Skip to main content Accessibility help
×
Hostname: page-component-848d4c4894-wzw2p Total loading time: 0 Render date: 2024-04-30T16:18:17.104Z Has data issue: false hasContentIssue false

Part III - Computational Modeling of Basic Cognitive Functionalities

Published online by Cambridge University Press:  21 April 2023

Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References

Aha, D. W., & Goldstone, R. L. (1992). Concept learning and flexible weighting. In Proceedings of the fourteenth annual conference of the Cognitive Science Society (vol. 534, p. 539).Google Scholar
Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98(3), 409429.Google Scholar
Ashby, F. G., & Alfonso-Reese, L. A. (1995). Categorization as probability density estimation. Journal of Mathematical Psychology, 39(2), 216233.CrossRefGoogle Scholar
Ashby, F. G., Alfonso-Reese, L. A., Turken, A. U., & Waldron, E. M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105(3), 442481.CrossRefGoogle ScholarPubMed
Ashby, F. G., & Maddox, W. T. (1993). Relations between prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology, 37(3), 372400.Google Scholar
Ashby, F. G., & Maddox, W.T. (2005). Human category learning. Annual Review of Psychology, 56, 149178.Google Scholar
Ashby, F. G., & Rosedahl, L. (2017). A neural interpretation of exemplar theory. Psychological Review, 124(4), 472482.CrossRefGoogle ScholarPubMed
Austerweil, J. L., & Griffiths, T. L. (2013). A nonparametric Bayesian framework for constructing flexible feature representations. Psychological Review, 120(4), 817851.Google Scholar
Austerweil, J. L., Liew, S. X., Conaway, N., & Kurtz, K. J. (under review). Creating something different: similarity, contrast, and representativeness in categorization.Google Scholar
Barsalou, L. W. (1983). Ad hoc categories. Memory & Cognition, 11(3), 211227.Google Scholar
Battleday, R. M., Peterson, J. C., & Griffiths, T. L. (2020). Capturing human categorization of natural images by combining deep networks and cognitive models. Nature Communications, 11(1), 114.Google Scholar
Brooks, L.R. (1978). Nonanalytic concept formation and memory for instances. In Rosch, E. & Lloyd, B., (Eds.), Cognition and Categorization, (pp. 169211). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Conaway, N., & Kurtz, K. J. (2017a). Similar to the category, but not the exemplars: a study of generalization. Psychonomic Bulletin & Review, 24(4), 13121323.CrossRefGoogle Scholar
Conaway, N., & Kurtz, K. J. (2017b). Solving nonlinearly separable classifications in a single-layer neural network. Neural Computation, 29(3), 861866.CrossRefGoogle Scholar
Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107140.Google Scholar
Feldman, J. (2000). Minimization of Boolean complexity in human concept learning. Nature, 407(6804), 630633.Google Scholar
Fried, L. S., & Holyoak, K. J. (1984). Induction of category distributions: a framework for classification learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(2), 234257.Google Scholar
Gentner, D., & Kurtz, K. J. (2005). Learning and using relational categories. In Ahn, W. L., Goldstone, R. L., Love, B. C., Markman, A. B., & Wolff, P. W. (Eds.). Categorization Inside and Outside the Lab. Washington, DC: American Psychological Association.Google Scholar
Gluck, M. A., & Bower, G. H. (1988). From conditioning to category learning: an adaptive network model. Journal of Experimental Psychology: General, 117(3), 227247.Google Scholar
Goldstone, R. L. (1994). The role of similarity in categorization: providing a groundwork. Cognition, 52, 125157.CrossRefGoogle ScholarPubMed
Goldstone, R. L., Kersten, A., & Carvalho, P. F. (2018). Categorization and concepts. In J. Wixted (Ed.), Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience (vol. 3, pp. 143). New York, NY: Wiley.Google Scholar
Goldstone, R. L., Schyns, P. G., & Medin, D. L. (1997). Learning to bridge between perception and cognition. The Psychology of Learning and Motivation, 36, 114.Google Scholar
Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. L. (2008). A rational analysis of rule‐based concept learning. Cognitive Science, 32(1), 108154.Google Scholar
Gureckis, T. M., & Love, B. C. (2003). Towards a unified account of supervised and unsupervised category learning. Journal of Experimental & Theoretical Artificial Intelligence, 15(1), 124.CrossRefGoogle Scholar
Gureckis, T. M., & Markant, D. B. (2012). Self-directed learning: a cognitive and computational perspective. Perspectives on Psychological Science, 7(5), 464481.Google Scholar
Hampton, J. A. (1981). An investigation of the nature of abstract concepts. Memory & Cognition, 9(2), 149156.Google Scholar
Homa, D., Sterling, S., & Trepel, L. (1981). Limitations of exemplar-based generalization and the abstraction of categorical information. Journal of Experimental Psychology: Human Learning and Memory, 7(6), 418439.Google Scholar
Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991). Adaptive mixtures of local experts. Neural Computation, 3(1), 7987.Google Scholar
Jäkel, F., Schölkopf, B., & Wichmann, F. A. (2008). Generalization and similarity in exemplar models of categorization: insights from machine learning. Psychonomic Bulletin & Review, 15(2), 256271.Google Scholar
Jäkel, F., Schölkopf, B., & Wichmann, F. A. (2009). Does cognitive science need kernels?. Trends in Cognitive Sciences, 13(9), 381388.Google Scholar
Jones, M., & Love, B. C. (2011). Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition. Behavioral and Brain Sciences, 34(4), 169188.Google Scholar
Katz, J. J., & Fodor, J. A. (1963). The structure of a semantic theory. Language, 39(2), 170210.CrossRefGoogle Scholar
Kemp, C. (2012). Exploring the conceptual universe. Psychological Review, 119(4), 685722.Google Scholar
Knapp, A. G., & Anderson, J. A. (1984). Theory of categorization based on distributed memory storage. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(4), 616637.Google Scholar
Kruschke, J. K. (1992). ALCOVE: an exemplar-based connectionist model of category learning. Psychological Review, 99(1), 2244.Google Scholar
Kruschke, J. K. (1993). Human category learning: implications for backpropagation models. Connection Science, 5(1), 336.Google Scholar
Kruschke, J. K. (2006). Locally Bayesian learning with applications to retrospective revaluation and highlighting. Psychological Review, 113(4), 677699.Google Scholar
Kruschke, J. K. (2008). Models of categorization. In Sun, R. (Ed.), The Cambridge Handbook of Computational Psychology, (pp. 267301). Cambridge: Cambridge University Press.Google Scholar
Kruschke, J. K., & Johansen, M. K. (1999). A model of probabilistic category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(5), 10831119.Google Scholar
Kurtz, K. J. (2007). The divergent autoencoder (DIVA) model of category learning. Psychonomic Bulletin & Review, 14(4), 560576.Google Scholar
Kurtz, K. J. (2015). Human category learning: toward a broader explanatory account. In B. H. Ross (Ed.), Psychology of Learning and Motivation, (vol. 63, pp. 77114). New York, NY: Academic Press.Google Scholar
Kurtz, K. J., & Conaway, N. (under review). Exemplar models can’t see the forest for the trees: a critical test and model comparison.Google Scholar
Kurtz, K. J., Levering, K. R., Stanton, R. D., Romero, J., & Morris, S. N. (2013). Human learning of elemental category structures: revising the classic result of Shepard, Hovland, and Jenkins (1961). Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(2), 552572.Google ScholarPubMed
Kurtz, K. J., Mason, M., & Wetzel, M. (2020). Investigating discriminative constraints to the divergent autoencoder (DIVA) model of human category learning. Poster presented at the 2020 Annual Meeting of the Psychonomic Society.Google Scholar
Kurtz, K. J., & Silliman, D. C. (2019). Warning: the exemplars in your category representation may not be the ones experienced during learning. In Goel, A., Seifert, C., & Freska, C. (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society (pp. 5657). Cognitive Science Society.Google Scholar
Kurtz, K. J, & Wetzel, M. (2021). On the generalization of simple alternating category structures. Cognitive Science, 45(4), e12972.Google Scholar
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 13321338.CrossRefGoogle ScholarPubMed
Lamberts, K. (1998). The time course of categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24(3), 695711.Google Scholar
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436444.CrossRefGoogle ScholarPubMed
Lee, M. D., & Navarro, D. J. (2002). Extending the ALCOVE model of category learning to featural stimulus domains. Psychonomic Bulletin & Review, 9(1), 4358.CrossRefGoogle ScholarPubMed
Levering, K. R., Conaway, N., & Kurtz, K. J. (2020). Revisiting the linear separability constraint: new implications for theories of human category learning. Memory & Cognition, 48, 335347.Google Scholar
Levering, K. R., & Kurtz, K. J. (2015). Observation versus classification in supervised category learning. Memory & Cognition, 43(2), 266282.CrossRefGoogle ScholarPubMed
Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111(2), 309332.Google Scholar
Luce, R. D. (1963). Detection and recognition. In Luce, R. D., Bush, R. R., & Galanter, E. (Eds.), Handbook of Mathematical Psychology, (pp. 103189). New York, NY: Wiley.Google Scholar
Markman, A. B., & Ross, B. H. (2003). Category use and category learning. Psychological Bulletin, 129, 592613.Google Scholar
McClelland, J. L., & Rumelhart, D. E. (1986). A distributed model of memory. In Rumelhart, D. L., & McClelland, J. L., (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Vol II. Applications (pp. 170215). Cambridge MA: MIT Press.Google Scholar
Medin, D. L. (1989). Concepts and conceptual structure. American Psychologist, 44(12), 14691481.CrossRefGoogle ScholarPubMed
Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207238.Google Scholar
Medin, D. L., & Schwanenflugel, P. J. (1981). Linear separability in classification learning. Journal of Experimental Psychology: Human Learning and Memory, 7(5), 355368.Google Scholar
Minda, J. P., & Smith, J. D. (2001). Prototypes in category learning: the effects of category size, category structure, and stimulus complexity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27(3), 775799.Google Scholar
Minda, J. P., & Smith, J. D. (2002). Comparing prototype-based and exemplar-based accounts of category learning and attentional allocation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28(2), 275292.Google Scholar
Murphy, G. L. (2002). The Big Book of Concepts. Cambridge, MA: MIT press.Google Scholar
Murphy, G. L. (2003). Ecological validity and the study of concepts. In Ross, B., (Ed.), The Psychology of Learning and Motivation (vol. 43, pp. 141). San Diego, CA: Elsevier Academic Press.Google Scholar
Murphy, G. L. (2005). The study of concepts inside and outside the laboratory: Medin versus Medin. In Ahn, W. L., Goldstone, R. L., Love, B. C., Markman, A. B., & Wolff, P. W., (Eds.), Categorization Inside and Outside the Laboratory, (pp. 179195). Washington, DC: American Psychological Association.Google Scholar
Murphy, G. L. (2016). Is there an exemplar theory of concepts?. Psychonomic Bulletin & Review, 23(4), 10351042.Google Scholar
Murphy, G. L., & Medin, D. L. (1985). The role of theories in conceptual coherence. Psychological Review, 92, 289316.Google Scholar
Murphy, G. L., & Ross, B. H. (1994). Predictions from uncertain categorizations. Cognitive Psychology, 27, 148193.CrossRefGoogle ScholarPubMed
Navarro, D. J. (2005). Analyzing the RULEX model of category learning. Journal of Mathematical Psychology, 49(4), 259275.Google Scholar
Nosofsky, R. M. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(1), 104114.Google Scholar
Nosofsky, R. M. (1986). Attention, similarity, and the identification–categorization relationship. Journal of Experimental Psychology: General, 115(1), 3957.Google Scholar
Nosofsky, R. M. (1992). Similarity scaling and cognitive process models. Annual Review of Psychology, 43(1), 2553.Google Scholar
Nosofsky, R. M., Gluck, M., Palmeri, T. J., McKinley, S. C., & Glauthier, P. (1994). Comparing models of rule-based classification learning: a replication and extension of Shepard, Hovland, and Jenkins (1961). Memory & Cognition, 22, 352369.Google Scholar
Nosofsky, R. M., & Palmeri, T. J. (1997). An exemplar-based random walk model of speeded classification. Psychological Review, 104(2), 266300.Google Scholar
Nosofsky, R. M., & Palmeri, T. J. (1998). A rule-plus-exception model for classifying objects in continuous-dimension spaces. Psychonomic Bulletin & Review, 5(3), 345369.Google Scholar
Nosofsky, R. M., Palmeri, T. J., & McKinley, S. K. (1994). Rule-plus-exception model of classification learning. Psychological Review, 101, 5579.Google Scholar
Nosofsky, R. M., Sanders, C. A., Gerdom, A., Douglas, B. J., & McDaniel, M. A. (2017). On learning natural-science categories that violate the family-resemblance principle. Psychological Science, 28(1), 104114.Google Scholar
Palmeri, T. J., Love, B. C., & Turner, B. M. (2017). Model-based cognitive neuroscience. Journal of Mathematical Psychology, 76(Part B), 5964.Google Scholar
Pape, A. D., Kurtz, K. J., & Sayama, H. (2015). Complexity measures and concept learning. Journal of Mathematical Psychology, 64, 6675.Google Scholar
Pitt, M. A., Myung, I. J., & Zhang, S. (2002). Toward a method of selecting among computational models of cognition. Psychological Review, 109(3), 472491.Google Scholar
Poggio, T., & Girosi, F. (1990). Regularization algorithms for learning that are equivalent to multilayer networks. Science, 247(4945), 978982.Google Scholar
Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353363.Google Scholar
Pothos, E. M., Perlman, A., Bailey, T. M., et al. (2011). Measuring category intuitiveness in unconstrained categorization tasks. Cognition, 121(1), 83100.CrossRefGoogle ScholarPubMed
Pothos, E. M., & Wills, A. J. (2011). Formal Approaches in Categorization. Cambridge: Cambridge University Press.Google Scholar
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81106.Google Scholar
Reed, S. K. (1972). Pattern recognition and categorization. Cognitive Psychology, 3(3), 382407.CrossRefGoogle Scholar
Rehder, B., & Hoffman, A. B. (2005). Eyetracking and selective attention in category learning. Cognitive Psychology, 51(1), 141.Google Scholar
Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. In Black, A. H., & Prokasy, W. F. (Eds.), Classical Conditioning II: Current Research and Theory (pp. 6499). New York, NY: Appleton-Century-Crofts.Google Scholar
Roads, B. D., & Love, B. C. (2020). Enriching ImageNet with human similarity judgments and psychological embeddings. arXiv preprint arXiv:2011.11015Google Scholar
Rosch, E., & Mervis, C. B. (1975). Family resemblances: studies in the internal structure of categories. Cognitive Psychology, 7(4), 573605.Google Scholar
Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386408.CrossRefGoogle ScholarPubMed
Ross, B. H., Taylor, E. G., Middleton, E. L., & Nokes, T. J. (2008). Concept and category learning in humans. In Roediger, H. L., III (Ed.), Cognitive Psychology of Memory (pp. 535557). Oxford: Elsevier.Google Scholar
Rosseel, Y. (2002). Mixture models of categorization. Journal of Mathematical Psychology, 46(2), 178210.Google Scholar
Rumelhart, D. E. (1980). Schemata: the building blocks. In R. J. Spiro, B. C. Bruce, & W. F. Brewer (Eds.), Theoretical Issues in Reading Comprehension. London: Routledge.Google Scholar
Rumelhart, D. E. (1989). Toward a microstructural account of human reasoning. In Vosniadou, S. and Ortony, A. (Eds.), Similarity and Analogical Reasoning. New York, NY: Cambridge University Press.Google Scholar
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. In Rumelhart, D. E. & McClelland, J. L. (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Vol 1. Foundations (pp. 318362). Cambridge, MA: Bradford Books/MIT Press.Google Scholar
Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2010). Rational approximations to rational models: alternative algorithms for category learning. Psychological Review, 117(4), 11441167.Google Scholar
Sanders, C. A., & Nosofsky, R. M. (2020). Training deep networks to construct a psychological feature space for a natural-object category domain. Computational Brain & Behavior, 2020, 123.CrossRefGoogle Scholar
Shanks, D. R. (1991). Categorization by a connectionist network. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17(3), 433443.Google Scholar
Schwenk, H. (1998). The diabolo classifier. Neural Computation, 10(8), 21752200.Google Scholar
Schyns, P. G., Goldstone, R. L. & Thibaut, J. (1998). The development of features in object concepts. Behavioral and Brain Sciences, 21, 154.Google Scholar
Shepard, R. N. (1957). Stimulus and response generalization: a stochastic model relating generalization to distance in psychological space. Psychometrika, 22, 325345.CrossRefGoogle Scholar
Shepard, R. N. (1962). The analysis of proximities: multidimensional scaling with an unknown distance function. I. Psychometrika, 27(2), 125140.Google Scholar
Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237(4820), 13171323.Google Scholar
Shepard, R. N., Hovland, C. I., & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75(13), 142.CrossRefGoogle Scholar
Smith, E. E., & Medin, D. L. (1981). Categories and Concepts. Cambridge, MA: Harvard University Press.Google Scholar
Solomon, K. O., Medin, D. L. & Lynch, E. (1999). Concepts do more than categorize. Trends in Cognitive Sciences, 3, 99104.Google Scholar
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: statistics, structure, and abstraction. Science, 331(6022), 12791285.Google Scholar
Vanpaemel, W., & Lee, M. D. (2012). The Bayesian evaluation of categorization models: comment on Wills and Pothos (2012). Psychological Bulletin, 138(6), 12531258.Google Scholar
Vanpaemel, W., & Storms, G. (2008). In search of abstraction: the varying abstraction model of categorization. Psychonomic Bulletin & Review, 15(4), 732749.Google Scholar
Vigo, R. (2009). Categorical invariance and structural complexity in human concept learning. Journal of Mathematical Psychology, 53(4), 203221.Google Scholar
Widrow, B., & Hoff, M. E. (1960). Adaptive Switching Circuits (No. TR-1553-1). Stanford, CA: Stanford Electronics Labs.Google Scholar
Wills, A. J. & Pothos, E. M. (2012). On the adequacy of current empirical evaluations of formal models of categorization. Psychological Bulletin, 138, 102125.Google Scholar
Yang, L., & Lewandowsky, S. (2004). Knowledge partitioning in categorization: constraints on exemplar models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 10451064.Google Scholar
Zeithamova, D., Mack, M. L., Braunlich, K., et al. (2019). Brain mechanisms of concept learning. Journal of Neuroscience, 39(42), 82598266.Google Scholar

References

Aizenstein, H. J., MacDonald, A. W., Stenger, V. A., et al. (2000). Complementary category learning systems identified using event-related functional MRI. Journal of Cognitive Neuroscience, 12(6), 977987.Google Scholar
Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience, 9(1), 357381.CrossRefGoogle ScholarPubMed
Amos, A. (2000). A computational model of information processing in the frontal cortex and basal ganglia. Journal of Cognitive Neuroscience, 12(3), 505519.Google Scholar
Apicella, P., Legallet, E., & Trouche, E. (1997). Responses of tonically discharging neurons in the monkey striatum to primary rewards delivered during different behavioral states. Experimental Brain Research, 116(3), 456466.Google Scholar
Arbuthnott, G., Ingham, C., & Wickens, J. (2000). Dopamine and synaptic plasticity in the neostriatum. Journal of Anatomy, 196(4), 587596.Google Scholar
Ashby, F. G. (2018). Computational cognitive neuroscience. In Batchelder, W., Colonius, H., Dzhafarov, E., & Myung, J. (Eds.), New Handbook of Mathematical Psychology (vol. 2, pp. 223270). New York, NY: Cambridge University Press.Google Scholar
Ashby, F. G., Alfonso-Reese, L. A., Turken, A. U., & Waldron, E. M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105(3), 442481.Google Scholar
Ashby, F. G., & Casale, M. B. (2003). The cognitive neuroscience of implicit category learning. In Jiménez, L. (Ed.), Attention and Implicit Learning, (vol. 48, pp. 109142). New York, NY: John Benjamins Publishing Company.Google Scholar
Ashby, F. G., & Crossley, M. J. (2011). A computational model of how cholinergic interneurons protect striatal-dependent learning. Journal of Cognitive Neuroscience, 23(6), 15491566.Google Scholar
Ashby, F. G., & Crossley, M. J. (2012). Automaticity and multiple memory systems. Wiley Interdisciplinary Reviews Cognitive Science, 3(3), 363376.Google Scholar
Ashby, F. G., Ell, S. W., Valentin, V. V., & Casale, M. B. (2005). FROST: a distributed neurocomputational model of working memory maintenance. Journal of Cognitive Neuroscience, 17(11), 17281743.Google Scholar
Ashby, F. G., & Ennis, J. M. (2006). The role of the basal ganglia in category learning. Psychology of Learning and Motivation, 46, 136.Google Scholar
Ashby, F. G., Ennis, J. M., & Spiering, B. J. (2007). A neurobiological theory of automaticity in perceptual categorization. Psychological Review, 114(3), 632656.Google Scholar
Ashby, F. G., & Gott, R. E. (1988). Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 3353.Google ScholarPubMed
Ashby, F. G., Isen, A. M., & Turken, A. (1999). A neuropsychological theory of positive affect and its influence on cognition. Psychological Review, 106(3), 529550.Google Scholar
Ashby, F. G., & O’Brien, J. B. (2005). Category learning and multiple memory systems. Trends in Cognitive Sciences, 2, 8389.Google Scholar
Ashby, F. G., Paul, E. J., & Maddox, W. T. (2011). COVIS. In Pothos, E. M. & Wills, A. (Eds.), Formal Approaches in Categorization (pp. 6587). New York, NY: Cambridge University Press.CrossRefGoogle Scholar
Ashby, F. G., & Rosedahl, L. (2017). A neural interpretation of exemplar theory. Psychological Review, 124(4), 472482.Google Scholar
Ashby, F. G., & Valentin, V. V. (2017). Multiple systems of perceptual category learning: theory and cognitive tests. In Cohen, H. & Lefebvre, C. (Eds.), Handbook of Categorization in Cognitive Science, 2nd ed. (pp. 157188). Amsterdam: Elsevier.Google Scholar
Ashby, F. G., & Valentin, V. V. (2018). The categorization experiment: experimental design and data analysis. In Wagenmakers, E. J. & Wixted, J. T. (Eds.), Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 4th ed., vol. 5: Methodology (pp. 307347). New York, NY: Wiley.Google Scholar
Ashby, F. G., & Waldron, E. M. (1999). On the nature of implicit categorization. Psychonomic Bulletin & Review, 6(3), 363378.Google Scholar
Asmus, F., Huber, H., Gasser, T., & Schöls, L. (2008). Kick and rush paradoxical kinesia in parkinson disease. Neurology, 71(9), 695.Google Scholar
Bennett, B. D., & Wilson, C. J. (2000). Synaptology and physiology of neostriatal neurones. In Miller, R. & Wickens, J. R. (Eds.), Brain Dynamics and the Striatal Complex (pp. 111140). Amsterdam: Harwood Academic Publishers.Google Scholar
Braver, T. S., Cohen, J. D., Nystrom, L. E., Jonides, J., Smith, E. E., & Noll, D. C. (1997). A parametric study of prefrontal cortex involvement in human working memory. Neuroimage, 5(1), 4962.CrossRefGoogle ScholarPubMed
Bunge, S. A. (2004). How we use rules to select actions: a review of evidence from cognitive neuroscience. Cognitive, Affective, & Behavioral Neuroscience, 4(4), 564579.Google Scholar
Calabresi, P., Pisani, A., Mercuri, N. B., & Bernardi, G. (1996). The corticostriatal projection: from synaptic plasticity to dysfunctions of the basal ganglia. Trends in Neurosciences, 19(1), 1924.Google Scholar
Cantwell, G., Crossley, M. J., & Ashby, F. G. (2015). Multiple stages of learning in perceptual categorization: evidence and neurocomputational theory. Psychonomic Bulletin & Review, 22(6), 15981613.Google Scholar
Cantwell, G., Riesenhuber, M., Roeder, J. L., & Ashby, F. G. (2017). Perceptual category learning and visual processing: an exercise in computational cognitive neuroscience. Neural Networks, 89, 3138.Google Scholar
Casale, M. B., & Ashby, F. G. (2008). A role for the perceptual representation memory system in category learning. Perception & Psychophysics, 70(6), 983999.Google Scholar
Chersi, F., Mirolli, M., Pezzulo, G., & Baldassarre, G. (2013). A spiking neuron model of the cortico-basal ganglia circuits for goal-directed and habitual action learning. Neural Networks, 41, 212224.Google Scholar
Cools, R. (2006). Dopaminergic modulation of cognitive function-implications for l-dopa treatment in Parkinson’s disease. Neuroscience & Biobehavioral Reviews, 30(1), 123.Google Scholar
Cools, R., Lewis, S. J., Clark, L., Barker, R. A., & Robbins, T. W. (2007). L-dopa disrupts activity in the nucleus accumbens during reversal learning in Parkinson’s disease. Neuropsychopharmacology, 32(1), 180189.Google Scholar
Crossley, M. J., Ashby, F. G., & Maddox, W. T. (2013). Erasing the engram: the unlearning of procedural skills. Journal of Experimental Psychology: General, 142(3), 710741.Google Scholar
Crossley, M. J., Ashby, F. G., & Maddox, W. T. (2014). Context-dependent savings in procedural category learning. Brain & Cognition, 92, 110.Google Scholar
Crossley, M. J., Horvitz, J. C., Balsam, P. D., & Ashby, F. G. (2016). Expanding the role of striatal cholinergic interneurons and the midbrain dopamine system in appetitive instrumental conditioning. Journal of Neurophysiology, 115, 240254.Google Scholar
Crossley, M. J., Madsen, N. R., & Ashby, F. G. (2012). Procedural learning of unstructured categories. Psychonomic Bulletin & Review, 19(6), 12021209.Google Scholar
Curtis, C. E., & D’Esposito, M. (2003). Persistent activity in the prefrontal cortex during working memory. Trends in Cognitive Sciences, 7(9), 415423.Google Scholar
Davis, T., Love, B. C., & Preston, A. R. (2011). Learning the exception to the rule: model-based fMRI reveals specialized representations for surprising category members. Cerebral Cortex, 22(2), 260273.Google Scholar
Desmurget, M., & Turner, R. S. (2010). Motor sequences and the basal ganglia: kinematics, not habits. The Journal of Neuroscience, 30(22), 76857690.Google Scholar
Dunn, J. C., Newell, B. R., & Kalish, M. L. (2012). The effect of feedback delay and feedback type on perceptual category learning: the limits of multiple systems. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(4), 840859.Google Scholar
Eichenbaum, H., & Cohen, N. J. (2001). From Conditioning to Conscious Recollection: Memory Systems of the Brain. Oxford: Oxford University Press.Google Scholar
Engel, T. A., Chaisangmongkon, W., Freedman, D. J., & Wang, X.-J. (2015). Choice-correlated activity fluctuations underlie learning of neuronal category representation. Nature Communications, 6, 6454.Google Scholar
Estes, W. K. (1986). Array models for category learning. Cognitive Psychology, 18(4), 500549.Google Scholar
Feldman, D. E. (2009). Synaptic mechanisms for plasticity in neocortex. Annual Review of Neuroscience, 32, 3355.Google Scholar
Filoteo, J. V., Maddox, W. T., Salmon, D. P., & Song, D. D. (2005). Information-integration category learning in patients with striatal dysfunction. Neuropsychology, 19(2), 212222.Google Scholar
Filoteo, J. V., Paul, E. J., Ashby, F. G., et al. (2014). Simulating category learning and set shifting deficits in patients weight-restored from anorexia nervosa. Neuropsychology, 28(5), 741751.Google Scholar
Frank, M. J., & O’Reilly, R. C. (2006). A mechanistic account of striatal dopamine function in human cognition: psychopharmacological studies with cabergoline and haloperidol. Behavioral Neuroscience, 120(3), 497517.Google Scholar
Heaton, R. K. (1981). Wisconsin Card Sorting Test Manual. Odessa, FL: Psychological Assessment Resources.Google Scholar
Hélie, S., Paul, E. J., & Ashby, F. G. (2012a). A neurocomputational account of cognitive deficits in Parkinson’s disease. Neuropsychologia, 50(9), 22902302.Google Scholar
Hélie, S., Paul, E. J., & Ashby, F. G. (2012b). Simulating the effects of dopamine imbalance on cognition: from positive affect to Parkinson’s disease. Neural Networks, 32, 7485.Google Scholar
Helie, S., Roeder, J. L., Vucovich, L., Rünger, D., & Ashby, F. G. (2015). A neurocomputational model of automatic sequence production. Journal of Cognitive Neuroscience, 27(7), 14561469.Google Scholar
Hélie, S., Waldschmidt, J. G., & Ashby, F. G. (2010). Automaticity in rule-based and information-integration categorization. Attention, Perception, & Psychophysics, 72(4), 10131031.Google Scholar
Hopkins, R. O., Myers, C. E., Shohamy, D., Grossman, S., & Gluck, M. (2004). Impaired probabilistic category learning in hypoxic subjects with hippocampal damage. Neuropsychologia, 42(4), 524535.Google Scholar
Houk, J. C., Adams, J. L., & Barto, A. G. (1995). A model of how the basal ganglia generate and use neural signals that predict reinforcement. In Houk, J. C., Davis, J. L., & Beiser, D. G. (Eds.), Models of Information Processing in the Basal Ganglia (pp. 249270). Cambridge, MA: MIT Press.Google Scholar
Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14(6), 15691572.Google Scholar
Janowsky, J. S., Shimamura, A. P., Kritchevsky, M., & Squire, L. R. (1989). Cognitive impairment following frontal lobe damage and its relevance to human amnesia. Behavioral Neuroscience, 103(3), 548560.Google Scholar
Kehagia, A. A., Cools, R., Barker, R. A., & Robbins, T. W. (2009). Switching between abstract rules reflects disease severity but not dopaminergic status in Parkinson’s disease. Neuropsychologia, 47(4), 11171127.Google Scholar
Knowlton, B. J., Mangels, J. A., & Squire, L. R. (1996). A neostriatal habit learning system in humans. Science, 273(5280), 13991402.Google Scholar
Kovacs, P., Hélie, S., Tran, A. N., & Ashby, F. G. (2021). A neurocomputational theory of how rule-guided behaviors become automatic. Psychological Review, 128(3), 488508.Google Scholar
Kruschke, J. K. (1996). Dimensional relevance shifts in category learning. Connection Science, 8(2), 225247.CrossRefGoogle Scholar
Leng, N. R., & Parkin, A. J. (1988). Double dissociation of frontal dysfunction in organic amnesia. British Journal of Clinical Psychology, 27(4), 359362.Google Scholar
Lisman, J., Schulman, H., & Cline, H. (2002). The molecular basis of CaMKII function in synaptic and behavioural memory. Nature Reviews Neuroscience, 3(3), 175190.Google Scholar
Logothetis, N. K., & Sheinberg, D. L. (1996). Visual object recognition. Annual Review of Neuroscience, 19(1), 577621.Google Scholar
Lopez-Paniagua, D., & Seger, C. A. (2011). Interactions within and between corticostriatal loops during component processes of category learning. Journal of Cognitive Neuroscience, 23(10), 30683083.Google Scholar
Love, B. C., & Gureckis, T. M. (2007). Models in search of a brain. Cognitive, Affective, & Behavioral Neuroscience, 7(2), 90108.Google Scholar
Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111(2), 309332.Google Scholar
Maddox, W. T., Ashby, F. G., & Bohil, C. J. (2003). Delayed feedback effects on rule-based and information-integration category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 650662.Google Scholar
Maddox, W. T., Filoteo, J. V., Hejl, K. D., et al. (2004). Category number impacts rule-based but not information-integration category learning: further evidence for dissociable category-learning systems. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(1), 227235.Google Scholar
Maddox, W. T., Glass, B. D., O’Brien, J. B., Filoteo, J. V., & Ashby, F. G. (2010). Category label and response location shifts in category learning. Psychological Research, 74(2), 219236.Google Scholar
Maddox, W. T., & Ing, A. D. (2005). Delayed feedback disrupts the procedural-learning system but not the hypothesis-testing system in perceptual category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31(1), 100107.Google Scholar
Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. New York, NY: W. H. Freeman.Google Scholar
Matsumoto, N., Minamimoto, T., Graybiel, A. M., & Kimura, M. (2001). Neurons in the thalamic CM-Pf complex supply striatal neurons with information about behaviorally significant sensory events. Journal of Neurophysiology, 85(2), 960976.Google Scholar
Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85(3), 207238.Google Scholar
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24(1), 167202.Google Scholar
Mishkin, M., Malamut, B., & Bachevalier, J. (1984). Memories and habits: two neural systems. In Lynch, G., McGaugh, J. L., & Weinberger, N. M. (Eds.), Neurobiology of Human Learning and Memory (pp. 6577). New York, NY: Guilford Press.Google Scholar
Monchi, O., Petrides, M., Doyon, J., Postuma, R. B., Worsley, K., & Dagher, A. (2004). Neural bases of set-shifting deficits in Parkinson’s disease. The Journal of Neuroscience, 24(3), 702710.Google Scholar
Monchi, O., Petrides, M., Petre, V., Worsley, K., & Dagher, A. (2001). Wisconsin card sorting revisited: distinct neural circuits participating in different stages of the task identified by event-related functional magnetic resonance imaging. The Journal of Neuroscience, 21(19), 77337741.Google Scholar
Monchi, O., Taylor, J. G., & Dagher, A. (2000). A neural model of working memory processes in normal subjects, Parkinson’s disease and schizophrenia for fMRI design and predictions. Neural Networks, 13(8–9), 953973.Google Scholar
Moustafa, A. A., & Gluck, M. A. (2011). A neurocomputational model of dopamine and prefrontal–striatal interactions during multicue category learning by Parkinson patients. Journal of Cognitive Neuroscience, 23(1), 151167.Google Scholar
Nomura, E., Maddox, W., Filoteo, J., et al. (2007). Neural correlates of rule-based and information-integration visual category learning. Cerebral Cortex, 17(1), 3743.Google Scholar
Norman, K. A., & O’Reilly, R. C. (2003). Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach. Psychological Review, 110(4), 611646.Google Scholar
Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 3957.Google Scholar
O’Reilly, R. C., Hazy, T. E., & Herd, S. A. (2016). The Leabra cognitive architecture: how to play 20 principles with nature. The Oxford Handbook of Cognitive Science, 91, 91116.Google Scholar
O’Reilly, R. C., Munakata, Y., Frank, M., Hazy, T., et al. (2012). Computational Cognitive Neuroscience. Mainz: PediaPress.Google Scholar
O’Reilly, R. C., Noelle, D. C., Braver, T. S., & Cohen, J. D. (2002). Prefrontal cortex and dynamic categorization tasks: representational organization and neuromodulatory control. Cerebral Cortex, 12(3), 246257.Google Scholar
O’Reilly, R. C., Wyatte, D., Herd, S., Mingus, B., & Jilk, D. J. (2013). Recurrent processing during object recognition. Frontiers in Psychology, 4, 124.Google Scholar
Pakhotin, P., & Bracci, E. (2007). Cholinergic interneurons control the excitatory input to the striatum. The Journal of Neuroscience, 27(2), 391400.Google Scholar
Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353363.Google Scholar
Price, A., Filoteo, J. V., & Maddox, W. T. (2009). Rule-based category learning in patients with Parkinson’s disease. Neuropsychologia, 47(5), 12131226.CrossRefGoogle ScholarPubMed
Rall, W. (1967). Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input. Journal of Neurophysiology, 30(5), 11381168.Google Scholar
Reber, P. J., & Squire, L. R. (1999). Intact learning of artificial grammars and intact category learning by patients with Parkinson’s disease. Behavioral Neuroscience, 113(2), 235242.Google Scholar
Reber, P. J., Stark, C. E., & Squire, L. R. (1998). Contrasting cortical activity associated with category memory and recognition memory. Learning & Memory, 5(6), 420428.Google Scholar
Reynolds, J. N., & Wickens, J. R. (2002). Dopamine-dependent plasticity of corticostriatal synapses. Neural Networks, 15(4), 507521.Google Scholar
Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2(11), 10191025.Google Scholar
Riesenhuber, M., & Poggio, T. (2002). Neural mechanisms of object recognition. Current Opinion in Neurobiology, 12(2), 162168.Google Scholar
Rougier, N. P., & O’Reilly, R. C. (2002). Learning representations in a gated prefrontal cortex model of dynamic task switching. Cognitive Science, 26(4), 503520.Google Scholar
Rudy, J. W. (2014). The Neurobiology of Learning and Memory. Sunderland, MA: Sinauer.Google Scholar
Sanders, B. (1971). Factors affecting reversal and nonreversal shifts in rats and children. Journal of Comparative and Physiological Psychology, 74, 192202.Google Scholar
Schneider, W., & Shiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. Psychological Review, 84(1), 166.Google Scholar
Seamans, J. K., & Yang, C. R. (2004). The principal features and mechanisms of dopamine modulation in the prefrontal cortex. Progress in Neurobiology, 74(1), 158.Google Scholar
Seger, C. A., & Cincotta, C. M. (2005). The roles of the caudate nucleus in human classification learning. The Journal of Neuroscience, 25(11), 29412951.Google Scholar
Seger, C. A., & Miller, E. K. (2010). Category learning in the brain. Annual Review of Neuroscience, 33, 203219.CrossRefGoogle ScholarPubMed
Seger, C. A., Peterson, E. J., Cincotta, C. M., Lopez-Paniagua, D., & Anderson, C. W. (2010). Dissociating the contributions of independent corticostriatal systems to visual categorization learning through the use of reinforcement learning modeling and Granger causality modeling. NeuroImage, 50(2), 644656.Google Scholar
Serre, T., Oliva, A., & Poggio, T. (2007). A feedforward architecture accounts for rapid categorization. Proceedings of the National Academy of Sciences, 104(15), 64246429.Google Scholar
Smith, Y., Raju, D. V., Pare, J.-F., & Sidibe, M. (2004). The thalamostriatal system: a highly specific network of the basal ganglia circuitry. Trends in Neurosciences, 27(9), 520527.Google Scholar
Sreenivasan, K. K., Curtis, C. E., & D’Esposito, M. (2014). Revisiting the role of persistent neural activity during working memory. Trends in Cognitive Sciences, 18(2), 8289.Google Scholar
Tachibana, K., Suzuki, K., Mori, E., et al. (2009). Neural activity in the human brain signals logical rule identification. Journal of Neurophysiology, 102(3), 15261537.Google Scholar
Valentin, V. V., Maddox, W. T., & Ashby, F. G. (2014). A computational model of the temporal dynamics of plasticity in procedural learning: sensitivity to feedback timing. Frontiers in Psychology, 5(643). https://doi.org/10.3389/fpsyg.2014.00643Google Scholar
Varrone, A., & Halldin, C. (2014). Human brain imaging of dopamine transporters. In Seeman, P. & Madras, B. (Eds.), Imaging of the Human Brain in Health and Disease (pp. 203240). Amsterdam: Elsevier.Google Scholar
Waldron, E. M., & Ashby, F. G. (2001). The effects of concurrent task interference on category learning: evidence for multiple category learning systems. Psychonomic Bulletin & Review, 8(1), 168176.Google Scholar
Wallis, J. D., & Miller, E. K. (2003). From rule to response: neuronal processes in the premotor and prefrontal cortex. Journal of Neurophysiology, 90(3), 17901806.Google Scholar
Wickens, J. (1993). A Theory of the Striatum. Oxford: Pergamon Press.Google Scholar
Willingham, D. B. (1998). A neuropsychological theory of motor skill learning. Psychological Review, 105, 558584.Google Scholar
Willingham, D. B., Nissen, M. J., & Bullemer, P. (1989). On the development of procedural knowledge. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15(6), 10471060.Google Scholar
Wills, A., Noury, M., Moberly, N. J., & Newport, M. (2006). Formation of category representations. Memory & Cognition, 34(1), 1727.Google Scholar
Wilson, C. J. (1995). The contribution of cortical neurons to the firing pattern of striatal spiny neurons. In Houk, J. C., Davis, J. L., & Beiser, D. G. (Eds.), Models of Information Processing in the Basal Ganglia (pp. 2950). Cambridge, MA: MIT Press.Google Scholar
Worthy, D. A., Markman, A. B., & Maddox, W. T. (2013). Feedback and stimulus-offset timing effects in perceptual category learning. Brain and Cognition, 81(2), 283293.Google Scholar
Wyatte, D., Herd, S., Mingus, B., & O’Reilly, R. (2012). The role of competitive inhibition and top-down feedback in binding during object recognition. Frontiers in Psychology, 3, 182.Google Scholar
Yagishita, S., Hayashi-Takagi, A., Ellis-Davies, G. C., Urakubo, H., Ishii, S., & Kasai, H. (2014). A critical time window for dopamine actions on the structural plasticity of dendritic spines. Science, 345(6204), 16161620.Google Scholar
Zeithamova, D., & Maddox, W. T. (2006). Dual-task interference in perceptual category learning. Memory & Cognition, 34(2), 387398.Google Scholar

References

Anderson, J. R. (1991). The adaptive nature of human categorization, Psychological Review, 98, 409429.Google Scholar
Blok, S. V., Medin, D. L., & Osherson, D. N. (2007). Induction as conditional probability judgment. Memory & Cognition, 36(6), 13531364.Google Scholar
Bonawitz, E., & Shafto, P. (2016). Computational models of development, social influences. Current Opinion in Behavioral Sciences, 7, 95100.Google Scholar
Bowers, J. S., & Davis, C. J. (2012). Bayesian just-so stories in psychology and neuroscience. Psychological Bulletin, 138(3), 389414.CrossRefGoogle ScholarPubMed
Bright, A. K., & Feeney, A. (2014). The engine of thought is a hybrid: roles of associative and structured knowledge in reasoning. Journal of Experimental Psychology: General, 143(6), 20822102.Google Scholar
Carey, S. (1985). Conceptual Change in Childhood. Cambridge, MA: Bradford Books.Google Scholar
Carnap, R. (1968). Inductive logic and inductive intuition. In Lakatos, I. (Ed.), Studies in Logic and the Foundations of Mathematics (vol. 51, pp. 258314). Amsterdam: Elsevier.Google Scholar
Cassey, P., Hawkins, G. E., Donkin, C., & Brown, S. D. (2016). Using alien coins to test whether simple inference is Bayesian. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42(3), 497503.Google Scholar
Coley, J. D., & Vasilyeva, N. Y. (2010). Generating inductive inferences: premise relations and property effects. Psychology of Learning and Motivation: Advances in Research and Theory, 53, 183226.Google Scholar
Collins, A. & Michalski, R. (1989). The logic of plausible reasoning: a core theory. Cognitive Science, 13(1), l49.Google Scholar
Dunsmoor, J. E., & Murphy, G. L. (2014). Stimulus typicality determines how broadly fear is generalized. Psychological Science, 25, 18161821.Google Scholar
Evans, J. St. B. T., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: advancing the debate. Perspectives on Psychological Science, 8, 223241.Google Scholar
Feeney, A. (2017). Forty years of progress on category-based inductive reasoning. In Ball, L. J. & Thompson, V. A. (Eds.), International Handbook of Thinking and Reasoning (pp. 167185). London: Routledge.Google Scholar
Feeney, A., & Heit, E. (2011). Properties of the diversity effect in category-based inductive reasoning. Thinking & Reasoning, 17, 156181.Google Scholar
Feeney, A., Shafto, P., & Dunning, D. (2007). Who is susceptible to conjunction fallacies in category-based induction? Psychonomic Bulletin & Review, 14, 884889.Google Scholar
Feiler, D., Tong, J., & Larrick, R. (2013). Biased judgment in censored environments. Management Science, 59, 573591.Google Scholar
Fisher, A. V. (2015). Development of inductive generalization. Child Development Perspectives, 9(3), 172177.Google Scholar
Frank, M. C., Goldwater, S., Griffiths, T. L., & Tenenbaum, J. B. (2010). Modeling human performance in statistical word segmentation. Cognition, 117, 107125.CrossRefGoogle ScholarPubMed
Gelman, S. A., & Markman, E. M. (1986). Categories and induction in young children. Cognition, 23(3), 183209.Google Scholar
Gershman, S. J., & Beck, J. M. (2018). Complex probabilistic inference. In Moustafa, A. A. (Ed). Computational Models of Brain and Behavior, (pp. 453466). Hoboken, NJ: Wiley.Google Scholar
Goodman, N. (1972). Seven strictures on similarity. In Goodman, N., Problems and Projects (pp. 437447). Indianapolis, IN: Bobbs-Merrill.Google Scholar
Griffiths, T. L., Lieder, F., & Goodman, N. D. (2015). Rational use of cognitive resources: level of analysis between computational and the algorithmic. Topics in Cognitive Science, 7, 217229.Google Scholar
Handley, S. J., & Trippas, D. (2015). Dual processes and the interplay between knowledge and structure: a new parallel processing model. Psychology of Learning and Motivation, 62, 3358.Google Scholar
Hayes, B. K., Banner, S., Forrester, S., & Navarro, D. J. (2019). Selective sampling and inductive inference: drawing inferences based on observed and missing evidence. Cognitive Psychology, 113, 101221.CrossRefGoogle ScholarPubMed
Hayes, B. K., Banner, S., & Navarro, D. J. (2017). Sampling frames, Bayesian inference and inductive reasoning. In Gunzelmann, G., Howes, A., Tenbrink, T., & Davelaar, E. (Eds.), Proceedings of the 39th Annual Meeting of the Cognitive Science Society (pp. 488493). Austin, TX: Cognitive Science Society.Google Scholar
Hayes, B. K., & Heit, E. (2018). Inductive reasoning 2.0. Wiley Interdisciplinary Reviews Cognitive Science, 9(3), 113, e1459.Google Scholar
Hayes, B. K., Navarro, D. J., Stephens, R. G., Ransom, K., & Dilevski, N. (2019). The diversity effect in inductive reasoning depends on sampling assumptions. Psychonomic Bulletin & Review, 26, 10431050.Google Scholar
Hayes, B. K. Stephens, R. G., Ngo, J., Dunn, J. C., (2018). The dimensionality of reasoning: evidence for a single process account of inductive and deductive inference. Journal of Experimental Psychology: Learning, Memory and Cognition, 44, 13331351.Google Scholar
Hayes, B. K., & Thompson, S. P. (2007). Causal relation and feature similarity in children’s inductive reasoning. Journal of Experimental Psychology: General, 136, 470484.Google Scholar
Hayes, B. K., Wei, P., Dunn, J. C., & Stephens, R. G. (2019). Why is logic so likeable? A single-process account of argument evaluation with logic and liking judgments. Journal of Experimental Psychology: Learning, Memory and Cognition. 46, 699719.Google Scholar
Heit, E. (1998). A Bayesian analysis of some forms of inductive reasoning. In Oaksford, M. & Chater, N. (Eds.), Rational Models of Cognition (pp. 248274). Oxford: Oxford University Press.Google Scholar
Heit, E., & Feeney, A. (2005). Relations between premise similarity and inductive strength. Psychonomic Bulletin & Review, 12(2), 340344.Google Scholar
Heit, E., & Rotello, C. M. (2010). Relations between inductive reasoning and deductive reasoning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 805812.Google Scholar
Heit, E., & Rubinstein, J. (1994). Similarity and property effects in inductive reasoning. Journal of Experimental Psychology, 20(2), 411422.Google Scholar
Hendrickson, A. T., Perfors, A., Navarro, D. J., & Ransom, K. (2019). Sample size, number of categories and sampling assumptions: exploring some differences between categorization and generalization. Cognitive Psychology, 111, 80102.Google Scholar
Hogarth, R., Lejarraga, T., & Soyer, E. (2015). The two settings of kind and wicked learning environments. Current Directions in Psychological Science, 24, 379385.Google Scholar
Kemp, C., & Jern, A. (2013). A taxonomy of inductive problems. Psychological Bulletin and Review, 21, 2346.Google Scholar
Kemp, C., & Tenenbaum, J. B. (2009). Structured statistical models of inductive reasoning. Psychological Review, 116, 2058.Google Scholar
Lawson, C. A., & Kalish, C. W. (2009). Sample selection and inductive generalization. Memory & Cognition, 37(5), 596607.Google Scholar
Le Mens, G., & Denrell, J. (2011). Rational learning and information sampling: on the “naivety” assumption in sampling explanations of judgment biases. Psychological Review, 118(2), 379392.Google Scholar
Lee, J. C., Lovibond, P. F., Hayes, B. K., & Navarro, D. (2019). Negative evidence and inductive reasoning in generalization of associative learning. Journal of Experimental Psychology: General, 148, 289303.Google Scholar
López, A., Gelman, S. A., Gutheil, G., & Smith, E. E. (1992). The development of category-based induction. Child Development, 63(5), 10701090.Google Scholar
Marr, D. (1982). Vision. San Francisco, CA: W. H. Freeman.Google Scholar
McClelland, J. L., & Rogers, T. T. (2003). The parallel distributed processing approach to semantic cognition. Nature Reviews Neuroscience, 4(4), 310322.Google Scholar
McKenzie, C. R. (2003). Rational models as theoriesnot standards – of behavior. Trends in Cognitive Sciences, 7, 403406.Google Scholar
Medin, D. L., Coley, J. D., Storms, G., & Hayes, B. K. (2003). A relevance theory of induction. Psychonomic Bulletin & Review, 10, 517532.Google Scholar
Medin, D. L., Wattenmaker, W. D., & Hampson, S. E. (1987). Family resemblance, conceptual cohesiveness, and category construction. Cognitive Psychology, 19, 242279.Google Scholar
Mitchell, T. (1997). Machine Learning. London: McGraw-Hill.Google Scholar
Murphy, G. L., & Medin, D. L. (1985). The role of theories in conceptual coherence. Psychological Review, 92(3), 289316.Google Scholar
Navarro, D. J., & Perfors, A. F. (2010). Similarity, feature discovery, and the size principle. Acta Psychologica, 133, 256268.Google Scholar
Navarro, D. J., Dry, M. J., & Lee, M. D. (2012). Sampling assumptions in inductive generalization. Cognitive Science, 36(2), 187223.Google Scholar
Nisbett, R. E., Krantz, D. H., Jepson, C., & Kunda, Z. (1983). The use of statistical heuristics in everyday inductive reasoning. Psychological Review, 90, 339363.Google Scholar
Oaksford, M., & Chater, N. (2007). Bayesian Rationality: The Probabilistic Approach to Human Reasoning. Oxford: Oxford University Press.Google Scholar
Oaksford, M., & Chater, N. (2013). Dynamic inference and everyday conditional reasoning in the new paradigm. Thinking & Reasoning, 19, 346379.Google Scholar
Osherson, D. N., Smith, E. E., Wilkie, O., & Lopez, A. (1990). Category-based induction. Psychological Review, 97, 185200.Google Scholar
Ransom, K. J., Perfors, A., & Navarro, D. J. (2016). Leaping to conclusions: why premise relevance affects argument strength. Cognitive Science, 40, 17751796.Google Scholar
Rehder, B. (2009). Causal-based property generalization. Cognitive Science, 33, 301343.Google Scholar
Rips, L. J. (1975). Inductive judgements about natural categories. Journal of Verbal Learning & Verbal Behavior, 14, 665681.Google Scholar
Rogers, T. T., & McClelland, J. L. (2004). Semantic Cognition: A Parallel Distributed Processing Approach. Cambridge, MA: MIT Press.Google Scholar
Rogers, T. T., & McClelland, J. L. (2014). Parallel distributed processing at 25: further explorations in the microstructure of cognition. Cognitive Science, 38, 10241077.Google Scholar
Sanborn, A. N., & Chater, N. (2016). Bayesian brains without probabilities. Trends in Cognitive Sciences, 20(12), 883893.Google Scholar
Sanjana, N. E., & Tenenbaum, J. B. (2003). Bayesian models of inductive generalization. In Jordan, M. I., LeCun, Y., & Solla, S. A. (Eds.), Advances in Neural Information Processing Systems (pp. 5966). Cambridge, MA: MIT Press.Google Scholar
Shafto, P., Coley, J. D., & Baldwin, D. (2007). Effects of time pressure on context-sensitive property induction. Psychonomic Bulletin & Review, 14, 890894.Google Scholar
Shafto, P., Goodman, N. D., & Frank, M. C. (2012). Learning from others: the consequences of psychological reasoning for human learning. Perspectives on Psychological Science, 7(4), 341351.Google Scholar
Shafto, P., Kemp, C., Bonawitz, E. B., Coley, J. D., & Tenenbaum, J. B. (2008). Inductive reasoning about causally transmitted properties. Cognition, 109, 175192.Google Scholar
Shi, L., Griffiths, T. L., Feldman, N. H., & Sanborn, A. N. (2010). Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin & Review, 17(4), 443464.Google Scholar
Sloman, S. A. (1993). Feature-based induction. Cognitive Psychology, 25, 231280.Google Scholar
Sloman, S. A. (1998). Categorical inference is not a tree: the myth of inheritance hierarchies. Cognitive Psychology, 35, 133.Google Scholar
Smith, E. E., Lopéz, A., & Osherson, D. (1992). Category membership, similarity, and naive induction. In Healy, A. F., Kosslyn, S. M., & Shiffrin, R. M. (Eds.), Essays in Honor of William K. Estes, Vol. 2. From Learning Processes to Cognitive Processes (pp. 181206). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Stephens, R. G., Dunn, J. C., & Hayes, B. K. (2018). Are there two processes in reasoning? The dimensionality of inductive and deductive inferences. Psychological Review, 125(2), 218244.Google Scholar
Stephens, R. G., Matzke, D., & Hayes, B. K. (2019). Disappearing dissociations in experimental psychology: using state-trace analysis to test for multiple processes. Journal of Mathematical Psychology, 90, 322.Google Scholar
Sun, R. (1995). Robust reasoning: integrating rule-based and similarity-based reasoning. Artificial Intelligence, 75, 241295.Google Scholar
Sun, R., & Zhang, X. (2006). Accounting for a variety of reasoning data within a cognitive architecture. Journal of Experimental and Theoretical Artificial Intelligence, 18(2), 169191.Google Scholar
Tauber, S., Navarro, D. J., Perfors, A., & Steyvers, M. (2017). Bayesian models of cognition revisited: setting optimality aside and letting data drive psychological theory. Psychological Review, 124, 410441.Google Scholar
Tenenbaum, J. B., & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24, 629640.Google Scholar
Voorspoels, W., Navarro, D. J., Perfors, A., Ransom, K., & Storms, G. (2015). How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning. Cognitive Psychology, 81, 125.Google Scholar
Xie, B., Hayes, B. K., & Navarro, D. J. (2018). Adding types, but not tokens, affects the breadth of property induction. In Rogers, T., Rau, M., Zhu, X., & Kalish, C. W. (Eds.), Proceedings of the 40th Annual Meeting of the Cognitive Science Society (pp. 11991204). Austin, TX: Cognitive Science Society.Google Scholar
Xu, F., & Tenenbaum, J. B. (2007). Word learning as Bayesian inference. Psychological Review, 114, 245275.Google Scholar

References

Bowdle, B. F., & Gentner, D. (2005). The career of metaphor. Psychological Review, 112, 193216.Google Scholar
Bowers, J. S. (2017). Parallel distributed processing theory in the age of deep networks. Trends in Cognitive Sciences, 21(12), 950961.Google Scholar
Chen, D., Peterson, J. C., & Griffiths, T. L. (2017). Evaluating vector-space models of analogy. In Proceedings of the 39th Annual Conference of the Cognitive Science Society.Google Scholar
Cunningham, J., & Shepard, R. (1974). Monotone mapping of similarities into a general metric space. Journal of Mathematical Psychology, 11, 335363.Google Scholar
Doumas, L. A., & Hummel, J. E. (2005). Approaches to modeling human mental representations: what works, what doesn’t and why. In K. J. Holyoak, , & Morrison, R. G. (Eds.), The Cambridge Handbook of Thinking and Reasoning (pp. 7394). Cambridge: Cambridge University Press.Google Scholar
Doumas, L. A. A., Hummel, J. E., & Sandhofer, C. M. (2008). A theory of the discovery and predication of relational concepts. Psychological Review, 115(1), 143.Google Scholar
Doumas, L. A. A., Puebla, G., Martin, A. E., & Hummel, J. E. (2022). A theory of relation learning and cross-domain generalization. Psychological Review (advance online publication). https://doi.org/10.1037/rev0000346Google Scholar
Ehresman, D., & Wessel, D. L. (1978). Report: Perception of Timbral Analogies. Paris: Centre Georges Pompidou.Google Scholar
Falkenhainer, B., Forbus, K. D., & Gentner, D. (1989). The structure-mapping engine: algorithm and examples. Artificial Intelligence, 41, 163.Google Scholar
Forbus, K. D., Gentner, D., & Law, K. (1995). MAC/FAC: a model of similarity-based retrieval. Cognitive Science, 19, 141205.Google Scholar
Forbus, K. D., & Hinrichs, T. R. (2017). Analogy and qualitative representations in the companion cognitive architecture. AI Magazine, 2017, 34–42.Google Scholar
Gentner, D. (1983). Structure-mapping: a theoretical framework for analogy. Cognitive Science, 7, 155170.Google Scholar
Gentner, D. (2003). Why we’re so smart. In Gentner, D. & Goldin-Meadow, S. (Eds.), Language in Mind: Advances in the Study of Language and Thought (pp. 195235). Cambridge, MA: MIT Press.Google Scholar
Gick, M. L., & Holyoak, K. J. (1980). Analogical problem solving. Cognitive Psychology, 12, 306355.Google Scholar
Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15, 138.Google Scholar
Halford, G. S. (1992). Analogical reasoning and conceptual complexity in cognitive development. Human Development, 35, 193217.Google Scholar
Halford, G. S., Wilson, W. H., & Phillips, S. (1998). Processing capacity defined by relational complexity: implications for comparative, developmental, and cognitive psychology. Brain and Behavioral Sciences, 21, 803864.Google Scholar
Hill, F., Santoro, A., Barrett, D. G., Morcos, A. S., & Lillicrap, T. (2019). Learning to make analogies by contrasting abstract relational structure. arXiv:1902.00120Google Scholar
Hofstadter, D. R., & Mitchell, M. (1994). An overview of the Copycat project. In Holyoak, K. J. & Barnden, J. A. (Eds.), Advances in Connectionist and Neural Computation Theory, Vol. 2: Analogical Connections (pp. 31112). Norwood, NJ: Erlbaum.Google Scholar
Hofstadter, D., & Sander, E. (2013). Surfaces and Essences: Analogy as the Fuel and Fire of Thinking. New York, NY: Basic Books.Google Scholar
Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (1986). Induction: Processes of Inference, Learning, and Discovery. Cambridge, MA. MIT Press.Google Scholar
Holyoak, K. J. (2019). The Spider’s Thread: Metaphor in Mind, Brain and Poetry. Cambridge, MA: MIT Press.Google Scholar
Holyoak, K. J., & Thagard, P. (1989). Analogical mapping by constraint satisfaction. Cognitive Science, 13, 295355.Google Scholar
Holyoak, K. J., & Thagard, P. (1995). Mental Leaps: Analogy in Creative Thought. Cambridge, MA: MIT Press.Google Scholar
Hu, S., Ma, Y., Liu, X., Wei, Y., & Bai, S. (2020). Hierarchical rule induction network for abstract visual reasoning. arXiv:2002.06838.Google Scholar
Hummel, J. E. (2010). Symbolic vs. associative learning. Cognitive Science, 34, 958965.Google Scholar
Hummel, J. E. (2011). Getting symbols out of a neural architecture. Connection Science, 23, 109118.Google Scholar
Hummel, J. E., & Biederman, I. (1992). Dynamic binding in a neural network for shape recognition. Psychological Review, 99, 480517.Google Scholar
Hummel, J. E., & Holyoak, K. J. (1992). Indirect analogical mapping. In Proceedings of the 14th Annual Conference of the Cognitive Science Society (pp. 516521). Hillsdale, NJ: Erlbaum.Google Scholar
Hummel, J. E., & Holyoak, K. J. (1997). Distributed representations of structure: a theory of analogical access and mapping. Psychological Review, 104, 427466.Google Scholar
Hummel, J. E., & Holyoak, K. J. (2003). A symbolic-connectionist theory of relational inference and generalization. Psychological Review, 110, 220264.Google Scholar
Hummel, J. E., Landy, D. H., & Devnich, D. (2008). Toward a process model of explanation with implications for the type-token problem. In Naturally Inspired AI: Papers from the AAAI Fall Symposium. Technical Report FS-08-06, 79-86.Google Scholar
Hummel, J. E., Licato, J., & Bringsjord, S. (2014). Analogy, explanation, and proof. Frontiers in Human Neuroscience (online). http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00867/abstractGoogle Scholar
Jung, W., & Hummel, J. E., (2015a). Making probabilistic relational categories learnable. Cognitive Science, 39, 12591291. https://doi.org/10.1111/cogs.12199Google Scholar
Jung, W., & Hummel, J. E. (2015b). Revisiting Wittgenstein’s puzzle: hierarchical encoding and comparison facilitate learning of probabilistic relational categories. Frontiers in Psychology, 6, 110. https://doi.org/10.3389/fpsyg.2015.00110Google Scholar
Kittur, A., Hummel, J. E., & Holyoak, K. J. (2004). Feature- vs. relation-defined categories: probab(alistical)ly not the same. In Proceedings of the 26th Annual Conference of the Cognitive Science Society (pp. 696–701).Google Scholar
Kittur, A., Hummel, J. E., & Holyoak, K. J. (2006). Ideals aren’t always typical: dissociating goodness-of-exemplar from typicality judgments. In Proceedings of the 28th Annual Conference of the Cognitive Science Society.Google Scholar
Knowlton, B. J., Morrison, R. G., Hummel, J. E., & Holyoak, K. J. (2012). A neurocomputational system for relational reasoning. Trends in Cognitive Sciences, 17, 373381.Google Scholar
Kogut, P., Gordon, J., Morgenthaler, D., et al. (2011). Recognizing geospatial patterns with biologically-inspired relational reasoning. In Second International Conference on Biologically Inspired Cognitive Architectures (BICA 2011).Google Scholar
Kubose, T. T., Holyoak, K. J., & Hummel, J. E. (2002). The role of textual coherence in incremental analogical mapping. Journal of Memory and Language, 47, 407435.Google Scholar
Lakoff, G. (1987). Women, Fire and Dangerous Things: What Categories Reveal About the Mind. Chicago, IL: University of Chicago Press.Google Scholar
Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. Chicago, IL: University of Chicago Press.Google Scholar
Leech, R., Mareschal, D., & Cooper, R.P. (2008). Analogy as relational priming: a developmental and computational perspective on the origins of a complex cognitive skill. Behavioral and Brain Sciences, 31(4), 378414.Google Scholar
Licato, J., Bringsjord, S., & Hummel, J. E. (2012). Exploring the role of analogico-deductive reasoning in the balance-beam task. In Rethinking Cognitive Development: Proceedings of the 42nd Annual Meeting of the Jean Piaget Society.Google Scholar
Lin, T. -J., Anderson, R. C., Hummel, J. E., et al. (2012). Children’s use of analogy during Collaborative Reasoning. Child Development, 83, 14291443.Google Scholar
Lovett, A., & Forbus, K. (2017). Modeling visual problem solving as analogical reasoning. Psychological Review, 124(1), 6090.Google Scholar
Lu, H., Chen, D., & Holyoak, K. J., (2012). Bayesian analogy with relational transformations. Psychological Review, 119, 617648.Google Scholar
Malhotra, G., Evans, B., & Bowers, J. (2020). Hiding a plane behind a pixel: shape-bias in CNNs and the benefit of building in biological constraints. Vision Research, 174, 5778.Google Scholar
Marcus, G. F. (1998). Rethinking eliminative connectionism. Cognitive Psychology, 37(3), 243282.Google Scholar
Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. New York, NY: W.H. Freeman.Google Scholar
McClelland, J. L., & Rogers, T. T. (2003). The parallel distributed processing approach to semantic cognition. Nature Reviews Neuroscience, 4(4), 310322.Google Scholar
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Jordan, M. I., LeCun, Y., & Solla, S. A. (Eds.), Advances in Neural Information Processing Systems (pp. 31113119). Cambridge, MA: MIT Press.Google Scholar
Morrison, R. G., Doumas, L. A., & Richland, L. E. (2011). A computational account of children’s analogical reasoning: balancing inhibitory control in working memory and relational representation. Developmental Science, 14(3), 516529.Google Scholar
Morrison, R. G., Krawczyk, D. C., Holyoak, K. J., et al. (2004). A neurocomputational model of analogical reasoning and its breakdown in frontotemporal lobar degeneration. Journal of Cognitive Neuroscience, 16, 260271.Google Scholar
Penn, D. C., Holyoak, K. J., & Povinelli, D. J. (2008). Darwin’s mistake: explaining the discontinuity between human and nonhuman minds. Behavioral and Brain Sciences, 31(2), 109130.Google Scholar
Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: global vectors for word representation. Empirical Methods in Natural Language Processing, 14, 15321543.Google Scholar
Peyre, J., Laptev, I., Schmid, C., & Sivic, J. (2019). Detecting unseen visual relations using analogies. In Proceedings of the IEEE International Conference on Computer Vision (pp. 19811990).Google Scholar
Rabagliati, H., Doumas, L. A., & Bemis, D. K. (2017). Representing composed meanings through temporal binding. Cognition, 162, 6172.Google Scholar
Rabovsky, M., Hansen, S. S., & McClelland, J. L. (2018). Modelling the N400 brain potential as change in a probabilistic representation of meaning. Nature Human Behavior, 2(9), 693705.Google Scholar
Ross, B. (1987). This is like that: the use of earlier problems and the separation of similarity effects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 629639.Google Scholar
Rumelhart, D. E., & Abrahamson, A. A. (1973). A model for analogical reasoning. Cognitive Psychology, 5(1), 128.Google Scholar
Sandhofer, C. M., & Doumas, L. A. (2008). Order of presentation effects in learning color categories. Journal of Cognition and Development, 9(2), 194221.Google Scholar
Santoro, A., Raposo, D., Barrett, D. G., et al. (2017). A simple neural network module for relational reasoning. In Jordan, M. I., LeCun, Y., & Solla, S. A. (Eds.), Advances in Neural Information Processing Systems (pp. 49674976). Cambridge, MA: MIT Press.Google Scholar
Son, J. Y., Doumas, L. A., & Goldstone, R. L. (2010). When do words promote analogical transfer? The Journal of Problem Solving, 3(1), 4.Google Scholar
St. John, M. F. (1992). The Story Gestalt: a model of knowledge-intensive processes in text comprehension. Cognitive Science, 16, 271302.Google Scholar
St. John, M. F., & McClelland, J. L. (1990). Learning and applying contextual constraints in sentence comprehension. Artificial Intelligence, 46, 217257.Google Scholar
Taylor, E. G., & Hummel, J. E. (2009). Finding similarity in a model of relational reasoning. Cognitive Systems Research, 10, 229239.Google Scholar
Tversky, A. (1977). Features of similarity. Psychological Review, 84, 327352.Google Scholar
Viskontas, I., Morrison, R., Holyoak, K. J., Hummel, J. E., & Knowlton, B. J. (2004). Relational integration, inhibition, and analogical reasoning in older adults. Psychology and Aging, 19, 581591.Google Scholar
Zhou, L., Cui, P., Yang, S., Zhu, W., & Tian, Q. (2019). Learning to learn image classifiers with visual analogy. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 11497–11506).Google Scholar

References

Barrouillet, P., & Lecas, J. F. (1999). Mental models in conditional reasoning and working memory. Thinking & Reasoning, 5, 289302.Google Scholar
Beth, E. W., & Piaget, J. (1966). Mathematical Epistemology and Psychology. Dordrecht: Reidel.Google Scholar
Boole, G. (1854). An Investigation of the Laws of Thought. London: Macmillan.Google Scholar
Braine, M. D. S. (1978). On the relation between the natural logic of reasoning and standard logic. Psychological Review, 85, 121.Google Scholar
Bucciarelli, M., & Johnson-Laird, P. N. (1999). Strategies in syllogistic reasoning. Cognitive Science, 23, 247303.Google Scholar
Byrne, R. M. J. (2005). The Rational Imagination: How People Create Alternatives to Reality. Cambridge, MA: MIT Press.Google Scholar
Byrne, R. M. J., & Johnson-Laird, P. N. (1989). Spatial reasoning. Journal of Memory and Language, 28, 564575.Google Scholar
Byrne, R. M. J., & Johnson-Laird, P. N. (2019). If and or: real and counterfactual possibilities in their truth and probability. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46, 760780.Google Scholar
Carey, S. (2009). The Origin of Concepts. New York, NY: Oxford University Press.Google Scholar
Cherubini, P., & Johnson-Laird, P. N. (2004). Does everyone love everyone? The psychology of iterative reasoning. Thinking & Reasoning, 10, 3153.Google Scholar
Cook, S. A. (1971). The complexity of theorem proving procedures. Proceedings of the Third Annual Association of Computing Machinery Symposium on the Theory of Computing, 3, 151158.Google Scholar
Goodwin, G. P. (2014). Is the basic conditional probabilistic? Journal of Experimental Psychology: General, 143, 12141241.Google Scholar
Hempel, C. G. (1945). Studies in the logic of confirmation, Parts I and II. Mind, 54, 126, 97–121. http://dx.doi.org/10.1093/mind/LIV.213.1Google Scholar
Hinterecker, T., Knauff, M., & Johnson-Laird, P. N. (2016). Modality, probability, and mental models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42, 16061620.Google Scholar
Hughes, G. E., & Cresswell, M. J. (1996). A New Introduction to Modal Logic. London: Routledge.Google Scholar
Jeffrey, R. (1981). Formal Logic: Its Scope and Limits (2nd ed.). New York, NY: McGraw-Hill.Google Scholar
Johnson-Laird, P. N. (1975). Models of deduction. In Falmagne, R. (Ed.), Reasoning: Representation and Process (pp. 754). Springdale, NJ: Erlbaum.Google Scholar
Johnson-Laird, P. N. (1983). Mental Models. Cambridge: Cambridge University Press.Google Scholar
Johnson-Laird, P. N. (2006). How We Reason. New York, NY: Oxford University Press.Google Scholar
Johnson-Laird, P. N., Girotto, V., & Legrenzi, P. (2004). Reasoning from inconsistency to consistency. Psychological Review, 111, 640661.Google Scholar
Johnson-Laird, P. N., Legrenzi, P., Girotto, P., & Legrenzi, M. (2000). Illusions in reasoning about consistency. Science, 288, 531532.Google Scholar
Johnson-Laird, P. N., Legrenzi, P., Girotto, V., Legrenzi, M., & Caverni, J-P. (1999). Naive probability: a mental model theory of extensional reasoning. Psychological Review, 106, 6288.Google Scholar
Johnson-Laird, P. N., & Ragni, M. (2019). Possibilities as the foundation of reasoning. Cognition, 193, 130950.Google Scholar
Johnson-Laird, P. N., & Wason, P. C. (1970a). A theoretical analysis of insight into a reasoning task. Cognitive Psychology, 1, 134148. http://dx.doi.org/10.1016/0010-0285(70)90009-5Google Scholar
Johnson-Laird, P. N., & Wason, P. C. (1970b). Insight into a logical relation. Quarterly Journal of Experimental Psychology, 22, 4961. http://dx.doi.org/10.1080/14640747008401901Google Scholar
Kelly, L., Khemlani, S., & Johnson-Laird, P.N. (2020). Reasoning about durations. Journal of Cognitive Neuroscience, 32 (11), 2103–2116.Google Scholar
Khemlani, S. (2021). Psychological theories of syllogistic reasoning. In Knauff, M. & Spohn, W. (Eds.), Handbook of Rationality. Cambridge, MA: MIT Press.Google Scholar
Khemlani, S. S., Byrne, R. M. J., & Johnson-Laird, P. N. (2018). Facts and possibilities: a model-based theory of sentential reasoning. Cognitive Science, 2018, 1–38. https://doi.org/10.1111/cogs.12634Google Scholar
Khemlani, S., & Johnson-Laird, P. N. (2012). Theories of the syllogism: a meta-analysis. Psychological Bulletin, 138, 427457.Google Scholar
Khemlani, S., & Johnson-Laird, P. N. (2013). Cognitive changes from explanations. Journal of Cognitive Psychology, 25, 139146.Google Scholar
Khemlani, S., & Johnson-Laird, P. N. (2017). Illusions in reasoning. Minds and Machines, 27, 1135.Google Scholar
Khemlani, S., & Johnson-Laird, P. N. (2022). Reasoning about properties: a computational theory. Psychological Review (advance online publication). https://doi.org/10.1037/rev0000240Google Scholar
Khemlani, S., Lotstein, M., & Johnson-Laird, P. N. (2015). Naive probability: model-based estimates of unique events. Cognitive Science, 39, 12161258.Google Scholar
Khemlani, S., Mackiewicz, R., Bucciarelli, M., & Johnson-Laird, P. N. (2013). Kinematic mental simulations in abduction and deduction. Proceedings of the National Academy of Sciences, 110 (42), 1676616771. www.pnas.org/cgi/doi/10.1073/pnas.1316275110Google Scholar
Knauff, M. (2013). Space to Reason. Cambridge, MA: MIT Press.Google Scholar
Miller, G. A., & Johnson-Laird, P. N. (1976). Language and Perception. Cambridge, MA: Harvard University Press.Google Scholar
Newell, A. (1973). You can’t play 20 questions with nature and win. In Chase, W. G., (Ed.), Visual Information Processing. New York, NY: Academic Press.Google Scholar
Oaksford, M., & Chater N., (1996). Rational explanation of the selection task. Psychological Review, 103, 381391.Google Scholar
Oaksford, M., & Chater, N. (2020). New paradigms in the psychology of reasoning. Annual Review of Psychology, 71, 12.1–12.26. https://doi.org/10.1146/annurev-psych-010419-%20051132Google Scholar
Osherson, D. N. (1974–1976). Logical Abilities in Children (vols. 1–4). Hillsdale, NJ: Erlbaum.Google Scholar
Popper, K. R. (1959). The Logic of Scientific Discovery. New York, NY: Basic Books.Google Scholar
Ragni, M., Dames, H., & Johnson-Laird, P. N. (2019). A meta-analysis of conditional reasoning. In preparation.Google Scholar
Ragni, M., & Knauff, M. (2013). A theory and a computational model of spatial reasoning with preferred mental modelsPsychological Review120, 561588.Google Scholar
Ragni, M., Kola, I., & Johnson-Laird, P. N. (2018). On selecting evidence to test hypotheses. Psychological Bulletin, 144, 779796. http://dx.doi.org/10.1037/bul0000146Google Scholar
Ramsey, F. R. (1990). F. R. Ramsey, Philosophical Papers. In Mellor, D. H., (Ed.). Cambridge: Cambridge University Press.Google Scholar
Rips, L. J. (1994). The Psychology of Proof. Cambridge, MA: MIT Press.Google Scholar
Schaeken, W., Johnson-Laird, P. N., & d’Ydewalle, G. (1996). Mental models and temporal reasoning. Cognition, 60, 205234.Google Scholar
Stenning, K., & Van Lambalgen, M. (2012). Human Reasoning and Cognitive Science. Cambridge, MA: MIT Press.Google Scholar
Sun, R. (2016). Anatomy of the Mind: Exploring Psychological Mechanisms and Processes with the Clarion Cognitive Architecture. New York, NY: Oxford University Press.Google Scholar
Tversky, B. (1993). Cognitive maps, cognitive collages, and spatial mental models. In Frank, A. U. & Campari, I. (Eds.), Spatial Information Theory: A Theoretical Basis for GIS, Proceedings COSIT ’93. Lecture Notes in Computer Science, 716, pp. 1424. Berlin: Springer. https://doi.org/10.1007/3-540-57207-4_2Google Scholar
Tversky, A., & Kahneman, D. (1983). Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment. Psychological Review, 90 (4), 293315.Google Scholar
Wason, P. C. (1968). Reasoning about a rule. The Quarterly Journal of Experimental Psychology, 20, 273281.Google Scholar
Wason, P. C. (1969). Regression in reasoning? British Journal of Psychology, 60, 471480.Google Scholar

References

Allais, M. (1953). Le comportement de l’homme rationnel devant le risque: critique des postulats et axiomes de l’école américaine. Econometrica: Journal of the Econometric Society, 21(4), 503546.Google Scholar
Anderson, J. R. (1996). ACT: a simple theory of complex cognition. American Psychologist, 51, 355365.Google Scholar
Beach, L. R., & Mitchell, T. R. (1978). A contingency model for the selection of decision strategies. Academy of Management Review, 3(3), 439449.Google Scholar
Bergner, A. S., Oppenheimer, D. M., & Detre, G. (2019). VAMP (Voting Agent Model of Preferences): a computational model of individual multi-attribute choice. Cognition, 192, 103971.Google Scholar
Berkowitsch, N. A., Scheibehenne, B., & Rieskamp, J. (2014). Rigorously testing multialternative decision field theory against random utility models. Journal of Experimental Psychology: General, 143(3), 1331.Google Scholar
Bhatia, S. (2013). Associations and the accumulation of preference. Psychological Review, 120(3), 522.Google Scholar
Bhatia, S. (2014). Sequential sampling and paradoxes of risky choice. Psychonomic Bulletin & Review, 21(5), 10951111.Google Scholar
Bhatia, S., & Pleskac, T. J. (2019). Preference accumulation as a process model of desirability ratings. Cognitive Psychology, 109, 4767.Google Scholar
Birnbaum, M. H. (2008). New paradoxes of risky decision making. Psychological Review, 115(2), 463.Google Scholar
Birnbaum, M. H., & Stegner, S. E. (1979). Source credibility in social judgment: bias, expertise, and the judge’s point of view. Journal of Personality and Social Psychology, 37, 4874.Google Scholar
Bostic, R., Herrnstein, R. J., & Luce, R. D. (1990). The effect on the preference-reversal phenomenon of using choice indifferences. Journal of Economic Behavior & Organization, 13(2), 193212.Google Scholar
Busemeyer, J. R., & Diederich, A. (2002). Survey of decision field theory. Mathematical Social Sciences, 43(3), 345370.Google Scholar
Busemeyer, J. R., Gluth, S., Rieskamp, J., & Turner, B. M. (2019). Cognitive and neural bases of multi-attribute, multi-alternative, value-based decisions. Trends in Cognitive Sciences, 23(3), 251263.Google Scholar
Busemeyer, J. R., & Johnson, J. G. (2008). Micro-process models of decision making. In R. Sun (Ed.), Cambridge Handbook of Computational Psychology, (pp. 302–321).Google Scholar
Busemeyer, J. R., & Townsend, J. T. (1992). Fundamental derivations from decision field theory. Mathematical Social Sciences, 23(3) (pp. 302–321).Google Scholar
Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review, 100(3), 432.Google Scholar
Busemeyer, J. R., Wang, Z., & Townsend, J. T. (2006). Quantum dynamics of human decision-making. Journal of Mathematical Psychology, 50(3), 220241.Google Scholar
Cheng, J., & González‐Vallejo, C. (2017). Action dynamics in intertemporal choice reveal different facets of decision process. Journal of Behavioral Decision Making, 30(1), 107122.Google Scholar
Colas, J. T. (2017). Value-based decision making via sequential sampling with hierarchical competition and attentional modulation. PloS One, 12(10), e0186822.Google Scholar
Diederich, A. (1997). Dynamic stochastic models for decision making under time constraints. Journal of Mathematical Psychology, 41(3), 260274.Google Scholar
Diederich, A., & Busemeyer, J. R. (1999). Conflict and the stochastic-dominance principle of decision making. Psychological Science, 10(4), 353359.Google Scholar
Diederich, A., & Busemeyer, J. R. (2003). Simple matrix methods for analyzing diffusion models of choice probability, choice response time, and simple response time. Journal of Mathematical Psychology, 47(3), 304322.Google Scholar
Diederich, A., & Trueblood, J. S. (2018). A dynamic dual process model of risky decision making. Psychological Review, 125(2), 270.Google Scholar
Ellsberg, D. (1961). Risk, ambiguity, and the Savage axioms. The Quarterly Journal of Economics, 75(4), 643669.Google Scholar
Fiedler, S., & Glöckner, A. (2012). The dynamics of decision making in risky choice: an eye-tracking analysis. Frontiers in Psychology, 3, 335.Google Scholar
Fifić, M., Houpt, J. W., & Rieskamp, J. (2019). Response times as identification tools for cognitive processes underlying decisions. In M. Schulte-Mecklenbeck, A. Kuehberger, & J. G. Johnson (Eds.), A Handbook of Process Tracing Methods for Decision Research (p. 184). New York, NY: Psychology Press.Google Scholar
Frame, M. E. (2019). EEG and ERPs as neural process tracing methodologies in decision-making research. In M. Schulte-Mecklenbeck, A. Kuehberger, & J. G. Johnson (Eds.), A Handbook of Process Tracing Methods (pp. 217233). London: Routledge.Google Scholar
Frame, M. E., Johnson, J. G., & Thomas, R. D. (2018). A neural indicator of response competition in preferential choice. Decision, 5(4), 272.Google Scholar
Gao, J., & Lee, J. D. (2006). Extending the decision field theory to model operators’ reliance on automation in supervisory control situations. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 36(5), 943959.Google Scholar
Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451482.Google Scholar
Glöckner, A., & Betsch, T. (2008). Multiple-reason decision making based on automatic processing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34(5), 1055.Google Scholar
Glöckner, A., Heinen, T., Johnson, J. G., & Raab, M. (2012). Network approaches for expert decisions in sports. Human Movement Science, 31(2), 318333.Google Scholar
Glöckner, A., Hilbig, B. E., & Jekel, M. (2014). What is adaptive about adaptive decision making? A parallel constraint satisfaction account. Cognition, 133(3), 641666.Google Scholar
Gold, J. I., & Shadlen, M. N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30, 535574.Google Scholar
Gonzalez, R., & Wu, G. (1999). On the shape of the probability weighting function. Cognitive Psychology, 38(1), 129166.Google Scholar
Grossberg, S., & Gutowski, W. E. (1987). Neural dynamics of decision making under risk: affective balance and cognitive-emotional interactions. Psychological Review, 94(3), 300.Google Scholar
Hotaling, J. M., Busemeyer, J. R., & Li, J. (2010). Theoretical developments in decision field theory: comment on Tsetsos, Usher, and Chater (2010). Psychological Review, 117(4), 12941298.Google Scholar
Huber, J., Payne, J. W., & Puto, C. (1982). Adding asymmetrically dominated alternatives: violations of regularity and the similarity hypothesis. Journal of Consumer Research, 9(1), 9098.Google Scholar
Johnson, J. G. (2006). Cognitive modeling of decision making in sports. Psychology of Sport and Exercise, 7(6), 631652.Google Scholar
Johnson, J. G., & Busemeyer, J. R. (2005). A dynamic, stochastic, computational model of preference reversal phenomena. Psychological Review, 112(4), 841.Google Scholar
Johnson, J. G., & Busemeyer, J. R. (2016). A computational model of the attention process in risky choice. Decision, 3(4), 254.Google Scholar
Johnson, J. G., & Frame, M. E. (2019). Using process tracing data to define and test process models. In Schulte-Mecklenbeck, M., Kuhberger, A., & Johnson, J. G. (Eds.), A Handbook of Process Tracing Methods (2nd ed.) (pp. 374387). New York, NY: Routledge.Google Scholar
Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk, Econometrica, 47, 263291.Google Scholar
Kahneman, D., & Tversky, A. (2013). Prospect theory: an analysis of decision under risk. In L. C. MacLean & W. T. Ziemba (Eds.), Handbook of the Fundamentals of Financial Decision Making: Part I (pp. 99127).Google Scholar
Keeney, R. L., & Raiffa, H. (1993). Decisions with Multiple Objectives: Preferences and Value Trade-offs. Cambridge: Cambridge University Press.Google Scholar
Kieslich, P. J., Henninger, F., Wulff, D. U., Haslbeck, J. M., & Schulte-Mecklenbeck, M. (2019). Mouse tracking: a practical guide to implementation and analysis. In Schulte-Mecklenbeck, M., Kuhberger, A., & Johnson, J. G. (Eds.), A Handbook of Process Tracing Methods (2nd ed.) (pp. 111130). New York, NY: Routledge.Google Scholar
Koop, G. J., & Johnson, J. G. (2013). The response dynamics of preferential choice. Cognitive Psychology, 67(4), 151185.Google Scholar
Krajbich, I., Armel, C., & Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience, 13(10), 1292.Google Scholar
Krajbich, I., Lu, D., Camerer, C., & Rangel, A. (2012). The attentional drift-diffusion model extends to simple purchasing decisions. Frontiers in Psychology, 3, 193.Google Scholar
Krajbich, I., & Rangel, A. (2011). Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions. Proceedings of the National Academy of Sciences, 108(33), 1385213857.Google Scholar
Kvam, P. D., & Busemeyer, J. R. (2020). A distributional and dynamic theory of pricing and preference. Psychological Review, 127(6), 1053. https://doi.org/0.1037/rev0000215Google Scholar
Laird, J. E. (2012). The Soar Cognitive Architecture. Cambridge, MA: MIT Press.Google Scholar
Lee, S., & Son, Y. J. (2020). Extended decision field theory with social-learning for long-term decision-making processes in social networks. Information Sciences, 512, 12931307. https://doi.org/10.1016/j.ins.2019.10.025Google Scholar
Lejarraga, T., Dutt, V., & Gonzalez, C. (2012). Instance‐based learning: a general model of repeated binary choice. Journal of Behavioral Decision Making, 25(2), 143153.Google Scholar
Lichtenstein, S., & Slovic, P. (1971). Reversals of preference between bids and choices in gambling decisions. Journal of Experimental Psychology, 89(1), 46.Google Scholar
Lieder, F., & Griffiths, T. L. (2017). Strategy selection as rational metareasoning. Psychological Review, 124(6), 762.Google Scholar
Lindman, H. R. (1971). Inconsistent preferences among gambles. Journal of Experimental Psychology, 89(2), 390.Google Scholar
Link, S. W., & Heath, R. A. (1975). A sequential theory of psychological discrimination. Psychometrika, 40(1), 77105.Google Scholar
Marewski, J. N., & Mehlhorn, K. (2011). Using the ACT-R architecture to specify 39 quantitative process models of decision making. Judgment and Decision Making, 6(6), 439519.Google Scholar
Marley, A. A. J., & Colonius, H. (1992). The “horse race” random utility model for choice probabilities and reaction times, and its competing risks interpretation. Journal of Mathematical Psychology, 36, 120.Google Scholar
Noguchi, T., & Stewart, N. (2018). Multialternative decision by sampling: a model of decision making constrained by process data. Psychological Review, 125(4), 512.Google Scholar
Nosofsky, R. M., & Palmeri, T. J. (1997). An exemplar-based random walk model of speeded classification. Psychological Review, 104(2), 266.Google Scholar
Nunez, M. D., Vandekerckhove, J., & Srinivasan, R. (2017). How attention influences perceptual decision making: single-trial EEG correlates of drift-diffusion model parameters. Journal of Mathematical Psychology, 76, 117130.Google Scholar
Oppenheimer, D. M., & Kelso, E. (2015). Information processing as a paradigm for decision making. Annual Review of Psychology, 66, 277294.Google Scholar
Otter, T., Allenby, G. M., & Van Zandt, T. (2008). An integrated model of discrete choice and response time. Journal of Marketing Research, 45(5), 593607.Google Scholar
Payne, J. W. (1976). Task complexity and contingent processing in decision making: an information search and protocol analysis. Organizational Behavior and Human Performance, 16(2), 366387.Google Scholar
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1988). Adaptive strategy selection in decision making. Journal of experimental psychology: Learning, Memory, and Cognition, 14(3), 534.Google Scholar
Payne, J. W., & Braunstein, M. L. (1978). Risky choice: an examination of information acquisition behavior. Memory & Cognition, 6(5), 554561.Google Scholar
Payne, J. W., Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The Adaptive Decision Maker. Cambridge: Cambridge University Press.Google Scholar
Pleskac, T. J., & Busemeyer, J. R. (2010). Two-stage dynamic signal detection: a theory of choice, decision time, and confidence. Psychological Review, 117(3), 864.Google Scholar
Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59108.Google Scholar
Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016). Diffusion decision model: current issues and history. Trends in Cognitive Sciences, 20(4), 260281.Google Scholar
Rieskamp, J., Busemeyer, J. R., & Mellers, B. A. (2006). Extending the bounds of rationality: evidence and theories of preferential choice. Journal of Economic Literature, 44(3), 631661.Google Scholar
Rieskamp, J., & Otto, P. E. (2006). SSL: a theory of how people learn to select strategies. Journal of Experimental Psychology: General, 135(2), 207.Google Scholar
Roe, R. M., Busemeyer, J. R., & Townsend, J. T. (2001). Multialternative decision field theory: a dynamic connectionst model of decision making. Psychological Review, 108(2), 370.Google Scholar
Rottenstreich, Y., & Hsee, C. K. (2001). Money, kisses, and electric shocks: on the affective psychology of risk. Psychological Science, 12(3), 185190.Google Scholar
Schulte-Mecklenbeck, M., Johnson, J. G., Böckenholt, U., et al. (2017). Process-tracing methods in decision making: on growing up in the 70s. Current Directions in Psychological Science, 26(5), 442450.Google Scholar
Shah, A. K., & Oppenheimer, D. M. (2007). Easy does it: the role of fluency in cue weighting. Judgment and Decision Making, 2(6), 371379.Google Scholar
Simonson, I. (1989). Choice based on reasons: the case of attraction and compromise effects. Journal of Consumer Research, 16(2), 158174.Google Scholar
Smith, P. L., & Ratcliff, R. (2004). Psychology and neurobiology of simple decisions. Trends in Neurosciences, 27(3), 161168.Google Scholar
Stewart, N., & Simpson, K. (2008). A decision-by-sampling account of decision under risk. In N. Chater & M. Oaksford (Eds.), The Probabilistic Mind. Prospects for Bayesian Cognitive Science (pp. 261276). Oxford: Oxford University Press.Google Scholar
Stewart, N., Hermens, F., & Matthews, W. J. (2016). Eye movements in risky choice. Journal of Behavioral Decision Making, 29(2–3), 116136.Google Scholar
Sun, R. (2016). Anatomy of the Mind: Exploring Psychological Mechanisms and Processes with the Clarion Cognitive Architecture. Oxford: Oxford University Press.Google Scholar
Thorngate, W. (1980). Efficient decision heuristics. Behavioral Science, 25(3), 219225.Google Scholar
Townsend, J. T., & Ashby, F. G. (1983). Stochastic modeling of elementary psychological processes. Cambridge University Press Archive.Google Scholar
Townsend, J. T., & Busemeyer, J. R. (1989) Approach-avoidance: return to dynamic decision behavior. In Izawa, C., (Ed.), Current Issues in Cognitive Processes: The Tulane Flowerree Symposium on Cognition. Hillsdale, NJ: Erlbaum.Google Scholar
Trueblood, J. S., Brown, S. D., & Heathcote, A. (2014). The multiattribute linear ballistic accumulator model of context effects in multialternative choice. Psychological Review, 121(2), 179.Google Scholar
Tsetsos, K., Usher, M., & Chater, N. (2010). Preference reversal in multiattribute choice. Psychological Review, 117(4), 1275.Google Scholar
Turner, B. M., Schley, D. R., Muller, C., & Tsetsos, K. (2018). Competing theories of multialternative, multiattribute preferential choice. Psychological Review, 125(3), 329.Google Scholar
Turner, B. M., van Maanen, L., & Forstmann, B. U. (2015). Informing cognitive abstractions through neuroimaging: the neural drift diffusion model. Psychological Review, 122(2), 312.Google Scholar
Tversky, A. (1972). Elimination by aspects: a theory of choice. Psychological Review, 79(4), 281.Google Scholar
Tversky, A. (1977). Features of similarity. Psychological Review, 84(4), 327.Google Scholar
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297323.Google Scholar
Tversky, A., & Simonson, I. (1993). Context-dependent preferences. Management Science, 39(10), 11791189.Google Scholar
Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: the leaky, competing accumulator model. Psychological Review, 108(3), 550.Google Scholar
Usher, M., & McClelland, J. L. (2004). Loss aversion and inhibition in dynamical models of multialternative choice. Psychological Review, 111(3), 757.Google Scholar
van Vugt, M. K., Simen, P., Nystrom, L. E., Holmes, P., & Cohen, J. D. (2012). EEG oscillations reveal neural correlates of evidence accumulation. Frontiers in Neuroscience, 6, 106.Google Scholar
van Vugt, M. K., Simen, P., Nystrom, L., Holmes, P., & Cohen, J. D. (2014). Lateralized readiness potentials reveal properties of a neural mechanism for implementing a decision threshold. PloS One, 9(3), e90943.Google Scholar
von Neumann, J., & Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton, NJ: Princeton University Press.Google Scholar
Wallsten, T. S., & Barton, C. (1982). Processing probabilistic multidimensional information for decisions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 8(5), 361.Google Scholar
Weber, E., & Kirsner, B. (1997). Reasons for rank-dependent utility evaluation. Journal of Risk and Uncertainty, 14(1), 4161.Google Scholar
Wedell, D. H. (2015). Multialternative choice models. The Wiley Blackwell Handbook of Judgment and Decision Making, 2, 117140.Google Scholar
Wollschläger, L. M., & Diederich, A. (2019). Similarity, attraction, and compromise effects: original findings, recent empirical observations, and computational cognitive process models. American Journal of Psychology (online). https://doi.org/10.5406/amerjpsyc.133.1.0001Google Scholar
Yechiam, E., Busemeyer, J. R., Stout, J. C., & Bechara, A. (2005). Using cognitive models to map relations between neuropsychological disorders and human decision-making deficits. Psychological Science, 16(12), 973978.Google Scholar

References

Ackerman, P. L. (1990). A correlational analysis of skill specificity: learning, abilities, and individual differences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 883901.Google Scholar
Altmann, E. M., & Trafton, J. G. (2002). Memory for goals: an activation-based model. Cognitive Science, 26, 3983.Google Scholar
Amir, E., & Maynard-Zhang, P. (2004). Logic-based subsumption architecture. Artificial Intelligence, 153, 167237.Google Scholar
Anderson, J. R. (1976). Language, Memory, and Thought. Hillsdale, NJ: Erlbaum.Google Scholar
Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369406.Google Scholar
Anderson, J. R. (1983). The Architecture of Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Anderson, J. R. (1986). Knowledge compilation: the general learning mechanism. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach (vol. 2, pp. 289310). Los Altos, CA: Kaufmann.Google Scholar
Anderson, J. R. (1987). Skill acquisition: compilation of weak-method problem solutions. Psychological Review, 94, 192210.Google Scholar
Anderson, J. (1989). The analogical origins of errors in problem solving. In Klahr, D. & Kotovsky, K. (Eds.), Complex Information Processing: The Impact of Herbert A. Simon. Hillsdale, NJ: Erlbaum.Google Scholar
Anderson, J. R. (1993). Rules of the Mind. Hillsdale, NJ: Erlbaum.Google Scholar
Anderson, J. R. (2007). How Can the Human Mind Occur in the Physical Universe? New York, NY: Oxford University Press.Google Scholar
Anderson, J. R., Betts, S., Bothell, D., Hope, R., & Lebiere, C. (2019). Learning rapid and precise skills. Psychological Review, 126, 727760.Google Scholar
Anderson, J. R., Kline, P., & Beasley, C. (1978). A Theory of the Acquisition of Cognitive Skills. New Haven, CT: Yale University Press.Google Scholar
Anderson, J. R., Kline, P. J., & Beasley, C. M., Jr. (1979). A general learning theory and its application to schema abstraction. In Bower, G. H. (Ed.), The Psychology of Learning and Motivation: Advances in Research and Theory (vol. 13, pp. 277318). New York, NY: Academic Press.Google Scholar
Anderson, J. R., & Thompson, R. (1989). Use of analogy in a production system architecture. In Vosniadou, S. & Ortony, A. (Eds.), Similarity and Analogical Reasoning (pp. 267297). Cambridge: Cambridge University Press.Google Scholar
Anzai, Y., & Simon, H. A. (1979). The theory of learning by doing. Psychological Review, 86, 124140.Google Scholar
Bharadwaj, K. K., & Jain, N. K. (1992). Hierarchical censored production rule (HCPRs) system. Data & Knowledge Engineering, 8, 1934.Google Scholar
Bhatnagar, N., & Mostow, J. (1994). On-line learning from search failure. Machine Learning, 15, 69117.Google Scholar
Blessing, S. B., & Anderson, J. R. (1996). How people learn to skip steps. Journal of Experimental Psychology: Learning, Memory, & Cognition, 22, 576598. [Reprinted in Polk & Seifert, 2002, pp. 577–620.]Google Scholar
Brown, J. S., & VanLehn, K. (1980). Repair theory: a generative theory of bugs in procedural skills. Cognitive Science, 4, 379426.Google Scholar
Buchanan, B. & Mitchell, T. (1978). Model-directed learning of production rules. In Waterman, D. & Hayes-Roth, F. (Eds.), Pattern-Directed Inference Systems (pp. 297312). New York, NY: Academic Press.Google Scholar
Bush, R. R., & Mosteller, F. (1951). A model for stimulus generalization and discrimination. Psychological Review, 58, 413423.Google Scholar
Carbonell, J. G. (1983). Learning by analogy: formulating and generalizing plans from past experience. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach (pp. 137161). Palo Alto, CA: Tioga.Google Scholar
Carbonell, J. G. (1986). Derivational analogy: a theory of reconstructive problem solving and expertise acquisition. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach (vol. 2, pp. 371392). Los Altos, CA: Morgan Kauffmann.Google Scholar
Carroll, J. B. (1993). Human Cognitive Abilities. Cambridge: Cambridge University Press.Google Scholar
Choi, D., & Ohlsson, S. (2011). Effects of multiple learning mechanisms in a cognitive architecture. In Carlson, L., Hölscher, C., & Shipley, T. (Eds.), Proceedings of the 33rd Annual Meeting of the Cognitive Science Society (pp. 3003–3008). Austin, TX: Cognitive Science Society Boston.Google Scholar
Christiansen, M. H. (2019). Implicit statistical learning. Topics in Cognitive Science, 11, 468481.Google Scholar
Conway, F., & Siegelman, J. (2005). Dark Hero of the Information Age: In Search of Norbert Wiener the Father of Cybernetics. New York, NY: Basic Books.Google Scholar
Cooper, R. P., Ruh, N., & Mareschal, D. (2014). The goal circuit model: a hierarchical, multi-route model of the acquisition and control of routine sequential action in humans. Cognitive Science, 3, 244274.Google Scholar
Corrigan-Halpern, A., & Ohlsson, S. (2002). Feedback effects in the acquisition of a hierarchical skill. In Gray, W. D. & Schunn, C. D. (Eds.), Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society (pp. 226231). Mahwah, NJ: Erlbaum.Google Scholar
Crevier, D. (1993). AI: The Tumultuous History of the Search for Artificial Intelligence. New York, NY: Basic Books.Google Scholar
Crossman, E. (1959). A theory of the acquisition of speed-skill. Ergonomics, 2, 152166.Google Scholar
Davis, R., & King, J. (1977) An overview of production systems. In Elcock, E. & Michie, D. (Eds.), Machine Intelligence 8 (pp. 300332). Chichester: Horwood.Google Scholar
De Jong, G. (Ed.). (2012). Investigating Explanation-Based Learning (vol. 120). London: Springer Science & Business Media.Google Scholar
Doane, S. M., Sohn, Y. W., McNamara, D. S., & Adams, D. (2000). Comprehension-based skill acquisition. Cognitive Science, 24, 152.Google Scholar
Donald, M. (1991). Origins of the Modern Mind: Three Stages in the Evolution of Culture and Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Douglass, S. A., & Anderson, J. R. (2008). A model of language processing and spatial reasoning using skill acquisition to situate action. In Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 2218–2286).Google Scholar
Ebbinghaus, H. (1964/1885). Memory: A Contribution to Experimental Psychology. New York, NY: Dover.Google Scholar
Elio, R., & Scharf, P. B. (1990). Modeling novice-to-expert shifts in problem-solving strategy and knowledge organization. Cognitive Science, 14, 579639.Google Scholar
Ericsson, K. A., Charness, N., Feltovich, P. J., & Hoffman, R. R. (2006). The Cambridge Handbook of Expertise and Expert Performance. Cambridge: Cambridge University Press.Google Scholar
Ericsson, K. A., Krampe, R. Th., & Tesch-Romer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100, 363406.Google Scholar
Falkenhainer, B., Forbus, K. D., & Gentner, D. (1989). The structure-mapping engine: algorithm and examples. Artificial Intelligence, 41, 163.Google Scholar
Fischer, K. W. (1980). A theory of cognitive development: the control and construction of hierarchies of skills. Psychological Review, 87, 477531.Google Scholar
Fitts, P. (1964). Perceptual-motor skill learning. In Melton, A. (Ed.), Categories of Human Learning (pp. 243285). New York, NY: Academic Press.Google Scholar
Forgy, C. L. (1982). Rete: a fast algorithm for the many pattern/many object pattern match problem. Artificial Intelligence, 19 , 1737.Google Scholar
Fu, W.-T., & Gray, W. D. (2004). Resolving the paradox of the active user: stable suboptimal performance in interactive tasks. Cognitive Science, 28, 901935.Google Scholar
Gagne, R. M. (1970). The Conditions of Learning (2nd ed.). London: Holt, Rinehart & Winston.Google Scholar
Gardner, H. (1985). The Mind’s New Science: A History of the Cognitive Revolution. New York, NY: Basic Books.Google Scholar
Gentner, D. (1983). Structure-mapping: a theoretical framework for analogy. Cognitive Science, 7, 155170.Google Scholar
Giunchiglia, E., Lee, J., Lifschitz, V., McCain, N. & Tuner, H. (2004) Nonmonotonic causal theories. Artificial Intelligence, 153, 49104.Google Scholar
Graesser, A. C., Millis, K., & Graesser, A. (2011). Discourse and cognition. In T. A. Van Dijk (Ed.), Discourse Studies: A Multidisciplinary Introduction (pp. 126142). London: SAGE Publications.Google Scholar
Gray, W. D., & Boehm-Davis, D. A. (2000). Milliseconds matter: an introduction to microstrategies and to their use in describing and predicting interactive behavior. Journal of Experimental Psychology: Applied, 6, 322335.Google Scholar
Gray, W. D., Schoelles, M. J., & Sims, C. R. (2005). Adapting to the task environment: explorations in expected value. Cognitive Systems Research, 6, 2740.Google Scholar
Grefenstette, J. J. (1988). Credit assignment in rule discovery systems based on genetic algorithms. Machine Learning, 3, 225245.Google Scholar
Hagert, G., Waern, Y., & Tärnlund, S.-Å. (1982). Open and closed models of understanding in conditional reasoning. Acta Psychologica, 52, 4159.Google Scholar
Hayes-Roth, F., Klahr, P., & Mostow, D. (1981). Advice taking and knowledge refinement: an iterative view of skill acquisition. In Anderson, J. (Ed.), Cognitive Skills and Their Acquisition (pp. 231253). Hillsdale, NJ: Erlbaum.Google Scholar
Hilgard, E. R., & Bower, G. H. (1966). Theories of Learning (3rd ed.). New York, NY: Appleton-Century-Crofts.Google Scholar
Holland, J., Holyoak, K., Nisbett, R., & Thagard, P. (1986). Induction: The Processes of Inference, Learning, and Discovery. Cambridge, MA: MIT Press.Google Scholar
Holyoak, K. J. (1985). The pragmatics of analogical transfer. In Bower, G. H. (Ed.), The Psychology of Learning and Motivation (vol. 19, pp. 5987). New York, NY: Academic Press.Google Scholar
Holyoak, K. J., & Thagard, P. R. (1989a). A computational model of analogical problem solving. In Vosniadou, S. & Ortony, A. (Eds.), Similarity and Analogical Reasoning (pp. 242266). Cambridge: Cambridge University Press.Google Scholar
Holyoak, K. J., & Thagard, P. (1989b). Analogical mapping by constraint satisfaction. Cognitive Science, 13, 295355.Google Scholar
Holyoak, K. J., & Thagard, P. (1994). Mental Leaps: Analogy in Creative Thought. Cambridge, MA: MIT Press.Google Scholar
Huffman, S. B., & Laird, J. E. (1995). Flexibly instructable agents. Journal of Artificial Intelligence Research, 3, 271324.Google Scholar
Hummel, J. E., & Holyoak, K. J. (1997). Distributed representations of structure: a theory of analogical access and mapping. Psychological Review, 104, 427466.Google Scholar
Hummel, J. E., & Holyoak, K. J. (2003). A symbolic-connectionist theory of relational inference and generalization. Psychological Review, 110, 220264.Google Scholar
Jain, N. K., & Bharadwaj, K. K. (1998). Some learning techniques in hierarchical censored production rules (HCPRs) system. International Journal of Intelligent Systems, 13, 319344.Google Scholar
James, W. (1890). Principles of Psychology (vols. 1 and 2). London: Macmillan.Google Scholar
Jones, G., Ritter, F. E., & Wood, D. J. (2000). Using a cognitive architecture to examine what develops. Psychological Science, 11(2), 93100.Google Scholar
Jones, R. M., & Langley, P. A. (2005). A constrained architecture for learning and problem solving. Computational Intelligence, 21, 480502.Google Scholar
Jones, R. M., & VanLehn, K. (1994). Acquisition of children’s addition strategies: a model of impasse-free, knowledge-level learning. Machine Learning, 16, 1136. [Reprinted in Polk & Seifert, 2002, pp. 623–646.]Google Scholar
Keane, M. T., Ledgeway, T., & Duff, S. (1994). Constraints on analogical mapping: a comparison of three models. Cognitive Science, 18, 338387.Google Scholar
Kieras, D., & Bovair, S. (1986). The acquisition of procedures from text: a production-system analysis of transfer of training. Journal of Memory and Language, 25, 507524.Google Scholar
Kim, J. W., Ritter, F. E., & Koubek, R. .J. (2013). An integrated theory for improved skill acquisition retention in the three stages of learning. Theoretical Issues in Ergonomic Science, 14(1), 3237.Google Scholar
Kintsch, W. (1998). Comprehension: A Paradigm for Cognition. Cambridge: Cambridge University Press.Google Scholar
Koedinger, K. R., & Anderson, J. R. (1990). Abstract planning and perceptual chunks: elements of expertise in geometry. Cognitive Science, 14, 511550.Google Scholar
Kokinov, B. N., & Petrov, A. A. (2001). Integrating memory and reasoning in analogy-making: the AMBR model. In Gentner, D., Holyoak, K. J., & Kokinov, B. N. (Eds.), The Analogical Mind: Perspectives from Cognitive Science (pp. 59124). Cambridge, MA: MIT Press.Google Scholar
Laird, J. E. (2012). The Soar Cognitive Architecture. Cambridge, MA: MIT Press.Google Scholar
Lane, N. (1987). Skill Acquisition Rates and Patterns: Issues and Training Implications. New York, NY: Springer-Verlag.Google Scholar
Langley, P. (1983). Learning search strategies through discrimination. International Journal of Man-Machine Studies, 18, 513541.Google Scholar
Langley, P. (1985). Learning to search: from weak methods to domain-specific heuristics. Cognitive Science, 9, 217260.Google Scholar
Langley, P. (1987). A general theory of discrimination learning. In Klahr, D., Langley, P., & Neches, R. (Eds.), Production System Models of Learning and Development (pp. 99161). Cambridge, MA: MIT Press.Google Scholar
Langley, P., & Choi, D. (2006). Learning recursive control programs from problem solving. Journal of Machine Learning Research, 7, 493518.Google Scholar
Larkin, J. H. (1981). Enriching formal knowledge: a model for learning to solve textbook physics problems. In Anderson, J. R. (Ed.), Cognitive Skills and Their Acquisition (pp. 311334). Hillsdale, NJ: Erlbaum.Google Scholar
Larkin, J. H., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Models of competence in solving physics problems. Cognitive Science, 4, 317345.Google Scholar
Lenat, D. B. (1983). Toward a theory of heuristics. In Groner, R., Groner, M., & Bischof, W. F. (Eds.), Methods of Heuristics (pp. 351404). Hillsdale, NJ: Erlbaum.Google Scholar
Lewis, C. (1987). Composition of productions. In Klahr, D., Langley, P., & Neches, R. (Eds.), Production System Models of Learning and Development (pp. 329358). Cambridge, MA: MIT Press.Google Scholar
Lewis, C. (1988). Why and how to learn why: analysis-based generalization of procedures. Cognitive Science, 12, 211356.Google Scholar
Lifschitz, V. (Ed.). (1990). Formalizing Common Sense: Papers by John McCarthy. Norwoord, NJ: Ablex.Google Scholar
Logan, G. D. (1998). Toward an instance theory of automatization. Psychological Review, 95, 492527.Google Scholar
Luchins, A. S., & Luchins, E. H. (1959). Rigidity of Behavior. Eugene, OR: University of Oregon Press.Google Scholar
McCarthy, J. (1959). Programs with common sense. Proceedings of the Teddington Conference on the Mechanization of Thought Processes (pp. 75–91). London: Her Majesty’s Stationery Office. [Reprinted as section 7.1 of J. McCarthy, “Programs with common sense,” in Minsky (Ed.), 1968.]Google Scholar
McCarthy, J. (1963). Situations, Actions and Causal Laws. Stanford Artificial Intelligence Project Memo No. 2. Stanford, CA: Stanford University. [Reprinted as section 7.2 of J. McCarthy (Ed.), “Programs with common sense,” in Minsky (Ed.), 1968.]Google Scholar
McDermott, J., & Forgy, C. (1978). Production system conflict resolution strategies. In Waterman, D. & Hayes-Roth, F. (Eds.), Pattern-Directed Inference Systems (pp. 177199). New York, NY: Academic Press.Google Scholar
Miller, G. A., Galanter, E., & Pribram, K. H. (1960). Plans and the Structure of Behavior. New York, NY: Holt, Rinehart & Winston.Google Scholar
Minsky, M. (Ed.). (1968). Semantic Information Processing. Cambridge, MA: MIT Press.Google Scholar
Mitrovic, A., Ohlsson, S., & Barrow, D. K. (2013). The effect of positive feedback in a constraint-based intelligent tutoring system. Computers & Education, 60, 264272.Google Scholar
Mostow, D. J. (1983). Machine transformation of advice into a heuristic search procedure. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach (pp. 367404). Palo Alto, CA: Tioga.Google Scholar
Nason, S., & Laird, J. E. (2005). Soar-RL: integrating reinforcement learning with Soar. Cognitive Systems Research, 6, 5159.Google Scholar
Neches, R. (1987). Learning through incremental refinement of procedures. In Klahr, D., Langley, P., & Neches, R. (Eds.), Production System Models of Learning and Development (pp. 163219). Cambridge, MA: MIT Press.Google Scholar
Neches, R., Langley, P., & Klahr, D. (1987). Learning, development, and production systems. In Klahr, D., Langley, P., & Neches, R. (Eds.), Production System Models of Learning and Development (pp. 153). Cambridge, MA: MIT Press.Google Scholar
Neimark, E. D., & Estes, W. K. (Eds.). (1967). Stimulus Sampling Theory. San Francisco, CA: Holden-Day.Google Scholar
Nerb, J., Ritter, F. E., & Krems, J. F. (1999). Knowledge level learning and the power law: a Soar model of skill acquisition in scheduling. Kognitionswissenschaft, 8, 2029.Google Scholar
Neves, D. M., & Anderson, J. R. (1981). Knowledge compilation: mechanisms for the automatization of cognitive skills. In Anderson, J. R (Ed.), Cognitive Skills and Their Acquisition (pp. 5784). Hillsdale, NJ: Erlbaum.Google Scholar
Newell, A. (1972). A theoretical exploration of mechanisms for coding the stimulus. In Melton, A. W. & Martin, E. (Eds.), Coding Processes in Human Memory (pp. 373434). New York, NY: Wiley.Google Scholar
Newell, A. (1973). Production systems: models of control structures. In Chase, W. G. (Ed.), Visual Information Processing (pp. 463526). New York, NY: Academic Press.Google Scholar
Newell, A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Newell, A., & Rosenbloom, P. (1981). Mechanisms of skill acquisition and the law of practice. In Anderson, J. (Ed.), Cognitive Skills and Their Acquisition (pp. 155). Hillsdale, NJ: Erlbaum.Google Scholar
Newell, A., Shaw, J. C., & Simon, H. A. (1958). Elements of a theory of human problem solving. Psychological Review, 65, 151166.Google Scholar
Newell, A., & Simon, H. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Ohlsson, S. (1987a). Transfer of training in procedural learning: a matter of conjectures and refutations? In Bolc, L. (Ed.), Computational Models of Learning (pp. 5588). Berlin: Springer-Verlag.Google Scholar
Ohlsson, S. (1987b). Truth versus appropriateness: relating declarative to procedural knowledge. In Klahr, D., Langley, P., & Neches, R. (Eds.), Production System Models of Learning and Development (pp. 287327). Cambridge, MA: MIT Press.Google Scholar
Ohlsson, S. (1992). Artificial instruction: a method for relating learning theory to instructional design. In Winne, P. & Jones, M. (Eds.), Foundations and Frontiers in Instructional Computing Systems. New York, NY: Springer-Verlag.Google Scholar
Ohlsson, S. (1993). The interaction between knowledge and practice in the acquisition of cognitive skills. In Chipman, S. & Meyrowitz, A. L. (Eds.), Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning (pp. 147208). Boston, MA: Kluwer.Google Scholar
Ohlsson, S. (1996). Learning from performance errors. Psychological Review, 103, 241262.Google Scholar
Ohlsson, S. (2006). Order effects in constraint-based skill acquisition. In Ritter, F. E., Nerb, J., O’Shea, T., & Lehtinen, E. (Eds.), In Order to Learn: How Ordering Effects in Machine Learning Illuminates Human Learning and Vice Versa (pp. 151165). New York, NY: Oxford University Press.Google Scholar
Ohlsson, S. (2011). Deep Learning: How The Mind Overrides Experience. Cambridge: Cambridge University Press.Google Scholar
Ohlsson, S., Ernst, A. M., & Rees, E. (1992). The cognitive complexity of doing and learning arithmetic. Journal of Research in Mathematics Education, 23(5), 441467.Google Scholar
Ohlsson, S., & Jewett, J. J. (1997). Ideal adaptive agents and the learning curve. In Brzezinski, J., Krause, B., & Maruszewski, T. (Eds.), Idealization VIII: Modelling in Psychology (pp. 139176). Amsterdam: Rodopi.Google Scholar
Ohlsson, S., & Rees, E. (1991a). The function of conceptual understanding in the learning of arithmetic procedures. Cognition and Instruction, 8, 103179.Google Scholar
Ohlsson, S., & Rees, E. (1991b). Adaptive search through constraint violation. Journal of Experimental and Theoretical Artificial Intelligence, 3, 3342.Google Scholar
Osisanwo, F. Y., Akinsola, J. E. T., Awodele, O., Hinmikaiye, J. O., Olakanmi, O., & Akinjobi, J. (2017). Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology, 48, 128138.Google Scholar
Paik, J., Kim, J. W., Ritter, F. E., & Reitter, D. (2005). Predicting user performance and learning in human-computer interaction with the Herbal compiler. Transactions on Computer-Human Interaction, 22, Article 25.Google Scholar
Pirolli, P. (1986). A cognitive model and computer tutor for programming recursion. Human-Computer Interaction, 2, 319355.Google Scholar
Pirolli, P. (1991). Effects of examples and their explanations in a lesson on recursion: a production system analysis. Cognition and Instruction, 8, 207259.Google Scholar
Pirolli, P., & Recker, M. (1994). Learning strategies and transfer in the domain of programming. Cognition and Instruction, 12, 235275.Google Scholar
Polk, T. A., & Seifert, C. M. (Eds.). (2002). Cognitive Modeling. Cambridge, MA: MIT Press.Google Scholar
Reason, J. (1990). Human Error. Cambridge: Cambridge University Press.Google Scholar
Reimann, P., Schult, T. J., & Wichmann, S. (1993). Understanding and using worked-out examples: a computational model. In Strube, G. & Wender, K. (Eds.), The Cognitive Psychology of Knowledge (pp. 177201). Amsterdam: North-Holland.Google Scholar
Restle, R. (1955). A theory of discrimination learning. Psychological Review, 62, 1119.Google Scholar
Ritter, F. E., & Bibby, P. (2001). Modeling how and when learning happens in a simple fault-finding task. In Proceedings of the Fourth International Conference on Cognitive Modeling (pp. 187192). Mahwah, NJ: Erlbaum.Google Scholar
Ritter, F. E., & Bibby, P. A. (2008). Modeling how, when, and what is learned in a simple fault‐finding task. Cognitive Science, 32, 862892.Google Scholar
Ritter, F. E., Jones, R. M., & Baxter, G. D. (1998). Reusable models and graphical interfaces: realizing the potential of a unified theory of cognition. In Schmid, U., Krems, J. K., & Wysotzki, F. W. (Eds.), Mind Modeling: A Cognitive Science Approach to Reasoning, Learning and Discovery (pp. 83109). Lengerich: Pabst Scientific Publishing.Google Scholar
Rosenbloom, P., & Newell, A. (1986). The chunking of goal hierarchies: a generalized model of practice. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach (vol. 2, pp. 247288). Los Altos, CA: Kaufmann.Google Scholar
Rosenbloom, P., & Newell, A. (1987). Learning by chunking: a production system model of practice. In Klahr, D., Langley, P., & Neches, R. (Eds.), Production System Models of Learning and Development (pp. 221286). Cambridge, MA: MIT Press.Google Scholar
Rosenbloom, P. S., Laird, J. E., & Newell, A. (Eds.). (1993). The Soar Papers: Research on Integrated Intelligence (Volumes 1 and 2). Cambridge, MA: MIT Press.Google Scholar
Ruiz, D., & Newell, A. (1993). Tower-noticing triggers strategy-change in the Tower of Hanoi: a Soar model. In Rosenbloom, P. S., Laird, J. E., & Newell, A. (Eds.), The Soar Papers: Research on Integrated Intelligence (vol. 2, pp. 934941). Cambridge, MA: MIT Press.Google Scholar
Rumelhart, D. E., McClelland, J. L., & the PDP Research Group (Eds.). (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Volumes 1 and 2). Cambridge, MA: MIT Press.Google Scholar
Rychener, M. D. (1983). The instructible production system: a retrospective approach. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach (pp. 429459). Palo Alto, CA: Tioga.Google Scholar
Rychener, M. D., & Newell, A. (1978). An instructable production system: basic design issues. In Waterman, D. A. & Hayes-Roth, F. (Eds.), Pattern-Directed Inference Systems (pp. 135153). New York, NY: Academic Press.Google Scholar
Ryle, G. (1968/1949). The Concept of Mind. London: Penguin.Google Scholar
Salomon, G., & Perkins, D. N. (1989). Rocky roads to transfer: rethinking mechanisms of a neglected phenomenon. Educational Psychologist, 24, 113142.Google Scholar
Salvucci, D. D. (2013). Integration and reuse in cognitive skill acquisition. Cognitive Science, 37, 829860.Google Scholar
Salvucci, D. D., & Anderson, J. R. (1998). Analogy. In Anderson, J. R. & Lebiere, C. (Eds.), The Atomic Components of Thought (pp. 343383). Mahwah, NJ: Erlbaum.Google Scholar
Salvucci, D. D., & Anderson, J. R. (2001). Integrating analogical mapping and general problem solving: the path-mapping theory. Cognitive Science, 25, 67110.Google Scholar
Schneider, W., & Chein, J. M. (2003). Controlled & automatic processing: behavior, theory, and biological mechanisms. Cognitive Science, 27, 525559.Google Scholar
Schneider, W., & Oliver, W. L. (1991). An instructable connectionist/control architecture: using rule-based instructions to accomplish connectionist learning in a human time scale. In VanLehn, K. (Ed.), Architectures for Intelligence (pp. 113145). Hillsdale, NJ: Erlbaum.Google Scholar
Shrager, J., Hogg, T., & Huberman, B. A. (1988). A graph-dynamic model of the power law of practice and the problem-solving fan effect. Science, 242, 414416.Google Scholar
Shrager, J., & Siegler, R. S. (1998). A model of children’s strategy choices and strategy discoveries. Psychological Science, 9, 405410.Google Scholar
Siegler, R., & Araya, R. (2005). A computational model of conscious and unconscious strategy discovery. In Kail, R. V. (Ed.), Advances in Child Development and Behavior (vol. 33, pp. 142). Oxford: Elsevier.Google Scholar
Siegler, R. S., & Shipley, C. (1995). Variation, selection, and cognitive change. In Simon, T. J. & Halford, G. S. (Eds.), Developing Cognitive Competencies: New Approaches to Process Modeling (pp. 3176). Hillsdale, NJ: Erlbaum.Google Scholar
Siegler, R. S., & Shrager, J. (1984). Strategy choices in addition and subtraction: how do children know what to do? In Sophian, C. (Ed.), Origins of Cognitive Skills (pp. 229293). Hillsdale, NJ: Erlbaum.Google Scholar
Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Revew, 63, 129138.Google Scholar
Simon, H. A. (1972). On reasoning about actions. In Simon, H. A. & Siklossy, L. (Eds.), Representation and Meaning (pp. 414430). Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Singley, M. K., & Anderson, J. R. (1989). The Transfer of Cognitive Skill. Cambridge, MA: Harvard University Press.Google Scholar
Spellman, B. A., & Holyoak, K. J. (1996). Pragmatics in analogical mapping. Cognitive Psychology, 31, 307346.Google Scholar
Stearns, B., & Laird, J. E. (2018). Modeling instruction fetch in procedural learning. In 16th International Conference on Cognitive Modelling (ICCM), Madison, WI.Google Scholar
Stevens, J. C., & Savin, H. B. (1962). On the form of learning curves. Journal of the Experimental Analysis of Behavior, 5 , 1518.Google Scholar
Sun, R., Merrill, E., & Peterson, T. (2001). From implicit skills to explicit knowledge: a bottom-up model of skill learning. Cognitive Science, 25, 203244.Google Scholar
Sun, R., Slusarz, P., & Terry, C. (2005). The interaction of the explicit and the implicit in skill learning: a dual-process approach. Psychological Review, 112, 159192.Google Scholar
Taatgen, N. A. (2005). Modeling parallelization and flexibility improvements in skill acquisition: from dual tasks to complex dynamic skills. Cognitive Science, 29, 421455.Google Scholar
Taatgen, N. A. (2013). The nature and transfer of cognitive skills. Psychological Review, 120, 439471.Google Scholar
Taatgen, N. A., & Anderson, J. R. (2002). Why do children learn to say “Broke”? A model of learning the past tense without feedback. Cognition, 86, 123155.Google Scholar
Taatgen, N. A., & Lee, F. J. (2003). Production compilation: a simple mechanism to model complex skill acquisition. Human Factors, 45, 6176.Google Scholar
Taylor, M. E., & Stone, P. (2009). Transfer learning for reinforcement learning domains: a survey. Journal of Machine Learning Research, 10, 16331685.Google Scholar
Tenison, C., Fincham, J. M., & Anderson, J. A. (2016). Phases of learning: how skill acquisition impacts cognitive processing. Cognitive Psychology, 87, 128.Google Scholar
Thorndike, E. L. (1898). Animal intelligence: an experimental study of the associative processes in animals. Dissertation, Ph.D., Columbia University.Google Scholar
Thorndike, E. L. (1911). The Principles of Teaching Based on Psychology. New York, NY: A. G. Seiler.Google Scholar
Thorndike, E. L. (1927). The law of effect. American Journal of Psychology, 39, 212222.Google Scholar
VanLehn, K. (1983 ). Felicity Conditions for Human Skill Acquisition: Validating an AI Based Theory (Technical Report CIS 21). Palo Alto, CA: Xerox Palo Alto Research Centers.Google Scholar
VanLehn, K. (1987). Learning one subprocedure per lesson. Artificial Intelligence, 31, 140.Google Scholar
VanLehn, K. (1988). Toward a theory of impasse-driven learning. In Mandl, H. & Lesgold, A. (Eds.), Learning Issues for Intelligent Tutoring Systems (pp. 1941). New York, NY: Springer Verlag.Google Scholar
VanLehn, K. (1990). Mind Bugs: The Origins of Procedural Misconceptions. Cambridge, MA: MIT Press.Google Scholar
VanLehn, K. (1998). Analogy events: how examples are used during problem solving. Cognitive Science, 22, 347388.Google Scholar
VanLehn, K. (1999). Rule-learning events in the acquisition of a complex skill: an evaluation of Cascade. The Journal of the Learning Sciences, 8, 71125.Google Scholar
VanLehn, K., & Jones, R. (1993). Learning by explaining examples to oneself: a computational model. In Chipman, S. & Meyrowitz, A. L. (Eds.), Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning (pp. 2582). Boston, MA: Kluwer.Google Scholar
VanLehn, K., Jones, R. M., & Chi, M. T. H. (1992). A model of the self-explanation effect. The Journal of the Learning Sciences, 2, 159.Google Scholar
VanLehn, K., Ohlsson, S., & Nason, R. (1994) Applications of simulated students: an exploration. Journal of Artificial Intelligence and Education, 5, 135175.Google Scholar
Veloso, M. M., & Carbonell, J. G. (1993). Derivational analogy in Prodigy: automating case acquisition, storage and utilization. Machine Learning, 10, 249278.Google Scholar
Waterman, D., & Hayes-Roth, F. (1978). An overview of pattern-directed inference systems. In Waterman, D. & Hayes-Roth, F. (Eds.), Pattern-Directed Inference Systems (pp. 322). New York, NY: Academic Press.Google Scholar
Watson, J. B. (1913). Psychology as the behaviorist views it. Psychological Review, 20, 158177.Google Scholar
Weiner, N. (1948). Cybernetics. Wiley, NY: Technology Press.Google Scholar
Welford, A. T. (1968). Fundamentals of Skill. London: Methuen.Google Scholar
Wilson, W. H., Halford, G. S., Gray, B., & Phillips, S. (2001). The STAR-2 model for mapping hierarchically structured analogs. In Gentner, D., Holyoak, K. J., & Kokinov, B. N. (Eds.), The Analogical Mind: Perspectives from Cognitive Science (pp. 125159). Cambridge, MA: MIT Press.Google Scholar
Winograd, T. (1975). Frame representations and the declarative/procedural controversy. In Bobrow, D. & Collins, A. (Eds.), Representation and Understanding: Studies in Cognitive Science (pp. 185210). New York, NY: Academic Press.Google Scholar
Winston, P. H. (1986). Learning by augmenting rules and accumulating censors. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach (vol. 3, pp. 4561). Los Altos, CA: Kaufmann.Google Scholar
Woltz, D. J., Gardner, M. K., & Bell, B. G. (2000). Negative transfer errors in sequential skills: strong-but-wrong sequence application. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(3), 601625.Google Scholar
Woodworth, R. S. (1938). Experimental Psychology. New York, NY: Henry Holt.Google Scholar

References

Anacker, C., & Hen, R. (2017). Adult hippocampal neurogenesis and cognitive flexibility linking memory and mood. Nature Reviews Neuroscience, 18, 335346.Google Scholar
Anderson, J. R., Bothell, D., Lebiere, C., & Matessa, M. (1998). An integrated theory of list memory. Journal of Memory and Language, 38(4), 341380. https://doi.org/10.1006/jmla.1997.2553Google Scholar
Annis, J., Lenes, J. G., Westfall, H. A., Criss, A. H., & Malmberg, K. J. (2015). The list-length effect does not discriminate between models of recognition memory. Journal of Memory and Language, 85, 2741. https://doi.org/10.1016/j.jml.2015.06.001Google Scholar
Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: a proposed system and its control processes. Psychology of Learning and Motivation, 2, 89195.Google Scholar
Baddeley, A. D., & Hitch, G. (1974). Working memory. Psychology of Learning and Motivation, 8, 4789. https://doi.org/10.1016/S0079-7421(08)60452-1Google Scholar
Baldassano, C., Chen, J., Zadbood, A., Pillow, J. W., Hasson, U., & Norman, K. A. (2017). Discovering event structure in continuous narrative perception and memory. Neuron, 95(3), 709721.e5. https://doi.org/10.1016/j.neuron.2017.06.041Google Scholar
Bjork, R. A., & Whitten, W. B. (1974). Recency-sensitive retrieval processes in long-term free recall. Cognitive Psychology, 6(2), 173189.Google Scholar
Bousfield, W. A. (1953). The occurrence of clustering in the recall of randomly arranged associates. The Journal of General Psychology, 48, 229240.Google Scholar
Bower, G. H. (1981). Mood and memory. American Psychologist, 36(2), 129148.Google Scholar
Bowles, N. L., & Glanzer, M. (1983). An analysis of interference in recognition memory. Memory & Cognition, 11, 307315.Google Scholar
Brady, T. F., Konkle, T., Gill, J., Oliva, A., & Alvarez, G. A. (2013). Visual long-term memory has the same limit on fidelity as visual working memory. Psychological Science, 24(6), 981990.Google Scholar
Bright, I. M., Meister, M. L. R., Cruzado, N. A., Tiganj, Z., Buffalo, E. A., & Howard, M. W. (2020). A temporal record of the past with a spectrum of time constants in the monkey entorhinal cortex. Proceedings of the National Academy of Sciences, 117(33), 2027420283.Google Scholar
Brown, G. D. A., Neath, I., & Chater, N. (2007). A temporal ratio model of memory. Psychological Review, 114(3), 539576. https://doi.org/10.1037/0033-295X.114.3.539Google Scholar
Bruce, D., & Papay, J. P. (1970). Primacy effect in single-trial free recall. Journal of Verbal Learning and Verbal Behavior, 9(5), 472486.Google Scholar
Burgess, N., Maguire, E. A., & O’Keefe, J. (2002). The human hippocampus and spatial and episodic memory. Neuron, 35(4), 625641. https://doi.org/10.1016/S0896-6273(02)00830-9Google Scholar
Byrne, P., Becker, S., & Burgess, N. (2007). Remembering the past and imagining the future: a neural model of spatial memory and imagery. Psychological Review, 114(2), 340375. https://doi.org/10.1037/0033-295X.114.2.340Google Scholar
Caporale, N., & Dan, Y. (2008). Spike timing: a Hebbian learning rule. Annual Review of Neuroscience, 31(1), 2546. https://doi.org/10.1146/annurev.neuro.31.060407.125639Google Scholar
Cho, K. W., & Neely, J. H. (2013). Null category-length and targetlure relatedness effects in episodic recognition: a constraint on item-noise interference models. Quarterly Journal of Experimental Psychology, 66(7), 13311355.Google Scholar
Clark, S. E., & Gronlund, S. D. (1996). Global matching models of recognition memory: how the models match the data. Psychonomic Bulletin & Review, 3(1), 3760. https://doi.org/10.3758/BF03210740Google Scholar
Cowan, N. (2001). The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87114. https://doi.org/10.1017/S0140525X01003922Google Scholar
Criss, A. H., Malmberg, K. J., & Shiffrin, R. M. (2011). Output interference in recognition memory. Journal of Memory and Language, 64(4), 316326. https://doi.org/10.1016/j.jml.2011.02.003Google Scholar
Criss, A. H., & Shiffrin, R. M. (2004). Context noise and item noise jointly determine recognition memory: a comment on Dennis and Humphreys (2001). Psychological Review, 111(3), 800807. https://doi.org/10.1037/0033-295X.111.3.800Google Scholar
Curran, T. (2000). Brain potentials of recollection and familiarity. Memory & Cognition, 28(6), 923938. https://doi.org/10.3758/BF03209340Google Scholar
Curran, T., & Cleary, A. M. (2003). Using ERPs to dissociate recollection from familiarity in picture recognition. Cognitive Brain Research, 15(2), 191205. https://doi.org/10.1016/S0926-6410(02)00192-1Google Scholar
Darby, K. P., & Sederberg, P. B. (2022). Transparency, replicability, and discovery in cognitive aging research: a computational modeling approach. Psychology and Aging, 37(1), 10. https://doi.org/10.1037/pag0000665Google Scholar
Daselaar, S. M., Fleck, M. S., & Cabeza, R. (2006). Triple dissociation in the medial temporal lobes: recollection, familiarity, and novelty. Journal of Neurophysiology, 96(4), 19021911. https://doi.org/10.1152/jn.01029.2005Google Scholar
Daselaar, S. M., Fleck, M. S., Dobbins, I. G., Madden, D. J., & Cabeza, R. (2006). Effects of healthy aging on hippocampal and rhinal memory functions: an event-related fMRI study. Cerebral Cortex, 16(12), 17711782. https://doi.org/10.1093/cercor/bhj112Google Scholar
Davachi, L. (2006). Item, context and relational episodic encoding in humans. Current Opinion in Neurobiology, 16(6), 693700. https://doi.org/10.1016/j.conb.2006.10.012Google Scholar
Davachi, L., Mitchell, J. P., & Wagner, A. D. (2003). Multiple routes to memory: distinct medial temporal lobe processes build item and source memories. Proceedings of the National Academy of Sciences, 100(4), 21572162. https://doi.org/10.1073/pnas.0337195100Google Scholar
Davachi, L., & Preston, A. R. (2014). The medial temporal lobe and memory. In The Cognitive Neurosciences (5th ed., pp. 539546). Cambridge, MA: MIT Press.Google Scholar
Davelaar, E. J., Goshen-Gottstein, Y., Ashkenazi, A., Haarmann, H. J., & Usher, M. (2005). The demise of short-term memory revisited: empirical and computational investigations of recency effects. Psychological Review, 112(1), 342.Google Scholar
Deese, J. (1959a). Influence of inter-item associative strength upon immediate free recall. Psychological Reports, 5, 305312. https://doi.org/10.2466/PR0.5.3.305-312Google Scholar
Deese, J. (1959b). On the prediction of occurrence of particular verbal intrusions in immediate recall. Journal of Experimental Psychology, 58(1), 1722. https://doi.org/10.1037/h0046671Google Scholar
Deffenbacher, K. A., Johanson, J., Vetter, T., & O’Toole, A. J. (2000). The face typicality-recognizability relationship: encoding or retrieval locus? Memory & Cognition, 28(7), 11731182. https://doi.org/10.3758/BF03211818Google Scholar
Dennis, S., & Humphreys, M. S. (2001). A context noise model of episodic word recognition. Psychological Review, 108(2), 452478.Google Scholar
Dennis, S., Lee, M. D., & Kinnell, A. (2008). Bayesian analysis of recognition memory: the case of the list-length effect. Journal of Memory and Language, 59(3), 361376.Google Scholar
Diller, D. E., Nobel, P. A., & Shiffrin, R. M. (2001). An ARC model for accuracy and response time in recognition and recall. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27(2), 414435. https://doi.org/10.1037/0278-7393.27.2.414Google Scholar
Dudukovic, N. M., & Wagner, A. M. (2007). Goal-dependent modulation of declarative memory: neural correlates of temporal recency decisions and novelty detection. Neuropsychologia, 45(11), 26082620.Google Scholar
Ebbinghaus, H. (1885). Memory: A Contribution to Experimental Psychology. New York, NY: Teachers College, Columbia University.Google Scholar
Eichenbaum, H. (2014). Time cells in the hippocampus: a new dimension for mapping memories. Nature Reviews Neuroscience, 15, 732744.Google Scholar
Estes, W. K. (1955). Statistical theory of distributional phenomena in learning. Psychological Review, 62(5), 369377.Google Scholar
Farrell, S. (2010). Dissociating conditional recency in immediate and delayed free recall: a challenge for unitary models of recency. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36(2), 324347.Google Scholar
Farrell, S. (2012). Temporal clustering and sequencing in short-term memory and episodic memory. Psychological Review, 119(2), 223271. https://doi.org/10.1037/a0027371Google Scholar
Fiser, J., & Aslin, R. N. (2001). Unsupervised statistical learning of higher-order spatial structures from visual scenes. Psychological Science, 12(6), 499504. https://doi.org/10.1111/1467-9280.00392Google Scholar
Fox, J., Dennis, S., & Osth, A. F. (2020). Accounting for the build-up of proactive interference across lists in a list length paradigm reveals a dominance of item-noise in recognition memory. Journal of Memory and Language, 110, 104126.Google Scholar
Gershman, S. J., Moore, C. D., Todd, M. T., Norman, K. A., & Sederberg, P. B. (2012). The successor representation and temporal context. Neural Computation, 24(6), 15531568. https://doi.org/10.1162/NECO_a_00282Google Scholar
Glanzer, M., & Adams, J. K. (1985). The mirror effect in recognition memory. Memory & Cognition, 13(1), 820. https://doi.org/10.3758/BF03198438Google Scholar
Glanzer, M., & Adams, J. K. (1990). The mirror effect in recognition memory: data and theory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16(1), 516. https://doi.org/10.1037/0278-7393.16.1.5Google Scholar
Glanzer, M., & Bowles, N. (1976). Analysis of the word-frequency effect in recognition memory. Journal of Experimental Psychology: Human Learning and Memory, 2(1), 2131. https://doi.org/10.1037/0278-7393.2.1.21Google Scholar
Glenberg, A. M., & Swanson, N. G. (1986). A temporal distinctiveness theory of recency and modality effects. Journal of Experimental Psychology: Learning, Memory, and Cogntion, 12(1), 315.Google Scholar
Godden, D. R., & Baddeley, A. D. (1965). Context-dependent memory in two natural environments: on land and underwater. British Journal of Psychology, 6(3), 325331.Google Scholar
Gold, A. E., & Kesner, R. P. (2005). The role of the CA3 subregion of the dorsal hippocampus in spatial pattern completion in the rat. Hippocampus, 15, 808814.Google Scholar
Gravina, M. T., & Sederberg, P. B. (2017). The neural architecture of prediction over a continuum of spatiotemporal scales. Current Opinion in Behavioral Sciences, 17, 194202. https://doi.org/10.1016/j.cobeha.2017.09.001Google Scholar
Hall, J. F. (1954). Learning as a function of word-frequency. The American Journal of Psychology, 67(1), 138140. https://doi.org/10.2307/1418080Google Scholar
Harris, J. J., Jolivert, R., & Attwell, D. (2012). Synaptic energy use and supply. Neuron, 75(5), 762777.Google Scholar
Healey, M. K., Long, N. M., & Kahana, M. J. (2019). Contiguity in episodic memory. Psychonomic Bulletin & Review, 26(3), 699720.Google Scholar
Hebb, D. O. (1949). The Organization of Behavior: A Neuropsychological Theory. New York, NY: Wiley.Google Scholar
Hintzman, D. L. (1984). MINERVA 2: a simulation model of human memory. Behavior Research Methods, Instruments, & Computers, 16(2), 96101. https://doi.org/10.3758/BF03202365Google Scholar
Honey, C. J., Thesen, T., Donner, T. H., et al. (2012). Slow cortical dynamics and the accumulation of information over long timescales. Neuron, 76(2), 423434. https://doi.org/10.1016/j.neuron.2012.08.011Google Scholar
Horner, A. J., Bisby, J. A., Bush, D., Lin, W.-J., & Burgess, N. (2015). Evidence for holistic episodic recollection via hippocampal pattern completion. Nature Communications, 6, 7462.Google Scholar
Howard, M. W., & Kahana, M. J. (1999). Contextual variability and serial position effects in free recall. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(4), 923941.Google Scholar
Howard, M. W., & Kahana, M. J. (2002a). A distributed representation of temporal context. Journal of Mathematical Psychology, 46(3), 269299.Google Scholar
Howard, M. W., & Kahana, M. J. (2002b). When does semantic similarity help episodic retrieval? Journal of Memory and Language, 46(1), 8598. https://doi.org/10.1006/jmla.2001.2798Google Scholar
Howard, M. W., MacDonald, C. J., Tiganj, Z., et al. (2014). A unified mathematical framework for coding time, space, and sequences in the hippocampal region. Journal of Neuroscience, 34(13), 46924707. https://doi.org/10.1523/JNEUROSCI.5808-12.2014Google Scholar
Howard, M. W., Shankar, K. H., Aue, W. R., & Criss, A. H. (2015). A distributed representation of internal time. Psychological Review, 122(1), 2453. https://doi.org/10.1037/a0037840Google Scholar
Howard, M. W., Shankar, K. H., & Jagadisan, U. K. K. (2011). Constructing semantic representations from a gradually changing representation of temporal context. Topics in Cognitive Science, 3(1), 4873. https://doi.org/10.1111/j.1756-8765.2010.01112.xGoogle Scholar
Howard, M. W., Youker, T. E., & Venkatadass, V. S. (2008). The persistence of memory: contiguity effects across hundreds of seconds. Psychonomic Bulletin & Review, 15(1), 5863. https://doi.org/10.3758/PBR.15.1.58Google Scholar
Humphreys, M. S., Bain, J. D., & Pike, R. (1989). Different ways to cue a coherent memory system: a theory for episodic, semantic, and procedural tasks. Psychological Review, 96(2), 208233. https://doi.org/10.1037/0033-295X.96.2.208Google Scholar
Kahana, M. J. (1996). Associative retrieval processes in free recall. Memory & Cognition, 24(1), 103109.Google Scholar
Kahana, M. J., Howard, M. W., & Polyn, S. M. (2008). Associative retrieval processes in episodic memory. In J. H. Byrne (Ed.), Learning and Memory: A Comprehensive Reference: Vol. 2. Cognitive Psychology of Memory (pp. 467490). Oxford: Elsevier.Google Scholar
Kesner, R. P., & Rolls, E. T. (2015). A computational theory of hippocampal function, and tests of the theory: new developments. Neuroscience & Biobehavioral Reviews, 48, 92147. https://doi.org/10.1016/j.neubiorev.2014.11.009Google Scholar
Kimball, D. R., Smith, T. A., & Kahana, M. J. (2007). The fSAM model of false recall. Psychological Review, 114, 954993.Google Scholar
Kinnell, A., & Dennis, S. (2011). The list length effect in recognition memory: an analysis of potential confounds. Memory & Cognition, 39(2), 348363. https://doi.org/10.3758/s13421-010-0007-6Google Scholar
Kinnell, A., & Dennis, S. (2012). The role of stimulus type in list length effects in recognition memory. Memory & Cognition, 40(3), 311325.Google Scholar
Kragel, J. E., Morton, N. W., & Polyn, S. M. (2015). Neural activity in the medial temporal lobe reveals the fidelity of mental time travel. Journal of Neuroscience, 35(7), 29142926. https://doi.org/10.1523/JNEUROSCI.3378-14.2015Google Scholar
Kumaran, D., Hassabis, D., & McClelland, J. L. (2016). What learning systems do intelligent agents need? Complementary learning systems theory updated. Trends in Cognitive Sciences, 20(7), 512534. https://doi.org/10.1016/j.tics.2016.05.004Google Scholar
Laming, D. (2010). Serial position curves in free recall. Psychological Review, 117(1), 93133.Google Scholar
Lee, S.-H., Kravitz, D. J., & Baker, C. I. (2013). Goal-dependent dissociation of visual and prefrontal cortices during working memory. Nature Neuroscience, 16, 997999.Google Scholar
Lennie, P. (2003). The cost of cortical computation. Current Biology, 13(6), 493497.Google Scholar
Levy, W. B. (1996). A sequence predicting CA3 is a flexible associator that learns and uses context to solve hippocampal-like tasks. Hippocampus, 6(6), 579590. https://doi.org/10.1002/(SICI)1098-1063(1996)6:6%3C579::AID-HIPO3%3E3.0.CO;2-CGoogle Scholar
Levy, W. B., & Baxter, R. A. (1996). Energy efficient neural codes. Neural Computation, 8(3), 531543.Google Scholar
Light, L. L., Kayra-Stuart, F., & Hollander, S. (1979). Recognition memory for typical and unusual faces. Journal of Experimental Psychology: Human Learning and Memory, 5(3), 212228. https://doi.org/10.1037/0278-7393.5.3.212Google Scholar
Lohnas, L. J., Polyn, S. M., & Kahana, M. J. (2015). Expanding the scope of memory search: modeling intralist and interlist effects in free recall. Psychological Review, 122(2), 337363.Google Scholar
Long, N. M., Danoff, M. S., & Kahana, M. J. (2015). Recall dynamics reveal the retrieval of emotional context. Psychonomic Bulletin & Review, 22(5), 13281333. https://doi.org/10.3758/s13423-014-0791-2Google Scholar
MacDonald, C. J., LePage, K. Q., Eden, U. T., & Eichenbaum, H. (2012). Hippocampal “time cells” bridge the gap in memory for discontiguous events. Neuron, 71(4), 737749.Google Scholar
Malmberg, K. J. (2008). Recognition memory: a review of the critical findings and an integrated theory for relating them. Cognitive Psychology, 57(4), 335384. https://doi.org/10.1016/j.cogpsych.2008.02.004Google Scholar
Malmberg, K. J., Holden, J. E., & Shiffren, R. M. (2004). Modeling the effects of repetitions, similarity, and normative word frequency on old-new recognition and judgments of frequency. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(2), 319331. https://doi.org/10.1037/0278-7393.30.2.319Google Scholar
Malmberg, K. J., & Shiffrin, R. M. (2005). The “one-shot” hypothesis for context storage. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31(2), 322336. https://doi.org/10.1037/0278-7393.31.2.322Google Scholar
Manning, J. R., Norman, K. A., & Kahana, M. J. (2015). The Role of Context in Episodic Memory. Cambridge, MA: MIT Press.Google Scholar
Marshall, P. H., & Werder, P. R. (1972). The effects of the elimination of rehearsal on primacy and recency. Journal of Verbal Learning and Verbal Behavior, 11(5), 649653. https://doi.org/10.1016/S0022-5371(72)80049-5Google Scholar
McClelland, J. L. (1994). The organization of memory: a parallel distributed processing perspective. Revue Neurologique, 150(8–9), 570579.Google Scholar
McClelland, J. L., & Chappell, M. (1998). Familiarity breeds differentiation: a subjective-likelihood approach to the effects of experience in recognition memory. Psychological Review, 105(4), 724760. https://doi.org/10.1037/0033-295X.105.4.734-760Google Scholar
McClelland, J. L., McNaughton, B. L., & O’Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102(3), 419457. https://doi.org/10.1037/0033-295X.102.3.419Google Scholar
Meeter, M., & Murre, J. (2004). Simulating episodic memory deficits in semantic dementia with the TraceLink model. Memory, 12(3), 272287. https://doi.org/10.1080/09658210244000658Google Scholar
Mensink, G.-J., & Raaijmakers, J. G. W. (1988). A model for interference and forgetting. Psychological Review, 95(4), 434455.Google Scholar
Miletic, S., & van Maanen, L. (2019). Caution in decision-making under time pressure is mediated by timing ability. Cognitive Psychology, 110, 1629.Google Scholar
Miller, J. F., Neufang, M., Solway, A., et al. (2013). Neural activity in human hippocampal formation reveals the spatial context of retrieved memories. Science, 342(6162), 11111114.Google Scholar
Modigliani, V., & Hedges, D. G. (1987). Distributed rehearsals and the primacy effect in single-trial free recall. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13(3), 426436.Google Scholar
Molitor, R. J., Sherrill, K. R., Morton, N. W., Miller, A. A., & Preston, A. R. (2021). Memory reactivation during learning simultaneously promotes dentate gyrus/CA2,3 pattern differentiation and CA1 memory integration. Journal of Neuroscience, 41(4), 726738. https://doi.org/10.1523/JNEUROSCI.0394-20.2020Google Scholar
Momennejad, I., Russek, E. M., Cheong, J. H., Botvinick, M. M., Daw, N. D., & Gershman, S. J. (2017). The successor representation in human reinforcement learning. Nature Human Behaviour, 1(9), 680692. https://doi.org/10.1038/s41562-017-0180-8Google Scholar
Morton, N. W., Kahana, M. J., Rosenberg, E. A., et al. (2013). Category-specific neural oscillations predict recall organization during memory search. Cerebral Cortex, 23(10), 24072422. https://doi.org/10.1093/cercor/bhs229Google Scholar
Morton, N. W., & Polyn, S. M. (2016). A predictive framework for evaluating models of semantic organization in free recall. Journal of Memory and Language, 86, 119140.Google Scholar
Morton, N. W., & Polyn, S. M. (Submitted). A neurocognitive theory of episodic and semantic interactions during memory search.Google Scholar
Mullennix, J. W., Ross, A., Smith, C., Kuykendall, K., Conard, J., & Barb, S. (2011). Typicality effects on memory for voice: implications for earwitness testimony. Applied Cognitive Psychology, 25(1), 2934. https://doi.org/10.1002/acp.1635Google Scholar
Müller, G. E., & Pilzecker, A. (1900). Experimentelle Beiträge zur Lehre vom Gedächtniss. Leipzig: J. A. Barth.Google Scholar
Murdock, B. B. (1962). The serial position effect of free recall. Journal of Experimental Psychology, 64(5), 482488.Google Scholar
Murdock, B. B. (1982). A theory for the storage and retrieval of item and associative information. Psychological Review, 89(6), 609626. https://doi.org/10.1037/0033-295X.89.6.609Google Scholar
Murdock, B. B. (1997). Context and mediators in a theory of distributed associative memory (TODAM2). Psychological Review, 104(4), 839862. https://doi.org/10.1037/0033-295X.104.4.839Google Scholar
Murdock, B. B., & Okada, R. (1970). Interresponse times in single-trial free recall. Journal of Experimental Psychology, 86(2), 263267. https://doi.org/10.1037/h0029993Google Scholar
Murre, J. M. J., Graham, K. S., & Hodges, J. R. (2001). Semantic dementia: relevance to connectionist models of long-term memory. Brain, 124(4), 647675. https://doi.org/10.1093/brain/124.4.647Google Scholar
Nielson, D. M., Smith, T. A., Sreekumar, V., Dennis, S., & Sederberg, P. B. (2015). Human hippocampus represents space and time during retrieval of real-world memories. Proceedings of the National Academy of Sciences, 112(35), 1107811083. https://doi.org/10.1073/pnas.1507104112Google Scholar
Norman, K. A., & O’Reilly, R. C. (2003). Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach. Psychological Review, 110(4), 611646.Google Scholar
Osth, A. F., & Dennis, S. (2015). Sources of interference in item and associative recognition memory. Psychological Review, 122(2), 260311.Google Scholar
Osth, A. F., & Dennis, S. (2020). Global matching models of recognition memory (advance online publication).https://doi.org/10.31234/osf.io/mja6cGoogle Scholar
Palestro, J. J., Bahg, G., Sederberg, P. B., Lu, Z.-L., Steyvers, M., & Turner, B. M. (2018). A tutorial on joint models of neural and behavioral measures of cognition. Journal of Mathematical Psychology, 84, 2048. https://doi.org/10.1016/j.jmp.2018.03.003Google Scholar
Phillips, J. L., Shiffrin, R. M., & Atkinson, R. C. (1967). Effects of list length on short-term memory. Journal of Verbal Learning and Verbal Behavior, 6(3), 303311.Google Scholar
Polyn, S. M., Norman, K. A., & Kahana, M. J. (2009a). A context maintenance and retrieval model of organizational processes in free recall. Psychological Review, 116(1), 129156. https://doi.org/10.1037/a0014420Google Scholar
Polyn, S. M., Norman, K. A., & Kahana, M. J. (2009b). Task context and organization in free recall. Neuropsychologia, 47(11), 21582163.Google Scholar
Post, E. L. (1930). Generalized differentiation. Transactions of the American Mathematical Society, 32(4), 723723. https://doi.org/10.1090/S0002-9947-1930-1501560-XGoogle Scholar
Preston, A. R., & Eichenbaum, H. (2013). Interplay of hippocampus and prefrontal cortex in memory. Current Biology, 23(17), R764R773. https://doi.org/10.1016/j.cub.2013.05.041Google Scholar
Preston, A. R., Shrager, Y., Dudukovic, N. M., & Gabrieli, J. D. (2004). Hippocampal contribution to the novel use of relational information in declarative memory. Hippocampus, 14(2), 148152.Google Scholar
Raaijmakers, J. G. W. (2003). Spacing and repetition effects in human memory: application of the SAM model. Cognitive Science, 27(3), 431452.Google Scholar
Raaijmakers, J. G. W., & Shiffrin, R. M. (1981). Search of associative memory. Psychological Review, 88(2), 93134.Google Scholar
Ranganath, C., & Ritchey, M. (2012). Two cortical systems for memory-guided behaviour. Nature Reviews Neuroscience, 13(10), 713726. https://doi.org/10.1038/nrn3338Google Scholar
Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59108.Google Scholar
Ratcliff, R., Voskuilen, C., & Teodorescu, A. (2018). Modeling 2-alternative forced-choice tasks: accounting for both magnitude and difference effects. Cognitive Psychology, 103, 122.Google Scholar
Roediger, H. L., & McDermott, K. B. (1995). Creating false memories: remembering words not presented in lists. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21(4), 803814. https://doi.org/10.1037/0278-7393.21.4.803Google Scholar
Rolls, E. T. (2013). The mechanisms for pattern completion and pattern separation in the hippocampus. Frontiers in Systems Neuroscience, 7, 121.Google Scholar
Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science, 274(5294), 19261928. https://doi.org/10.1126/science.274.5294.1926Google Scholar
Schapiro, A. C., Gregory, E., Landau, B., McCloskey, M., & Turk-Browne, N. B. (2014). The necessity of the medial temporal lobe for statistical learning. Journal of Cognitive Neuroscience, 26(8), 17361747. https://doi.org/10.1162/jocn_a_00578Google Scholar
Schapiro, A. C., Turk-Browne, N. B., Botvinick, M. M., & Norman, K. A. (2017). Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1711), 20160049. https://doi.org/10.1098/rstb.2016.0049Google Scholar
Schlichting, M. L., & Preston, A. R. (2017). The hippocampus and memory integration: building knowledge to navigate future decisions. In Hannula, D. E. & Duff, M. C. (Eds.), The Hippocampus from Cells to Systems: Structure, Connectivity, and Functional Contributions to Memory and Flexible Cognition (pp. 405437). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-50406-3_13Google Scholar
Schmidt, S. R. (1996). Category typicality effects in episodic memory: testing models of distinctiveness. Memory & Cognition, 24(5), 595607. https://doi.org/10.3758/BF03201086Google Scholar
Schooler, L. J., Shiffrin, R. M., & Raaijmakers, J. G. W. (2001). A Bayesian model for implicit effects in perceptual identification. Psychological Review, 108(1), 257272. https://doi.org/10.1037/0033-295X.108.1.257Google Scholar
Sederberg, P. B., Gauthier, L. V., Terushkin, V., Miller, J. F., Barnathan, J. A., & Kahana, M. J. (2006). Oscillatory correlates of the primacy effect in episodic memory. Neuroimage, 32(3), 14221431.Google Scholar
Sederberg, P. B., Gershman, S. J., Polyn, S. M., & Norman, K. A. (2011). Human memory reconsolidation can be explained using the temporal context model. Psychonomic Bulletin and Review, 18(3), 455468.Google Scholar
Sederberg, P. B., Howard, M. W., & Kahana, M. J. (2008). A context-based theory of recency and contiguity in free recall. Psychological Review, 115(4), 893912.Google Scholar
Sederberg, P. B., Miller, J. F., Howard, M. W., & Kahana, M. J. (2010). The temporal contiguity effect predicts episodic memory performance. Memory & Cognition, 88, 389399.Google Scholar
Shankar, K. H., & Howard, M. W. (2012). A scale-invariant internal representation of time. Neural Computation, 24(1), 134193. https://doi.org/10.1162/NECO_a_00212Google Scholar
Shankar, K. H., & Howard, M. W. (2013). Optimally fuzzy temporal memory. Journal of Machine Learning Research, 14(83), 37853812.Google Scholar
Shiffrin, R. M., & Steyvers, M. (1997). A model for recognition memory: REM retrieving effectively from memory. Psychonomic Bulletin & Review, 4(2), 145166.Google Scholar
Siefke, B. M., Smith, T. A., & Sederberg, P. B. (2019). A context-change account of temporal distinctiveness. Memory & Cognition, 47(6), 11581172. https://doi.org/10.3758/s13421-019-00925-5Google Scholar
Sirotin, Y. B., Kimball, D. R., & Kahana, M. J. (2005). Going beyond a single list: modeling the effects of prior experience on episodic free recall. Psychonomic Bulletin & Review, 12, 787805.Google Scholar
Smith, D. A., & Graesser, A. C. (1981). Memory for actions in scripted activities as a function of typicality, retention interval, and retrieval task. Memory & Cognition, 9(6), 550559. https://doi.org/10.3758/BF03202349Google Scholar
Smith, S. M., & Vela, E. (2001). Environmental context-dependent memory: a review and meta-analysis. Psychonomic Bulletin & Review, 8, 203220.Google Scholar
Socher, R., Gershman, S. J., Perotte, A. J., Sederberg, P. B., Blei, D. M., & Norman, K. A. (2009). A Bayesian analysis of dynamics in free recall. In M. I. Jordan, Y. LeCun, & S. A. Solla (Eds.), Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press.Google Scholar
Staudigl, T., & Hanslmayr, S. (2013). Theta oscillations at encoding mediate the context-dependent nature of human episodic memory. Current Biology, 23(12), 11011106.Google Scholar
Steyvers, M., Shiffrin, R. M., & Nelson, D. L. (2005). Word association spaces for predicting semantic similarity effects in episodic memory. In A. F. Healy (Ed.), Experimental Cognitive Psychology and Its Applications (pp. 237249). Washington, DC: American Psychological Association. https://doi.org/10.1037/10895-018Google Scholar
Strong, E. K. (1912). The effect of length of series upon recognition memory. Psychological Review, 19(6), 447462.Google Scholar
Talmi, D., Lohnas, L. J., & Daw, N. D. (2019). A retrieved context model of the emotional modulation of memory. Psychological Review, 126(4), 455485. https://doi.org/10.1037/rev0000132Google Scholar
Talmi, D., & Moscovitch, M. (2004). Can semantic relatedness explain the enhancement of memory for emotional words? Memory & Cognition, 32(5), 742751. https://doi.org/10.3758/BF03195864Google Scholar
Tan, L., & Ward, G. (2000). A recency-based account of the primacy effect in free recall. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(6), 15891625. https://doi.org/10.1037/0278-7393.26.6.1589Google Scholar
Tsao, A., Sugar, J., Lu, L., et al. (2018). Integrating time from experience in the lateral entorhinal cortex. Nature, 561, 5762.Google Scholar
Tulving, E. (1985). Memory and consciousness. Canadian Psychology/Psychologie Canadienne, 26(1), 112. https://doi.org/10.1037/h0080017Google Scholar
Tulving, E. (1993). What is episodic memory? Current Directions in Psychological Science, 2(3), 6770. https://doi.org/10.1111/1467-8721.ep10770899Google Scholar
Turner, B. M., & Sederberg, P. B. (2012). Approximate Bayesian computation with differential evolution. Journal of Mathematical Psychology, 56(5), 375385.Google Scholar
Turner, B. M., & Sederberg, P. B. (2014). A generalized, likelihood-free method for posterior estimation. Psychonomic Bulletin & Review, 21(2), 227250.Google Scholar
Turner, B. M., Sederberg, P. B., Brown, S. D., & Steyvers, M. (2013). A method for efficiently sampling from distributions with correlated dimensions. Psychological Methods, 18(3), 368384.Google Scholar
Turner, B. M., & Van Zandt, T. (2012). A tutorial on approximate Bayesian computation. Journal of Mathematical Psychology, 56(2), 6985.Google Scholar
Urgolites, Z. J., & Wood, J. N. (2013). Visual long-term memory stores high-fidelity representations of observed actions. Psychological Science, 24(4), 403411.Google Scholar
Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: the leaky, competing accumulator model. Psychological Review, 108(3), 550592. https://doi.org/10.1037/0033-295X.108.3.550Google Scholar
Usher, M., Olami, Z., & McClelland, J. L. (2002). Hick’s Law in a stochastic race model with speed-accuracy tradeoff. Journal of Mathematical Psychology, 46, 704715.Google Scholar
van Ravenzwaaij, D., Brown, S. D., Marley, A. A. J., & Heathcote, A. (2020). Accumulating advantages: a new conceptualization of rapid multiple choice. Psychological Review, 127(2), 186215.Google Scholar
Wagenmakers, E.-J., Steyvers, M., Raaijmakers, J. G. W., Shiffrin, R. M., van Rijn, H., & Zeelenberg, R. (2004). A model for evidence accumulation in the lexical decision task. Cognitive Psychology, 48(3), 332367. https://doi.org/10.1016/j.cogpsych.2003.08.001Google Scholar
Wixted, J. T. (2007). Dual-process theory and signal-detection theory of recognition memory. Psychological Review, 114(1), 152176. https://doi.org/10.1037/0033-295X.114.1.152Google Scholar
Xu, J., & Malmberg, K. J. (2007). Modeling the effects of verbal and nonverbal pair strength on associative recognition. Memory & Cognition, 35(3), 526544. https://doi.org/10.3758/BF03193292Google Scholar
Yonelinas, A. P. (2002). The nature of recollection and familiarity: a review of 30 years of research. Journal of Memory and Language, 46(3), 441517. https://doi.org/10.1006/jmla.2002.2864Google Scholar

References

Adams, E. J., Nguyen, A. T., & Cowan, N. (2018). Theories of working memory: differences in definition, degree of modularity, role of attention, and purpose. Language, Speech, and Hearing Services in Schools, 49(3), 340355. https://doi.org/10.1044/2018%20LSHSS-17-0114Google Scholar
Alexander, G., DeLong, M., & Strick, P. (1986 ). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience, 9, 357381.Google Scholar
Alexander, G. E. (1987 ). Selective neuronal discharge in monkey putamen reflects intended direction of planned limb movements. Experimental Brain Research, 67, 623634.Google Scholar
Anderson, J. R., & Lebiere, C. (1998). The Atomic Components of Thought (1st ed.). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Arbib, M. A., & Dominey, P. F. (1995 ). Modeling the roles of basal ganglia in timing and sequencing saccadic eye movements. In Houk, J. C., Davis, J. L., & Beiser, D. G. (Eds.), Models of Information Processing in the Basal Ganglia (pp. 149162). Cambridge, MA: MIT Press.Google Scholar
Arnsten, A. F. T., Wang, M. J., & Paspalas, C. D. (2012 ). Neuromodulation of thought: flexibilities and vulnerabilities in prefrontal cortical network synapses. Neuron, 76(1), 223239. https://doi.org/10.1016/%20j.neuron.2012.08.038Google Scholar
Ashby, F. G., Ell, S. W., Valentin, V. V., & Casale, M. B. (2005). FROST: a distributed neurocomputational model of working memory maintenance. Journal of Cognitive Neuroscience, 17(11), 17281743. https://doi.org/10.1162/089892905774589271Google Scholar
Baddeley, A. D. (1986). Working Memory. New York, NY: Oxford University Press.Google Scholar
Baddeley, A. D., & Hitch, G. J. (1974). Working memory. In Bower, G. (Ed.), The Psychology of Learning and Motivation (vol. VIII, pp. 4789). New York, NY: Academic Press.Google Scholar
Badre, D., & Frank, M. J. (2012 ). Mechanisms of hierarchical reinforcement learning in cortico-striatal circuits 2: evidence from FMRI. Cerebral Cortex, 22(3), 527–536.Google Scholar
Barak, O., & Tsodyks, M. (2014). Working models of working memory. Current Opinion in Neurobiology, 25, 2024. https://doi.org/10.1016/j.conb.2013.10.008Google Scholar
Basso, M. A., & Wurtz, R. H. (2002). Neuronal activity in substantia nigra pars reticulata during target selection. Journal of Neuroscience, 22(5), 18831894.Google Scholar
Bays, P. M., Catalao, R. F. G., & Husain, M. (2009). The precision of visual working memory is set by allocation of a shared resource. Journal of Vision, 9(10), 77. https://doi.org/10.1167/9.10.7Google Scholar
Bays, P. M., & Husain, M. (2008). Dynamic shifts of limited working memory resources in human vision. Science, 321(5890), 851854. https://doi.org/10.1126/science.1158023Google Scholar
Beiser, D. G., & Houk, J. C. (1998). Model of cortical-basal ganglionic processing: encoding the serial order of sensory events. Journal of Neurophysiology, 79, 31683188.Google Scholar
Bhandari, A., & Badre, D. (2018). Learning and transfer of working memory gating policies. Cognition, 172, 89100. https://doi.org/10.1016/j.cognition.2017.12.001Google Scholar
Bogacz, R. (2013). Basal ganglia: beta oscillations. In Jaeger, D. & Jung, R. (Eds.), Encyclopedia of Computational Neuroscience (pp. 15). New York, NY: Springer. https://doi.org/10.1007/978-1-4614-7320-6%2082-1Google Scholar
Botvinick, M. M., & Plaut, D. C. (2004). Doing without schema hierarchies: a recurrent connectionist approach to normal and impaired routine sequential action. Psychological Review, 111(2), 395429.Google Scholar
Botvinick, M. M., & Plaut, D. C. (2006). Short-term memory for serial order: a recurrent neural network model. Psychological Review, 113, 201233.Google Scholar
Braver, T. S., & Cohen, J. D. (2000). On the control of control: the role of dopamine in regulating prefrontal function and working memory. In Monsell, S. & Driver, J. (Eds.), Control of Cognitive Processes: Attention and Performance XVIII (pp. 713737). Cambridge, MA: MIT Press.Google Scholar
Braver, T. S., Paxton, J. L., Locke, H. S., & Barch, D. M. (2009). Flexible neural mechanisms of cognitive control within human prefrontal cortex. Proceedings of the National Academy of Sciences USA, 106(18), 73517356.Google Scholar
Brown, J. W., Bullock, D., & Grossberg, S. (2004). How laminar frontal cortex and basal ganglia circuits interact to control planned and reactive saccades. Neural Networks, 17, 471510.Google Scholar
Brown, R. G., & Marsden, C. D. (1990). Cognitive function in Parkinson’s disease: from description to theory. Trends in Neurosciences, 13, 2129.Google Scholar
Brown, V. J., & Bowman, E. M. (2002). Rodent models of prefrontal cortical function. Trends in Neurosciences, 25, 340343.Google Scholar
Burgess, N., & Hitch, G. (2005). Computational models of working memory: putting long-term memory into context. Trends in Cognitive Sciences, 9(11), 535541. https://doi.org/10.1016/j.tics.2005.09.011Google Scholar
Chatham, C. H., & Badre, D. (2015). Multiple gates on working memory. Current Opinion in Behavioral Sciences, 1, 2331. https://doi.org/10.1016/j.cobeha.2014.08.001Google Scholar
Chatham, C. H., Frank, M., & Badre, D. (2014). Corticostriatal output gating during selection from working memory. Neuron, 81(4), 930942.Google Scholar
Chatham, C. H., Herd, S. A., Brant, A. M., et al. (2011). From an executive network to executive control: a computational model of the n-back task. Journal of Cognitive Neuroscience, 23, 35983619.Google Scholar
Choi, E. Y., Yeo, B. T. T., & Buckner, R. L. (2012). The organization of the human striatum estimated by intrinsic functional connectivity. Journal of Neurophysiology, 108(8), 22422263. https://doi.org/10.1152/%20jn.00270.2012Google Scholar
Clascá, F., Rubio-Garrido, P., & Jabaudon, D. (2012). Unveiling the diversity of thalamocortical neuron subtypes. European Journal of Neuroscience, 35(10), 15241532. https://doi.org/10.1111/j.1460-9568.2012.08033.xGoogle Scholar
Cleeremans, A., Servan-Schreiber, D., & McClelland, J. L. (1989). Finite state automata and simple recurrent networks. Neural Computation, 1(3), 372381.Google Scholar
Cohen, J. D., Dunbar, K., & McClelland, J. L. (1990). On the control of automatic processes: a parallel distributed processing model of the Stroop effect. Psychological Review, 97(3), 332361.Google Scholar
Cole, M. W., Bagic, A., Kass, R., & Schneider, W. (2010). Prefrontal dynamics underlying rapid instructed task learning reverse with practice. Journal of Neuroscience, 30(42), 1424514254.Google Scholar
Collins, A. G. E., & Frank, M. J. (2013). Cognitive control over learning: creating, clustering, and generalizing task-set structure. Psychological Review, 120(1), 190229.Google Scholar
Collins, A. G. E., & Frank, M. J. (2014). Opponent actor learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive. Psychological Review, 121(3), 337366.Google Scholar
Collins, A. G. E., & Frank, M. J. (2016). Surprise! Dopamine signals mix action, value and error. Nature Neuroscience, 19(1), 35. https://doi.org/10.1038/nn.4207Google Scholar
Courtemanche, R., Fujii, N., & Graybiel, A. M. (2003). Synchronous, focally modulated beta-band oscillations characterize local field potential activity in the striatum of awake behaving monkeys. Journal of Neuroscience, 23(37), 1174111752.Google Scholar
Cowan, N. (2001). The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87185.Google Scholar
Cowan, N. (2011). The focus of attention as observed in visual working memory tasks: making sense of competing claims. Neuropsychologia, 49(6), 14011406. https://doi.org/10.1016/j.neuropsychologia.2011.01.035Google Scholar
Cowan, N. (2017). The many faces of working memory and short-term storage. Psychonomic Bulletin & Review, 24(4), 11581170. https://doi.org/10.3758/s13423-016-1191-6Google Scholar
Cowan, N. (2019). Short-term memory based on activated long-term memory: a review in response to Norris (2017). Psychological Bulletin, 145(8), 822847. https://doi.org/10.1037/bul0000199Google Scholar
Cowan, N., Nugent, L. D., Elliott, E. M., Ponomarev, I., & Saults, J. S. (1999). The role of attention in the development of short-term memory: age differences in the verbal span of apprehension. Child Development, 70(5), 10821097.Google Scholar
Dahlin, E., Neely, A. S., Larsson, A., Backman, L., & Nyberg, L. (2008). Transfer of learning after updating training mediated by the striatum. Science, 320(5882), 15101512.Google Scholar
Dayan, P. (2007). Bilinearity, rules, and prefrontal cortex. Frontiers in Computational Neuroscience, 1(1), 114.Google Scholar
Dayan, P. (2008). Simple substrates for complex cognition. Frontiers in Computational Neuroscience, 2(2), 255.Google Scholar
Dominey, P. F., & Arbib, M. A. (1992). Cortico-subcortical model for generation of spatially accurate sequential saccades. Cerebral Cortex, 2, 153175.Google Scholar
Dominey, P. F., Arbib, M., & Joseph, J.-P. (1995). A model of corticostriatal plasticity for learning oculomotor associations and sequences. Journal of Cognitive Neuroscience, 7(3), 311336. https://doi.org/10.1162/jocn.1995.7.3.311Google Scholar
Dunbar, K., & MacLeod, C. M. (1984). A horse race of a different color: Stroop interference patterns with transformed words. Journal of Experimental Psychology. Human Perception and Performance, 10, 622639.Google Scholar
Durstewitz, D., Seamans, J. K., & Sejnowski, T. J. (2000). Neurocomputational models of working memory. Nature Neuroscience, 3 suppl., 11841191.Google Scholar
Economo, M. N., Viswanathan, S., Tasic, B., et al. (2018). Distinct descending motor cortex pathways and their roles in movement. Nature, 563(7729), 7984. https://doi.org/10.1038/s41586-018-0642-9Google Scholar
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179211.Google Scholar
Elston, G. N. (2003). Cortex, cognition and the cell: new insights into the pyramidal neuron and prefrontal function. Cerebral Cortex, 13(11), 11241138.Google Scholar
Engle, R. W., Tuholski, S. W., Laughlin, J. E., & Conway, A. R. (1999). Working memory, short-term memory, and general fluid intelligence: a latent-variable approach. Journal of Experimental Psychology. General, 128, 309331.Google Scholar
Ferry, A. T., Öngür, D., An, X., & Price, J. L. (2000). Prefrontal cortical projections to the striatum in macaque monkeys: evidence for an organization related to prefrontal networks. Journal of Comparative Neurology, 425(3), 447470.Google Scholar
Flaherty, A. W., & Graybiel, A. M. (1993a). Output architecture of the primate putamen. Journal of Neuroscience, 13(8), 32223237.Google Scholar
Flaherty, A. W., & Graybiel, A. M. (1993b). Two input systems for body representations in the primate striatal matrix: experimental evidence in the squirrel monkey. Journal of Neuroscience, 13(3), 11201137.Google Scholar
Frank, M. J. (2005). When and when not to use your subthalamic nucleus: lessons from a computational model of the basal ganglia. In Seth, A. K., Prescott, T. J., & Bryson, J. J. (Eds.), Modelling Natural Action Selection: Proceedings of an International Workshop (pp. 5360). Sussex: AISB.Google Scholar
Frank, M. J., & Badre, D. (2012). Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: computational analysis. Cerebral Cortex, 22(3), 509526.Google Scholar
Frank, M. J., Loughry, B., & O’Reilly, R. C. (2001). Interactions between the frontal cortex and basal ganglia in working memory: a computational model. Cognitive, Affective, and Behavioral Neuroscience, 1, 137160.Google Scholar
Frank, M. J., & O’Reilly, R. C. (2006). A mechanistic account of striatal dopamine function in human cognition: psychopharmacological studies with cabergoline and haloperidol. Behavioral Neuroscience, 120, 497517.Google Scholar
Friedman, N., Miyake, A., Corley, R., Young, S., Defries, J., & Hewitt, J. (2006). Not all executive functions are related to intelligence. Psychological Science, 17(2), 172179.Google Scholar
Fries, W. (1984). Cortical projections to the superior colliculus in the macaque monkey: a retrograde study using horseradish peroxidase. Journal of Comparative Neurology, 230(1), 5576. https://doi.org/10.1002/%20cne.902300106Google Scholar
Fukuda, K., Vogel, E., Mayr, U., & Awh, E. (2010). Quantity, not quality: the relationship between fluid intelligence and working memory capacity. Psychonomic Bulletin & Review, 17(5), 673679. https://doi.org/10.3758/17.5.673Google Scholar
Funahashi, S., Bruce, C. J., & Goldman-Rakic, P. S. (1989). Mnemonic coding of visual space in the monkey’s dorsolateral prefrontal cortex. Journal of Neurophysiology, 61(2), 331349.Google Scholar
Fusi, S., Miller, E. K., & Rigotti, M. (2016). Why neurons mix: high dimensionality for higher cognition. Current Opinion in Neurobiology, 37, 6674. https://doi.org/10.1016/j.conb.2016.01.010Google Scholar
Fuster, J. M., & Alexander, G. E. (1971). Neuron activity related to short-term memory. Science, 173, 652654.Google Scholar
Gayet, S., Paffen, C. L. E., & Van der Stigchel, S. (2013). Information matching the content of visual working memory is prioritized for conscious access. Psychological Science, 24(12), 24722480. https://doi.org/10.1177/0956797613495882Google Scholar
Gerfen, C. R., & Surmeier, D. J. (2011). Modulation of striatal projection systems by dopamine. Annual Review of Neuroscience, 34, 441466.Google Scholar
Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: continual prediction with LSTM. Neural Computation, 12, 24512471.Google Scholar
Giguere, M., & Goldman-Rakic, P. S. (1988). Mediodorsal nucleus: areal, laminar, and tangential distribution of afferents and efferents in the frontal lobe of rhesus monkeys. Journal of Comparative Neurology, 277(2), 195213. https://doi.org/10.1002/cne.902770204Google Scholar
Goldman-Rakic, P. S. (1995). Cellular basis of working memory. Neuron, 14(3), 477485.Google Scholar
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA: MIT Press.Google Scholar
Gorgoraptis, N., Catalao, R. F. G., Bays, P. M., & Husain, M. (2011). Dynamic updating of working memory resources for visual objects. Journal of Neuroscience, 31(23), 85028511. https://doi.org/10.1523/%20JNEUROSCI.0208-11.2011Google Scholar
Graybiel, A. M. (1995). Building action repertoires: memory and learning functions of the basal ganglia. Current Opinion in Neurobiology, 5(6), 733741.Google Scholar
Graybiel, A. M., Flaherty, A. W., & Gimenez-Amaya, J. M. (1991). Striosomes and matrisomes. In Bernardi, G., Carpenter, M. B., Di Chiara, G., Morelli, M., & Stanzione, P. (Eds.), The Basal Ganglia III: Proceedings of the Third Triennial Meeting of the International Basal Ganglia Society (pp. 312). New York, NY: Plenum Press.Google Scholar
Gruber, A. J., Dayan, P., Gutkin, B. S., & Solla, S. A. (2006). Dopamine modulation in the basal ganglia locks the gate to working memory. Journal of Computational Neuroscience, 20(2), 153166.Google Scholar
Guo, Z. V., Inagaki, H. K., Daie, K., Druckmann, S., Gerfen, C. R., & Svoboda, K. (2017). Maintenance of persistent activity in a frontal thalamocortical loop. Nature, 545(7653), 181186. https://doi.org/10.1038/nature22324Google Scholar
Haber, S. N. (2003). The primate basal ganglia: parallel and integrative networks. Journal of Chemical Neuroanatomy, 26(4), 317330.Google Scholar
Haber, S. N., & Knutson, B. (2010). The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology, 35, 426.Google Scholar
Haith, A. M., Pakpoor, J., & Krakauer, J. W. (2016). Independence of movement preparation and movement initiation. Journal of Neuroscience, 36(10), 30073015. https://doi.org/10.1523/JNEUROSCI.3245-15.2016Google Scholar
Hardman, C. D., Henderson, J. M., Finkelstein, D. I., Horne, M. K., Paxinos, G., & Halliday, G. M. (2002). Comparison of the basal ganglia in rats, marmosets, macaques, baboons, and humans: volume and neuronal number for the output, internal relay, and striatal modulating nuclei. Journal of Comparative Neurology, 445(3), 238255.Google Scholar
Harris, K. D., & Shepherd, G. M. G. (2015). The neocortical circuit: themes and variations. Nature Neuroscience, 18(2), 170181. https://doi.org/10.1038/nn.3917Google Scholar
Hattox, A. M., & Nelson, S. B. (2007). Layer V neurons in mouse cortex projecting to different targets have distinct physiological properties. Journal of Neurophysiology, 98, 33303340.Google Scholar
Hazy, T. E., Frank, M. J., & O’Reilly, R. C. (2006). Banishing the homunculus: making working memory work. Neuroscience, 139, 105118.Google Scholar
Hazy, T. E., Frank, M. J., & O’Reilly, R. C. (2007). Towards an executive without a homunculus: computational models of the prefrontal cortex/basal ganglia system. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 362(1485), 16011613.Google Scholar
Herd, S. A., O’Reilly, R. C., Hazy, T. E., Chatham, C. H., Brant, A. M., & Friedman, N. P. (2014). A neural network model of individual differences in task switching abilities. Neuropsychologia, 62, 375–389. https://doi.org/10.1016/j.neuropsychologia.2014.04.014.Google Scholar
Hikida, T., Kimura, K., Wada, N., Funabiki, K., & Nakanishi, S. (2010). Distinct roles of synaptic transmission in direct and indirect striatal pathways to reward and aversive behavior. Neuron, 66, 896907.Google Scholar
Hikosaka, O., Sakamoto, M., & Usui, S. (1989). Functional properties of monkey caudate neurons. III. Activities related to expectation of target and reward. Journal of Neurophysiology, 61(4), 814832.Google Scholar
Hikosaka, O., & Wurtz, R. H. (1983). Visual and oculomotor functions of monkey substantia nigra pars reticulata. III. Memory-contingent visual and saccade responses. Journal of Neurophysiology, 49(5), 12681284.Google Scholar
Hinton, G. E., McClelland, J. L., & Rumelhart, D. E. (1986). Distributed representations. In Rumelhart, D. E., McClelland, J. L., & P. R. Group (Eds.), Parallel Distributed Processing. Volume 1: Foundations (pp. 77109). Cambridge, MA: MIT Press.Google Scholar
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 17351780.Google Scholar
Houk, J. C. (2005). Agents of the mind. Biological Cybernetics, 92(6), 427437.Google Scholar
Huang, T.-R., Hazy, T. E., Herd, S. A., & O’Reilly, R. C. (2013). Assembling old tricks for new tasks: a neural model of instructional learning and control. Journal of Cognitive Neuroscience, 25(6), 843851.Google Scholar
Ilinsky, I. A., Jouandet, M. L., & Goldman-Rakic, P. S. (1985). Organization of the nigrothalamocortical system in the rhesus monkey. Journal of Comparative Neurology, 236(3), 315330. https://doi.org/10.1002/%20cne.902360304Google Scholar
Jilk, D., Lebiere, C., O’Reilly, R. C., & Anderson, J. (2008). SAL: an explicitly pluralistic cognitive architecture. Journal of Experimental & Theoretical Artificial Intelligence, 20(3), 197218.Google Scholar
Joel, D., & Weiner, I. (2000). The connections of the dopaminergic system with the striatum in rats and primates: an analysis with respect to the functional and compartmental organization of the striatum. Neuroscience, 96, 451474.Google Scholar
Jones, E. G. (1998a). A new view of specific and nonspecific thalamocortical connections. Advances in Neurology, 77, 4971.Google Scholar
Jones, E. G. (1998b). Viewpoint: the core and matrix of thalamic organization. Neuroscience, 85(2), 331345. https://doi.org/10.1016/S0306-4522(97)00581-2Google Scholar
Jones, E. G. (2007). The Thalamus (2nd ed.). Cambridge: Cambridge University Press.Google Scholar
Jordan, M. I. (1986). Attractor dynamics and parallelism in a connectionist sequential machine. In Proceedings of the 8th Confererence of the Cognitive Science Society (pp. 531546). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Jung, W. H., Jang, J. H., Park, J. W., et al. (2014). Unravelling the intrinsic functional organization of the human striatum: a parcellation and connectivity study based on resting-state fMRI. PLOS One, 9(9), e106768. https://doi.org/10.1371/%20journal.pone.0106768Google Scholar
Kansky, K., Silver, T., Mély, D. A., et al. (2017). Schema networks: zero-shot transfer with a generative causal model of intuitive physics. arXiv:1706.04317 [cs].Google Scholar
Kimura, M., Kato, M., & Shimazaki, H. (1990). Physiological properties of projection neurons in the monkey striatum to the globus pallidus. Experimental Brain Research, 82(3), 672676. https://doi.org/10.1007/%20bf00228811Google Scholar
Kravitz, A. V., Tye, L. D., & Kreitzer, A. C. (2012). Distinct roles for direct and indirect pathway striatal neurons in reinforcement. Nature Neuroscience, 15(6), 816818.Google Scholar
Kriete, T., Noelle, D. C., Cohen, J. D., & O’Reilly, R. C. (2013). Indirection and symbol-like processing in the prefrontal cortex and basal ganglia. Proceedings of the National Academy of Sciences, 110(41), 1639016395.Google Scholar
Kritzer, M. F., & Goldman-Rakic, P. S. (1995). Intrinsic circuit organization of the major layers and sublayers of the dorsolateral prefrontal cortex in the rhesus monkey. Journal of Comparative Neurology, 359(1), 131143.Google Scholar
Krystal, J. H., Abi-Saab, W., Perry, E., et al. (2005). Preliminary evidence of attenuation of the disruptive effects of the NMDA glutamate receptor antagonist, ketamine, on working memory by pretreatment with the group II metabotropic glutamate receptor agonist, LY354740, in healthy human subjects. Psychopharmacology, 179(1), 303309. https://doi.org/10.1007/s00213-004-1982-8Google Scholar
Kubota, K., & Niki, H. (1971). Prefrontal cortical unit activity and delayed alternation performance in monkeys. Journal of Neurophysiology, 34(3), 337347.Google Scholar
Kuramoto, E., Furuta, T., Nakamura, K. C., Unzai, T., Hioki, H., & Kaneko, T. (2009). Two types of thalamocortical projections from the motor thalamic nuclei of the rat: a single neuron-tracing study using viral vectors. Cerebral Cortex, 19(9), 20652077.Google Scholar
Kuramoto, E., Ohno, S., Furuta, T., et al. (2015). Ventral medial nucleus neurons send thalamocortical afferents more widely and more preferentially to layer 1 than neurons of the ventral anterior–ventral lateral nuclear complex in the rat. Cerebral Cortex, 25(1), 221235. https://doi.org/10.1093/cercor/bht216Google Scholar
Lamme, V. A. F. (2006). Towards a true neural stance on consciousness. Trends in Cognitive Sciences, 10(11), 494501. https://doi.org/10.1016/j.tics.2006.09.001Google Scholar
Larkum, M. E., Petro, L. S., Sachdev, R. N. S., & Muckli, L. (2018). A perspective on cortical layering and layer-spanning neuronal elements. Frontiers in Neuroanatomy, 12, 19. https://doi.org/10.3389/fnana.2018.00056Google Scholar
Leichnetz, G. R., Spencer, R. F., Hardy, S. G., & Astruc, J. (1981). The prefrontal corticotectal projection in the monkey; an anterograde and retrograde horseradish peroxidase study. Neuroscience, 6(6), 10231041.Google Scholar
Levitt, J. B., Lewis, D. A., Yoshioka, T., & Lund, J. S. (1993). Topography of pyramidal neuron intrinsic connections in macaque monkey prefrontal cortex (areas 9 & 46). Journal of Comparative Neurology, 338, 360376.Google Scholar
Logie, R. H. (2018). Scientific advance and theory integration in working memory: comment on Oberauer et al. (2018). Psychological Bulletin; Washington, 144(9), 959.Google Scholar
Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390(6657), 279281.Google Scholar
Luck, S. J., & Vogel, E. K. (2013). Visual working memory capacity: from psychophysics and neurobiology to individual differences. Trends in Cognitive Sciences, 17(8), 391400. https://doi.org/10.1016/%20j.tics.2013.06.006Google Scholar
Ma, W. J., Husain, M., & Bays, P. M. (2014). Changing concepts of working memory. Nature Neuroscience, 17(3), 347356. https://doi.org/10.1038/nn.3655Google Scholar
Mante, V., Sussillo, D., Shenoy, K. V., & Newsome, W. T. (2013). Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature, 503(7474), 7884. https://doi.org/10.1038/nature12742Google Scholar
Masse, N. Y., Yang, G. R., Song, H. F., Wang, X.-J., & Freedman, D. J. (2019). Circuit mechanisms for the maintenance and manipulation of information in working memory. Nature Neuroscience, 22(7), 11591167. https://doi.org/10.1038/s41593-019-0414-3Google Scholar
McNab, F., & Klingberg, T. (2008). Prefrontal cortex and basal ganglia control access to working memory. Nature Neuroscience, 11(1), 103107.Google Scholar
Middleton, F. A., & Strick, P. L. (2000). Basal ganglia output and cognition: evidence from anatomical, behavioral, and clinical studies. Brain and Cognition, 42(2), 183200.Google Scholar
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167202.Google Scholar
Miller, E. K., & Desimone, R. (1994). Parallel neuronal mechanisms for short-term memory. Science, 263, 520522.Google Scholar
Miller, E. K., Erickson, C. A., & Desimone, R. (1996). Neural mechanisms of visual working memory in prefrontal cortex of the macaque. Journal of Neuroscience, 16(16), 51545167.Google Scholar
Miller, G. A. (1956). The Magical Number Seven, Plus Or Minus Two: Some Limits On Our Capacity For Processing Information (vol. 101). Indiana: Bobbs-Merrill.Google Scholar
Miller, G. A., Galanter, E., & Pribram, K. H. (1960). Plans and the Structure of Behavior. New York, NY: Holt.Google Scholar
Mingus, B., Kriete, T., Herd, S., Wyatte, D., Latimer, K., & O’Reilly, R. (2011). Generalization of figure-ground segmentation from binocular to monocular vision in an embodied biological brain model. In J. Schmidhuber, K. R. Thórisson, & M. Looks (Eds.), Artificial General Intelligence (pp. 351–356). London: Springer. https://doi.org/10.1007/978-3-642-22887-2_42Google Scholar
Mink, J. W. (1996). The basal ganglia: focused selection and inhibition of competing motor programs. Progress in Neurobiology, 50(4), 381425.Google Scholar
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “Frontal Lobe” tasks: a latent variable analysis. Cognitive Psychology, 41, 49100.Google Scholar
Miyake, A., & Shah, P. (Eds.). (1999). Models of Working Memory: Mechanisms of Active Maintenance and Executive Control. New York, NY: Cambridge University Press.Google Scholar
Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529533.Google Scholar
Moghaddam, B., & Adams, B. W. (1998). Reversal of phencyclidine effects by a group II metabotropic glutamate receptor agonist in rats. Science, 281(5381), 13491352. https://doi.org/10.1126/%20science.281.5381.1349Google Scholar
Mollick, J. A., Hazy, T. E., Krueger, K. A., et al. (2020). A systems-neuroscience model of phasic dopamine. Psychological Review, 127(6), 9721021. https://doi.org/10.1037/rev0000199Google Scholar
Monchi, O., Petrides, M., Strafella, A. P., Worsley, K. J., & Doyon, J. (2006). Functional role of the basal ganglia in the planning and execution of actions. Annals of Neurology, 59(2), 257264.Google Scholar
Montague, P. R., Dayan, P., & Sejnowski, T. J. (1996). A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of Neuroscience, 16(5), 19361947.Google Scholar
Mountcastle, V. B. (1997). The columnar organization of the neocortex. Brain, 120 (Pt 4), 701722.Google Scholar
Moustafa, A. A., Sherman, S. J., & Frank, M. J. (2008). A dopaminergic basis for working memory, learning, and attentional shifting in Parkinson’s Disease. Neuropsychologia, 46, 31443156.Google Scholar
Münkle, M. C., Waldvogel, H. J., & Faull, R. L. M. (2000). The distribution of calbindin, calretinin and parvalbumin immunoreactivity in the human thalamus. Journal of Chemical Neuroanatomy, 19(3), 155173. https://doi.org/10.1016/S0891-0618(00)00060-0Google Scholar
Nassar, M. R., Helmers, J. C., & Frank, M. J. (2018). Chunking as a rational strategy for data compression in visual working memory. Psychological Review, 125(4), 486511. https://doi.org/10.1037/%20rev0000101Google Scholar
Newell, A., & Simon, H. (1956). The logic theory machine: a complex information processing system. IRE Transactions on Information Theory, 2(3), 6179. https://doi.org/10.1109/TIT.1956.1056797Google Scholar
Nyberg, L., Andersson, M., Forsgren, L., et al. (2009). Striatal dopamine D2 binding is related to frontal BOLD response during updating of long-term memory representations. NeuroImage, 46(4), 11941199.Google Scholar
Oberauer, K., Lewandowsky, S., Awh, E., et al. (2018a). Benchmarks for models of short-term and working memory. Psychological Bulletin, 144(9), 885958. https://doi.org/colorado.idm.oclc.org/10.1037/bul0000153Google Scholar
Oberauer, K., Lewandowsky, S., Awh, E., et al. (2018b). Benchmarks provide common ground for model development: reply to Logie (2018) and Vandierendonck (2018). Psychological Bulletin, 144(9), 972977. https://doi.org/colorado.idm.oclc.org/10.1037/bul0000165Google Scholar
Öngür, D., & Price, J. L. (2000). The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cerebral Cortex, 10(3), 206219.Google Scholar
O’Reilly, R. C. (1996). Biologically plausible error-driven learning using local activation differences: the generalized recirculation algorithm. Neural Computation, 8(5), 895938. https://doi.org/10.1162/neco.1996.8.5.895Google Scholar
O’Reilly, R. C. (2006). Biologically based computational models of high-level cognition. Science, 314(5796), 9194.Google Scholar
O’Reilly, R. C., Braver, T. S., & Cohen, J. D. (1999). A biologically based computational model of working memory. In Miyake, A. & Shah, P. (Eds.), Models of Working Memory: Mechanisms of Active Maintenance and Executive Control (pp. 375411). New York, NY: Cambridge University Press.Google Scholar
O’Reilly, R. C., & Frank, M. J. (2006). Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia. Neural Computation, 18(2), 283328.Google Scholar
O’Reilly, R. C., Frank, M. J., Hazy, T. E., & Watz, B. (2007). PVLV: the primary value and learned value Pavlovian learning algorithm. Behavioral Neuroscience, 121(1), 3149.Google Scholar
O’Reilly, R. C., Hazy, T. E., & Herd, S. A. (2016). The Leabra cognitive architecture: how to play 20 principles with nature and win! In Chipman, S. (Ed.), Oxford Handbook of Cognitive Science. Oxford: Oxford University Press.Google Scholar
O’Reilly, R. C., Munakata, Y., Frank, M. J., Hazy, T. E., & Contributors. (2012). Computational Cognitive Neuroscience. Wiki Book, 1st ed. Available from:https://compcogneuro.orgGoogle Scholar
O’Reilly, R. C., Nair, A., Russin, J. L., & Herd, S. A. (2020). How sequential interactive processing within frontostriatal loops supports a continuum of habitual to controlled processing. Frontiers in Psychology, 11, 380. https://doi.org/10.3389/fpsyg.2020.00380Google Scholar
O’Reilly, R. C., Noelle, D. C., Braver, T. S., & Cohen, J. D. (2002). Prefrontal cortex and dynamic categorization tasks: representational organization and neuromodulatory control. Cerebral Cortex, 12, 246257.Google Scholar
O’Reilly, R. C., Petrov, A. A., Cohen, J. D., Lebiere, C. J., Herd, S. A., & Kriete, T. (2014). How limited systematicity emerges: a computational cognitive neuroscience approach. In Calvo, I. P. & Symons, J. (Eds.), The Architecture of Cognition: Rethinking Fodor and Pylyshyn’s Systematicity Challenge. Cambridge, MA: MIT Press.Google Scholar
O’Reilly, R. C., Russin, J. L., & Herd, S. A. (2019). Computational models of motivated frontal function. In D’Esposito, M. & Grafman, J. (Eds.), Handbook of Clinical Neurology (vol. 163, pp. 317332). Amsterdam: Elsevier.Google Scholar
O’Reilly, R. C., Russin, J. L., Zolfaghar, M., & Rohrlich, J. (2020). Deep predictive learning in neocortex and pulvinar. arXiv:2006.14800 [q-bio]Google Scholar
Pakkenberg, B., & Gundersen, H. J. (1997). Neocortical neuron number in humans: effect of sex and age. Journal of Comparative Neurology, 384(2), 312320.Google Scholar
Pauli, W. M., O’Reilly, R. C., Yarkoni, T., & Wager, T. D. (2016). Regional specialization within the human striatum for diverse psychological functions. Proceedings of the National Academy of Sciences, 113(7), 19071912. https://doi.org/10.1073/pnas.1507610113Google Scholar
Pertzov, Y., Bays, P. M., Joseph, S., & Husain, M. (2013). Rapid forgetting prevented by retrospective attention cues. Journal of Experimental Psychology. Human Perception and Performance, 39(5), 12241231. https://doi.org/10.1037/a0030947Google Scholar
Phillips, J. W., Schulmann, A., Hara, E., et al. (2019). A repeated molecular architecture across thalamic pathways. Nature Neuroscience, 22(11), 19251935. https://doi.org/10.1038/s41593-019-0483-3Google Scholar
Plenz, D., & Wickens, J. R. (2010). The striatal skeleton: medium spiny projection neurons and their lateral connections. In Steiner, H. & Tseng, K. Y. (Eds.), Handbook of Basal Ganglia Structure and Function (pp. 99112). New York, NY: Academic Press.Google Scholar
Rac-Lubashevsky, R., & Frank, M. J. (2020). Analogous computations in working memory input, output and motor gating: electrophysiological and computational modeling evidence. bioRxiv, 2020.12.21.423791. https://doi.org/10.1101/2020.12.21.423791Google Scholar
Ramaswamy, S., & Markram, H. (2015). Anatomy and physiology of the thick-tufted layer 5 pyramidal neuron. Frontiers in Cellular Neuroscience, 9, 19. https://doi.org/10.3389/fncel.2015.00233Google Scholar
Rao, S. G., Williams, G. V., & Goldman-Rakic, P. S. (1999). Isodirectional tuning of adjacent interneurons and pyramidal cells during working memory: evidence for microcolumnar organization in PFC. Journal of Neurophysiology, 81(4), 19031916.Google Scholar
Redondo, R. L., & Morris, R. G. M. (2011). Making memories last: the synaptic tagging and capture hypothesis. Nature Reviews Neuroscience, 12(1), 1730. https://doi.org/10.1038/nrn2963Google Scholar
Rikhye, R. V., Gilra, A., & Halassa, M. M. (2018). Thalamic regulation of switching between cortical representations enables cognitive flexibility. Nature Neuroscience, 21(12), 17531763. https://doi.org/10.1038/s41593-018-0269-zGoogle Scholar
Roberts, B. M., Shaffer, C. L., Seymour, P. A., Schmidt, C. J., Williams, G. V., & Castner, S. A. (2010). Glycine transporter inhibition reverses ketamine-induced working memory deficits. NeuroReport, 21(5), 390394. https://doi.org/10.1097/WNR.0b013e3283381a4eGoogle Scholar
Robinson, A. J., & Fallside, F. (1987). The utility driven dynamic error propagation network (Tech. Rep. No. CUED/F-INFENG/TR.1). Cambridge: Cambridge University Engineering Department.Google Scholar
Rougier, N. P., Noelle, D., Braver, T. S., Cohen, J. D., & O’Reilly, R. C. (2005). Prefrontal cortex and the flexibility of cognitive control: rules without symbols. Proceedings of the National Academy of Sciences, 102(20), 73387343.Google Scholar
Rougier, N. P., & O‘Reilly, R. C. (2002). Learning representations in a gated prefrontal cortex model of dynamic task switching. Cognitive Science, 26, 503520.Google Scholar
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(9), 533536.Google Scholar
Sanders, H., Berends, M., Major, G., Goldman, M. S., & Lisman, J. E. (2013). NMDA and GABAB (KIR) conductances: the “perfect couple” for bistability. Journal of Neuroscience, 33(2), 424429. https://doi.org/10.1523/JNEUROSCI.1854-12.2013Google Scholar
Schmidhuber, J., Gers, F., & Eck, D. (2002). Learning nonregular languages: a comparison of simple recurrent networks and LSTM. Neural Computation, 14(9), 20392042.Google Scholar
Schmidt, R., Ruiz, M. H., Kilavik, B. E., Lundqvist, M., Starr, P. A., & Aron, A. R. (2019). Beta oscillations in working memory, executive control of movement and thought, and sensorimotor function. Journal of Neuroscience, 39(42), 82318238. https://doi.org/10.1523/JNEUROSCI.1163-19.2019Google Scholar
Schroll, H., Vitay, J., & Hamker, F. H. (2012). Working memory and response selection: a computational account of interactions among cortico-basalganglio-thalamic loops. Neural Networks, 26, 5974. https://doi.org/10.1016/j.neunet.2011.10.008Google Scholar
Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 15931599.Google Scholar
Seamans, J. K., & Yang, C. R. (2004). The principal features and mechanisms of dopamine modulation in the prefrontal cortex. Progress in Neurobiology, 74(1), 157.Google Scholar
Seth, A. K., Dienes, Z., Cleeremans, A., Overgaard, M., & Pessoa, L. (2008). Measuring consciousness: relating behavioural and neurophysiological approaches. Trends in Cognitive Sciences, 12(8), 314321. https://doi.org/10.1016/j.tics.2008.04.008Google Scholar
Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending, and a general theory. Psychological Review, 84, 127190.Google Scholar
Sommer, M. A., & Wurtz, R. H. (2000). Composition and topographic organization of signals sent from the frontal eye field to the superior colliculus. Journal of Neurophysiology, 83(4), 19792001.Google Scholar
Stelzel, C., Basten, U., Montag, C., Reuter, M., & Fiebach, C. J. (2010). Frontostriatal involvement in task switching depends on genetic differences in D2 receptor density. Journal of Neuroscience, 30(42), 1420514212.Google Scholar
Stocco, A., Lebiere, C., & Anderson, J. (2010). Conditional routing of information to the cortex: a model of the basal ganglia’s role in cognitive coordination. Psychological Review, 117, 541574.Google Scholar
Stokes, M. G. (2015). ‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework. Trends in Cognitive Sciences, 19(7), 394405. https://doi.org/10.1016/j.tics.2015.05.004Google Scholar
Stokes, M. G., Kusunoki, M., Sigala, N., Nili, H., Gaffan, D., & Duncan, J. (2013). Dynamic coding for cognitive control in prefrontal cortex. Neuron, 78(2), 364375. https://doi.org/10.1016/j.neuron.2013.01.039Google Scholar
Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643662.Google Scholar
Tanibuchi, I., Kitano, H., & Jinnai, K. (2009a). Substantia nigra output to prefrontal cortex via thalamus in monkeys. I. Electrophysiological identification of thalamic relay neurons. Journal of Neurophysiology, 102(5), 29332945.Google Scholar
Tanibuchi, I., Kitano, H., & Jinnai, K. (2009b). Substantia nigra output to prefrontal cortex via thalamus in monkeys. II. Activity of thalamic relay neurons in delayed conditional go/no-go discrimination task. Journal of Neurophysiology, 102(5116), 29462954.Google Scholar
Todd, M. T., Niv, Y., & Cohen, J. D. (2008). Learning to use working memory in partially observable environments through dopaminergic reinforcement. In Koller, D. (Ed.), Advances in Neural Information Processing Systems (NIPS) (vol. 21). Red Hook, NY: Curran Associates.Google Scholar
Uylings, H., Groenewegen, H., & Kolb, B. (2003). Do rats have a prefrontal cortex? Behavioural Brain Research, 146(1–2), 317.Google Scholar
van Moorselaar, D., Theeuwes, J., & Olivers, C. N. L. (2014). In competition for the attentional template: can multiple items within visual working memory guide attention? Journal of Experimental Psychology. Human Perception and Performance, 40(4), 14501464. https://doi.org/10.1037/a0036229Google Scholar
Vandierendonck, A. (2018). Working memory benchmarks: a missed opportunity. Comment on Oberauer et al. (2018). Psychological Bulletin, 144(9), 963971. https://doi.org/colorado.idm.oclc.org/10.1037/bul0000159Google Scholar
Vinyals, O., Babuschkin, I., Czarnecki, W. M., et al. (2019). Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 575(7782), 350354. https://doi.org/10.1038/s41586-019-1724-zGoogle Scholar
Voytek, B., & Knight, R. T. (2010). Prefrontal cortex and basal ganglia contributions to visual working memory. Proceedings of the National Academy of Sciences, 107(42), 1816718172.Google Scholar
Wang, M., Yang, Y., Wang, C.-J., et al. (2013). NMDA receptors subserve persistent neuronal firing during working memory in dorsolateral prefrontal cortex. Neuron, 77(4), 736749. https://doi.org/10.1016/j.neuron.2012.12.032Google Scholar
Wang, X.-J. (2001). Synaptic reverberation underlying mnemonic persistent activity. Trends in Neurosciences, 24(8), 455463.Google Scholar
Wang, Y., Markram, H., Goodman, P. H., Berger, T. K., Ma, J., & Goldman-Rakic, P. S. (2006). Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nature Neuroscience, 9(4), 534542.Google Scholar
Watanabe, Y., & Funahashi, S. (2012). Thalamic mediodorsal nucleus and working memory. Neuroscience & Biobehavioral Reviews, 36(1), 134142. https://doi.org/10.1016/j.neubiorev.2011.05.003Google Scholar
Watanabe, Y., Takeda, K., & Funahashi, S. (2009). Population vector analysis of primate mediodorsal thalamic activity during oculomotor delayed-response performance. Cerebral Cortex, 19, 13131321.Google Scholar
Wei, Z., Wang, X.-J., & Wang, D.-H. (2012). From distributed resources to limited slots in multiple-item working memory: a spiking network model with normalization. Journal of Neuroscience, 32(33), 1122811240.Google Scholar
Werbos, P. (1974). Beyond regression: new tools for prediction and analysis in the behavioral sciences. (Unpublished doctoral dissertation). Cambridge, MA: Harvard University Press.Google Scholar
Werbos, P. (1990). Backpropagation through time: what it does and how to do it. Proceedings of the IEEE, 78(10), 15501560. https://doi.org/10.1109/5.58337Google Scholar
Whittington, J. C. R., & Bogacz, R. (2019). Theories of error back-propagation in the brain. Trends in Cognitive Sciences, 23(3), 235250. https://doi.org/10.1016/j.tics.2018.12.005Google Scholar
Wickens, J. R., Alexander, M. E., & Miller, R. (1991). Two dynamic modes of striatal function under dopaminergic-cholinergic control: simulation and analysis of a model. Synapse, 8(1), 112. https://doi.org/10.1002/syn.890080102Google Scholar
Wilken, P., & Ma, W. J. (2004). A detection theory account of change detection. Journal of Vision, 4(12), 11201135. https://doi.org/10.1167/4.12.11Google Scholar
Williams, A., & Phillips, J. (2020). Transfer reinforcement learning using output-gated working memory. Proceedings of the AAAI Conference on Artificial Intelligence, 34(2), 13241331. https://doi.org/10.1609/aaai.v34i02.5488Google Scholar
Williams, R. J., & Zipser, D. (1992). Gradient-based learning algorithms for recurrent networks and their computational complexity. In Chauvin, Y. & Rumelhart, D. E. (Eds.), Backpropagation: Theory, Architectures and Applications. Hillsdale, NJ: Erlbaum.Google Scholar
Winnubst, J., Bas, E., Ferreira, T. A., et al. (2019). Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain. Cell, 179(1), 268281.e13. https://doi.org/10.1016/j.cell.2019.07.042Google Scholar
Wyder, M. T., Massoglia, D. P., & Stanford, T. R. (2004). Contextual modulation of central thalamic delay-period activity: representation of visual and saccadic goals. Journal of Neurophysiology, 91(6), 26282648.Google Scholar
Yehene, E., Meiran, N., & Soroker, N. (2008). Basal ganglia play a unique role in task switching within the frontal-subcortical circuits: evidence from patients with focal lesions. Journal of Cognitive Neuroscience, 20, 10791093.Google Scholar
Yttri, E. A., & Dudman, J. T. (2016). Opponent and bidirectional control of movement velocity in the basal ganglia. Nature, 533(7603), 402406. https://doi.org/10.1038/nature17639Google Scholar
Zalocusky, K. A., Ramakrishnan, C., Lerner, T. N., Davidson, T. J., Knutson, B., & Deisseroth, K. (2016). Nucleus accumbens D2R cells signal prior outcomes and control risky decision-making. Nature, 531(7596), 642646. https://doi.org/10.1038/nature17400Google Scholar
Zhang, W., & Luck, S. J. (2008). Discrete fixed-resolution representations in visual working memory. Nature, 453(7192), 233235.Google Scholar

References

Aarts, E., & Roelofs, A. (2011). Attentional control in anterior cingulate cortex based on probabilistic cueing. Journal of Cognitive Neuroscience, 23(3), 716727. https://doi.org/10.1162/jocn.2010.21435Google Scholar
Alexander, W. H., & Brown, J. W. (2010). Computational models of performance monitoring and cognitive control. Topics in Cognitive Science, 2(4), 658677. https://doi.org/10.1111/j.1756-8765.2010.01085.xGoogle Scholar
Alexander, W. H., & Brown, J. W. (2011). Medial prefrontal cortex as an action-outcome predictor. Nature Neuroscience, 14(10), 13381344. https://doi.org/10.1038/nn.2921Google Scholar
Alexander, W. H., & Brown, J. W. (2014). A general role for medial prefrontal cortex in event prediction. Frontiers in Computational Neuroscience, 8, 111. https://doi.org/10.3389/fncom.2014.00069Google Scholar
Alexander, W. H., & Brown, J. W. (2015). Hierarchical error representation: a computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Computation, 27, 23542410.Google Scholar
Altmann, E. M., & Gray, W. D. (2008). An integrated model of cognitive control in task switching. Psychological Review, 115(3), 602639. https://doi.org/10.1037/0033-295x.115.3.602Google Scholar
Anderson, J. R. (1996). A simple theory of complex cognition. American Psychologist, 51(4), 355365. https://doi.org/10.1037//0003-066x.51.4.355Google Scholar
Ardid, S., Wang, X.-J., & Compte, A. (2007). An integrated microcircuit model of attentional processing in the neocortex. The Journal of Neuroscience, 27(32), 84868495. https://doi.org/10.1523/jneurosci.1145-07.2007Google Scholar
Aston-Jones, G., & Cohen, J. D. (2005). An integrative theory of locus coeruleus-norepinephrine: adaptive gain and optimal performance. Annual Review of Neuroscience, 28(1), 403450. https://doi.org/10.1146/annurev.neuro.28.061604.135709Google Scholar
Badre, D., Bhandari, A., Keglovits, H., & Kikumoto, A. (2021). The dimensionality of neural representations for control. Current Opinion in Behavioral Sciences, 38, 2028. https://doi.org/10.1016/j.cobeha.2020.07.002Google Scholar
Barch, D. M., & Ceaser, A. (2012). Cognition in schizophrenia: core psychological and neural mechanisms. Trends in Cognitive Sciences, 16(1), 2734. https://doi.org/10.1016/j.tics.2011.11.015Google Scholar
Barch, D. M., Culbreth, A., & Sheffield, J. (2018). Systems level modeling of cognitive control in psychiatric disorders: a focus on schizophrenia. In A. Anticevic & J. Murray (Eds.), Computational Psychiatry: Mathematical Modeling of Mental Illness (pp. 145173). London: Elsevier.Google Scholar
Behrens, T. E. J., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. S. (2007). Learning the value of information in an uncertain world. Nature Neuroscience, 10(9), 12141221. https://doi.org/10.1038/nn1954Google Scholar
Bench, C. J., Frith, C. D., Grasby, P. M., et al. (1993). Investigations of the functional anatomy of attention using the Stroop test. Neuropsychologia, 31(9), 907922. https://doi.org/10.1016/0028-3932(93)90147-rGoogle Scholar
Bengtsson, S. L., Haynes, J.-D., Sakai, K., Buckley, M. J., & Passingham, R. E. (2008). The representation of abstract task rules in the human prefrontal cortex. Cerebral Cortex, 19(8), 19291936. https://doi.org/10.1093/cercor/bhn222Google Scholar
Berlyne, D. E. (1957). Uncertainty and conflict: a point of contact between information-theory and behavior-theory concepts. Psychological Review, 64(6), 329339. https://doi.org/10.1037/h0041135Google Scholar
Blais, C., Harris, M. B., Guerrero, J. V., & Bunge, S. A. (2012). Rethinking the role of automaticity in cognitive control. The Quarterly Journal of Experimental Psychology, 65(2), 268276. https://doi.org/10.1080/17470211003775234Google Scholar
Blei, D. M., Griffiths, T. L., & Jordan, M. I. (2010). The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. Journal of the ACM, 57(2), 7. https://doi.org/10.1145/1667053.1667056Google Scholar
Botvinick, M. M. (2007). Conflict monitoring and decision making: reconciling two perspectives on anterior cingulate function. Cognitive, Affective, & Behavioral Neuroscience, 7(4), 356366. https://doi.org/10.3758/cabn.7.4.356Google Scholar
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108(3), 624652. https://doi.org/10.1037/0033-295x.108.3.624Google Scholar
Botvinick, M. M., & Cohen, J. D. (2014). The computational and neural basis of cognitive control: charted territory and new frontiers. Cognitive Science, 38, 12491285. https://doi.org/10.1111/cogs.12126Google Scholar
Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring and anterior cingulate cortex: an update. Trends in Cognitive Sciences, 8(12), 539546. https://doi.org/10.1016/j.tics.2004.10.003Google Scholar
Botvinick, M. M., Niv, Y., & Barto, A. C. (2009). Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective. Cognition, 113(3), 262280. https://doi.org/10.1016/j.cognition.2008.08.011Google Scholar
Boureau, Y., Sokol-Hessner, P., & Daw, N. D. (2015). Deciding how to decide: self-control and meta-decision making. Trends in Cognitive Sciences, 19(11), 700710. https://doi.org/10.1016/j.tics.2015.08.013Google Scholar
Brass, M., Ullsperger, M., Knoesche, T. R., Cramon, D. Y. von, & Phillips, N. A. (2005). Who comes first? The role of the prefrontal and parietal cortex in cognitive control. Journal of Cognitive Neuroscience, 17(9), 13671375. https://doi.org/10.1162/0898929054985400Google Scholar
Braver, T. S. (2012). The variable nature of cognitive control: a dual mechanisms framework. Trends in Cognitive Sciences, 16(2), 106113. https://doi.org/10.1016/j.tics.2011.12.010Google Scholar
Braver, T. S., Barch, D. M., & Cohen, J. D. (1999). Cognition and control in schizophrenia: a computational model of dopamine and prefrontal function. Biological Psychiatry, 46(3), 312328. http://www.ncbi.nlm.nih.gov/pubmed/10435197Google Scholar
Braver, T. S., Barch, D. M., Keys, B. A., et al. (2001). Context processing in older adults: evidence for a theory relating cognitive control to neurobiology in healthy aging. Journal of Experimental Psychology: General, 130(4), 746763. https://doi.org/10.1037//0096-3445.130.4.746Google Scholar
Braver, T. S., & Cohen, J. D. (2000). On the control of control: the role of dopamine in regulating prefrontal function and working memory. In Monsell, S. & Driver, J. (Eds.), Making Working Memory Work (pp. 551581). Cambridge, MA: MIT Press. https://doi.org/10.1016/s0165-0173(03)00143-7Google Scholar
Braver, T. S., & Cohen, J. D. (2001). Working memory, cognitive control, and the prefrontal cortex: computational and empirical studies. Cognitive Processing, 2, 2555.Google Scholar
Braver, T. S., & Ruge, H. (2006). Functional neuroimaging of executive functions. In Cabeza, R. & Kingstone, A. (Eds.), Handbook of Functional Neuroimaging of Cognition (2nd ed., pp. 307348). Cambridge, MA: MIT Press.Google Scholar
Brown, J. W. (2013). Beyond conflict monitoring: cognitive control and the neural basis of thinking before you act. Current Directions in Psychological Science, 22(3), 179185. https://doi.org/10.1177/0963721412470685Google Scholar
Brown, J. W., & Braver, T. S. (2005). Learned predictions of error likelihood in the anterior cingulate cortex. Science, 307(5712), 11101121.Google Scholar
Brown, J. W., Reynolds, J. R., & Braver, T. S. (2007). A computational model of fractionated conflict-control mechanisms in task-switching. Cognitive Psychology, 55(1), 3785. https://doi.org/10.1016/j.cogpsych.2006.09.005Google Scholar
Bustamante, L., Lieder, F., Musslick, S., Shenhav, A., & Cohen, J. (2021). Learning to overexert cognitive control in a Stroop task. Cognitive, Affective, & Behavioral Neuroscience, 21(3), 453471. https://doi.org/10.3758/s13415-020-00845-xGoogle Scholar
Carter, C. S., Braver, T. S., Barch, D. M., Botvinick, M. M., Noll, D., & Cohen, J. D. (1998). Anterior cingulate cortex, error detection, and the online monitoring of performance. Science, 280(5364), 747749. https://doi.org/10.1126/science.280.5364.747Google Scholar
Carter, C. S., & Veen, V. van. (2007). Anterior cingulate cortex and conflict detection: an update of theory and data. Cognitive, Affective, & Behavioral Neuroscience, 7(4), 367379. https://doi.org/10.3758/cabn.7.4.367Google Scholar
Cavanagh, J. F., Masters, S. E., Bath, K., & Frank, M. J. (2014). Conflict acts as an implicit cost in reinforcement learning. Nature Communications, 5, 110. https://doi.org/10.1038/ncomms6394Google Scholar
Chatham, C. H., Herd, S. A., Brant, A. M., et al. (2011). From an executive network to executive control: a computational model of the N-back task. Journal of Cognitive Neuroscience, 11(23), 35983619. https://doi.org/10.1162/jocn_a_00047Google Scholar
Chen, Y., Spagna, A., Wu, T., et al. (2019). Testing a cognitive control model of human intelligence. Scientific Reports, 9(1), 117. https://doi.org/10.1038/s41598-019-39685-2Google Scholar
Chong, T. T. J., Apps, M., Giehl, K., Sillence, A., Grima, L. L., & Husain, M. (2017). Neurocomputational mechanisms underlying subjective valuation of effort costs. PLoS Biology, 15(2), 128. https://doi.org/10.1371/journal.pbio.1002598Google Scholar
Cohen, J. D. (2017). Cognitive control: core constructs and current considerations. In T. Egner (Ed.), The Wiley Handbook of Cognitive Control (pp. 327). Oxford: Wiley-Blackwell.Google Scholar
Cohen, J. D., Braver, T. S., & Brown, J. W. (2002). Computational perspectives on dopamine function in prefrontal cortex. Current Opinion in Neurobiology, 12(2), 223229. www.sciencedirect.com/science/article/pii/S0959438802003148Google Scholar
Cohen, J. D., Braver, T. S., & O’Reilly, R. C. (1996). A computational approach to prefrontal cortex, cognitive control and schizophrenia: recent developments and current challenges. Philosophical Transactions of the Royal Society of London, Series B, Biological Sciences, 351, 15151527.Google Scholar
Cohen, J. D., Dunbar, K., & McClelland, J. L. (1990). On the control of automatic processes: a parallel distributed processing account of the Stroop effect. Psychological Review, 97(3), 332361. https://doi.org/10.1037/0033-295x.97.3.332Google Scholar
Cohen, J. D., & Huston, T. A. (1994). Progress in the use of interactive models for understanding attention and performance. In Umilta, C. & Moscovitch, M. (Eds.), Attention and Performance XV: Conscious and Nonconscious Information Processing (pp. 453476). Cambridge, MA: MIT Press.Google Scholar
Cohen, J. D., Usher, M., & McClelland, J. L. (1998). A PDP approach to set size effects within the Stroop task: reply to Kanne, Balota, Spieler, and Faust (1998). Psychological Review, 105(1), 188194. https://doi.org/10.1037/0033-295x.105.1.188Google Scholar
Cole, M. W., Ito, T., & Braver, T. S. (2016). The behavioral relevance of task information in human prefrontal cortex. Cerebral Cortex, 26(6), 24972505. https://doi.org/10.1093/cercor/bhv072Google Scholar
Cole, M. W., Yarkoni, T., Repovs, G., Anticevic, A., & Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. Journal of Neuroscience, 32(26), 89888999. https://doi.org/10.1523/jneurosci.0536-12.2012Google Scholar
Cole, M. W., Yeung, N., Freiwald, W. A., & Botvinick, M. (2009). Cingulate cortex: diverging data from humans and monkeys. Trends in Neurosciences, 32(11), 566574. https://doi.org/10.1016/j.tins.2009.07.001Google Scholar
Collins, A. G. E. (2017). The cost of structure learning. Journal of Cognitive Neuroscience, 29(10), 16461655. https://doi.org/10.1162/jocn_a_01128Google Scholar
Collins, A. G. E., Cavanagh, J. F., & Frank, M. J. (2014). Human EEG uncovers latent generalizable rule structure during learning. The Journal of Neuroscience, 34(13), 46774685. https://doi.org/10.1523/jneurosci.3900-13.2014Google Scholar
Collins, A. G. E., & Frank, M. J. (2013). Cognitive control over learning: creating, clustering, and generalizing task-set structure. Psychological Review, 120(1), 190229. https://doi.org/10.1037/a0030852Google Scholar
Collins, A. G. E., & Frank, M. J. (2016). Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning. Cognition, 152, 160169. https://doi.org/10.1016/j.cognition.2016.04.002Google Scholar
Cools, R. (2016). The costs and benefits of brain dopamine for cognitive control. Wiley Interdisciplinary Reviews: Cognitive Science, 7, 317329. https://doi.org/10.1002/wcs.1401Google Scholar
Croxson, P. L., Walton, M. E., O’Reilly, J. X., Behrens, T. E. J., & Rushworth, M. F. S. (2009). Effort-based cost-benefit valuation and the human brain. Journal of Neuroscience, 29(14), 45314541. https://doi.org/10.1523/jneurosci.4515-08.2009Google Scholar
D’Ardenne, K., Eshel, N., Luka, J., et al. (2012). Role of prefrontal cortex and the midbrain dopamine system in working memory updating. Proceedings of the National Academy of Sciences, 109(49), 1990019909. https://doi.org/10.1073/pnas.1116727Google Scholar
Dayan, P. (2012). How to set the switches on this thing. Current Opinion in Neurobiology, 22(6), 10681074. https://doi.org/10.1016/j.conb.2012.05.011Google Scholar
Dayan, P., & Yu, A. J. (2009). Phasic norepinephrine: a neural interrupt signal for unexpected events. Network: Computation in Neural Systems, 17(4), 335350. https://doi.org/10.1080/09548980601004024Google Scholar
De Pisapia, N. D., Repovš, G., & Braver, T. S. (2008). Computational models of attention and cognitive control. In R. Sun (Ed.), The Cambridge Handbook of Computational Psychology (pp. 422450). Cambridge: Cambridge University Press. https://doi.org/10.1017/cbo9780511816772.019Google Scholar
Deco, G., & Rolls, E. T. (2003). Attention and working memory: a dynamical model of neuronal activity in the prefrontal cortex. European Journal of Neuroscience, 18(8), 23742390. https://doi.org/10.1046/j.1460-9568.2003.02956.xGoogle Scholar
Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18(1), 193222. https://doi.org/10.1146/annurev.ne.18.030195.001205Google Scholar
Dixon, M. L., & Christoff, K. (2012). The decision to engage cognitive control is driven by expected reward-value: neural and behavioral evidence. PLoS One, 7(12). https://doi.org/10.1371/journal.pone.0051637Google Scholar
Dixon, M. L., Vega, A. D. L., Mills, C., et al. (2018). Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proceedings of the National Academy of Sciences, 115(7), 201715766. https://doi.org/10.1073/pnas.1715766115Google Scholar
Domenech, P., & Koechlin, E. (2015). Executive control and decision-making in the prefrontal cortex. Current Opinion in Behavioral Sciences, 1, 101106. https://doi.org/10.1016/j.cobeha.2014.10.007Google Scholar
Doya, K. (2002). Metalearning and neuromodulation. Neural Networks, 15(4–6), 495506. https://doi.org/10.1016/s0893-6080(02)00044-8Google Scholar
Dreisbach, G., & Fischer, R. (2012). The role of affect and reward in the conflict-triggered adjustment of cognitive control. Frontiers in Human Neuroscience, 6, 342. https://doi.org/10.3389/fnhum.2012.00342Google Scholar
Dreisbach, G., & Fischer, R. (2015). Conflicts as aversive signals for control adaptation. Current Directions in Psychological Science, 24(4), 255260. https://doi.org/10.1177/0963721415569569Google Scholar
Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour. Trends in Cognitive Sciences, 14(4), 172179. https://doi.org/10.1016/j.tics.2010.01.004Google Scholar
Duncan, J. (2013). The structure of cognition: attentional episodes in mind and brain. Neuron, 80(1), 3550. https://doi.org/10.1016/j.neuron.2013.09.015Google Scholar
Duncan, J., & Owen, A. M. (2000). Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends in Neurosciences, 23(10), 475483. https://doi.org/10.1016/s0166-2236(00)01633-7Google Scholar
Durstewitz, D., & Seamans, J. K. (2002). The computational role of dopamine D1 receptors in working memory. Neural Networks, 15, 561572.Google Scholar
Duverne, S., & Koechlin, E. (2017). Rewards and cognitive control in the human prefrontal cortex. Cerebral Cortex, 27(10), 116. https://doi.org/10.1093/cercor/bhx210Google Scholar
Egner, T. (Ed.). (2017). The Wiley Handbook of Cognitive Control. Oxford: Wiley Blackwell.Google Scholar
Egner, T., & Hirsch, J. (2005). Cognitive control mechanisms resolve conflict through cortical amplification of task-relevant information. Nature Neuroscience, 8(12), 17841790. https://doi.org/10.1038/nn1594Google Scholar
Engle, R. W., & Kane, M. J. (2004). Executive attention, working memory capacity, and a two-factor theory of cognitive control. In B. H. Ross (Ed.),The Psychology of Learning and Motivation: Advances in Research and Theory (pp. 145199). New York, NY: Academic Press. https://doi.org/10.1016/s0079-7421(03)44005-xGoogle Scholar
Eppinger, B., Goschke, T., & Musslick, S. (2021). Meta-control: from psychology to computational neuroscience. Cognitive, Affective, & Behavioral Neuroscience, 21(3), 447452. https://doi.org/10.3758/s13415-021-00919-4Google Scholar
Feng, S. F., Schwemmer, M., Gershman, S. J., & Cohen, J. D. (2014). Multitasking versus multiplexing: toward a normative account of limitations in the simultaneous execution of control-demanding behaviors. Cognitive, Affective, & Behavioral Neuroscience, 14(1), 129146. https://doi.org/10.3758/s13415-013-0236-9Google Scholar
Flesch, T., Juechems, K., Dumbalska, T., Saxe, A., & Summerfield, C. (2022). Orthogonal representations for robust context-dependent task performance in brains and neural networks. Neuron, 110, 1258–1270. https://doi.org/10.1016/j.neuron.2022.01.005Google Scholar
Frank, M. J., & Badre, D. (2012). Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: computational analysis. Cerebral Cortex, 22(3), 509526. https://doi.org/10.1093/cercor/bhr114Google Scholar
Freund, M., Etzel, J., & Braver, T. (2021). Neural coding of cognitive control: the representational similarity analysis approach. Trends in Cognitive Sciences, 25, 622–638. https://doi.org/10.1016/j.tics.2021.03.011Google Scholar
Friedman, N. P., & Robbins, T. W. (2021). The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology, 47(1), 1–18. https://doi.org/10.1038/s41386-021-01132-0Google Scholar
Fritz, J., & Dreisbach, G. (2013). Conflicts as aversive signals: conflict priming increases negative judgments for neutral stimuli. Cognitive, Affective, Behavioral Neuroscience, 13(2), 311317. https://doi.org/10.3758/s13415-012-0147-1Google Scholar
Fröbose, M. I., & Cools, R. (2018). Chemical neuromodulation of cognitive control avoidance. Current Opinion in Behavioral Sciences, 22, 121127. https://doi.org/10.1016/j.cobeha.2018.01.027Google Scholar
Frömer, R., Lin, H., Wolf, C. K. D., Inzlicht, M., & Shenhav, A. (2021). Expectations of reward and efficacy guide cognitive control allocation. Nature Communications, 12(1), 1030. https://doi.org/10.1038/s41467–021-21315-zGoogle Scholar
Fusi, S., Miller, E. K., & Rigotti, M. (2016). Why neurons mix: high dimensionality for higher cognition. Current Opinion in Neurobiology, 37, 6674. https://doi.org/10.1016/j.conb.2016.01.010Google Scholar
Gehring, W. J., Goss, B., Coles, M. G. H., Meyer, D. E., & Donchin, E. (1993). A neural system for error detection and compensation. Psychological Science, 4(6), 385390. https://doi.org/10.1111/j.1467-9280.1993.tb00586.xGoogle Scholar
Gershman, S. J., Cohen, J. D., & Niv, Y. (2010). Learning to selectively attend. 32nd Annual Proceedings of the Cognitive Science Society, pp. 1270–1275.Google Scholar
Gilbert, S. J., & Shallice, T. (2002). Task switching: A PDP model. Cognitive Psychology, 44(3), 297337. https://doi.org/10.1006/cogp.2001.0770Google Scholar
Grahek, I., Musslick, S., & Shenhav, A. (2020). A computational perspective on the roles of affect in cognitive control. International Journal of Psychophysiology, 151, 2534. https://doi.org/10.1016/j.ijpsycho.2020.02.001Google Scholar
Gratton, G., Cooper, P., Fabiani, M., Carter, C. S., & Karayanidis, F. (2018). Dynamics of cognitive control: theoretical bases, paradigms, and a view for the future. Psychophysiology, 55, 1–29. https://doi.org/10.1111/psyp.13016Google Scholar
Gu, S., Pasqualetti, F., Cieslak, M., et al. (2015). Controllability of structural brain networks. Nature Communications, 6(1), 8414. https://doi.org/10.1038/ncomms9414Google Scholar
Hamid, A. A., Pettibone, J. R., Mabrouk, O. S., et al. (2016). Mesolimbic dopamine signals the value of work. Nature Neuroscience, 19(1), 117126. https://doi.org/10.1038/nn.4173Google Scholar
Hazy, T. E., Frank, M. J., & O’Reilly, R. C. (2007). Towards an executive without a homunculus: computational models of the prefrontal cortex/basal ganglia system. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1485), 16011613. https://doi.org/10.1098/rstb.2007.2055Google Scholar
Herd, S. A., O’Reilly, R. C., Hazy, T. E., et al. (2014). A neural network model of individual differences in task switching abilities. Neuropsychologia, 62, 375389. https://doi.org/10.1016/j.neuropsychologia.2014.04.014Google Scholar
Holroyd, C. B., & Coles, M. G. H. (2002). The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109(4), 679709. https://doi.org/10.1037//0033-295x.109.4.679Google Scholar
Holroyd, C. B., Nieuwenhuis, S., Yeung, N., et al. (2004). Dorsal anterior cingulate cortex shows fMRI response to internal and external error signals. Nature Neuroscience, 7(5), 497498. https://doi.org/10.1038/nn1238Google Scholar
Holroyd, C. B., Yeung, N., Coles, M. G. H., & Cohen, J. D. (2005). A mechanism for error detection in speeded response time tasks. Journal of Experimental Psychology: General, 134(2), 163191. https://doi.org/10.1037/0096-3445.134.2.163Google Scholar
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 25542558. https://doi.org/10.1073/pnas.79.8.2554Google Scholar
Kerns, J. G. (2004). Anterior cingulate conflict monitoring and adjustments in control. Science, 303(5660), 10231026. https://doi.org/10.1126/science.1089910Google Scholar
Khamassi, M., Quilodran, R., Enel, P., Dominey, P. F., & Procyk, E. (2015). Behavioral regulation and the modulation of information coding in the lateral prefrontal and cingulate cortex. Cerebral Cortex, 25(9), 31973218. https://doi.org/10.1093/cercor/bhu114Google Scholar
Kool, W., & Botvinick, M. (2014). A labor/leisure tradeoff in cognitive control. Journal of Experimental Psychology: General, 143(1), 131141. https://doi.org/10.1037/a0031048Google Scholar
Kool, W., Shenhav, A., & Botvinick, M. M. (2017). Cognitive control as cost-benefit decision making. In T. Egener (Ed.), The Wiley Handbook of Cognitive Control (pp. 167–189). Oxford: Wiley-Blackwell. https://doi.org/10.1002/9781118920497.ch10Google Scholar
Kouneiher, F., Charron, S., & Koechlin, E. (2009). Motivation and cognitive control in the human prefrontal cortex. Nature Neuroscience, 12(7), 939–945. https://doi.org/10.1038/nn.2321Google Scholar
Kriete, T., Noelle, D. C., Cohen, J. D., & O’Reilly, R. C. (2013). Indirection and symbol-like processing in the prefrontal cortex and basal ganglia. Proceedings of the National Academy of Sciences, 110(41), 1639016395. https://doi.org/10.1073/pnas.1303547110Google Scholar
Leng, X., Yee, D., Ritz, H., & Shenhav, A. (2021). Dissociable influences of reward and punishment on adaptive cognitive control. PLoS Computational Biology, 17(12), 121. https://doi.org/10.1371/journal.pcbi.1009737Google Scholar
Lieder, F., & Griffiths, T. L. (2019). Resource-rational analysis: understanding human cognition as the optimal use of limited computational resources. Behavioral and Brain Sciences, 43, 185. https://doi.org/10.1017/s0140525x1900061xGoogle Scholar
Lieder, F., Shenhav, A., Musslick, S., & Griffiths, T. L. (2018). Rational metareasoning and the plasticity of cognitive control. PLoS Computational Biology, 14(4), 127. https://doi.org/10.1371/journal.pcbi.1006043Google Scholar
Logan, G. D. (1989). Automaticity and cognitive control. In J. S. Uleman & J. A. Bargh, (Eds.), Unintended Thought (pp. 5274). Hove: Guilford Press.Google Scholar
Luks, T. L., Simpson, G. V., Feiwell, R. J., & Miller, W. L. (2002). Evidence for anterior cingulate cortex involvement in monitoring preparatory attentional set. NeuroImage, 17(2), 792802. https://doi.org/10.1006/nimg.2002.1210Google Scholar
MacDonald, A. W., Cohen, J. D., Stenger, V. A., & Carter, C. S. (2000). Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science, 288(5472), 18351838. https://doi.org/10.1126/science.288.5472.1835Google Scholar
MacLeod, C. M. (1991). Half a century of reseach on the Stroop effect: an integrative review. Psychological Bulletin, 109(2), 163203. https://doi.org/10.1037/0033-2909.109.2.163Google Scholar
Masís, J. A., Musslick, S., & Cohen, J. (2021). The value of learning and cognitive control allocation. In Proceedings of the Annual Meeting of the Cognitive Science Society. https://escholarship.org/uc/item/7w0223v0Google Scholar
McClelland, J. L. (1979). On the time relations of mental processes: an examination of systems of processes in cascade. Psychological Review, 86(4), 287330. https://doi.org/10.1037/0033-295x.86.4.287Google Scholar
McGuire, J. T., & Botvinick, M. M. (2010). Prefrontal cortex, cognitive control, and the registration of decision costs. Proceedings of the National Academy of Sciences, 107(17), 79227926. https://doi.org/10.1073/pnas.0910662107Google Scholar
Melcher, T., & Gruber, O. (2009). Decomposing interference during Stroop performance into different conflict factors: an event-related fMRI study. Cortex, 45(2), 189200. https://doi.org/10.1016/j.cortex.2007.06.004Google Scholar
Milham, M. P., & Banich, M. T. (2005). Anterior cingulate cortex: an fMRI analysis of conflict specificity and functional differentiation. Human Brain Mapping, 25(3), 328335. https://doi.org/10.1002/hbm.20110Google Scholar
Miller, E. K. (2000). The prefrontal cortex and cognitive control. Nature Reviews Neuroscience, 1, 5965.Google Scholar
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167202. https://doi.org/10.1146/annurev.neuro.24.1.167Google Scholar
Minai, A. A. (2015). Computational models of cognitive and motor control. In Kacprzyk, J. & Pedrycz, W. (Eds.), Springer Handbook of Computational Intelligence (pp. 665682). London: Springer. https://doi.org/10.1007/978-3-662-43505-2_35Google Scholar
Modirrousta, M., & Fellows, L. K. (2008). Medial prefrontal cortex plays a critical and selective role in ‘feeling of knowing’ meta-memory judgments. Neuropsychologia, 46(12), 29582965. https://doi.org/10.1016/j.neuropsychologia.2008.06.011Google Scholar
Momennejad, I., Russek, E. M., Cheong, J. H., Botvinick, M. M., Daw, N. D., & Gershman, S. J. (2017). The successor representation in human reinforcement learning. Nature Human Behaviour, 1(9), 680692. https://doi.org/10.1038/s41562-017-0180-8Google Scholar
Monsell, S. (2003). Task switching. Trends in Cognitive Sciences, 7(3), 134140. https://doi.org/10.1016/s1364-6613(03)00028-7Google Scholar
Montague, P., Dayan, P., & Sejnowski, T. (1996). A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of Neuroscience, 16(5), 19361947. https://doi.org/10.1523/jneurosci.16-05-01936.1996Google Scholar
Musslick, S., & Cohen, J. (2020). Rationalizing constraints on the capacity for cognitive control. PsyArXiv. https://psyarxiv.com/vtknh/Google Scholar
Musslick, S., Cohen, J. D., & Shenhav, A. (2019). Decomposing individual differences in cognitive control: a model-based approach. In Proceedings of the 41st Annual Meeting of the Cognitive Science Society.Google Scholar
Musslick, S., Shenhav, A., Botvinick, M. M., & Cohen, J. D. (2015). A computational model of control allocation based on the expected value of control. In Reinforcement Learning and Decision Making Conference. Edmonton, Alberta, Canada.Google Scholar
Nassar, M. R., & Frank, M. J. (2016). Taming the beast: extracting generalizable knowledge from computational models of cognition. Current Opinion in Behavioral Sciences, 11, 4954. https://doi.org/10.1016/j.cobeha.2016.04.003Google Scholar
Niendam, T. A., Laird, A. R., Ray, K. L., Dean, Y. M., Glahn, D. C., & Carter, C. S. (2012). Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cognitive, Affective, Behavioral Neuroscience, 12(2), 241268. https://doi.org/10.3758/s13415-011-0083-5Google Scholar
Norman, D. A., & Shallice, T. (1986). Attention to action: willed and automatic control of behavior. In Davidson, R., Schwartz, G, & Shapiro, D (Eds.), Consciousness and Self-Regulation: Advances in Research and Theory (pp. 1–18). London: Springer.Google Scholar
O’Reilly, R. C. (2006). Biologically based computational models of high-level cognition. Science, 314, 9194. https://doi.org/10.1126/science.1127242Google Scholar
O’Reilly, R. C., Braver, T. S., & Cohen, J. D. (1999). A biologically-based computational model of working memory. In A. Miyake & P. Shah (Eds.), Models of Working Memory: Mechanisms of Active Maintenance and Executive Control (pp. 375–411). Cambridge: Cambridge University Press. https://doi.org/10.1017/cbo9781139174909Google Scholar
O’Reilly, R. C., & Frank, M. J. (2006). Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia. Neural Computation, 18(2), 283328. https://doi.org/10.1162/089976606775093909Google Scholar
O’Reilly, R. C., Herd, S. A., & Pauli, W. M. (2010). Computational models of cognitive control. Current Opinion in Neurobiology, 20(2), 367377. https://doi.org/10.1016/j.conb.2010.01.008Google Scholar
O’Reilly, R. C., Munakata, Y., Frank, M. J., & Hazy, T. E. (2012). Computational Cognitive Neuroscience. Wiki Book, 4th ed. (2020). Available at: https://CompCogNeuro.orgGoogle Scholar
Ott, T., & Nieder, A. (2019). Dopamine and cognitive control in prefrontal cortex. Trends in Cognitive Sciences, 23(3), 213234. https://doi.org/10.1016/j.tics.2018.12.006Google Scholar
Posner, M. I., & Snyder, C. R. R. (1975). Attention and cognitive control. In Solso, R. L. (Ed.), Information Processing and Cognition: The Loyola Symposium (pp. 5585). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Ranti, C., Chatham, C. H., & Badre, D. (2015). Parallel temporal dynamics in hierarchical cognitive control. Cognition, 142, 205229. https://doi.org/10.1016/j.cognition.2015.05.003Google Scholar
Reverberi, C., Görgen, K., & Haynes, J.-D. (2012). Compositionality of rule representations in human prefrontal cortex. Cerebral Cortex, 22(6), 12371246. https://doi.org/10.1093/cercor/bhr200Google Scholar
Reynolds, J. R., Braver, T. S., Brown, J. W., & Stigchel, S. V. der. (2006). Computational and neural mechanisms of task switching. Neurocomputing, 69(10–12), 13321336. https://doi.org/10.1016/j.neucom.2005.12.102Google Scholar
Ridderinkhof, K. R., Ullsperger, M., Crone, E. A., & Nieuwenhuis, S. (2004). The role of the medial frontal cortex in cognitive control. Science, 306, 443447.Google Scholar
Rigotti, M., Barak, O., Warden, M. R., et al. (2013). The importance of mixed selectivity in complex cognitive tasks. Nature, 497(7451), 585590. https://doi.org/10.1038/nature12160Google Scholar
Roelofs, A., Turennout, M. van, & Coles, M. G. H. (2006). Anterior cingulate cortex activity can be independent of response conflict in Stroop-like tasks. Proceedings of the National Academy of Sciences, 103(37), 1388413889. https://doi.org/10.1073/pnas.0606265103Google Scholar
Rogers, R. D., & Monsell, S. (1995). Costs of a predictible switch between simple cognitive tasks. Journal of Experimental Psychology: General, 124(2), 207231. https://doi.org/10.1037/0096-3445.124.2.207Google Scholar
Rougier, N. P., Noelle, D. C., Braver, T. S., Cohen, J. D., & O’Reilly, R. C. (2005). Prefrontal cortex and flexible cognitive control: rules without symbols. Proceedings of the National Academy of Sciences, 102(20), 73387343. https://doi.org/10.1073/pnas.0502455102Google Scholar
Rumelhart, D. E., Hinton, G. E., & McClelland, J. L. (1986). A general framework for parallel distributed processing. In D. E. Rumelhart & J. L. McClelland, (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1 (pp. 4576). Cambridge, MA: MIT Press. www.csri.utoronto.ca/~hinton/absps/pdp2.pdfGoogle Scholar
Rumelhart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. E. (1986). Schemata and sequential thought processes in PDP models. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel Distributed Processing, Vol. 2 (pp. 757). Cambridge, MA: MIT Press. https://doi.org/10.1016/b978-1-4832-1446-7.50020-0Google Scholar
Sakai, K. (2008). Task set and prefrontal cortex. Neuroscience, 31(1), 219245. https://doi.org/10.1146/annurev.neuro.31.060407.125642Google Scholar
Schneider, W., & Chein, J. M. (2003). Controlled automatic processing: behavior, theory, and biological mechanisms. Cognitive Science, 27(3), 525559. https://doi.org/10.1016/s0364-0213(03)00011-9Google Scholar
Servan-Schreiber, D., Printz, H., & Cohen, J. D. (1990). A network model of catecholamine effects: gain, signal-to-noise ratio, and behavior. Science, 249(4971), 892895. https://doi.org/10.1126/science.2392679Google Scholar
Shenhav, A., Botvinick, M. M., & Cohen, J. D. (2013). The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron, 79(2), 217240. https://doi.org/10.1016/j.neuron.2013.07.007Google Scholar
Shenhav, A., Cohen, J. D., & Botvinick, M. M. (2016). Dorsal anterior cingulate cortex and the value of control. Nature Neuroscience, 19(10), 12861291. https://doi.org/10.1038/nn.4384Google Scholar
Shenhav, A., Musslick, S., Lieder, F., et al. (2017). Toward a rational and mechanistic account of mental effort. Annual Review of Neuroscience, 40(1), 99124. https://doi.org/10.1146/annurev-neuro-072116-031526Google Scholar
Sheth, S. A., Mian, M. K., Patel, S. R., et al. (2012). Human dorsal anterior cingulate cortex neurons mediate ongoing behavioural adaptation. Nature, 488, 15. https://doi.org/10.1038/nature11239Google Scholar
Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. Psychological Review, 84(2), 127190. https://doi.org/10.1037/0033-295x.84.2.127Google Scholar
Silvetti, M., Vassena, E., Abrahamse, E., & Verguts, T. (2018). Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner. PLoS Computational Biology, 14(8), e1006370. https://doi.org/10.1371/journal.pcbi.1006370Google Scholar
Sohn, M. H., & Anderson, J. R. (2001). Task preparation and task repetition: two-component model of task switching. Journal of Experimental Psychology: General, 130(4), 764778. https://doi.org/10.1037/0096-3445.130.4.764Google Scholar
Spunt, R. P., Lieberman, M. D., Cohen, J. R., & Eisenberger, N. I. (2012). The phenomenology of error processing: the dorsal ACC response to stop-signal errors tracks reports of negative affect. Journal of Cognitive Neuroscience, 24(8), 17531765. https://doi.org/10.1162/jocn_a_00242Google Scholar
Steenbergen, H. van. (2014). Affective modulation of cognitive control: a biobehavioral perspective. In G. H. E. Gendolla, M. Tops, & S. L. Koole (Eds.), Handbook of Biobehavioral Approaches to Self-Regulation (pp. 89–107). New York, NY: Springer. https://doi.org/10.1007/978-1-4939-1236-0_7Google Scholar
Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18(6), 643662. https://doi.org/10.1037/h0054651Google Scholar
Sutton, R., & Barto, A. (1998). Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press.Google Scholar
Tervo, D. G. R., Tenenbaum, J. B., & Gershman, S. J. (2016). Toward the neural implementation of structure learning. Current Opinion in Neurobiology, 37, 99105. https://doi.org/10.1016/j.conb.2016.01.014Google Scholar
Unsworth, N., & Robison, M. K. (2017). A locus coeruleus-norepinephrine account of individual differences in working memory capacity and attention control. Psychonomic Bulletin & Review, 24(4), 12821311. https://doi.org/10.3758/s13423-016-1220-5Google Scholar
Vassena, E., Deraeve, J., & Alexander, W. H. (2017). Predicting motivation: computational models of PFC can explain neural coding of motivation and effort-based decision-making in health and disease. Journal of Cognitive Neuroscience, 29(10), 16331645. https://doi.org/10.1162/jocn_a_01160Google Scholar
Vassena, E., Deraeve, J., & Alexander, W. H. (2019). Task-specific prioritization of reward and effort information: novel insights from behavior and computational modeling. Cognitive, Affective, & Behavioral Neuroscience, 19(3), 619636. https://doi.org/10.3758/s13415-018-00685-wGoogle Scholar
Vassena, E., Deraeve, J., & Alexander, W. H. (2020). Surprise, value and control in anterior cingulate cortex during speeded decision-making. Nature Human Behaviour, 4(4), 412422. https://doi.org/10.1038/s41562-019-0801-5Google Scholar
Vassena, E., Holroyd, C. B., & Alexander, W. H. (2017). Computational models of anterior cingulate cortex: at the crossroads between prediction and effort. Frontiers in Neuroscience, 11, 19. https://doi.org/10.3389/fnins.2017.00316Google Scholar
Veen, V. V., & Carter, C. S. (2002). The anterior cingulate as a conflict monitor: fMRI and ERP studies. Physiology Behavior, 77, 477482.Google Scholar
Venkatraman, V., Rosati, A. G., Taren, A. A., & Huettel, S. A. (2009). Resolving response, decision, and strategic control: evidence for a functional topography in dorsomedial prefrontal cortex. The Journal of Neuroscience, 29(42), 1315813164. https://doi.org/10.1523/jneurosci.2708-09.2009Google Scholar
Verguts, T. (2017). Computational models of cognitive control. In Egner, T. (Ed.), The Wiley Handbook of Cognitive Control (pp. 125142). Oxford: Wiley-Blackwell. https://doi.org/10.1002/9781118920497.ch8Google Scholar
Verguts, T., & Notebaert, W. (2008). Hebbian learning of cognitive control: dealing with specific and nonspecific adaptation. Psychological Review, 115(2), 518525. https://doi.org/10.1037/0033-295x.115.2.518Google Scholar
Verguts, T., & Notebaert, W. (2009). Adaptation by binding: a learning account of cognitive control. Trends in Cognitive Sciences, 13(6), 252257. https://doi.org/10.1016/j.tics.2009.02.007Google Scholar
Vermeylen, L., Wisniewski, D., Gonzalez-Garcia, C., Hoofs, V., Notebaert, W., & Braem, S. (2020). Shared neural representations of cognitive conflict and negative affect in the medial frontal cortex. Journal of Neuroscience, 40(45), 87158725. https://doi.org/10.1523/jneurosci.1744-20.2020Google Scholar
Wang, J. X., Kurth-Nelson, Z., Kumaran, D., et al. (2018). Prefrontal cortex as a meta-reinforcement learning system. Nature Neuroscience, 21(6), 860868. https://doi.org/10.1038/s41593-018-0147-8Google Scholar
Wang, X.-J. (2013). The prefrontal cortex as a quintessential “cognitive-type” neural circuit: working memory and decision making. In Stuss, D. T. & Knight, R. T. (Eds.), Principles of Frontal Lobe Function (pp. 226248). Cambridge: Cambridge University Press.Google Scholar
Waszak, F., Hommel, B., & Allport, A. (2003). Task-switching and long-term priming: role of episodic stimulus–task bindings in task-shift costs. Cognitive Psychology, 46(4), 361413. https://doi.org/10.1016/s0010-0285(02)00520-0Google Scholar
Westbrook, A., Bosch, R. van den, Määttä, J. I., et al. (2020). Dopamine promotes cognitive effort by biasing the benefits versus costs of cognitive work. Science, 367(6484), 13621366. https://doi.org/10.1126/science.aaz5891Google Scholar
Westbrook, A., & Braver, T. S. (2015). Cognitive effort: a neuroeconomic approach. Cognitive, Affective, Behavioral Neuroscience, 15, 395415. https://doi.org/10.3758/s13415-015-0334-yGoogle Scholar
Westbrook, A., & Braver, T. S. (2016). Dopamine does double duty in motivating cognitive effort. Neuron, 89(4), 695710. https://doi.org/10.1016/j.neuron.2015.12.029Google Scholar
Westbrook, A., Lamichhane, B., & Braver, T. (2019). The subjective value of cognitive effort is encoded by a domain-general valuation network. Journal of Neuroscience, 39(20), 39343947. https://doi.org/10.1523/jneurosci.3071-18.2019Google Scholar
Wood, J. N., & Grafman, J. (2003). Human prefrontal cortex: processing and representational perspectives. Nature Reviews Neuroscience, 4(2), 139147. https://doi.org/10.1038/nrn1033Google Scholar
Woolgar, A., Hampshire, A., Thompson, R., & Duncan, J. (2011). Adaptive coding of task-relevant information in human frontoparietal cortex. Journal of Neuroscience, 31(41), 1459214599. https://doi.org/10.1523/jneurosci.2616-11.2011Google Scholar
Wylie, G., & Allport, A. (2000). Task switching and the measurement of “switch costs.” Psychological Research, 63(3–4), 212233. https://doi.org/10.1007/s004269900003Google Scholar
Yang, G. R., Joglekar, M. R., Song, H. F., Newsome, W. T., & Wang, X.-J. (2019). Task representations in neural networks trained to perform many cognitive tasks. Nature Neuroscience, 22(2), 297306. https://doi.org/10.1038/s41593-018-0310-2Google Scholar
Yee, D. M., & Braver, T. S. (2018). Interactions of motivation and cognitive control. Current Opinion in Behavioral Sciences, 19, 8390. https://doi.org/10.1016/j.cobeha.2017.11.009Google Scholar
Yee, D. M., & Braver, T. S. (2020). Computational models of cognitive control: past and current approaches. In Series, P. (Ed.), Computational Psychiatry: A Primer (pp. 83104). Cambridge, MA: MIT Press.Google Scholar
Yee, D. M., Crawford, J. L., Lamichhane, B., & Braver, T. S. (2021). Dorsal anterior cingulate cortex encodes the integrated incentive motivational value of cognitive task performance. Journal of Neuroscience, 41(16), 37073720. https://doi.org/10.1523/jneurosci.2550-20.2021Google Scholar
Yee, D. M., Leng, X., Shenhav, A., & Braver, T. S. (2022). Aversive motivation and cognitive control. Neuroscience and Biobehavioral Reviews, 133, 104493. https://doi.org/10.1016/j.neubiorev.2021.12.016Google Scholar
Yeung, N. (2013). Conflict monitoring and cognitive control. In Oschner, K. N. & Kosslyn, S. (Eds.), The Oxford Handbook of Cognitive Neuroscience: Volume 2: The Cutting Edges. Oxford: Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199988709.013.0018Google Scholar
Yeung, N., Botvinick, M. M., & Cohen, J. D. (2004). The neural basis of error detection: conflict monitoring and the error-related negativity. Psychological Review, 111(4), 931959. https://doi.org/10.1037/0033-295x.111.4.931Google Scholar
Yu, A. J., & Dayan, P. (2005). Uncertainty, neuromodulation, and attention. Neuron, 46(4), 681692. https://doi.org/10.1016/j.neuron.2005.04.026Google Scholar

References

Adams, C. D. (1982) Variations in the sensitivity of instrumental responding to reinforcer devaluation. Quarterly Journal of Experimental Psychology, 34B, 7798.Google Scholar
Aitken, M. R., & Dickinson, A. (2005). Simulations of a modified SOP model applied to retrospective revaluation of human causal learning. Learning & Behavior, 33, 147159.Google Scholar
Atkinson, R. C., & Estes, W. K. (1963). Stimulus sampling theory. In Luce, R. D., Bush, R. R., & Galanter, E. (Eds.), Handbook of Mathematical Psychology (vol. 2, pp. 121268). New York, NY: Wiley.Google Scholar
Baetu, I., Burns, N. R., Yu, E., & Baker, A. G. (2018). Fluid abilities and rule learning: patterning and biconditional discriminations. Journal of Intelligence, 6, 7.Google Scholar
Balleine, B. W., Dickinson, A. (1998). Goal-directed instrumental action: contingency and incentive learning and their cortical substrates. Neuropharmacology, 37, 407419.Google Scholar
Balleine, B. W., & Ostlund, S. B. (2007). Still at the choice‐point: action selection and initiation in instrumental conditioning. Annals of the New York Academy of Sciences, 1104, 147171.Google Scholar
Beckers, T., Miller, R. R., De Houwer, J., & Urushihara, K. (2006). Reasoning rats: forward blocking in Pavlovian animal conditioning is sensitive to constraints of causal inference. Journal of Experimental Psychology: General, 135(1), 92102.Google Scholar
Behrens, T. E., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. (2007). Learning the value of information in an uncertain world. Nature Neuroscience, 10(9), 12141221.Google Scholar
Bellingham, W. P., Gillette-Bellingham, K., & Kehoe, E. J. (1985). Summation and configuration 2016 schedules with the rat and rabbit. Animal Learning & Behavior, 13, 152164.Google Scholar
Blough, D. S. (1975). Steady state data and a quantitative model of operant generalization and discrimination. Journal of Experimental Psychology: Animal Behavior Processes, 1, 321.Google Scholar
Boakes, R. A. (1977). Performance on learning to associate a stimulus with positive reinforcement. In Davis, H. & Hurwitz, H. M. B. (Eds.), Operant–Pavlovian Interactions (pp. 67101). Hillsdale, NJ: Erlbaum.Google Scholar
Bouton, M. E. (1994). Conditioning, remembering, and forgetting. Journal of Experimental Psychology: Animal Behavior Processes, 20, 219231.Google Scholar
Bouton, M. E. (2004). Context and behavioral processes in extinction. Learning & Memory, 11, 485494.Google Scholar
Bouton, M. E., & Bolles, R. C. (1979). Contextual control of the extinction of conditioned fear. Learning and Motivation, 10, 445466.Google Scholar
Bouton, M., Doyle-Burr, C. & Vurbic, D. (2012). Asymmetrical generalization of conditioning and extinction from compound to element and element to compound. Journal of Experimental Psychology: Animal Behavior Processes, 38, 381393.Google Scholar
Bouton, M. E., & King, D. A. (1983). Contextual control of the extinction of conditioned fear: tests for the associative value of the context. Journal of Experimental Psychology: Animal Behavior Processes, 9, 248265.Google Scholar
Bouton, M. E., & Swartzentruber, D. (1986). Analysis of the associative and occasion setting properties of contexts participating in a Pavlovian discrimination. Journal of Experimental Psychology: Animal Behavior Processes, 12, 333350.Google Scholar
Brogden, W. J. (1939). Sensory pre-conditioning. Journal of Experimental Psychology, 25(4), 323332.Google Scholar
Bush, R. R., & Mosteller, F. (1951). A model for stimulus generalization and discrimination. Psychological Review, 58, 413423.Google Scholar
Byrom, N. C., & Murphy, R. A. (2014). Sampling capacity underlies individual differences in human associative learning. Journal of Experimental Psychology: Animal Learning and Cognition, 40, 133143.Google Scholar
Cartoni, E., Puglisi-Allegra, S., Baldassarre, G. (2013). The three principles of action: a Pavlovian-instrumental transfer hypothesis. Frontiers in Behavioral Neuroscience, 7, 153.Google Scholar
Dayan, P., Kakade, S., & Montague, P. R. (2000). Learning and selective attention. Nature Neuroscience, 3, 12181223.Google Scholar
Delamater, A. R., Sosa, W., & Katz, M. (1999). Elemental and configural processes in patterning discrimination learning. The Quarterly Journal of Experimental Psychology, 52B, 97124.Google Scholar
Delamater, A. R., & Westbrook, R. F. (2014). Psychological and neural mechanisms of experimental extinction: a selective review. Neurobiology of Learning and Memory, 108, 3851.Google Scholar
Denniston, J. C., Savastano, H. I., & Miller, R. R. (2001). The extended comparator hypothesis: learning by contiguity, responding by relative strength. In Mowrer, R. R. & Klein, S. B. (Eds.), Handbook of Contemporary Learning Theories (pp. 65117). Mahwah, NJ: Erlbaum.Google Scholar
Dickinson, A., & Balleine, B. W. (1993). Actions and responses: the dual psychology of behaviour. In Eilan, N., McCarthy, R., & Brewer, M. W., (Eds.), Spatial Representation (pp. 277293). Oxford: Blackwells.Google Scholar
Dickinson, A., & Burke, J. (1996). Within-compound associations mediate the retrospective revaluation of causality judgements. Quarterly Journal of Experimental Psychology, 49B, 6080.Google Scholar
Dickinson, A., Hall, G., & Mackintosh, N. J. (1976). Surprise and the attenuation of blocking. Journal of Experimental Psychology: Animal Behavior Processes, 2, 313322.Google Scholar
Dickinson, A., Squire, S., Varga, Z., & Smith, J. W. (1998). Omission learning after instrumental pretraining. Quarterly Journal of Experimental Psychology, 51B, 271286.Google Scholar
Don, H. J., Beesley, T., & Livesey, E. J. (2019). Learned predictiveness models predict opposite attention biases in the inverse base-rate effect. Journal of Experimental Psychology: Animal Learning & Cognition, 45, 143162.Google Scholar
Don, H. J., Goldwater, M. B., Greenaway, J. K., Hutchings, R., & Livesey, E. J. (2020) Relational rule discovery in complex discrimination learning. Journal of Experimental Psychology: Learning, Memory & Cognition, 46, 18071827.Google Scholar
Don, H. J., Goldwater, M. B., Otto, R., & Livesey, E. J. (2016). Rule abstraction, model-based choice and cognitive reflection. Psychonomic Bulletin & Review, 23, 16151623.Google Scholar
Don, H. J., Worthy, D. A., & Livesey, E. J. (2021). Hearing hooves, thinking zebras: a review of the inverse base-rate effect. Psychonomic Bulletin & Review, 28, 11421163.Google Scholar
Esber, G. R., & Haselgrove, M. (2011). Reconciling the influence of predictiveness and uncertainty on stimulus salience: a model of attention in associative learning. Proceedings of the Royal Society B: Biological Sciences, 278(1718), 25532561.Google Scholar
Estes, W. K. (1943). Discriminative conditioning I. A discriminative property of conditioned anticipation. Journal of Experimental Psychology, 32, 150155.Google Scholar
Estes, W. K. (1948). Discriminative conditioning II. Effects of a Pavlovian conditioned stimulus upon a subsequently established operant response. Journal of Experimental Psychology, 38, 173177.Google Scholar
Estes, W. K. (1950). Towards a statistical theory of learning. Psychological Review, 57, 94107.Google Scholar
Flagel, S. B., Akil, H., & Robinson, T. E. (2009). Individual differences in the attribution of incentive salience to reward-related cues: implications for addiction. Neuropharmacology, 56, 139148.Google Scholar
Fletcher, P. C., Anderson, J. M., Shanks, D. R., et al. (2001). Responses of human frontal cortex to surprising events are predicted by formal associative learning theory. Nature Neuroscience, 4, 10431048.Google Scholar
Fraser, K. M., & Holland, P. C. (2019). Occasion setting. Behavioral Neuroscience, 133, 145175.Google Scholar
Frost, R., Armstrong, B. C., & Christiansen, M. H. (2019). Statistical learning research: a critical review and possible new directions. Psychological Bulletin, 145(12), 11281153.Google Scholar
George, D. N., & Pearce, J. M. (2012). A configural theory of attention and associative learning. Learning & Behavior, 40, 241254.Google Scholar
Gershman, S. J. (2015). A unifying probabilistic view of associative learning. PLoS Computational Biology, 11, e1004567.Google Scholar
Gershman, S. J., Blei, D. M., & Niv, Y. (2010). Context, learning, and extinction. Psychological Review, 117, 197209.Google Scholar
Ghirlanda, S. (2015). On elemental and configural models of associative learning. Journal of Mathematical Psychology, 64–65, 816.Google Scholar
Ghirlanda, S., & Enquist, M. (1998). Artificial neural networks as models of stimulus control. Animal Behaviour, 56, 13831389.Google Scholar
Gibson, E. J., & Walk, R. D. (1956). The effect of prolonged exposure to visually presented patterns on learning to discriminate them. Journal of Comparative and Physiological Psychology, 49, 239242.Google Scholar
Gluck, M. A., & Bower, G. H. (1988). From conditioning to category learning: an adaptive network model. Journal of Experimental Psychology: General, 117(3), 227.Google Scholar
Goldwater, M. B., Don, H. J., Krusche, M., & Livesey, E. J. (2018). Relational discovery in category learning. Journal of Experimental Psychology: General, 147, 135.Google Scholar
Hall, G., & Rodriguez, G. (2010). Associative and nonassociative processes in latent inhibition: an elaboration of the Pearce-Hall model. In Lubow, R. E. & Weiner, I. (Eds.), Latent Inhibition: Data, Theories, and Applications to Schizophrenia (pp. 114136). Cambridge: Cambridge University Press.Google Scholar
Hanson, H. M. (1957). Discrimination training effect on stimulus generalization gradient for spectrum stimuli. Science, 125, 888889.Google Scholar
Harris, J. A. (2006). Elemental representations of stimuli in associative learning. Psychological Review, 113, 584605.Google Scholar
Harris, J. A. (2011). The acquisition of conditioned responding. Journal of Experimental Psychology: Animal Behavior Processes, 37(2), 151164.Google Scholar
Harris, J. A., & Livesey, E. J. (2008). Comparing patterning and biconditional discriminations in humans. Journal of Experimental Psychology: Animal Behavior Processes, 34, 144154.Google Scholar
Harris, J. A., & Livesey, E. J. (2010). An attention-modulated associative network. Learning & Behavior, 38, 126.Google Scholar
Harris, J. A., Livesey, E. J., Ghareai, S., & Westbrook, R. F. (2008). Negative patterning is easier than a biconditional discrimination. Journal of Experimental Psychology: Animal Behavior Processes, 34, 494500.Google Scholar
Haselgrove, M. (2010). Reasoning rats or associative animals? A common-element analysis of the effects of additive and subadditive pretraining on blocking. Journal of Experimental Psychology: Animal Behavior Processes, 36(2), 296306.Google Scholar
Heyes, C. (2012). Simple minds: a qualified defence of associative learning. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1603), 26952703.Google Scholar
Holland, P. C. (1983). Occasion setting in Pavlovian feature positive discriminations. In Commons, M. L., Herrnstein, R. J., & Wagner, A. R. (Eds.), Quantitative Analyses of Behavior: Volume 4. Discrimination Processes (pp. 183206). New York, NY: Ballinger.Google Scholar
Holmes, N. M., Chan, Y. Y., & Westbrook, R. F. (2020). An application of Wagner’s standard operating procedures or sometimes opponent processes (SOP) model to experimental extinction. Journal of Experimental Psychology: Animal Learning and Cognition, 46(3), 215234.Google Scholar
Honey, R. C., Dwyer, D. M., & Iliescu, A. F. (2020). HeiDI: a model for Pavlovian learning and performance with reciprocal associations. Psychological Review, 127(5), 829852.Google Scholar
Hull, C. L. (1943). Principles of Behavior: An Introduction to Behavior Theory. New York, NY: Appleton-Century.Google Scholar
Hume, D. (1741/1978). A Treatise of Human Nature, edited by L. A. Selby-Bigge, 2nd ed. revised by P. H. Nidditch. Oxford: Clarendon Press.Google Scholar
Inman, R. A., & Pearce, J. M. (2018). The discrimination of magnitude: a review and theoretical analysis. Neurobiology of Learning and Memory, 153, 118130.Google Scholar
Kamin, L. J. (1968). “Attention-like” processes in classical conditioning. In Jones, M. R. (Ed.), Miami Symposium on the Prediction of Behavior: Aversive Stimulation (pp. 931). Miami, FL: University of Miami Press.Google Scholar
Kehoe, E. J. 1988. A layered network model of associative learning: learning to learn and configuration. Psychological Review, 95, 411433.Google Scholar
Kehoe, E. J., 1998. Can the whole be something other than the sum of its parts? In Wynne, C. D. L. & Staddon, J. E. R., (Eds.), Models of Action: Mechanisms for Adaptive Behavior (pp. 87126). Mahwah, NJ: Erlbaum.Google Scholar
Kehoe, E. J., Horne, A. J., Horne, P. S., & Macrae, M. (1994). Summation and configuration between and within sensory modalities in classical conditioning of the rabbit. Animal Learning & Behavior, 22, 1926.Google Scholar
Kehoe, E. J., Ludvig, E. A., Dudeney, J. E., Neufeld, J., & Sutton, R. S. (2008). Magnitude and timing of nictitating membrane movements during classical conditioning of the rabbit (Oryctolagus cuniculus). Behavioral Neuroscience, 122, 471476.Google Scholar
Kinder, A., & Lachnit, H. (2003). Similarity and discrimination in human Pavlovian conditioning. Psychophysiology, 40(2), 226234.Google Scholar
Konorski, J. (1967). Integrative Activity of the Brain. Chicago, IL: University of Chicago Press.Google Scholar
Kremer, E. F. (1978). The Rescorla-Wagner model: losses in associative strength in compound conditioned stimuli. Journal of Experimental Psychology: Animal Behavior Processes, 4(1), 2236.Google Scholar
Kruschke, J. K. (2001). Toward a unified model of attention in associative learning. Journal of Mathematical Psychology, 45, 812863.Google Scholar
Lashley, K. S. (1929). Brain Mechanisms and Intelligence. Chicago, IL: University of Chicago Press.Google Scholar
Le Pelley, M. E. (2004). The role of associative history in models of associative learning: a selective review and a hybrid model. The Quarterly Journal of Experimental Psychology, 57B, 193243.Google Scholar
Le Pelley, M. E. (2012). Metacognitive monkeys or associative animals? Simple reinforcement learning explains uncertainty in nonhuman animals. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(3), 686708.Google Scholar
Le Pelley, M. E., & McLaren, I. P. L. (2003). Learned associability and associative change in human causal learning. The Quarterly Journal of Experimental Psychology, 56B, 6879.Google Scholar
Le Pelley, M. E., Mitchell, C. J., Beesley, T., George, D. N., & Wills, A. J. (2016). Attention and associative learning in humans: an integrative review. Psychological Bulletin, 142, 11111140.Google Scholar
Le Pelley, M. E., Oakeshott, S. M., & McLaren, I. P. L. (2005). Blocking and unblocking in human causal learning. Journal of Experimental Psychology: Animal Behavior Processes, 31, 5670.Google Scholar
Le Pelley, M. E., Schmidt-Hansen, M., Harris, N. J., Lunter, C. M., & Morris, C. S. (2010). Disentangling the attentional deficit in schizophrenia: pointers from schizotypy. Psychiatry Research, 176(2–3), 143149.Google Scholar
Livesey, E. J., Don, H. J., Uengoer, M., & Thorwart, A. (2019). Transfer of associability and relational structure in human associative learning. Journal of Experimental Psychology: Animal Learning & Cognition, 45, 125142.Google Scholar
Livesey, E. J., Greenaway, J., Schubert, S., & Thorwart, A. (2019). Testing the deductive inferential account of blocking in causal learning. Memory & Cognition, 47, 11201132.Google Scholar
Livesey, E. J. & McLaren, I. P. L. (2011). An elemental model of associative learning and memory. In Pothos, E. & Wills, A. J. (Eds.), Formal Approaches in Categorization (pp. 153172). Cambridge: Cambridge University Press.Google Scholar
Livesey, E. J. & McLaren, I. P. L. (2019). Revisiting peak shift on an artificial dimension: effects of stimulus variability on generalization. Quarterly Journal of Experimental Psychology, 72, 132150.Google Scholar
Livesey, E. J., Thorwart, A., & Harris, J. A. (2011). Comparing positive and negative patterning in human learning. Quarterly Journal of Experimental Psychology, 64, 23162333.Google Scholar
Lochmann, T., & Wills, A. J. (2003). Predictive history in an allergy prediction task. In Schmalhofer, F., Young, R. M., & Katz, G. (Eds.), Proceedings of EuroCogSci: The European Conference of the Cognitive Science Society (pp. 217222). Mahwah, NJ: Erlbaum.Google Scholar
Lotz, A., Uengoer, M., Koenig, S., Pearce, J. M., & Lachnit, H. (2012). An exploration of the feature-positive effect in adult humans. Learning & Behavior, 40, 222230.Google Scholar
Lovibond, P. F., Been, S. L., Mitchell, C. J., Bouton, M. E., & Frohardt, R. (2003). Forward and backward blocking of causal judgment is enhanced by additivity of effect magnitude. Memory & Cognition, 31(1), 133142.Google Scholar
Lubow, R. E., & Moore, A. U. (1959). Latent inhibition: the effect of nonreinforced pre-exposure to the conditional stimulus. Journal of Comparative and Physiological Psychology, 52, 415419.Google Scholar
Luce, R. D. (1959). Individual Choice Behavior. New York, NY: Wiley.Google Scholar
Luzardo, A., Alonso, E., & Mondragón, E. (2017). A Rescorla-Wagner drift-diffusion model of conditioning and timing. PLOS Computational Biology, 13(11), e1005796.Google Scholar
Mackintosh, N. (1975). A theory of attention: variations in the associability of stimuli with reinforcement. Psychological Review, 82, 276298. https://doi.org/10.1037/h0076778Google Scholar
Mackintosh, N. J., & Turner, C. (1971). Blocking as a function of novelty of CS and predictability of UCS. The Quarterly Journal of Experimental Psychology, 23(4), 359366.Google Scholar
Maes, E., Boddez, Y., Alfei, J. M., et al. (2016). The elusive nature of the blocking effect: 15 failures to replicate. Journal of Experimental Psychology: General, 145(9), e49e71.Google Scholar
Maes, E., Vanderoost, E., D’Hooge, R., De Houwer, J., & Beckers, T. (2017). Individual difference factors in the learning and transfer of patterning discriminations. Frontiers in Psychology, 8, 1262.Google Scholar
McDaniel, M. A., Cahill, M. J., Robbins, M., & Wiener, C. (2014). Individual differences in learning and transfer: stable tendencies for learning exemplars versus abstracting rules. Journal of Experimental Psychology: General, 143, 668.Google Scholar
McLaren, I. P. L., Kaye, H., & Mackintosh, N. J. (1989). An associative theory of the representation of stimuli: applications to perceptual learning and latent inhibition. In Morris, R. G. M. (Ed.), Parallel Distributed Processing: Implications for Psychology and Neurobiology (pp. 102130). Oxford: Oxford University Press.Google Scholar
McLaren, I. P. L., & Mackintosh, N. J. (2000). An elemental model of associative learning: I. Latent inhibition and perceptual learning. Animal Learning & Behavior, 28, 211246.Google Scholar
McLaren, I. P. L., & Mackintosh, N. J. (2002). Associative learning and elemental representation: II. Generalization and discrimination. Animal Learning & Behavior, 30, 177200.Google Scholar
Medin, D. L., & Edelson, S. M. (1988). Problem structure and the use of base-rate information from experience. Journal of Experimental Psychology: General, 1, 6885.Google Scholar
Melchers, K. G., Shanks, D. R., & Lachnit, H. (2008). Stimulus coding in human associative learning: flexible representations of parts and wholes. Behavioural Processes, 77, 413427.Google Scholar
Miller, R. R., & Matzel, L. D. (1988). The comparator hypothesis: a response rule for the expression of associations. In Bower, G. H. (Ed.), The Psychology of Learning and Motivation (vol. 22, pp. 5192). San Diego, CA: Academic Press.Google Scholar
Mitchell, C. J., De Houwer, J., & Lovibond, P. F. (2009). The propositional nature of human associative learning. Behavioral and Brain Science, 32, 183246.Google Scholar
Paskewitz, S., & Jones, M. (2020). Dissecting EXIT. Journal of Mathematical Psychology, 97, 102371.Google Scholar
Patitucci, E., Nelson, A. J. D., Dwyer, D. M., & Honey, R. C. (2016). The origins of individual differences in how learning is expressed in rats: a general-process perspective. Journal of Experimental Psychology: Animal Learning and Cognition, 42, 313324.Google Scholar
Pavlov, I. P. (1927). Conditioned Reflexes. London: Oxford University Press.Google Scholar
Pearce, J. M. (1987). A model for stimulus generalization in Pavlovian conditioning. Psychological Review, 94, 6173. https://doi.org/10.1037/0033-295X.94.1.61Google Scholar
Pearce, J. M. (1994). Similarity and discrimination: a selective review and a connectionist model. Psychological Review, 101, 587607. https://doi.org/10.1037/0033-295X.101.4.587Google Scholar
Pearce, J. M. (2002). Evaluation and development of a connectionist theory of configural learning. Animal Learning & Behavior, 30, 7395.Google Scholar
Pearce, J. M., Dopson, J. C., Haselgrove, M., & Esber, G. R. (2012). The fate of redundant cues during blocking and a simple discrimination. Journal of Experimental Psychology: Animal Behavior Processes, 38, 167179. https://doi.org/10.1037/a0027662Google Scholar
Pearce, J. M., & Hall, G. (1980). A model for Pavlovian learning: variations in the effectiveness of conditioned but not of unconditioned stimuli. Psychological Review, 87, 532552. https://doi.org/10.1037/0033-295x.87.6.532Google Scholar
Pearce, J. M., & Mackintosh, N. J. (2010). Two theories of attention: a review and a possible integration. In Mitchell, C. J. & Le Pelley, M. E. (Eds.), Attention and Associative Learning: From Brain to Behaviour (pp. 1140). Oxford: Oxford University Press.Google Scholar
Perruchet, P., & Pacton, S. (2006). Implicit learning and statistical learning: one phenomenon, two approaches. Trends in Cognitive Sciences, 10, 233238.Google Scholar
Polack, C. W., Laborda, M. A., & Miller, R. R. (2012). Extinction context as a conditioned inhibitor. Learning & Behavior, 40, 2433.Google Scholar
Redish, A., Jensen, S., Johnson, A., & Kurth-Nelson, A. (2007). Reconciling reinforcement learning models with behavioral extinction and renewal: implications for addiction, relapse, and problem gambling. Psychological Review, 114, 784805.Google Scholar
Relkin, E. M., & Doucet, J. R. (1997). Is loudness simply proportional to the auditory nerve spike count? The Journal of the Acoustical Society of America, 101, 27352740.Google Scholar
Rescorla, R. A. (1967). Pavlovian conditioning and its proper control procedures. Psychological Review, 74, 7181.Google Scholar
Rescorla, R. A. (1968). Probability of shock in the presence and absence of CS in fear conditioning. Journal of Comparative and Physiological Psychology, 66, 15.Google Scholar
Rescorla, R. A. (1969). Pavlovian conditioned inhibition. Psychological Bulletin, 72, 7794.Google Scholar
Rescorla, R. A. (1970). Reduction in the effectiveness of reinforcement after prior excitatory conditioning. Learning and Motivation, 1(4), 372381.Google Scholar
Rescorla, R. A. (1972). “ Configural” conditioning in discrete-trial bar pressing. Journal of Comparative and Physiological Psychology, 79(2), 307317.Google Scholar
Rescorla, R. A. (1988). Pavlovian conditioning: it’s not what you think it is. American Psychologist, 43(3), 151160.Google Scholar
Rescorla, R. A. (2006). Deepened extinction from compound stimulus presentation. Journal of Experimental Psychology: Animal Behavior Processes, 32(2), 135144.Google Scholar
Rescorla, R. A., & Solomon, R. L. (1967). Two-process learning theory: relationships between Pavlovian conditioning and instrumental learning. Psychological Review, 74, 151182.Google Scholar
Rescorla, R. A., & Wagner, A. (1972). A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and non-reinforcement. In Black, A., & Prokasy, W. (Eds.), Classical Conditioning. II. Current Research and Theory (pp. 6499). New York, NY: Appleton-Century-Crofts.Google Scholar
Rumelhart, D. E., Hinton, G. E., & Williams, G. E. (1986). Learning internal representations by error propagation. In Rumelhart, D. E. & McClelland, J. L. (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition (vol. 1). Cambridge, MA: MIT Press.Google Scholar
Saavedra, M. A. (1975). Pavlovian compound conditioning in the rabbit. Learning and Motivation, 6, 314326.Google Scholar
Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science, 274, 19261928.Google Scholar
Schmajuk, N. A., Di Carlo, J. J., (1992). Stimulus configuration, classical conditioning, and hippocampal function. Psychological Review, 99, 268305.Google Scholar
Schmajuk, N. A., Lamoureux, J. A., & Holland, P. C., 1998. Occasion setting: a neural network approach. Psychological Review, 105, 332.Google Scholar
Schultz, W. Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275, 15931599.Google Scholar
Sewell, D. K., Jach, H. K., Boag, R. J., & Van Heer, C. A. (2019). Combining error-driven models of associative learning with evidence accumulation models of decision-making. Psychonomic Bulletin & Review, 26(3), 868893.Google Scholar
Shanks, D. R. (1985). Forward and backward blocking in human contingency judgement. The Quarterly Journal of Experimental Psychology, 37B, 121.Google Scholar
Shanks, D. R. (1987). Acquisition functions in contingency judgment. Learning and Motivation, 18(2), 147166.Google Scholar
Soto, F. A. (2018). Contemporary associative learning theory predicts failures to obtain blocking: comment on Maes et al. (2016). Journal of Experimental Psychology: General, 147(4), 597602.Google Scholar
Soto, F. A., & Wasserman, E. A. (2010). Error-driven learning in visual categorization and object recognition: a common-elements model. Psychological Review, 117(2), 349381.Google Scholar
Spence, K. W. (1956). Behavior Theory and Conditioning. New Haven, CT: Yale University Press.Google Scholar
Stout, S. C., & Miller, R. R. (2007). Sometimes-competing retrieval (SOCR): a formalization of the comparator hypothesis. Psychological Review, 114(3), 759783.Google Scholar
Sutherland, N. S., & Mackintosh, N. J. (1971). Mechanisms of Animal Discrimination Learning. New York, NY: Academic Press.Google Scholar
Sutton, R. S. (1992). Gain adaptation beats least squares? In Proceedings of the Seventh Annual Yale Workshop on Adaptive and Learning Systems (pp. 161166). New Haven, CT: Yale University Press.Google Scholar
Sutton, R. S., & Barto, A. G. (1981). Toward a modern theory of adaptive networks: expectation and prediction. Psychological Review, 88, 135171.Google Scholar
Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning. Cambridge, MA: MIT Press.Google Scholar
Thein, T., Westbrook, R. F., & Harris, J. A. (2008). How the associative strengths of stimuli combine in compound: summation and overshadowing. Journal of Experimental Psychology: Animal Behavior Processes, 34, 155166.Google Scholar
Thorndike, E. L. (1898). Animal intelligence: an experimental study of the associative processes in animals. The Psychological Review: Monograph Supplements, 2(4), i.Google Scholar
Thorwart, A., & Lachnit, H. (2020). Inhibited elements model—implementation of an associative learning theory. Journal of Mathematical Psychology, 94, 102310.Google Scholar
Thorwart, A., & Livesey, E. J. (2016). Three ways that non-associative knowledge may affect associative learning processes. Frontiers in Psychology, 7, 2024. https://doi.org/10.3389/fpsyg.2016.02024Google Scholar
Thorwart, A., Livesey, E. J., & Harris, J. A. (2012). Normalisation between stimulus elements in a model of Pavlovian conditioning: showjumping on an elemental horse. Learning & Behavior, 40, 334346.Google Scholar
Thorwart, A., Uengoer, M., Livesey, E. J., & Harris, J. A. (2017). Summation effects in human learning: evidence from patterning discriminations in goal-tracking. Quarterly Journal of Experimental Psychology, 70, 13661379.Google Scholar
Tobler, P. N., O’Doherty, J. P., Dolan, R. J., & Schultz, W. (2006). Human neural learning depends on reward prediction errors in the blocking paradigm. Journal of Neurophysiology, 95, 301310.Google Scholar
Urushihara, K., & Miller, R. R. (2010). Backward blocking in first-order conditioning. Journal of Experimental Psychology: Animal Behavior Processes, 36(2), 281295.Google Scholar
Van Hamme, L. J., & Wasserman, E. A. (1994). Cue competition in causality judgments: the role of nonpresentation of compound stimulus elements. Learning & Motivation, 25, 127151.Google Scholar
Waelti, P., Dickinson, A., & Schultz, W. (2001). Dopamine responses comply with basic assumptions of formal learning theory. Nature, 412, 4348.Google Scholar
Wagner, A. R. (1978). Expectancies and the priming of STM. In Hulse, S. H., Fowler, H., & Honig, W. K. (Eds.), Cognitive Processes in Animal Behavior (pp. 177209). Hillsdale, NJ: Erlbaum.Google Scholar
Wagner, A. R. (1981). SOP: a model of automatic memory processing in animal behavior. In Spear, N. E. & Miller, R. R. (Eds.), Information Processing in Animals: Memory Mechanisms (pp. 547). Hillsdale, NJ: Erlbaum.Google Scholar
Wagner, A. R. (2003). Context-sensitive elemental theory. Quarterly Journal of Experimental Psychology, 56B, 729.Google Scholar
Wagner, A. R., & Brandon, S. E. (2001). A componential theory of Pavlovian conditioning. In Mowrer, R. R. & Klein, S. B. (Eds.), Handbook of Contemporary Learning Theories (pp. 2364). Mahwah, NJ: Erlbaum.Google Scholar
Wagner, A. R., Logan, F. A., Haberlandt, K., & Price, T. (1968). Stimulus selection in animal discrimination learning. Journal of Experimental Psychology, 76, 171180.Google Scholar
Wagner, A. R., & Rescorla, R. A. (1972). Inhibition in Pavlovian conditioning: applications of a theory. In Boakes, R. A. & Halliday, M. S. (Eds.), Inhibition and Learning (pp. 301336). New York, NY: Academic Press.Google Scholar
Whitlow Jr, J. W., & Wagner, A. R. (1972). Negative patterning in classical conditioning: summation of response tendencies to isolable and configural components. Psychonomic Science, 27, 299301.Google Scholar
Widrow, G., & Hoff, M. E. (1960). Adaptive switching circuits. Institute of Radio Engineers, Western Electronic Show and Convention, Convention Record, 4, 96194.Google Scholar
Williams, D. A., Overmier, J. B., & LoLordo, V. M. (1992). A reevaluation of Rescorla’s early dictums about Pavlovian conditioned inhibition. Psychological Bulletin, 111, 275290.Google Scholar
Wills, S., & Mackintosh, N. J. (1998). Peak shift on an artificial dimension. The Quarterly Journal of Experimental Psychology Section B: Comparative and Physiological Psychology, 51, 132.Google Scholar

References

Abbeel, P., & Ng, A. Y. (2004). Apprenticeship learning via inverse reinforcement learning. In 21st International Conference on Machine Learning, Banff, Canada.Google Scholar
Alexander, G. E., & Crutcher, M. D. (1990). Functional architecture of basal ganglia circuits: neural substrates of parallel processing. Trends in Neuroscience, 13, 266271. https://doi.org/10.1016/0166-2236(90)90107-LGoogle Scholar
Ardiel, E. L., & Rankin, C. H. (2010). An elegant mind: learning and memory in Caenorhabditis elegans. Learning and Memory, 17(4), 191201. https://doi.org/10.1101/lm.960510Google Scholar
Aston-Jones, G., & Cohen, J. D. (2005). An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. Annual Reviews in Neuroscience, 28, 403450. https://doi.org/10.1146/annurev.neuro.28.061604.135709Google Scholar
Bacon, P.-L., Harb, J., & Precup, D. (2017). The option-critic architecture. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17).Google Scholar
Baker, C. L., Saxe, R., & Tenenbaum, J. B. (2009). Action understanding as inverse planning. Cognition, 113(3), 329349. https://doi.org/10.1016/j.cognition.2009.07.005Google Scholar
Balleine, B. W., Dezfouli, A., Ito, M., & Doya, K. (2015). Hierarchical control of goal-directed action in the cortical–basal ganglia network. Current Opinion in Behavioral Sciences, 5, 17. https://doi.org/10.1016/j.cobeha.2015.06.001Google Scholar
Barreto, A., Hou, S., Borsa, D., Silver, D., & Precup, D. (2020). Fast reinforcement learning with generalized policy updates. Proceedings of the National Academy of Sciences (online). https://doi.org/10.1073/pnas.1907370117Google Scholar
Bavard, S., Lebreton, M., Khamassi, M., Coricelli, G., & Palminteri, S. (2018). Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences. Nature Communications, 9(1), 4503. https://doi.org/10.1038/s41467-018-06781-2Google Scholar
Behrens, T. E., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. (2007). Learning the value of information in an uncertain world. Nature Neuroscience, 10(9), 12141221. https://doi.org/10.1038/nn1954Google Scholar
Bellemare, M. G., Dabney, W., & Munos, R. (2017). A distributional perspective on reinforcement learning. In Proceedings of Machine Learning Research. http://proceedings.mlr.press/v70/bellemare17a.htmlGoogle Scholar
Bellman, R. (1952). On the theory of dynamic programming. Proceedings of the National Academy of Sciences, 38, 716719.Google Scholar
Belova, M. A., Paton, J. J., Morrison, S. E., & Salzman, C. D. (2007). Expectation modulates neural responses to pleasant and aversive stimuli in primate amygdala. Neuron, 55(6), 970984. https://doi.org/10.1016/j.neuron.2007.08.004Google Scholar
Bloem, B., Huda, R., Sur, M., & Graybiel, A. M. (2017). Two-photon imaging in mice shows striosomes and matrix have overlapping but differential reinforcement-related responses. eLife, 6. https://doi.org/10.7554/eLife.32353Google Scholar
Botvinick, M., & Toussaint, M. (2012). Planning as inference. Trends in Cognitive Sciences, 16(10), 485488. https://doi.org/10.1016/j.tics.2012.08.006Google Scholar
Boureau, Y. L., & Dayan, P. (2011). Opponency revisited: competition and cooperation between dopamine and serotonin. Neuropsychopharmacology, 36(1), 7497. https://doi.org/10.1038/npp.2010.151Google Scholar
Bromberg-Martin, E. S., Matsumoto, M., Hong, S., & Hikosaka, O. (2010). A pallidus-habenula-dopamine pathway signals inferred stimulus values. Journal of Neurophysiology, 104(2), 10681076. https://doi.org/10.1152/jn.00158.2010Google Scholar
Cassell, M. D., Freedman, L. J., & Shi, C. (1999). The intrinsic organization of the central extended amygdala. Annals of New York Academy of Sciences, 877, 217240.Google Scholar
Chen, C., Takahashi, T., Nakagawa, S., Inoue, T., & Kusumi, I. (2015). Reinforcement learning in depression: a review of computational research. Neuroscience and Biobehavioral Reviews, 55, 247267. https://doi.org/10.1016/j.neubiorev.2015.05.005Google Scholar
Cilden, E., & Polat, F. (2015). Toward generalization of automated temporal abstraction to partially observable reinforcement learning. IEEE Transactions on Cybernetics, 45(8), 14141425. https://doi.org/10.1109/TCYB.2014.2352038Google Scholar
Collins, A. G., & Frank, M. J. (2014). Opponent actor learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive. Psychological Review, 121(3), 337366. https://doi.org/10.1037/a0037015Google Scholar
Courville, A. C., Daw, N. D., & Touretzky, D. S. (2006). Bayesian theories of conditioning in a changing world. Trends in Cognitive Sciences, 10(7), 294300. https://doi.org/10.1016/j.tics.2006.05.004Google Scholar
Cui, G., Jun, S. B., Jin, X., et al. (2013). Concurrent activation of striatal direct and indirect pathways during action initiation. Nature, 494(7436), 238242. https://doi.org/10.1038/nature11846Google Scholar
Dabney, W., Kurth-Nelson, Z., Uchida, N., et al. (2020). A distributional code for value in dopamine-based reinforcement learning. Nature, 577(7792), 671675. https://doi.org/10.1038/s41586-019-1924-6Google Scholar
Dabney, W., Ostrovski, G., Silver, D., & Munos, R. M. (2018). Implicit quantile networks for distributional reinforcement learning. In 35th International Conference on Machine Learning (ICML 2018).Google Scholar
Daw, N. D., Gershman, S. J., Seymour, B., Dayan, P., & Dolan, R. J. (2011). Model-based influences on humans’ choices and striatal prediction errors. Neuron, 69(6), 12041215. https://doi.org/10.1016/j.neuron.2011.02.027Google Scholar
Daw, N. D., Kakade, S., & Dayan, P. (2002). Opponent interactions between serotonin and dopamine. Neural Networks, 15(4–6), 603616. www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12371515Google Scholar
Daw, N. D., Niv, Y., & Dayan, P. (2005). Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature Neuroscience, 8(12), 17041711. https://doi.org/10.1038/nn1560Google Scholar
Dayan, P. (1993). Improving generalization for temporal difference learning: the successor representation. Neural Computation, 5(4), 613624. https://doi.org/10.1162/neco.1993.5.4.613Google Scholar
Dayan, P., & Hinton, G. E. (1993). Feudal reinforcement learning. In S. J. Hanson, J. D. Cowan, & C. L. Giles (Eds.), Advances in Neural Information Processing Systems 5 (pp. 271278). San Francisco, CA: Morgan Kaufmann Publishers Inc.Google Scholar
Dayan, P., & Sejnowski, T. J. (1996). Exploration bonuses and dual control. Machine Learning, 25, 522.Google Scholar
Dearden, R., Friedman, N., & Russell, S. (1998). Bayesian Q-learning. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI).Google Scholar
Delong, M. R. (1990). Primate models of movement disorders of basal ganglia origin. Trends in Neurosciences, 13, 281285.Google Scholar
Devin, C., Gupta, A., Darrell, T., Abbeel, P., & Levine, S. (2017). Learning modular neural network policies for multi-task and multi-robot transfer. ICRA 2017 (online). https://doi.org/10.1109/ICRA.2017.7989250Google Scholar
Dietterich, T. G. (2000). Hierarchical reinforcement learning with the MAXQ value function decomposition. Journal of Artificial Intelligence Research, 13, 227303.Google Scholar
Doya, K. (1999). What are the computations of the cerebellum, the basal ganglia, and the cerebral cortex. Neural Networks, 12, 961974. https://doi.org/10.1016/S0893-6080(99)00046-5Google Scholar
Doya, K. (2000). Complementary roles of basal ganglia and cerebellum in learning and motor control. Current Opinion in Neurobiology, 10(6), 732739.Google Scholar
Doya, K. (2002). Metalearning and neuromodulation. Neural Networks, 15, 495506. https://doi.org/10.1016/S0893-6080(02)00044-8Google Scholar
Doya, K. (2008). Modulators of decision making. Nature Neuroscience, 11(4), 410416. https://doi.org/10.1038/nn2077Google Scholar
Doya, K. (2021). Canonical cortical circuits and the duality of Bayesian inference and optimal control. Current Opinion in Behavioral Sciences, 41, 160166. https://doi.org/10.1016/j.cobeha.2021.07.003Google Scholar
Doya, K., Miyazaki, K. W., & Miyazaki, K. (2021). Serotonergic modulation of cognitive computations. Current Opinion in Behavioral Sciences, 38, 116123. https://doi.org/10.1016/j.cobeha.2021.02.003Google Scholar
Doya, K., Samejima, K., Katagiri, K., & Kawato, M. (2002). Multiple model-based reinforcement learning. Neural Computation, 14(6), 13471369. https://doi.org/10.1162/089976602753712972Google Scholar
Doya, K., & Uchibe, E. (2005). The Cyber Rodent Project: exploration of adaptive mechanisms for self-preservation and self-reproduction. Adaptive Behavior, 13(2), 149160. https://doi.org/10.1177/105971230501300206Google Scholar
Elfwing, S., & Doya, K. (2014). Emergence of polymorphic mating strategies in robot colonies. PLoS One, 9(4), e93622. https://doi.org/10.1371/journal.pone.0093622Google Scholar
Elfwing, S., Uchibe, E., Doya, K., & Christensen, H. I. (2011). Darwinian embodied evolution of the learning ability for survival. Adaptive Behavior, 19(2), 101120. https://doi.org/10.1177/1059712310397633Google Scholar
Evans, R. C., Twedell, E. L., Zhu, M., Ascencio, J., Zhang, R., & Khaliq, Z. M. (2020). Functional dissection of basal ganglia inhibitory inputs onto substantia nigra dopaminergic neurons. Cell Reports, 32(11), 108156. https://doi.org/10.1016/j.celrep.2020.108156Google Scholar
Frank, M. J., Seeberger, L. C., & O’Reilly, R. C. (2004). By carrot or by stick: cognitive reinforcement learning in parkinsonism. Science, 306(5703), 19401943. https://doi.org/10.1126/science.1102941Google Scholar
Franklin, N. T., & Frank, M. J. (2018). Compositional clustering in task structure learning. PLoS Computational Biology, 14(4), e1006116. https://doi.org/10.1371/journal.pcbi.1006116Google Scholar
Friston, K. J., Lin, M., Frith, C. D., Pezzulo, G., Hobson, J. A., & Ondobaka, S. (2017). Active inference, curiosity and insight. Neural Computation, 29(10), 26332683. https://doi.org/10.1162/neco_a_00999Google Scholar
Fujimoto, A., & Takahashi, H. (2016). Flexible modulation of risk attitude during decision-making under quota. Neuroimage (online). https://doi.org/10.1016/j.neuroimage.2016.06.040Google Scholar
Fujimoto, A., Tsurumi, K., Kawada, R., et al. (2017). Deficit of state-dependent risk attitude modulation in gambling disorder. Translational Psychiatry, 7(4), e1085. https://doi.org/10.1038/tp.2017.55Google Scholar
Gerfen, C. R. (1984). The neostriatal mosaic: compartmentalization of corticostriatal input and striatonigral output systems. Nature, 311(5985), 461464. https://doi.org/10.1038/311461a0Google Scholar
Gerfen, C. R. (1992). The neostriatal mosaic: multiple levels of compartmental organization in the basal ganglia. Annual Review of Neuroscience, 15, 285320. https://doi.org/10.1146/annurev.ne.15.030192.001441Google Scholar
Gerfen, C. R., Engber, T. M., Mahan, L. C., et al. (1990). D1 and D2 dopamine receptor-regulated gene expression of striatonigral and striatopallidal neurons. Science, 250(4986), 14291432. https://doi.org/10.1126/science.2147780Google Scholar
Gershman, S. J. (2015). A unifying probabilistic view of associative learning. PLoS Computational Biology, 11(11), e1004567. https://doi.org/10.1371/journal.pcbi.1004567Google Scholar
Gershman, S. J., Blei, D. M., & Niv, Y. (2010). Context, learning, and extinction. Psychological Review, 117(1), 197209. https://doi.org/10.1037/a0017808Google Scholar
Glimcher, P. W., & Fehr, E. (2013). Neuroeconomics: Decision Making and the Brain (2nd ed.). London: Elsevier.Google Scholar
Graybiel, A. M. (1991). Basal ganglia: input, neural activity, and relation to the cortex. Current Opinion in Neurobiology, 1(4), 644651. https://doi.org/10.1016/s0959-4388(05)80043-1Google Scholar
Graybiel, A. M., & Ragsdale, C. W., Jr. (1978). Histochemically distinct compartments in the striatum of human, monkeys, and cat demonstrated by acetylthiocholinesterase staining. Proceedings of the National Academy of Sciences, 75(11), 57235726. https://doi.org/10.1073/pnas.75.11.5723Google Scholar
Haber, S. N., Fudge, J. L., & McFarland, N. R. (2000). Striatonigrostriatal pathways in primates form an ascending spiral from the shell to the dorsolateral striatum. Journal of Neuroscience, 20(6), 23692382. www.jneurosci.org/content/20/6/2369.full.pdfGoogle Scholar
Haber, S. N., & Knutson, B. (2010). The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology, 35(1), 426. https://doi.org/10.1038/npp.2009.129Google Scholar
Hamid, A. A., Frank, M. J., & Moore, C. I. (2021). Wave-like dopamine dynamics as a mechanism for spatiotemporal credit assignment. Cell, 184(10), P27332749.E16. https://doi.org/10.1016/j.cell.2021.03.046Google Scholar
Haruno, M., & Kawato, M. (2006). Heterarchical reinforcement-learning model for integration of multiple cortico-striatal loops: fMRI examination in stimulus-action-reward association learning. Neural Networks, 19(8), 12421254. https://doi.org/10.1016/j.neunet.2006.06.007Google Scholar
Haruno, M., Wolpert, D. M., & Kawato, M. (2001). Mosaic model for sensorimotor learning and control. Neural Computation, 13(10), 22012220. https://doi.org/10.1162/089976601750541778Google Scholar
Hasselmo, M. E. (1999). Neuromodulation: acetylcholine and memory consolidation. Trends in Cognitive Sciences, 3(9), 351359.Google Scholar
Hauert, C., Traulsen, A., Brandt, H., Nowak, M. A., & Sigmund, K. (2007). Via freedom to coercion: the emergence of costly punishment. Science, 316(5833), 19051907. https://doi.org/10.1126/science.1141588Google Scholar
Hikida, T., Kimura, K., Wada, N., Funabiki, K., & Nakanishi, S. (2010). Distinct roles of synaptic transmission in direct and indirect striatal pathways to reward and aversive behavior. Neuron, 66(6), 896907. https://doi.org/10.1016/j.neuron.2010.05.011Google Scholar
Hilbe, C., Simsa, S., Chatterjee, K., & Nowak, M. A. (2018). Evolution of cooperation in stochastic games. Nature, 559, 246–249. https://doi.org/10.1038/s41586-018-0277-xGoogle Scholar
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 17351780. https://doi.org/10.1162/neco.1997.9.8.1735Google Scholar
Hoover, J. E., & Strick, P. L. (1993). Multiple output channels in the basal ganglia. Science, 259(5096), 819821. https://doi.org/10.1126/science.7679223Google Scholar
Houk, J. C., Adams, J. L., & Barto, A. G. (1995). A model of how the basal ganglia generate and use neural signals that predict reinforcement. In Houk, J. C., Davis, J. L., & Beiser, D. G. (Eds.), Models of Information Processing in the Basal Ganglia (pp. 249270). Cambridge, MA: MIT Press.Google Scholar
Hu, H., Cui, Y., & Yang, Y. (2020). Circuits and functions of the lateral habenula in health and in disease. Nature Reviews Neuroscience, 21, 277–295. https://doi.org/10.1038/s41583-020-0292-4Google Scholar
Huys, Q. J., Eshel, N., O’Nions, E., Sheridan, L., Dayan, P., & Roiser, J. P. (2012). Bonsai trees in your head: how the Pavlovian system sculpts goal-directed choices by pruning decision trees. PLoS Computational Biology, 8(3), e1002410. https://doi.org/10.1371/journal.pcbi.1002410Google Scholar
Huys, Q. J. M., Browning, M., Paulus, M. P., & Frank, M. J. (2021). Advances in the computational understanding of mental illness. Neuropsychopharmacology, 46(1), 319. https://doi.org/10.1038/s41386-020-0746-4Google Scholar
Iigaya, K., Fonseca, M. S., Murakami, M., Mainen, Z. F., & Dayan, P. (2018). An effect of serotonergic stimulation on learning rates for rewards apparent after long intertrial intervals. Nature Communications, 9(1), 2477. https://doi.org/10.1038/s41467-018-04840-2Google Scholar
Ito, M., & Doya, K. (2011). Multiple representations and algorithms for reinforcement learning in the cortico-basal ganglia circuit. Current Opinion in Neurobiology, 21(3), 368373. https://doi.org/10.1016/j.conb.2011.04.001Google Scholar
Ito, M., & Doya, K. (2015a). Distinct neural representation in the dorsolateral, dorsomedial, and ventral parts of the striatum during fixed- and free-choice tasks. Journal of Neuroscience, 35(8), 34993514. https://doi.org/10.1523/JNEUROSCI.1962-14.2015Google Scholar
Ito, M., & Doya, K. (2015b). Parallel representation of value-based and finite state-based strategies in the ventral and dorsal striatum. PLoS Computational Biology, 11(11), e1004540. https://doi.org/10.1371/journal.pcbi.1004540Google Scholar
Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47(2), 263291.Google Scholar
Kakade, S., & Dayan, P. (2002). Dopamine: generalization and bonuses. Neural Networks, 15, 549559.Google Scholar
Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transactions of ASME, 82-D, 3545.Google Scholar
Kalman, R. E., & Koepcke, R. W. (1958). Optimal synthesis of linear sampling control systems using general performance indexes. Transactions of ASME, 80, 18201826.Google Scholar
Kaplan, F., & Oudeyer, P.-Y. (2007). In search of the neural circuits of intrinsic motivation. Frontiers in Neuroscience, 1(1), 225236. https://doi.org/10.3389/neuro.01.1.1.017.2007Google Scholar
Kappen, H. J., Gómez, V., & Opper, M. (2012). Optimal control as a graphical model inference problem. Machine Learning, 87(2), 159182. https://doi.org/10.1007/s10994-012-5278-7Google Scholar
Kim, H. R., Malik, A. N., Mikhael, J. G., et al. (2020). A unified framework for dopamine signals across timescales. Cell, 183(6), 16001616, e1625. https://doi.org/10.1016/j.cell.2020.11.013Google Scholar
Kravitz, A. V., Tye, L. D., & Kreitzer, A. C. (2012). Distinct roles for direct and indirect pathway striatal neurons in reinforcement. Nature Neuroscience, 15(6), 816818. https://doi.org/10.1038/nn.3100Google Scholar
Kurth-Nelson, Z., & Redish, A. D. (2009). Temporal-difference reinforcement learning with distributed representations. PLoS One, 4(10), e7362. https://doi.org/10.1371/journal.pone.0007362Google Scholar
Laibson, D. I. (1997). Golden eggs and hyperbolic discounting. The Quarterly Journal of Economics, 62, 443477.Google Scholar
Langdon, A. J., Sharpe, M. J., Schoenbaum, G., & Niv, Y. (2018). Model-based predictions for dopamine. Current Opinion in Neurobiology, 49, 17. https://doi.org/10.1016/j.conb.2017.10.006Google Scholar
Langdon, A. J., Song, M., & Niv, Y. (2019). Uncovering the “state”: tracing the hidden state representations that structure learning and decision-making. Behavioural Processes, 167, 103891. https://doi.org/10.1016/j.beproc.2019.103891Google Scholar
Levine, S. (2018). Reinforcement learning and control as probabilistic inference: tutorial and review. arXiv, 1805.00909Google Scholar
Levy, D. J., & Glimcher, P. W. (2011). Comparing apples and oranges: using reward-specific and reward-general subjective value representation in the brain. Journal of Neuroscience, 31(41), 1469314707. https://doi.org/10.1523/JNEUROSCI.2218-11.2011Google Scholar
Li, Y., Zhong, W., Wang, D., et al. (2016). Serotonin neurons in the dorsal raphe nucleus encode reward signals. Nature Communications, 7, 10503. https://doi.org/10.1038/ncomms10503Google Scholar
Liu, Z., Zhou, J., Li, Y., et al. (2014). Dorsal raphe neurons signal reward through 5-HT and glutamate. Neuron, 81(6), 13601374. https://doi.org/10.1016/j.neuron.2014.02.010Google Scholar
Lowet, A. S., Zheng, Q., Matias, S., Drugowitsch, J., & Uchida, N. (2020). Distributional reinforcement learning in the brain. Trends in Neurosciences, 43(12), 980–997. https://doi.org/10.1016/j.tins.2020.09.004Google Scholar
Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50(4), 370396. https://doi.org/10.1037/h0054346Google Scholar
Mathys, C., Daunizeau, J., Friston, K. J., & Stephan, K. E. (2011). A Bayesian foundation for individual learning under uncertainty. Frontiers in Human Neuroscience, 5, 39. https://doi.org/10.3389/fnhum.2011.00039Google Scholar
Matias, S., Lottem, E., Dugue, G. P., & Mainen, Z. F. (2017). Activity patterns of serotonin neurons underlying cognitive flexibility. Elife, 6 (online). https://doi.org/10.7554/eLife.20552Google Scholar
Matsumoto, M., & Hikosaka, O. (2007). Lateral habenula as a source of negative reward signals in dopamine neurons. Nature, 447(7148), 11111115. https://doi.org/10.1038/nature05860Google Scholar
Matsumoto, M., & Hikosaka, O. (2009). Two types of dopamine neuron distinctly convey positive and negative motivational signals. Nature, 459(7248), 837841. https://doi.org/10.1038/nature08028Google Scholar
Menegas, W., Akiti, K., Amo, R., Uchida, N., & Watabe-Uchida, M. (2018). Dopamine neurons projecting to the posterior striatum reinforce avoidance of threatening stimuli. Nature Neuroscience, 21, 1421–1430. https://doi.org/10.1038/s41593-018-0222-1Google Scholar
Miyazaki, K., Miyazaki, K. W., Sivori, G., Yamanaka, A., Tanaka, K. F., & Doya, K. (2020). Serotonergic projections to the orbitofrontal and medial prefrontal cortices differentially modulate waiting for future rewards. Science Advances, 6(48), eabc7246. https://doi.org/10.1126/sciadv.abc7246Google Scholar
Miyazaki, K., Miyazaki, K. W., Yamanaka, A., Tokuda, T., Tanaka, K. F., & Doya, K. (2018). Reward probability and timing uncertainty alter the effect of dorsal raphe serotonin neurons on patience. Nature Communications, 9(1), 2048. https://doi.org/10.1038/s41467-018-04496-yGoogle Scholar
Miyazaki, K. W., Miyazaki, K., Tanaka, K. F., et al. (2014). Optogenetic activation of dorsal raphe serotonin neurons enhances patience for future rewards. Current Biology, 24(17), 20332040. https://doi.org/10.1016/j.cub.2014.07.041Google Scholar
Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529533. https://doi.org/10.1038/nature14236Google Scholar
Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012). Computational psychiatry. Trends in Cognitive Sciences, 16(1), 7280. https://doi.org/10.1016/j.tics.2011.11.018Google Scholar
Mordatch, I., & Abbeel, P. (2017). Emergence of grounded compositional language in multi-agent populations. https://arxiv.org/abs/1703.04908Google Scholar
Morimoto, J., & Doya, K. (2001). Acquisition of stand-up behavior by a real robot using hierarchical reinforcement learning. Robotics and Autonomous Systems, 36, 3751. https://doi.org/10.1016/S0921-8890(01)00113-0Google Scholar
Muelling, K., Boularias, A., Mohler, B., Scholkopf, B., & Peters, J. (2014). Learning strategies in table tennis using inverse reinforcement learning. Biological Cybernetics (online). https://doi.org/10.1007/s00422-014-0599-1Google Scholar
Mukherjee, D., Lee, S., Kazinka, R., & Kable, J. W. (2020). Multiple facets of value-based decision making in major depressive disorder. Scientific Reports, 10(1), 3415. https://doi.org/10.1038/s41598-020-60230-zGoogle Scholar
Munuera, J., Rigotti, M., & Salzman, C. D. (2018). Shared neural coding for social hierarchy and reward value in primate amygdala. Nature Neuroscience, 21(3), 415423. https://doi.org/10.1038/s41593-018-0082-8Google Scholar
Nagai, Y., Takayama, K., Nishitani, N., et al. (2020). The role of dorsal raphe serotonin neurons in the balance between reward and aversion. International Journal of Molecular Sciences, 21(6). https://doi.org/10.3390/ijms21062160Google Scholar
Nakahara, H., Doya, K., & Hikosaka, O. (2001). Parallel cortico-basal ganglia mechanisms for acquisition and execution of visuo-motor sequences: a computational approach. Journal of Cognitive Neuroscience, 13(5), 626647. https://doi.org/10.1162/089892901750363208Google Scholar
Nassar, M. R., Wilson, R. C., Heasly, B., & Gold, J. I. (2010). An approximately Bayesian delta-rule model explains the dynamics of belief updating in a changing environment. Journal of Neuroscience, 30(37), 1236612378. https://doi.org/10.1523/JNEUROSCI.0822-10.2010Google Scholar
Ng, A. Y., & Russell, S. (2000). Algorithms for inverse reinforcement learning. In 17th International Conference on Machine Learning.Google Scholar
Nishijo, H., Ono, T., & Nishino, H. (1988). Topographic distribution of modality-specific amygdalar neurons in alert monkey. Journal of Neuroscience, 8(10), 35563569. https://doi.org/10.1523/jneurosci.08-10-03556.1988Google Scholar
Ohmura, Y., Iwami, K., Chowdhury, S., et al. (2021). Disruption of model-based decision making by silencing of serotonin neurons in the dorsal raphe nucleus. Current Biology, 31(11), 2446–2454. https://doi.org/10.1016/j.cub.2021.03.048Google Scholar
Ohtsuki, H., Hauert, C., Lieberman, E., & Nowak, M. A. (2006). A simple rule for the evolution of cooperation on graphs and social networks. Nature, 441(7092), 502505. https://doi.org/10.1038/nature04605Google Scholar
Ohtsuki, H., Iwasa, Y., & Nowak, M. A. (2009). Indirect reciprocity provides only a narrow margin of efficiency for costly punishment. Nature, 457(7225), 7982. https://doi.org/10.1038/nature07601Google Scholar
Pabba, M. (2013). Evolutionary development of the amygdaloid complex. Frontiers in Neuroanatomy, 7, 27. https://doi.org/10.3389/fnana.2013.00027Google Scholar
Palminteri, S., Khamassi, M., Joffily, M., & Coricelli, G. (2015). Contextual modulation of value signals in reward and punishment learning. Nature Communications, 6, 8096. https://doi.org/10.1038/ncomms9096Google Scholar
Palminteri, S., & Pessiglione, M. (2017). Opponent brain systems for reward and punishment learning: causal evidence from drug and lesion studies in humans. Decision Neuroscience, 2017, 291–303. https://doi.org/10.1016/B978-0-12-805308-9.00023-3Google Scholar
Parr, T., & Friston, K. J. (2017). Uncertainty, epistemics and active inference. Journal of the Royal Society Interface, 14(136). https://doi.org/10.1098/rsif.2017.0376Google Scholar
Pearce, J. M., & Bouton, M. E. (2001). Theories of associative learning in animals. Annual Review of Psychology, 52, 111139. https://doi.org/10.1146/annurev.psych.52.1.111Google Scholar
Redgrave, P., Prescott, T. J., & Gurney, K. (1999). Is the short-latency dopamine response too short to signal reward error? Trends in Neuroscience, 22(4), 146151. https://doi.org/10.1016/s0166-2236(98)01373-3Google Scholar
Redish, A. D. (2004). Addiction as a computational process gone awry. Science, 306, 19441947.Google Scholar
Redish, A. D., & Gordon, J. A. (2016). Computational Psychiatry. Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/9780262035422.001.0001Google Scholar
Reiss, S. (2012). Intrinsic and extrinsic motivation. Teaching of Psychology, 39(2), 152156. https://doi.org/10.1177/0098628312437704Google Scholar
Safra, L., Chevallier, C., & Palminteri, S. (2019). Depressive symptoms are associated with blunted reward learning in social contexts. PLoS Computational Biology, 15(7), e1007224. https://doi.org/10.1371/journal.pcbi.1007224Google Scholar
Sales, A. C., Friston, K. J., Jones, M. W., Pickering, A. E., & Moran, R. J. (2019). Locus Coeruleus tracking of prediction errors optimises cognitive flexibility: an active inference model. PLoS Computational Biology, 15(1), e1006267. https://doi.org/10.1371/journal.pcbi.1006267Google Scholar
Samejima, K., & Doya, K. (2007). Multiple representations of belief states and action values in corticobasal ganglia loops. Annals of the New York Academy of Sciences, 1104, 213228. https://doi.org/10.1196/annals.1390.024Google Scholar
Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80, 127.Google Scholar
Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275, 15931599. https://doi.org/10.1126/science.275.5306.1593Google Scholar
Schweighofer, N., & Doya, K. (2003). Meta-learning of reinforcement learning. Neural Networks, 16(1), 59. https://doi.org/10.1016/S0893-6080(02)00228-9Google Scholar
Singh, S. P. (1992). Transfer of learning by composing solutions of elemental sequential tasks. Machine Learning, 8(3/4), 323340. https://doi.org/10.1023/A:1022680823223Google Scholar
Sippy, T., Lapray, D., Crochet, S., & Petersen, C. C. (2015). Cell-type-specific sensorimotor processing in striatal projection neurons during goal-directed behavior. Neuron, 88(2), 298–305. https://doi.org/10.1016/j.neuron.2015.08.039Google Scholar
Soma, M., Aizawa, H., Ito, Y., et al. (2009). Development of the mouse amygdala as revealed by enhanced green fluorescent protein gene transfer by means of in utero electroporation. Journal of Comparative Neurology, 513(1), 113128. https://doi.org/10.1002/cne.21945Google Scholar
Stachenfeld, K. L., Botvinick, M. M., & Gershman, S. J. (2017). The hippocampus as a predictive map. Nature Neuroscience, 20(11), 16431653. https://doi.org/10.1038/nn.4650Google Scholar
Starkweather, C. K., & Uchida, N. (2021). Dopamine signals as temporal difference errors: recent advances. Current Opinion in Neurobiology, 67, 95105. https://doi.org/10.1016/j.conb.2020.08.014Google Scholar
Sugimoto, N., Haruno, M., Doya, K., & Kawato, M. (2012). MOSAIC for multiple-reward environments. Neural Computation, 24(3), 577606. https://doi.org/10.1162/NECO_a_00246Google Scholar
Sun, R. (2009). Motivational representations within a computational cognitive architecture. Cognitive Computation, 1(1), 91103. https://doi.org/10.1007/s12559-009-9005-zGoogle Scholar
Sun, R., & Sessions, C. (2000). Self-segmentation of sequences: automatic formation of hierarchies of sequential behaviors. IEEE Transactions on Systems, Man, and Cybernetics, 30(3), 403418. https://doi.org/10.1109/3477.846230Google Scholar
Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). Cambridge, MA: MIT Press.Google Scholar
Sutton, R. S., Precup, D., & Singh, S. (1999). Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning. Artificial Intelligence, 112(1–2), 181211. https://doi.org/10.1016/s0004-3702(99)00052-1Google Scholar
Takahashi, H. (2012). Monoamines and assessment of risks. Current Opinion in Neurobiology, 22(6), 10621067. https://doi.org/10.1016/j.conb.2012.06.003Google Scholar
Takahashi, H., Fujie, S., Camerer, C., et al. (2013). Norepinephrine in the brain is associated with aversion to financial loss. Molecular Psychiatry, 18(1), 34. https://doi.org/10.1038/mp.2012.7Google Scholar
Takeuchi, H., Kawada, R., Tsurumi, K., et al. (2015). Heterogeneity of loss aversion in pathological gambling. Journal of Gambling Studies, 32, 1143–1154. https://doi.org/10.1007/s10899-015-9587-1Google Scholar
Takeuchi, H., Tsurumi, K., Murao, T., et al. (2017). Common and differential brain abnormalities in gambling disorder subtypes based on risk attitude. Addictive Behaviors, 69, 4854. https://doi.org/10.1016/j.addbeh.2017.01.025Google Scholar
Tanaka, S. C., Yahata, N., Todokoro, A., et al. (2018). Preliminary evidence of altered neural response during intertemporal choice of losses in adult attention-deficit hyperactivity disorder. Scientific Reports, 8(1), 6703. https://doi.org/10.1038/s41598-018-24944-5Google Scholar
Tecuapetla, F., Jin, X., Lima, S. Q., & Costa, R. M. (2016). Complementary contributions of striatal projection pathways to action initiation and execution. Cell, 166(3), 703715. https://doi.org/10.1016/j.cell.2016.06.032Google Scholar
Thrun, S., & Pratt, L. (Eds.). (1998). Learning to Learn. New York, NY: Springer. https://doi.org/10.1007/978-1-4615-5529-2.Google Scholar
Todorov, E. (2008). General duality between optimal control and estimation. In The 47th IEEE Conference on Decision and Control.Google Scholar
Todorov, E. (2009). Parallels between sensory and motor information processing. In M. S. Gazzaniga (Ed.), The Cognitive Neurosciences, 4th ed. Cambridge, MA: MIT Press.Google Scholar
Uchibe, E. (2017). Model-free deep inverse reinforcement learning by logistic regression. Neural Processing Letters, 47, 891–905. https://doi.org/10.1007/s11063-017-9702-7Google Scholar
Uchibe, E., & Doya, K. (2014). Inverse reinforcement learning using Dynamic Policy Programming. In 4th International Conference on Development and Learning and on Epigenetic Robotics.Google Scholar
Uchibe, E., & Doya, K. (2021). Forward and inverse reinforcement learning sharing network weights and hyperparameters. Neural Networks, 144, 138153. https://doi.org/10.1016/j.neunet.2021.08.017Google Scholar
van den Bos, W., Talwar, A., & McClure, S. M. (2013). Neural correlates of reinforcement learning and social preferences in competitive bidding. Journal of Neuroscience, 33(5), 21372146. https://doi.org/10.1523/JNEUROSCI.3095-12.2013Google Scholar
von Neumann, J., & Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton, NJ: Princeton University Press.Google Scholar
Voorn, P., Vanderschuren, L. J., Groenewegen, H. J., Robbins, T. W., & Pennartz, C. M. (2004). Putting a spin on the dorsal-ventral divide of the striatum. Trends in Neuroscience, 27(8), 468474. https://doi.org/10.1016/j.tins.2004.06.006Google Scholar
Wang, J. X., Kurth-Nelson, Z., Kumaran, D., et al. (2018). Prefrontal cortex as a meta-reinforcement learning system. Nature Neuroscience, 21(6), 860868. https://doi.org/10.1038/s41593-018-0147-8Google Scholar
Watabe-Uchida, M., Eshel, N., & Uchida, N. (2017). Neural circuitry of reward prediction error. Annual Review of Neuroscience, 40, 373394. https://doi.org/10.1146/annurev-neuro-072116-031109Google Scholar
Wiering, M., & Schmidhuber, J. (1998). HQ-learning. Adaptive Behavior, 6, 219246.Google Scholar
Yamagata, N., Ichinose, T., Aso, Y., et al. (2014). Distinct dopamine neurons mediate reward signals for short- and long-term memories. Proceedings of the National Academy of Sciences, 112(2), 578–583. https://doi.org/10.1073/pnas.1421930112Google Scholar
Yamaguchi, S., Naoki, H., Ikeda, M., et al. (2018). Identification of animal behavioral strategies by inverse reinforcement learning. PLoS Computational Biology, 14(5), e1006122. https://doi.org/10.1371/journal.pcbi.1006122Google Scholar
Yang, G. R., Joglekar, M. R., Song, H. F., Newsome, W. T., & Wang, X. J. (2019). Task representations in neural networks trained to perform many cognitive tasks. Nature Neuroscience, 22(2), 297306. https://doi.org/10.1038/s41593-018-0310-2Google Scholar
Yoshida, W., Dolan, R. J., & Friston, K. J. (2008). Game theory of mind. PLoS Computational Biology, 4(12), e1000254. https://doi.org/10.1371/journal.pcbi.1000254Google Scholar
Yoshizawa, T., Ito, M., & Doya, K. (2018). Reward-predictive neural activities in striatal striosome compartments. eNeuro, 5(1), e0367–0317.2018. https://doi.org/10.1523/ENEURO.0367-17.2018Google Scholar
Yu, A. J., & Dayan, P. (2005). Uncertainty, neuromodulation, and attention. Neuron, 46(4), 681692. https://doi.org/10.1016/j.neuron.2005.04.026Google Scholar
Ziebart, B., Bagnell, J., & Dey, A. (2010). Modeling interaction via the principle of maximum causal entropy. In International Conference on Machine Learning.Google Scholar
Ziebart, B., Maas, A., Bagnell, J., & Dey, A. (2008). Maximum entropy inverse reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2008).Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×