Skip to main content Accessibility help
×
Hostname: page-component-848d4c4894-x24gv Total loading time: 0 Render date: 2024-04-30T17:33:13.046Z Has data issue: false hasContentIssue false

Part II - Cognitive Modeling Paradigms

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

Abel, S., Huber, W., & Dell, G. S. (2009). Connectionist diagnosis of lexical disorders in aphasia. Aphasiology, 23(11), 13531378.CrossRefGoogle Scholar
Abel, S., Willmes, K., & Huber, W. (2007). Model-oriented naming therapy: testing predictions of a connectionist model. Aphasiology, 21(5), 411447.Google Scholar
Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985). A learning algorithm for Boltzmann machines. Cognitive Science, 9, 147169.Google Scholar
Alireza, H., Fedor, A., & Thomas, M. S. C. (2017). Simulating behavioural interventions for developmental deficits: when improving strengths produces better outcomes than remediating weaknesses. In Gunzelmann, G., Howes, A., Tenbrink, T., & Davelaar, E., (Eds.), Proceedings of the 39th Annual Meeting of the Cognitive Science Society, London, UK.Google Scholar
Anderson, J., & Rosenfeld, E. (1988). Neurocomputing: Foundations of Research. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Anderson, J. A. (1977). Neural models with cognitive implications. In LaBerge, D. & Samuels, S. J., (Eds.), Basic Processes in Reading Perception and Comprehension, (pp. 2790). Hillsdale, NJ: Erlbaum.Google Scholar
Aru, J., & Vincente, R. (2018). What deep learning can tell us about higher cognitive functions like mindreading? arXiv:1803.10470v2Google Scholar
Bechtel, W., & Abrahamsen, A. (1991). Connectionism and the Mind. Oxford: Blackwell.Google Scholar
Berko, J. (1958). The child’s learning of English morphology. Word, 14, 150177.Google Scholar
Betti, A., & Gori, M. (2020). Backprop diffusion is biologically plausible. arXiv:1912.04635v2Google Scholar
Blakeman, S., & Mareschal, D. (2020). A complementary learning systems approach to temporal difference learning. Neural Networks, 22, 218230. https://doi.org/10.1016/j.neunet.2019.10.011CrossRefGoogle Scholar
Botvinick, M. & Plaut, D. C. (2004). Doing without schema hierarchies: a recurrent connectionist approach to normal and impaired routine sequential action. Psychological Review, 111, 395429.Google 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.12126CrossRefGoogle ScholarPubMed
Brown, T. B., Mann, B., Ryder, N., et al. (2020). Language models are few-shot learners. arXiv:2005.14165.Google Scholar
Burton, A. M., Bruce, V., & Johnston, R. A. (1990). Understanding face recognition with an interactive activation model. British Journal of Psychology, 81, 361380.Google Scholar
Bybee, J., & McClelland, J. L. (2005). Alternatives to the combinatorial paradigm of linguistic theory based on domain general principles of human cognition. The Linguistic Review, 22(24), 381410.CrossRefGoogle Scholar
Chang, F., Dell, G. S., & Bock, K. (2006). Becoming syntactic. Psychological Review, 113(2), 234272. https://doi.org/10.1037/0033-295X.113.2.234CrossRefGoogle ScholarPubMed
Chen, P. L., Lambon Ralph, M., & Rogers, T. T. (2017). A unified model of human semantic knowledge and its disorders. Nature Human Behaviour, 1, 0039. https://doi.org/10.1038/s41562-016-0039Google Scholar
Christiansen, M. H. & Chater, N. (2001). Connectionist Psycholinguistics. Westport, CT: Ablex.Google Scholar
Cleeremans, A., & Dienes, Z. (2008). Computational models of implicit learning. In R. Sun (Ed.), The Cambridge Handbook of Computational Psychology (pp. 396–421). Cambridge: Cambridge University Press. https://doi.org/10.1017/cbo9780511816772.018CrossRefGoogle Scholar
Cobb, M. (2020). The Idea of the Brain. London: Profile Books.Google Scholar
Cohen, G., Johnstone, R. A., & Plunkett, K. (2000). Exploring Cognition: Damaged Brains and Neural Networks. Hove: Psychology Press.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, 332361.CrossRefGoogle ScholarPubMed
Crick, F. (1989). The recent excitement about neural networks. Nature, 337, 129132. https://doi.org/10.1038/337129a0Google Scholar
Davelaar, E. J., & Usher, M. (2002). An activation-based theory of immediate item memory. In Bullinaria, J. A. & Lowe, W. (Eds.), Proceedings of the Seventh Neural Computation and Psychology Workshop: Connectionist Models of Cognition and Perception. Singapore: World Scientific.Google Scholar
Davies, M. (2005). Cognitive science. In Jackson, F. & Smith, M. (Eds.), The Oxford Handbook of Contemporary Philosophy. Oxford: Oxford University Press.Google Scholar
Devlin, J., Gonnerman, L., Andersen, E., & Seidenberg, M. S. (1997). Category specific semantic deficits in focal and widespread brain damage: a computational account. Journal of Cognitive Neuroscience, 10, 7794.Google Scholar
Dündar-Coecke, S., & Thomas, M. S. C. (2019). Modeling socioeconomic effects on the development of brain and behavior. In Goel, A. K., Seifert, C. M., & Freksa, C. (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society (pp. 16761682). Montreal: Cognitive Science Society.Google Scholar
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 179211.CrossRefGoogle Scholar
Elman, J. L. (1991). Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7, 195224.Google Scholar
Elman, J. L. (1993). Learning and development in neural networks: the importance of starting small. Cognition, 48, 7199.Google Scholar
Elman, J. L. (2005). Connectionist models of cognitive development: where next? Trends in Cognitive Sciences, 9, 111117.Google Scholar
Elman, J. L. & McRae, K. (2019). A model of event knowledge. Psychological Review, 126 (2), 252291. https://doi.org/10.1037/rev0000133CrossRefGoogle Scholar
Elman, J. L., Bates, E. A., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., & Plunkett, K. (1996). Rethinking Innateness: A Connectionist Perspective on Development. Cambridge, MA: MIT Press.Google Scholar
Ervin, S. M. (1964). Imitation and structural change in children’s language. In Lenneberg, E. H. (Ed.), New Directions in the Study of Language. Cambridge, MA: MIT Press.Google Scholar
Fahlman, S., & Lebiere, C. (1990). The cascade correlation learning architecture. In Touretzky, D. (Ed.), Advances in Neural Information Processing 2 (pp. 524532). Los Altos, CA: Morgan Kauffman.Google Scholar
Feldman, J. A. (1981). A connectionist model of visual memory. In Hinton, G. E. & Anderson, J. A. (Eds.), Parallel Models of Associative Memory (pp. 4981). Hillsdale, NJ: Erlbaum.Google Scholar
Fitz, H., & Chang, F. (2017). Meaningful questions: the acquisition of auxiliary inversion in a connectionist model of sentence production. Cognition, 166, 225250. https://doi.org/10.1016/j.cognition.2017.05.008CrossRefGoogle Scholar
Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: a critical analysis. Cognition, 78, 371.Google Scholar
French, R. M., Ans, B., & Rousset, S. (2001). Pseudopatterns and dual-network memory models: advantages and shortcomings. In French, R. & Sougné, J. (Eds.), Connectionist Models of Learning, Development and Evolution (pp. 1322). London: Springer.Google Scholar
Freud, S. (1895). Project for a scientific psychology. In Strachey, J. (Ed.), The Standard Edition of the Complete Psychological Works of Sigmund Freud. London: The Hogarth Press and the Institute of Psycho-Analysis.Google Scholar
Friston, K. (2009). The free-energy principle: a rough guide to the brain? Trends in Cognitive Sciences, 13(7), 293301. https://doi.org/10.1016/j.tics.2009.04.005Google Scholar
Friston, K., & Kiebel, S. (2009). Predictive coding under the free-energy principle. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 364(1521), 12111221. https://doi.org/10.1098/rstb.2008.0300Google Scholar
Goebel, R., & Indefrey, P. (2000). A recurrent network with short-term memory capacity learning the German –s plural. In Broeder, P. & Murre, J. (Eds.), Models of Language Acquisition: Inductive and Deductive Approaches (pp. 177200). Oxford: Oxford University Press.Google Scholar
Gordon, P. (2004). Numerical cognition without words: evidence from Amazonia. Science, 306(5695), 496499.Google Scholar
Grainger, J., Midgley, K., & Holcomb, P. J. (2010). Re-thinking the bilingual interactive-activation model from a developmental perspective (BIA-d). In Kail, M. & Hickmann, M. (Eds.), Language Acquisition Across Linguistic and Cognitive Systems (pp. 267283). Amsterdam: John Benjamins Publishing Company.Google Scholar
Green, D. C. (1998). Are connectionist models theories of cognition? Psycoloquy, 9(4).Google Scholar
Grossberg, S. (1976a). Adaptive pattern classification and universal recoding I: parallel development and coding of neural feature detectors. Biological Cybernetics, 23, 121–134..CrossRefGoogle Scholar
Grossberg, S. (1976b). Adaptive pattern classification and universal recoding II: feedback, expectation, olfaction, and illusions. Biological Cybernetics, 23, 187–202.Google Scholar
Haarmann, H., & Usher, M. (2001). Maintenance of semantic information in capacity limited item short-term memory. Psychonomic Bulletin & Review, 8, 568578.Google Scholar
Hackman, D. A., Farah, M. J., & Meaney, M. J. (2010). Socioeconomic status and the brain. Nature Reviews Neuroscience, 11, 651659.Google Scholar
Hahnloser, R., Sarpeshkar, R., Mahowald, M., et al. (2000). Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature, 405, 947951. https://doi.org/10.1038/35016072CrossRefGoogle Scholar
Harm, M. W. & Seidenberg, M. S. (1999). Phonology, reading acquisition, and dyslexia: insights from connectionist models. Psychological Review, 106 (3), 491528.Google Scholar
Hebb, D. O. (1949). The Organization of Behavior: A Neuropsychological Approach. New York, NY: John Wiley & Sons.Google Scholar
Hinton, G. E. (1989). Deterministic Boltzmann learning performs steepest descent in weight-space. Neural Computation, 1, 143150.Google Scholar
Hinton, G. E., & Anderson, J. A. (1981). Parallel Models of Associative Memory. Hillsdale, NJ: Erlbaum.Google Scholar
Hinton, G. E., & McClelland, J. L. (1988). Learning representations by recirculation. In Anderson, D. Z., (Ed.), Neural Information Processing Systems (pp. 358366). New York, NY: American Institute of Physics.Google Scholar
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313 (5786), 504507.CrossRefGoogle ScholarPubMed
Hinton, G. E., & Sejnowski, T. (1986). Learning and relearning in Boltzmann machines. In Rumelhart, D. & McClelland, J. (Eds.), Parallel Distributed Processing (vol. 1, pp. 282317). Cambridge, MA: MIT Press.Google Scholar
Hinton, G. E., & Sejnowski, T. J. (1983). Optimal perceptual inference. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC.Google Scholar
Hochreiter, S. (1991). Untersuchungen zu dynamischen neuronalen Netzen. Diploma thesis, Institut f. Informatik, Technische Univ. Munich.Google Scholar
Hochreiter, S., Bengio, Y., Frasconi, P., & Schmidhuber, J. (2001). Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. In Kremer, S. C. & Kolen, J. F. (Eds.), A Field Guide to Dynamical Recurrent Neural Networks. Piscataway, NJ: IEEE Press.Google 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
Hoeffner, J. H., & McClelland, J. L. (1993). Can a perceptual processing deficit explain the impairment of inflectional morphology in developmental dysphasia? A computational investigation. In Clark, E. V. (Ed.), Proceedings of the 25th Child Language Research Forum (pp. 3849). Stanford, CA: Center for the Study of Language and Information.Google Scholar
Hoffman, P., McClelland, J., & Lambon Ralph, M. (2018). Concepts, control and context: a connectionist account of normal and disordered semantic cognition. Psychological Review, 125(3), 293328. https://doi.org/10.1037/rev0000094Google Scholar
Hofstadter, D. (2018). The shallowness of Google Translate. The Atlantic. Available from: www.theatlantic.com/technology/archive/2018/01/the-shallowness-of-google-translate/551570/ [last accessed August 9, 2022].Google Scholar
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Science USA, 79, 25542558.Google Scholar
Houghton, G. (2005). Connectionist Models in Cognitive Psychology. Hove: Psychology Press.Google Scholar
James, W. (1890). Principles of Psychology. New York, NY: Holt.Google Scholar
Joanisse, M. F. & McClelland, J. L. (2015). Connectionist perspectives on language learning, representation, and processing. WIREs Cognitive Science (online). https://doi.org/10.1002/wcs.1340Google Scholar
Joanisse, M. F. & Seidenberg, M. S. (1999). Impairments in verb morphology following brain injury: a connectionist model. Proceedings of the National Academy of Science, 96, 75927597.Google Scholar
Joanisse, M. F. & Seidenberg, M. S. (2003). Phonology and syntax in specific language impairment: evidence from a connectionist model. Brain and Language, 86, 4056.Google Scholar
Jordan, M. I. (1986). Attractor dynamics and parallelism in a connectionist sequential machine. In Proceedings of the Eighth Annual Conference of Cognitive Science Society (pp. 531546). Hillsdale, NJ: Erlbaum.Google Scholar
Karaminis, T. N., & Thomas, M. S. C. (2010). A cross-linguistic model of the acquisition of inflectional morphology in English and Modern Greek. In Ohlsson, S. & Catrambone, R. (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, August 1114, 2010. Portland, Oregon, USA.Google Scholar
Karaminis, T. N., & Thomas, M. S. C. (2014). The multiple inflection generator: a generalized connectionist model for cross-linguistic morphological development. DNL Tech report 2014 (online). http://193.61.4.246/dnl/wp-content/uploads/2020/04/KT_TheMultipleInflectionGenerator2014.pdf [last accessed August 9, 2022].Google Scholar
Karmiloff-Smith, A. (1998). Development itself is the key to understanding developmental disorders. Trends in Cognitive Sciences, 2, 389398.Google Scholar
Karmiloff-Smith, A. (2009). Nativism versus neuroconstructivism: rethinking the study of developmental disorders. Developmental Psychology, 45(1), 5663.Google Scholar
Kirov, C. & Cotterell, R. (2018). Recurrent neural networks in linguistic theory: revisiting Pinker and Prince (1988) and the past tense debate. Transactions of the Association for Computational Linguistics, 6, 651665. https://doi.org/10.1162/tacl_a_00247CrossRefGoogle Scholar
Knopik, V. S., Neiderhiser, J. M., DeFries, J. C., & Plomin, R. (2016). Behavioral genetics (7th ed). New York, NY: Worth Publishers.Google Scholar
Kohonen, T. (1984). Self-Organization and Associative Memory. Berlin: Springer-Verlag.Google Scholar
Kollias, P. & McClelland, J. L. (2013). Context, cortex, and associations: a connectionist developmental approach to verbal analogies. Frontiers in Psychology, 4, 857. https://doi.org/10.3389/fpsyg.2013.00857Google Scholar
Kriegeskorte, N. (2015). Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual Review of Vision Science, 1, 417446.Google Scholar
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems, 1, 10971105.Google Scholar
Kuczaj, S. A. (1977). The acquisition of regular and irregular past tense forms. Journal of Verbal Learning and Verbal Behavior, 16, 589600.Google Scholar
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, e253.Google Scholar
Lashley, K. S. (1929). Brain Mechanisms and Intelligence: A Quantitative Study of Injuries to the Brain. New York, NY: Dover Publications, Inc.Google Scholar
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521 (7553), 436.Google Scholar
Lillicrap, T., Cownden, D., Tweed, D., & Akerman, C. J. (2016). Random synaptic feedback weights support error backpropagation for deep learning. Nature Communications, 7, 13276. https://doi.org/10.1038/ncomms13276CrossRefGoogle ScholarPubMed
Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. E. (2020). Backpropagation and the brain. Nature Reviews Neuroscience, 21, 335346. https://doi.org/10.1038/s41583–020-0277-3CrossRefGoogle ScholarPubMed
MacDonald, M. C., & Christiansen, M. H. (2002). Reassessing working memory: a comment on Just & Carpenter (1992) and Waters & Caplan (1996). Psychological Review, 109, 3554.Google Scholar
MacKay, D. J. (1992). A practical Bayesian framework for backpropagation networks. Neural Computation, 4, 448472.Google Scholar
Magnuson, J. S., Li, M., Luthra, S., You, H., & Steiner, R. (2019). Does predictive processing imply predictive coding in models of spoken word recognition? In Proceedings of the 41st Annual Meeting of the Cognitive Science Society (pp. 735740). Cognitive Science Society.Google Scholar
Manning, C. D., Clark, K., Hewitt, J., Khandelwal, U., & Levy, O. (2020) Emergent linguistic structure in artificial neural networks trained by self-supervision. Proceedings of the National Academy of Sciences, 117(48), 30046–30054.Google Scholar
Marcus, G. F. (2001). The Algebraic Mind: Integrating Connectionism and Cognitive Science. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Marcus, G., Pinker, S., Ullman, M., Hollander, J., Rosen, T., & Xu, F. (1992). Overregularisation in language acquisition. Monographs of the Society for Research in Child Development, 57 (228), 1178.Google Scholar
Mareschal, D., & Thomas, M. S. C. (2007). Computational modeling in developmental psychology. IEEE Transactions on Evolutionary Computation (Special Issue on Autonomous Mental Development), 11, 137150.Google Scholar
Mareschal, D., Johnson, M., Sirios, S., Spratling, M., Thomas, M. S. C., & Westermann, G. (2007). Neuroconstructivism: How the Brain Constructs Cognition. Oxford: Oxford University Press.CrossRefGoogle Scholar
Marr, D. (1982). Vision. San Francisco, CA: W. H. Freeman.Google Scholar
Marr, D., & Poggio, T. (1976). Cooperative computation of stereo disparity. Science, 194, 283287.Google Scholar
Mayor, J., Gomez, P., Chang, F., & Lupyan, G. (2014). Connectionism coming of age: legacy and future challenges. Frontiers In Psychology, 5, 187. https://doi.org/10.3389/fpsyg.2014.00187CrossRefGoogle ScholarPubMed
McClelland, J. L. (1981). Retrieving general and specific information from stored knowledge of specifics. In Proceedings of the Third Annual Meeting of the Cognitive Science Society (pp. 170172). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
McClelland, J. L. (1989). Parallel distributed processing: implications for cognition and development. In Morris, M. G. M. (Ed.), Parallel Distributed Processing, Implications for Psychology and Neurobiology (pp. 845). Oxford: Clarendon Press.Google Scholar
McClelland, J. L. (2013). Integrating probabilistic models of perception and interactive neural networks: a historical and tutorial review. Frontiers in Psychology, 4, 503. www.frontiersin.org/articles/10.3389/fpsyg.2013.00503/fullGoogle Scholar
McClelland, J. L., & Elman, J. L. (1986). The TRACE model of speech perception. Cognitive Psychology, 18, 186.Google Scholar
McClelland, J. L., Hill, F., Rudolph, M., Baldridge, J., & Schuetze, H. (2020). Placing language in an integrated understanding system: next steps toward human-level performance in neural language models. Proceedings of the National Academy of Sciences, 117(42), 2596625974. https://doi.org/10.1073/pnas.1910416117Google 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, 419457.Google Scholar
McClelland, J. L., Plaut, D. C., Gotts, S. J., & Maia, T. V. (2003). Developing a domain-general framework for cognition: what is the best approach? Commentary on a target article by Anderson and Lebiere. Behavioral and Brain Sciences, 22, 611614.Google Scholar
McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception. Part 1: An account of basic findings. Psychological Review, 88(5), 375405.Google Scholar
McClelland, J. L., Rumelhart, D. E. & the PDP Research Group (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 2: Psychological and Biological Models. Cambridge, MA: MIT Press.Google Scholar
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115133.Google Scholar
McLeod, P., Plunkett, K., & Rolls, E. T. (1998). Introduction to Connectionist Modelling of Cognitive Processes. Oxford: Oxford University Press.Google Scholar
Meynert, T. (1884). Psychiatry: A Clinical Treatise on Diseases of the Forebrain. Part I. The Anatomy, Physiology and Chemistry of the Brain. Trans. B. Sachs. New York, NY: G. P. Putnam’s Sons.Google Scholar
Minsky, M., & Papert, S. (1969). Perceptrons: An Introduction to Computational Geometry. Cambridge, MA: MIT Press.Google Scholar
Morton, J. (1969). Interaction of information in word recognition. Psychological Review, 76, 165178.Google Scholar
Morton, J. B., & Munakata, Y. (2002). Active versus latent representations: a neural network model of perseveration, dissociation, and decalage in childhood. Developmental Psychobiology, 40, 255265.Google Scholar
Moutoussis, M., Shahar, N., Hauser, T. U., & Dolan, R. J. (2017). Computation in psychotherapy, or how computational psychiatry can aid learning-based psychological therapies. Computational Psychiatry, 2, 5073. https://doi.org/10.1162/%20cpsy_a_00014Google Scholar
Movellan, J. R., & McClelland, J. L. (1993). Learning continuous probability distributions with symmetric diffusion networks. Cognitive Science, 17, 463496.Google Scholar
Munakata, Y. (1998). Infant perseveration and implications for object permanence theories: a PDP model of the AB task. Developmental Science, 1, 161184.Google Scholar
Munakata, Y. & McClelland, J. L. (2003). Connectionist models of development. Developmental Science, 6, 413429.Google Scholar
Newell, A. (1980). Physical symbol systems. Cognitive Science, 4(2), 135183.Google Scholar
Novikoff, A. (1962). Proceedings of the Symposium on the Mathematical Theory of Automata, 12, 615–622. New York, NY: Polytechnic Institute of Brooklyn.Google Scholar
O’Reilly, R. C. (1996). Biologically plausible error-driven learning using local activation differences: the generalized recirculation algorithm. Neural Computation, 8, 895938.Google Scholar
O’Reilly, R. C. (1998). Six principles for biologically based computational models of cortical cognition. Trends in Cognitive Sciences, 2, 455462.Google Scholar
O’Reilly, R. C., Bhattacharyya, R., Howard, M. D., & Ketza, N. (2014). Complementary learning systems. Cognitive Science, 38, 12291248. https://doi.org/10.1111/j.1551-6709.2011.01214.xGoogle 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. New York, NY: Cambridge University Press.Google Scholar
O’Reilly, R. C., & Munakata, Y. (2000). Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. Cambridge, MA: MIT Press.Google Scholar
Pater, J. (2019). Generative linguistics and neural networks at 60: foundation, friction, and fusion. Language, 95(1). Epub February 20, 2019. https://doi.org/10.1353/lan.2019.0005Google Scholar
Piazza, M., Pica, P., Izard, V., Spelke, E. S., & Dehaene, S. (2013). Education enhances the acuity of the nonverbal approximate number system. Psychological Science, 24(6), 10371043. https://doi.%20org/10.1177/09567%2097612%20464057.Google Scholar
Pinker, S. (1984). Language Learnability and Language Development. Cambridge, MA: Harvard University Press.Google Scholar
Pinker, S. (1999). Words and Rules. London: Weidenfeld & Nicolson.Google Scholar
Pinker, S., & Prince, A. (1988). On language and connectionism: analysis of a parallel distributed processing model of language acquisition. Cognition, 28, 73193.Google Scholar
Plaut, D. C., & Kello, C. T. (1999). The emergence of phonology from the interplay of speech comprehension and production: a distributed connectionist approach. In MacWhinney, B. (Ed.), The Emergence of Language (pp. 381415). Mahwah, NJ: Erlbaum.Google Scholar
Plaut, D. C. & McClelland, J. L. (1993). Generalization with componential attractors: word and nonword reading in an attractor network. In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society (pp. 824829). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. E. (1996). Understanding normal and impaired word reading: computational principles in quasi-regular domains. Psychological Review, 103, 56115.Google Scholar
Plunkett, K., & Marchman, V. (1991). U-shaped learning and frequency effects in a multi-layered perceptron: implications for child language acquisition. Cognition, 38, 160.Google Scholar
Plunkett, K., & Marchman, V. (1993). From rote learning to system building: acquiring verb morphology in children and connectionist nets. Cognition, 48, 2169.Google Scholar
Plunkett, K., & Marchman, V. (1996). Learning from a connectionist model of the English past tense. Cognition, 61, 299308.Google Scholar
Plunkett, K., & Nakisa, R. (1997). A connectionist model of the Arabic plural system. Language and Cognitive Processes, 12, 807836.Google Scholar
Rao, R., & Ballard, D. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience 2, 7987. https://doi.org/10.1038/4580Google Scholar
Rashevsky, N. (1935). Outline of a physico-mathematical theory of the brain. Journal of General Psychology, 13, 82112.Google Scholar
Reicher, G. M. (1969). Perceptual recognition as a function of meaningfulness of stimulus material. Journal of Experimental Psychology, 81, 274280.Google Scholar
Ritter, S., Barrett, D. G. T., Santoro, A., & Botvinick, M. M. (2017). Cognitive psychology for deep neural networks: a shape bias case study. arXiv:1706.08606v2Google Scholar
Rohde, D. L. T. & Plaut, D. C. (1999). Language acquisition in the absence of explicit negative evidence: how important is starting small? Cognition, 72, 67109.Google Scholar
Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386408.Google Scholar
Rosenblatt, F. (1962). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Washington, DC: Spartan Books.Google Scholar
Rumelhart, D. E., & McClelland, J. L. (1982). An interactive activation model of context effects in letter perception. Part 2: The contextual enhancement effect and some tests and extensions of the model. Psychological Review, 89, 6094.Google Scholar
Rumelhart, D. E., & McClelland, J. L. (1985). Levels indeed! Journal of Experimental Psychology General, 114(2), 193197.Google Scholar
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. In, D. E. Rumelhart, J. L. McClelland, , & the PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations (pp. 318362). Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Rumelhart, D. E., Hinton, G. E., & McClelland, J. L. (1986). A general framework for parallel distributed processing. In Rumelhart, D. E., McClelland, J. L., & the PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations (pp. 4576). Cambridge, MA: MIT Press.Google Scholar
Rumelhart, D. E., & McClelland, J. L. (1986). On learning the past tense of English verbs. In McClelland, J. L., Rumelhart, D. E., & the PDP Research Group (Eds.). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 2: Psychological and Biological Models (pp. 216271). Cambridge, MA: MIT Press.Google Scholar
Rumelhart, D. E., McClelland, J. L. & the PDP Research Group (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations. Cambridge, MA: MIT Press.Google Scholar
Rumelhart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. E. (1986). Schemata and sequential thought processes in PDP models. In, J. L. McClelland, D. E. Rumelhart, , & the PDP Research Group, Explorations in the Microstructure of Cognition Volume 2: Psychological and Biological Models (pp. 757). Cambridge, MA: MIT Press.Google Scholar
Sabatiel, S., McClelland, J. L., & Solstad, T. (2020). A computational model of learning to count in a multimodal, interactive environment. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.Google Scholar
Saffran, J. R., & Kirkham, N. Z. (2018). Infant statistical learning. Annual Review of Psychology, 69, 181203. https://doi.org/10.1146/annurev-psych-122216-011805Google Scholar
Saffran, J. R., Newport, E. L., & Aslin, R. N. (1996). Word segmentation: the role of distributional cues. Journal of Memory and Language, 35, 606621.Google Scholar
Scellier, B., & Bengio, Y. (2019). Equivalence of equilibrium propagation and recurrent backpropagation. Neural Computation, 31(2), 312329. https://doi.org/10.1162/neco_a_01160Google Scholar
Schmidhuber, J. (2015). Deep learning in neural networks: an overview. Neural Networks, 61, 85117. https://doi.org/10.1016/j.neunet.2014.09.003Google Scholar
Seidenberg, M. S. (1993). Connectionist models and cognitive theory. Psychological Science, 4(4), 228235.Google Scholar
Seidenberg, M. S. (2017). Language at the Speed of Sight. New York, NY: Basic Books.Google Scholar
Selfridge, O. G. (1959). Pandemonium: a paradigm for learning. In Symposium on the Mechanization of Thought Processes (pp. 511529). London: HMSO.Google Scholar
Shallice, T. (1988). From Neuropsychology to Mental Structure. Cambridge: Cambridge University Press.Google Scholar
Shultz, T. R. (2003). Computational Developmental Psychology. Cambridge, MA: MIT Press.Google Scholar
Smolensky, P. (1988). On the proper treatment of connectionism. Behavioral and Brain Sciences, 11, 174.Google Scholar
Spencer, H. (1872). Principles of Psychology (3rd ed.). London: Longman, Brown, Green, & Longmans.Google Scholar
Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 19291958.Google Scholar
Stoianov, I., & Zorzi, M. (2012). Emergence of a ‘visual number sense’ in hierarchical generative models. Nature Neuroscience, 15(2), 194196.Google Scholar
Storrs, K. R., & Kriegeskorte, N. (2019). Deep learning for cognitive neuroscience. arXiv:1903.01458v1Google Scholar
Sutton, R. S., & Barto, A. G. (1981). Toward a modern theory of adaptive networks: expectation and prediction. Psychological Review, 88(2), 135170.Google Scholar
Testolin, A., Zou, W. Y., & McClelland, J. L. (2020). Numerosity discrimination in deep neural networks: initial competence, developmental refinement and experience statistics. Developmental Science, 2020, e12940.Google Scholar
Thomas, M. S. C. (2016). Do more intelligent brains retain heightened plasticity for longer in development? A computational investigation. Developmental Cognitive Neuroscience, 19, 258269. https://doi.org/10.1016/j.dcn.2016.04.002Google Scholar
Thomas, M. S. C. (2018). A neurocomputational model of developmental trajectories of gifted children under a polygenic model: when are gifted children held back by poor environments? Intelligence, 69, 200212.Google Scholar
Thomas, M. S. C., & Brady, D. (2021). Quo vadis modularity in the 2020s? In Thomas, M. S. C., Mareschal, D., & Knowland, V. C. P. (Eds). Taking Development Seriously: A Festschrift for Annette Karmiloff-Smith. London: Routledge Psychology.Google Scholar
Thomas, M. S. C., Davis, R., Karmiloff-Smith, A., Knowland, V. C. P., & Charman, T. (2016). The over-pruning hypothesis of autism. Developmental Science, 9(2), 284305. https://doi.org/10.1111/desc.12303Google Scholar
Thomas, M. S. C., Fedor, A., Davis, R., Yang, J., Alireza, H., Charman, T., Masterson, J., & Best, W. (2019). Computational modelling of interventions for developmental disorders. Psychological Review, 26(5), 693726. https://doi.org/10.1037/rev0000151Google Scholar
Thomas, M. S. C., Forrester, N. A., & Richardson, F. M. (2006). What is modularity good for? In Proceedings of The 28th Annual Conference of the Cognitive Science Society (pp. 22402245), July 2629, Vancouver, BC, Canada.Google Scholar
Thomas, M. S. C., Forrester, N. A., & Ronald, A. (2013). Modeling socioeconomic status effects on language development. Developmental Psychology, 49(12), 23252343. https://doi.org/10.1037/a0032301Google Scholar
Thomas, M. S. C., Forrester, N. A., & Ronald, A. (2016). Multi-scale modeling of gene-behavior associations in an artificial neural network model of cognitive development. Cognitive Science, 40(1), 5199. https://doi.org/10.1111/cogs.12230Google Scholar
Thomas, M. S. C., & Karmiloff-Smith, A. (2002a). Are developmental disorders like cases of adult brain damage? Implications from connectionist modelling. Behavioral and Brain Sciences, 25(6), 727788.Google Scholar
Thomas, M. S. C., & Karmiloff-Smith, A. (2002b). Modelling typical and atypical cognitive development. In Goswami, U. (Ed.), Handbook of Childhood Development (pp. 575599). Oxford: Blackwell.Google Scholar
Thomas, M. S. C., & Karmiloff-Smith, A. (2003a). Modeling language acquisition in atypical phenotypes. Psychological Review, 110(4), 647682.Google Scholar
Thomas, M. S. C., & Karmiloff-Smith, A. (2003b). Connectionist models of development, developmental disorders and individual differences. In Sternberg, R. J., Lautrey, J., & Lubart, T. (Eds.), Models of Intelligence: International Perspectives, (pp. 133150). Washington, DC: American Psychological Association.Google Scholar
Thomas, M. S. C., & Knowland, V. C. P. (2014). Modelling mechanisms of persisting and resolving delay in language development. Journal of Speech, Language, and Hearing Research, 57(2), 467483. https://doi.org/10.1044/2013_JSLHR-L-12-0254Google Scholar
Thomas, M. S. C., & Van Heuven, W. (2005). Computational models of bilingual comprehension. In Kroll, J. F. & De Groot, A. M. B. (Eds.), Handbook of Bilingualism: Psycholinguistic Approaches (pp. 202225). Oxford: Oxford University Press.Google Scholar
Touretzky, D. S., & Hinton, G. E. (1988). A distributed connectionist production system. Cognitive Science, 12, 423466.Google Scholar
Tovar, A., Westermann, G., & Torres, A. (2017). From altered LTP/LTD to atypical learning: a computational model of Down syndrome. Cognition, 171, 1524. https://doi.org/10.1016/j.cognition.2017.10.021Google Scholar
Ueno, T., Saito, S., Rogers, T. T., & Lambon Ralph, M. A. (2011). Lichtheim 2: synthesizing aphasia and the neural basis of language in a neurocomputational model of the dual dorsal-ventral language pathways. Neuron, 72(2), 385396. https://doi.org/10.1016/j.neuron.2011.09.013Google Scholar
Usher, M., & McClelland, J. L. (2001). On the time course of perceptual choice: the leaky competing accumulator model. Psychological Review, 108, 550592.Google Scholar
van Gelder, T. (1991). Classical questions, radical answers: connectionism and the structure of mental representations. In Horgan, T. & Tienson, J. (Eds.), Connectionism and the Philosophy of Mind. Dordrecht: Kluwer Academic Publishers.Google Scholar
Verguts, T., & Fias, W. (2004). Representation of number in animals and humans: a neural model. Journal of Cognitive Neuroscience, 16(9), 14931504. https://doi.org/10.1162/0898929042568497Google Scholar
Westermann, G., Mareschal, D., Johnson, M. H., Sirois, S., Spratling, M. W., & Thomas, M. S. C. (2007). Neuroconstructivism. Developmental Science, 10, 7583.Google Scholar
Westermann, G., Thomas, M. S. C., & Karmiloff-Smith, A. (2010). Neuroconstructivism. In Goswami, U. (Ed.), Blackwell Handbook of Child Development (2nd ed.), (pp. 723748). Oxford: Blackwell.Google Scholar
Williams, R. J., & Zipser, D. (1995). Gradient-based learning algorithms for recurrent networks and their computational complexity. In Chauvin, Y. & Rumelhart, D. E. (Eds.), Back-propagation: Theory, Architectures and Applications. Hillsdale, NJ: Erlbaum.Google Scholar
Woollams, A. M. (2014). Connectionist neuropsychology: uncovering ultimate causes of acquired dyslexia. Philosophical Transactions of the Royal Society B, 369(1634), https://doi.org/10.1098/rstb.2012.0398Google Scholar
Wu, Y., Schuster, M., Chen, Z., et al. (2016). Google’s neural machine translation system: bridging the gap between human and machine translation. Available from: https://arxiv.org/abs/1609.08144 [last accessed August 9, 2022].Google Scholar
Xie, X., & Seung, H. S. (2003). Equivalence of backpropagation and contrastive Hebbian learning in a layered network. Neural Computation, 15, 441454.Google Scholar
Xu, F., & Pinker, S. (1995). Weird past tense forms. Journal of Child Language, 22, 531556.Google Scholar
Yamins, D. L., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences, 111(23), 86198624.Google Scholar

References

Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985). A learning algorithm for Boltzmann machines. Cognitive Science, 9, 147169.Google Scholar
Anderson, J. R. (1990). The Adaptive Character of Thought. Hillsdale, NJ: Erlbaum.Google Scholar
Ashby, F. G., & Alfonso-Reese, L. A. (1995). Categorization as probability density estimation. Journal of Mathematical Psychology, 39, 216233.Google Scholar
Atran, S. (1998). Folk biology and the anthropology of science: cognitive universals and cultural particulars. Behavioral and Brain Sciences, 21, 547609.Google Scholar
Bayes, T. (1763/1958). Studies in the history of probability and statistics: IX. Thomas Bayes’s essay towards solving a problem in the doctrine of chances. Biometrika, 45, 296315.Google Scholar
Bernardo, J. M., & Smith, A. F. M. (1994). Bayesian Theory. New York, NY: Wiley.Google Scholar
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York, NY: Springer.Google Scholar
Blei, D., Griffiths, T., Jordan, M., & Tenenbaum, J. (2004). Hierarchical topic models and the nested Chinese restaurant process. In Thrun, S., Saul, L. K., & Schölkopf, B. (Eds.), Advances in Neural Information Processing Systems 16. Cambridge, MA: MIT Press.Google Scholar
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 9931022.Google Scholar
Boas, M. L. (1983). Mathematical Methods in the Physical Sciences (2nd ed.). New York, NY: Wiley.Google Scholar
Bonawitz, E., Denison, S., Griffiths, T. L., & Gopnik, A. (2014). Probabilistic models, learning algorithms, and response variability: sampling in cognitive development. Trends in Cognitive Sciences, 18(10), 497500.Google Scholar
Bowers, J. S., & Davis, C. J. (2012). Bayesian just-so stories in psychology and neuroscience. Psychological Bulletin, 138, 389414.Google Scholar
Brainard, D. H., & Freeman, W. T. (1997). Bayesian color constancy. Journal of the Optical Society of America A, 14, 13931411.Google Scholar
Buehner, M., & Cheng, P. W. (1997). Causal induction: the Power PC theory versus the Rescorla-Wagner theory. In Shafto, M. & Langley, P. (Eds.), Proceedings of the 19th Annual Conference of the Cognitive Science Society (pp. 5561). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Buehner, M. J., Cheng, P. W., & Clifford, D. (2003). From covariation to causation: a test of the assumption of causal power. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 11191140.Google Scholar
Carey, S. (1985). Conceptual Change in Childhood. Cambridge, MA: MIT Press.Google Scholar
Charniak, E. (1993). Statistical Language Learning. Cambridge, MA: MIT Press.Google Scholar
Chater, N., Zhu, J.-Q., Spicer, J., Sundh, J., León-Villagrá, P., & Sanborn, A. (2020). Probabilistic biases meet the Bayesian brain. Current Directions in Psychological Science, 29(5), 506512.Google Scholar
Cheng, P. (1997). From covariation to causation: a causal power theory. Psychological Review, 104, 367405.Google Scholar
Clark, A. (2015). Surfing Uncertainty: Prediction, Action, and the Embodied Mind. Oxford: Oxford University Press.Google Scholar
Collins, A. M., & Loftus, E. F. (1975). A spreading activation theory of semantic processing. Psychological Review, 82, 407428.Google Scholar
Collins, A. M., & Quillian, M. R. (1969). Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behaviour, 8, 240247.Google Scholar
Dasgupta, I., Schulz, E., Tenenbaum, J. B., & Gershman, S. J. (2020). A theory of learning to infer. Psychological Review, 127(3), 412.Google Scholar
Davis, Z. J., Bramley, N. R., & Rehder, B. (2020). Causal structure learning in continuous systems. Frontiers in Psychology, 11, 244.Google Scholar
Denison, S., Bonawitz, E., Gopnik, A., & Griffiths, T. L. (2013). Rational variability in children’s causal inferences: the sampling hypothesis. Cognition, 126(2), 285300.Google Scholar
Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern Classification. New York, NY: Wiley.Google Scholar
Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. arXiv preprint arXiv:1703.03400Google Scholar
Friedman, N., & Koller, D. (2000). Being Bayesian about network structure. In Proceedings of the 16th Annual Conference on Uncertainty in AI (pp. 201210). Stanford, CA.Google Scholar
Friston, K., & Dolan, R. J. (2017). Computational psychiatry and the Bayesian brain. In Charney, D. S., Nestler, E. J., & Pamela Sklar, M. (Eds.), Charney & Nestler’s Neurobiology of Mental Illness. Oxford: Oxford University Press.Google Scholar
Froyen, V., Feldman, J., & Singh, M. (2015). Bayesian hierarchical grouping: perceptual grouping as mixture estimation. Psychological Review, 122(4), 575.Google Scholar
Gal, Y., & Ghahramani, Z. (2016). Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In the International Conference on Machine Learning (pp. 10501059).Google Scholar
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995). Bayesian Data Analysis. New York, NY: Chapman & Hall.Google Scholar
Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721741.Google Scholar
Gershman, S., Vul, E., & Tenenbaum, J. (2009). Perceptual multistability as Markov chain Monte Carlo inference. Advances in Neural Information Processing Systems, 22, 611619.Google Scholar
Gershman, S. J., Horvitz, E. J., & Tenenbaum, J. B. (2015). Computational rationality: a converging paradigm for intelligence in brains, minds, and machines. Science, 349(6245), 273278.Google Scholar
Ghahramani, Z. (2004). Unsupervised learning. In Bousquet, O., Raetsch, G., & von Luxburg, U. (Eds.), Advanced Lectures on Machine Learning. Berlin: Springer-Verlag.Google Scholar
Gigerenzer, G., Swijtink, Z., Porter, T., Daston, L., Beatty, J., & Kruger, L. (1989). The Empire of Chance. Cambridge: Cambridge University Press.Google Scholar
Gilks, W., Richardson, S., & Spiegelhalter, D. J. (Eds.). (1996). Markov Chain Monte Carlo in Practice. Suffolk: Chapman and Hall.Google Scholar
Glassen, T., & Nitsch, V. (2016). Hierarchical Bayesian models of cognitive development. Biological Cybernetics, 110(2–3), 217227.Google Scholar
Glymour, C. (2001). The Mind’s Arrows: Bayes Nets and Graphical Causal Models in Psychology. Cambridge, MA: MIT Press.Google Scholar
Glymour, C., & Cooper, G. (1999). Computation, Causation, and Discovery. Cambridge, MA: MIT Press.Google Scholar
Goldstein, H. (2003). Multilevel Statistical Models (3rd ed.). London: Hodder Arnold.Google Scholar
Good, I. J. (1980). Some history of the hierarchical Bayesian methodology. In Bernardo, J. M., DeGroot, M. H., Lindley, D. V., & Smith, A. F. M. (Eds.), Bayesian Statistics (pp. 489519). Valencia: Valencia University Press.Google Scholar
Goodman, N. D., & Frank, M. C. (2016). Pragmatic language interpretation as probabilistic inference. Trends in Cognitive Sciences, 20(11), 818829.Google Scholar
Goodman, N. D., Frank, M. C., Griffiths, T. L., Tenenbaum, J. B., Battaglia, P. W., & Hamrick, J. B. (2015). Relevant and robust: a response to Marcus and Davis (2013). Psychological Science, 26(4), 539541.Google Scholar
Goodman, N. D., Ullman, T. D., & Tenenbaum, J. B. (2011). Learning a theory of causality. Psychological Review, 118, 110119.Google Scholar
Gopnik, A., & Meltzoff, A. N. (1997). Words, Thoughts, and Theories. Cambridge, MA: MIT Press.Google Scholar
Grant, E., Finn, C., Levine, S., Darrell, T., & Griffiths, T. (2018). Recasting gradient-based meta-learning as hierarchical bayes. arXiv preprint arXiv:1801.08930Google Scholar
Griffiths, T. L. (2020). Understanding human intelligence through human limitations. Trends in Cognitive Sciences, 24(11), 873883.Google Scholar
Griffiths, T. L., Chater, N., Norris, D., & Pouget, A. (2012). How the Bayesians got their beliefs (and what those beliefs actually are): comment on Bowers and Davis (2012). Psychological Bulletin, 138(3), 415422.Google Scholar
Griffiths, T. L., Kemp, C., & Tenenbaum, J. B. (2008). Bayesian models of cognition. In Sun, R. (Ed.), Cambridge Handbook of Computational Cognitive Modeling. Cambridge: Cambridge University Press.Google Scholar
Griffiths, T. L., Lieder, F., & Goodman, N. D. (2015). Rational use of cognitive resources: levels of analysis between the computational and the algorithmic. Topics in Cognitive Science, 7(2), 217229.Google Scholar
Griffiths, T. L., & Pacer, M. (2011). A rational model of causal inference with continuous causes. In Leen, T. K. (Ed.), Advances in Neural Information Processing Systems (pp. 23842392). Cambridge, MA: MIT Press.Google Scholar
Griffiths, T. L., & Steyvers, M. (2002). A probabilistic approach to semantic representation. In Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Erlbaum.Google Scholar
Griffiths, T. L., & Steyvers, M. (2003). Prediction and semantic association. In Becker, S., Thrun, S., & Obermayer, K. (Eds.), Neural Information Processing Systems 15. Cambridge, MA: MIT Press.Google Scholar
Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Science, 101, 52285235.Google Scholar
Griffiths, T. L., Steyvers, M., Blei, D. M., & Tenenbaum, J. B. (2005). Integrating topics and syntax. In Saul, L. K., Weiss, Y., & Bottou, L. (Eds.), Advances in Neural Information Processing Systems 17. Cambridge, MA: MIT Press.Google Scholar
Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114, 211244.Google Scholar
Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51, 354384.Google Scholar
Griffiths, T. L., & Tenenbaum, J. B. (2009). Theory-based causal induction. Psychological Review, 116, 661716.Google Scholar
Griffiths, T. L., Vul, E., & Sanborn, A. N. (2012). Bridging levels of analysis for probabilistic models of cognition. Current Directions in Psychological Science, 21, 263268.Google Scholar
Hacking, I. (1975). The Emergence of Probability. Cambridge: Cambridg University Press.Google Scholar
Hagmayer, Y., & Mayrhofer, R. (2013). Hierarchical Bayesian models as formal models of causal reasoning. Argument & Computation, 4(1), 3645.Google Scholar
Hagmayer, Y., Sloman, S. A., Lagnado, D. A., & Waldmann, M. R. (2007). Causal reasoning through intervention. In A. Gopnik & L. Schulz (Eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford: Oxford University Press.Google Scholar
Hahn, U., & Oaksford, M. (2007). The rationality of informal argumentation: a Bayesian approach to reasoning fallacies. Psychological Review, 114(3), 704732.Google Scholar
Hastings, W. K. (1970). Monte Carlo methods using Markov chains and their applications. Biometrika, 57, 97109.Google Scholar
Heckerman, D. (1998). A tutorial on learning with Bayesian networks. In Jordan, M. I. (Ed.), Learning in Graphical Models (pp. 301354). Cambridge, MA: MIT Press.Google Scholar
Heibeck, T., & Markman, E. (1987). Word learning in children: an examination of fast mapping. Child Development, 58, 10211024.Google Scholar
Hofmann, T. (1999). Probabilistic latent semantic indexing. In Proceedings of the Twenty-Second Annual International SIGIR Conference.Google Scholar
Holyoak, K. J., & Cheng, P. W. (2011). Causal learning and inference as a rational process: the new synthesis. Annual Review of Psychology, 62, 135163.Google Scholar
Horvitz, E. J. (1990). Rational metareasoning and compilation for optimizing decisions under bounded resources (Tech. Rep.). Knowledge Systems Laboratory, Medical Computer Science, Stanford University, CA.Google Scholar
Huelsenbeck, J. P., & Ronquist, F. (2001). MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics, 17(8), 754755.Google Scholar
Jeffreys, W. H., & Berger, J. O. (1992). Ockham’s razor and Bayesian analysis. American Scientist, 80(1), 6472.Google Scholar
Jenkins, H. M., & Ward, W. C. (1965). Judgment of contingency between responses and outcomes. Psychological Monographs, 79(1), 117.Google Scholar
Jurafsky, D., & Martin, J. H. (2000). Speech and Language Processing. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773795.Google Scholar
Kemp, C., Perfors, A., & Tenenbaum, J. B. (2004). Learning domain structures. In Proceedings of the 26th Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Erlbaum.Google Scholar
Kemp, C., Perfors, A., & Tenenbaum, J. B. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10(3), 307321.Google Scholar
Kemp, C., & Tenenbaum, J. B. (2003). Theory-based induction. In Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.Google Scholar
Kemp, C., Tenenbaum, J. B., Niyogi, S., & Griffiths, T. L. (2010). A probabilistic model of theory formation. Cognition, 114, 165196.Google Scholar
Korb, K., & Nicholson, A. (2010). Bayesian Artificial Intelligence (2nd ed.). Boca Raton, FL: Chapman and Hall/CRC.Google Scholar
Lagnado, D., & Sloman, S. A. (2004). The advantage of timely intervention. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 856876.Google Scholar
Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: the Latent Semantic Analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104, 211240.Google Scholar
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436444.Google Scholar
Lee, M. D. (2006). A hierarchical Bayesian model of human decision-making on an optimal stopping problem. Cognitive Science, 30, 555580.Google Scholar
Lieder, F., & Griffiths, T. L. (2020). Resource-rational analysis: understanding human cognition as the optimal use of limited computational resources. Behavioral and Brain Sciences, 43, e1.Google Scholar
Lieder, F., Griffiths, T. L., & Hsu, M. (2018). Overrepresentation of extreme events in decision making reflects rational use of cognitive resources. Psychological review, 125(1), 1.Google Scholar
Lieder, F., Griffiths, T. L., Huys, Q. J., & Goodman, N. D. (2018). The anchoring bias reflects rational use of cognitive resources. Psychonomic Bulletin & Review, 25(1), 322349.Google Scholar
Lu, H., Rojas, R. R., Beckers, T., & Yuille, A. L. (2016). A Bayesian theory of sequential causal learning and abstract transfer. Cognitive Science, 40(2), 404439.Google Scholar
Lu, H., Yuille, A., Liljeholm, M., Cheng, P. W., & Holyoak, K. J. (2006). Modeling causal learning using Bayesian generic priors on generative and preventive powers. In Sun, R. & Miyake, N. (Eds.), Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society (pp. 519524). Mahwah, NJ: Erlbaum.Google Scholar
Lu, H., Yuille, A., Liljeholm, M., Cheng, P. W., & Holyoak, K. J. (2007). Bayesian models of judgments of causal strength: a comparison. In McNamara, D. S. & Trafton, G. (Eds.), Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society (pp. 12411246). Mahwah, NJ: Erlbaum.Google Scholar
Lu, H., Yuille, A. L., Liljeholm, M., Cheng, P. W., & Holyoak, K. J. (2008). Bayesian generic priors for causal learning. Psychological Review, 115(4), 955984.Google Scholar
Lucas, C. G., & Griffiths, T. L. (2010). Learning the form of causal relationships using hierarchical Bayesian models. Cognitive Science, 34, 113147.Google Scholar
Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instrumentation, and Computers, 28, 203208.Google Scholar
Mackay, D. J. C. (2003). Information Theory, Inference, and Learning Algorithms. Cambridge: Cambridge University Press.Google Scholar
Mandelbaum, E. (2019). Troubles with Bayesianism: an introduction to the psychological immune system. Mind & Language, 34(2), 141157.Google Scholar
Manning, C., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press.Google Scholar
Mansinghka, V. K., Kemp, C., Tenenbaum, J. B., & Griffiths, T. L. (2006). Structured priors for structure learning. In Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (UAI).Google Scholar
Marcus, G. F., & Davis, E. (2013). How robust are probabilistic models of higher-level cognition? Psychological Science, 24(12), 23512360.Google Scholar
Marr, D. (1982). Vision. San Francisco, CA: W. H. Freeman.Google Scholar
Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207238.Google Scholar
Metropolis, A. W., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equations of state calculations by fast computing machines. Journal of Chemical Physics, 21, 10871092.Google Scholar
Minka, T., & Lafferty, J. (2002). Expectation-Propagation for the generative aspect model. In Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence (UAI). San Francisco, CA: Morgan Kaufmann.Google Scholar
Myung, I. J., Forster, M. R., & Browne, M. W. (2000). Model selection [special issue]. Journal of Mathematical Psychology, 44, 1–2.Google Scholar
Myung, I. J., & Pitt, M. A. (1997). Applying Occam’s razor in modeling cognition: a Bayesian approach. Psychonomic Bulletin and Review, 4, 7995.Google Scholar
Navarro, D. J., & Kemp, C. (2017). None of the above: a Bayesian account of the detection of novel categories. Psychological Review, 124(5), 643677.Google Scholar
Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo methods (Tech. Rep. No. CRG-TR-93-1). Toronto, University of Toronto.Google Scholar
Nelson, D. L., McEvoy, C. L., & Schreiber, T. A. (1998). The university of south florida word association, rhyme, and word fragment norms. Available from: http://w3.usf.edu/FreeAssociation/ [last accessed August 9, 2022].Google Scholar
Newman, M. E. J., & Barkema, G. T. (1999). Monte Carlo Methods in Statistical Physics. Oxford: Clarendon Press.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(4), 339363.Google Scholar
Norris, D., & McQueen, J. M. (2008). Shortlist B: a Bayesian model of continuous speech recognition. Psychological Review, 115(2), 357.Google Scholar
Norris, J. R. (1997). Markov Chains. Cambridge: Cambridge University Press.Google Scholar
Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 3957.Google Scholar
Nosofsky, R. M. (1998). Optimal performance and exemplar models of classification. In Oaksford, M. & Chater, N. (Eds.), Rational Models of Cognition (pp. 218247). Oxford: Oxford University Press.Google Scholar
Osherson, D. N., Smith, E. E., Wilkie, O., Lopez, A., & Shafir, E. (1990). Category-based induction. Psychological Review, 97(2), 185200.Google Scholar
Pacer, M., & Griffiths, T. L. (2012). Elements of a rational framework for continuous-time causal induction. In Proceedings of the 34th Annual Conference of the Cognitive Science Society.Google Scholar
Pacer, M. D., & Griffiths, T. L. (2015). Upsetting the contingency table: causal induction over sequences of point events. In Proceedings of the 37th Annual Conference of the Cognitive Science Society.Google Scholar
Pajak, B., Fine, A. B., Kleinschmidt, D. F., & Jaeger, T. F. (2016). Learning additional languages as hierarchical probabilistic inference: insights from first language processing. Language Learning, 66(4), 900944.Google Scholar
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. San Francisco, CA: Morgan Kaufmann.Google Scholar
Pearl, J. (2000). Causality: Models, Reasoning and Inference. Cambridge: Cambridge University Press.Google Scholar
Pearl, J. (2018). The Book of Why: The New Science of Cause and Effect. New York, NY: Basic Books.Google Scholar
Pitman, J. (1993). Probability. New York, NY: Springer-Verlag.Google Scholar
Reed, S. K. (1972). Pattern recognition and categorization. Cognitive Psychology, 3, 393407.Google Scholar
Rice, J. A. (1995). Mathematical Statistics and Data Analysis (2nd ed.). Belmont, CA: Duxbury.Google Scholar
Rips, L. J. (1975). Inductive judgments about natural categories. Journal of Verbal Learning and Verbal Behavior, 14, 665681.Google Scholar
Russell, S. (1988). Analogy by similarity. In Helman, D. H. (Ed.), Analogical Reasoning (pp. 251269). New York, NY: Kluwer Academic Publishers.Google Scholar
Russell, S. J., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Saddle River, NJ: Pearson.Google Scholar
Sanborn, A. N., & Chater, N. (2016). Bayesian brains without probabilities. Trends in Cognitive Sciences, 20(12), 883893.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, 11441167.Google Scholar
Shen, S., & Ma, W. J. (2016). A detailed comparison of optimality and simplicity in perceptual decision making. Psychological Review, 123(4), 452480.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 and Review, 17, 443464.Google Scholar
Shiffrin, R. M., & Steyvers, M. (1997). A model for recognition memory: REM: Retrieving Effectively from Memory. Psychonomic Bulletin & Review, 4, 145166.Google Scholar
Sloman, S. (2005). Causal Models: How People Think About the World and Its Alternatives. Oxford: Oxford University Press.Google Scholar
Smith, L. B., Jones, S. S., Landau, B., Gershkoff-Stowe, L., & Samuelson, L. (2002). Object name learning provides on-the-job training for attention. Psychological Science, 13(1), 1319.Google Scholar
Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation Prediction and Search. New York, NY: Springer-Verlag.Google Scholar
Steyvers, M., Tenenbaum, J. B., Wagenmakers, E. J., & Blum, B. (2003). Inferring causal networks from observations and interventions. Cognitive Science, 27, 453489.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(4), 410441.Google Scholar
Tenenbaum, J. B., & Griffiths, T. L. (2001). Structure learning in human causal induction. In Leen, T., Dietterich, T., & Tresp, V. (Eds.), Advances in Neural Information Processing Systems 13 (pp. 5965). Cambridge, MA: MIT Press.Google Scholar
Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Science, 10, 309318.Google Scholar
Ullman, T. D., & Tenenbaum, J. B. (2020). Bayesian models of conceptual development: learning as building models of the world. Annual Review of Developmental Psychology, 2, 533558.Google Scholar
Vul, E., Goodman, N., Griffiths, T. L., & Tenenbaum, J. B. (2014). One and done? Optimal decisions from very few samples. Cognitive Science, 38(4), 599637.Google Scholar
Wellman, H. M., & Gelman, S. A. (1992). Cognitive development: foundational theories of core domains. Annual Review of Psychology, 43, 337375.Google Scholar
Xu, F., & Kushnir, T. (2013). Infants are rational constructivist learners. Current Directions in Psychological Science, 22(1), 2832.Google Scholar
Yeung, S., & Griffiths, T. L. (2015). Identifying expectations about the strength of causal relationships. Cognitive Psychology, 76, 129.Google Scholar
Yu, A. J. (2014). Bayesian models of attention. In Nobre, K., Nobre, A. C., & Kastner, S. (Eds.), The Oxford Handbook of Attention. Oxford: Oxford University Press.Google Scholar

References

Aamodt, A., & Plaza, E. (1994). Case-based reasoning: foundational issues, methodological variations, and system approaches. Artificial Intelligence Communications, 7(1), 3952.Google Scholar
Agre, P., & Chapman, D. (1990). What are plans for? In Maes, P. (Ed.), Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back. Cambridge, MA: MIT Press.Google Scholar
Anderson, J., & Lebiere, C. (1998). The Atomic Concepts of Thought. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Aristotle, (1989). Prior Analytics, translated by Robin Smith. Indianapolis, IN: Hackett.Google Scholar
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., et al. (2020). Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82115.Google Scholar
Avni, A., Bar-Eli, M., & Tenenbaum, G. (1990). Assessment and calculation in top chess players’ decision-making during competition: a theoretical model. Psychological Reports, 67(3), 899906.Google Scholar
Baader, F., Calvanese, D., McGuinness, D., Nardi, D., & Patel-Schneider, P. (2003). The Description Logic Handbook: Theory, Implementation, Applications. Cambridge: Cambridge University Press.Google Scholar
Baader, F., Horrocks, I., & Sattler, U. (2007). Description logics. In Van Harmelen, F., Lifschitz, V., & Porter, B. (Eds.), Handbook of Knowledge Representation. Abingdon: Elsevier.Google Scholar
Baader, F., & Nipkow, T. (1998). Term Rewriting and All That. Cambridge: Cambridge University Press.Google Scholar
Baader, F., & Nutt, W. (2003). Basic description logic. In Baader, F. et al. (Eds.), The Handbook of Description Logic: Theory, Implementation, and Applications. Cambridge: Cambridge University Press.Google Scholar
Bach, J. (2009). Principles of Synthetic Intelligence. An Architecture for Motivated Cognition. New York, NY: Oxford University Press.Google Scholar
Baker, C., Saxe, R., & Tenenbaum, J. (2011). Bayesian theory of mind: modeling joint belief-desire attribution. In Proceedings of the Thirty-third Annual Meeting of the Cognitive Science Society, Boston, MA.Google Scholar
Bechtel, W., Abrahamsen, A., & Graham, G. (2001). Cognitive science: history. In Smelser, N. & Baltes, P. (Eds.), International Encyclopedia of the Social & Behavioral Sciences (pp. 2154–2158). Abingdon: Elsevier.Google Scholar
Berners-Lee, T., Hendler, J., & Lassila, Ora (2001). The Semantic Web: a new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. Scientific American, 284(5), 3443.Google Scholar
Berov, L. (2017). Steering plot through personality and affect: an extended BDI model of fictional characters. In Kern-Isberner, G., Fürnkranz, J., & Thimm, M. (Eds.), KI 2017. Lecture Notes in Computer Science, Volume 10505. London: Springer. https://doi.org/10.1007/978-3-319-67190-1_23Google Scholar
Besold, T., Garcez, A., Bader, S., et al. (2022). Neural-symbolic learning and reasoning: a survey and interpretation. In Hitzler, P. & Sarker, K. (Eds.), Neuro-Symbolic Artificial Intelligence: The State of the Art. Amsterdam: IOS Press.Google Scholar
Besold, T., Kühnberger, K.-U., & Plaza, E. (2017). Towards a computational and algorithmic-level account of concept blending using analogies and amalgams. Connection Science, 29(4), 387413. https://doi.org/10.1080/09540091.2017.1326463Google Scholar
Bibel, W. (1993). Wissensrepräsentation und Inferenz: Eine grundlegende Einführung. Braunschweig, Wiesbaden: Vieweg Verlagsgesellschaft.Google Scholar
Bordini, R., Hubner, J., & Wooldridge, M. (2007). Programming Multi-Agent Systems in AgentSpeak Using Jason. Oxford: John Wiley & Sons.Google Scholar
Brachman, R., & Schmolze, J. (1985). An overview of the KL-ONE Knowledge Representation System. Cognitive Science, 9(2), 171216.Google Scholar
Bratko, I. (2012). Prolog Programming for Artificial Intelligence (4th ed.). Harlow: Addison-Wesley.Google Scholar
Bredeweg, B., & Struss, P. (2004). Current topics in qualitative reasoning. AI Magazine, 24(4).Google Scholar
Breiman, L., Friedman, J., Olshen, R. & Stone, C. (1984). Classification and Regression Trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software.Google Scholar
Brooks, R. (1999). Cambrian Intelligence: The Early History of the New AI. Cambridge, MA: MIT Press.Google Scholar
Brown, T., Mann, B., Ryder, N., et al. (2020). Language models are few-shot learners. ArXiv200514165 CsGoogle Scholar
Byrne, J. (2020). Learning and memory. In Neuroscience Online, the Open-Access Neuroscience Electronic Textbook. Available from: https://nba.uth.tmc.edu/neuroscience/m/index.htm [last accessed June 8, 2022].Google Scholar
Chater, N., Oaksford, M., Hahn, U., & Heit, E. (2010). Bayesian models of cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 1(6), 811823.Google Scholar
Chomsky, N. (1957). Syntactic Structures. The Hague/Paris: Mouton.Google Scholar
Chomsky, N. (1980a). On cognitive structures and their development: a reply to Piaget. In Piattelli-Palmarini, M. (Ed.), Language and Learning: The Debate between Jean Piaget and Noam Chomsky. Cambridge, MA: Harvard University Press.Google Scholar
Chomsky, N. (1980b). Rules and Representations. New York, NY: Blackwell.Google Scholar
Chomsky, N. (1981). Lectures on Government and Binding. Bonn: Foris Publications.Google Scholar
Confalonieri, R., Weyde, T., Besold, T. R., & del Prado Martín, F. M. (2021). Using ontologies to enhance human understandability of global post-hoc explanations of black-box models. Artificial Intelligence, 296, 103471.Google Scholar
Cropper, A., Dumancic, S., & Muggleton, S. (2020). Turning 30: new ideas in inductive logic programming. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Japan.Google Scholar
Davis, E., & Marcus, G. (2015). Commonsense reasoning and commonsense knowledge in artificial intelligence. Communications of the ACM, 58(9), 92103.Google Scholar
De Raedt, L., & Kersting, K. (2008). Probabilistic inductive logic programming. In De Raedt, L., Frasconi, P., Kersting, K., & Muggleton, S. (Eds.), Probabilistic Logic Programming – Theory and Applications (pp. 127). Berlin: Springer.Google Scholar
Donadello, I., Serafini, L., & Garcez, A. (2017). Logic tensor networks for semantic image interpretation. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence IJCAI (2017) (pp. 1596–1602).Google Scholar
Falkenhainer, B., Forbus, K., & Gentner, D. (1989). The structure-mapping engine: algorithm and examples. Artificial Intelligence, 41, 163.Google Scholar
Fauconnier, G., & Turner, M. (2003). The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities. New York, NY: Basic Books.Google Scholar
Fensel, D., van Harmelen, F., Andersson, B., et al. (2008 ). Towards LarKC: a platform for web-scale reasoning. In Proceedings of the 2008 IEEE International Conference on Semantic Computing ICSC, Santa Monica, CA.Google Scholar
Ferrucci, D., Levas, A., Bagchi, S., Gondek, D., & Mueller, E. (2013). Watson: beyond jeopardy!. Artificial Intelligence, 199, 93105.Google Scholar
Fillmore, C. J. (1976). Frame semantics and the nature of language. In Annals of the New York Academy of Sciences: Conference on the Origin and Development of Language and Speech, Vol. 280, pp. 2032.Google Scholar
Fincham, J. M., Anderson, H. S., & Anderson, J. R. (2020). Spatiotemporal analysis of event-related fMRI to reveal cognitive states. Human Brain Mapping, 41, 666–683. https://doi.org/10.1002/hbm.24831.Google Scholar
Flener, P., & Schmid, U. (2010). Inductive programming. In Sammut, C. & Webb, G. (Eds.), Encyclopedia of Machine Learning (pp. 537544). Berlin: Springer.Google Scholar
Fodor, J. (1981). Representations. Cambridge, MA: MIT Press.Google Scholar
Frege, G. (1879). Begriffsschrift. Eine der arithmetischen nachgebildete Formelsprache des reinen Denkens. Halle.Google Scholar
Garcez, A. S. D. A., Lamb, L. C., & Gabbay, D. M. (2007). Connectionist modal logic: representing modalities in neural networks. Theoretical Computer Science, 371(1–2), 3453.Google Scholar
Getoor, L., & Taskar, B. (2007). Introduction to Statistical Relational Learning. Cambridge, MA: MIT Press.Google Scholar
Guarino, N., Oberle, D., & Staab, S. (2009). What is an ontology? In Staab, S. and R.Studer, (Eds.), Handbook on Ontologies (pp. 1–17). Berlin: Springer. https://doi.org/10.1007/978-3-540-92673-3_0Google Scholar
Gulwani, S., Hernández-Orallo, J., Kitzelmann, E., Muggleton, S. H., Schmid, U., & Zorn, B. (2015). Inductive programming meets the real world. Communications of the ACM, 58(11), 9099.Google Scholar
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G. Z. (2019). XAI: explainable artificial intelligence. Science Robotics, 4(37), eaay7120.Google Scholar
Halstead, D. T. (2011). Statistical relational learning through structural analogy and probabilistic generalization. Doctoral dissertation, Northwestern University.Google Scholar
Heim, I., & Kratzer, A. (1998). Semantics in Generative Grammar. Oxford: Wiley-Blackwell.Google Scholar
Heim, S. (2007). The Resonant Interface. HCI Foundations for Interaction Design. London: Addison Wesley Publishing Company.Google Scholar
Hitzler, P., Krötzsch, M., & Rudolph, S. (2009). Foundations of Semantic Web Technologies. London: Chapman & Hall/CRC.Google Scholar
Hofmann, J., Kitzelmann, E., & Schmid, U. (2014). Applying inductive program synthesis to induction of number series a case study with IGOR2. In Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz) (pp. 2536). Cham: Springer.Google Scholar
Horrocks, I., & Patel-Schneider, P. (2004). Reducing OWL entailment to description logic satisfiability. Journal of Web Semantics, 1(4). http://dx.doi.org/10.2139/ssrn.3199027Google Scholar
Howard, R. (1960). Dynamic Programming and Markov Processes. Cambridge, MA: MIT Press.Google Scholar
Jara-Ettinger, J., Gweon, H., Tenenbaum, J. B., & Schulz, L. E. (2015). Children’s understanding of the costs and rewards underlying rational action. Cognition, 140, 1423.Google Scholar
Kamp, H., & Reyle, U. (1993). From Discourse to Logic: Introduction to Modeltheoretic Semantics of Natural Language, Formal Logic and Discourse Representation Theory. Dordrecht: Kluwer Academic Publishers.Google Scholar
Kaplan, R., & Bresnan, J. (1982). Lexical-functional grammar: a formal system for grammatical representation. In Bresnan, J. (Ed.), The Mental Representation of Grammatical Relations (pp. 173–281). Cambridge, MA: MIT Press.Google Scholar
Kifer, M., & Lausen, G. (1989). F-logic: a higher-order language for reasoning about objects, inheritance, and scheme. ACM SIGMOD, 18(2), 134146. https://doi.org/10.1145/66926.66939Google Scholar
Kindermann, R., & Snell, J. (1980). Markov random fields and their applications. In Meserve, B. E. (Ed.), Contemporary Mathematics. Providence, RI: American Mathematical Society.Google Scholar
Kitzelmann, E., & Schmid, U. (2006). Inductive synthesis of functional programs: an explanation-based generalization approach. Journal of Machine Learning Research, 7(2), 429454.Google Scholar
Klahr, D., Langley, P., & Neches, R. (Eds.). (1987). Production System Models of Learning and Development. Cambridge, MA: MIT Press.Google Scholar
Kleene, S. (1952). Introduction to Metamathematics. Amsterdam: North-Holland.Google Scholar
Kolodner, J. (1993). Case-Based Reasoning. San Mateo, CA: Morgan Kaufmann.Google Scholar
Kripke, S. (1959). A completeness theorem for modal logic. Journal of Symbolic Logic, 24(1), 114.Google Scholar
Laird, J. (2012). The Soar Cognitive Architecture. Cambridge, MA: MIT Press.Google Scholar
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 13321338.Google Scholar
Lee, M. D. (2011). How cognitive modeling can benefit from hierarchical Bayesian models. Journal of Mathematical Psychology, 55(1), 17.Google Scholar
Lehmann, J., Chan, M., & Bundy, A. (2013). A higher-order approach to ontology evolution in physics. Journal on Data Semantics, 2(4), 163187. https://doi.org/10.1007/s13740-012-0016-7Google Scholar
Leibniz, G. (1677). Preface to the general science. In: Wiener, P., (Ed.), Leibniz Selections. Oxford: Macmillan.Google Scholar
Leitgeb, H. (2005). Interpreted dynamical systems and qualitative laws: from neural networks to evolutionary systems. Synthese, 146(1), 189202.Google Scholar
Lenat, D., & Guha, R. (1989). Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Reading, MA: Addison-Wesley.Google Scholar
Lenat, D., Prakash, M., & Shepherd, M. (1986). CYC: using common sense knowledge to overcome brittleness and knowledge acquisition bottlenecks. AI Magazine, 6(4), 6585.Google Scholar
Magee, D., Needham, C. J., Santos, P., Cohn, A. G., & Hogg, D. C. (2004). Autonomous learning for a cognitive agent using continuous models and inductive logic programming from audio-visual input. In Proceedings of the AAAI workshop on Anchoring Symbols to Sensor Data (pp. 1724).Google Scholar
Martin, C. (1989). Pragmatic interpretation and ambiguity. In Proceedings of the Eleventh Annual Conference of the Cognitive Science Society, Ann Arbor, MI.Google Scholar
Matuszek, C., Cabral, J., Witbrock, M., & DeOliveira, J. (2006). An introduction to the syntax and content of Cyc. In Papers from the 2006 AAAI Spring Symposium “Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering,” Technical Report SS-06-05, Stanford, CA.Google Scholar
McCarthy, J. (1988). Review of the question of artificial intelligence. Annals of the History of Computing, 10(3), 224229.Google Scholar
Mehta, D., & Raghavan, V. (2002). Decision tree approximations of Boolean functions. Theoretical Computer Science, 270(1–2), 609623.Google Scholar
Miller, G., Beckwith, R., Fellbaum, C., Gross, D., & Miller, K. (1990). WordNet: an online lexical database. International Journal of Lexicography, 3(4), 235244.Google Scholar
Minsky, M. (1975). A framework for representing knowledge. In Winston, P., (Ed.), The Psychology of Computer Vision. New York, NY: McGraw-Hill.Google Scholar
Mitchell, T. (1982). Generalization as search. Artificial Intelligence, 18(2), 203226. https://doi.org/10.1016/0004-3702(82)90040-6CrossRefGoogle Scholar
Mizoguchi, F., Ohwada, H., Nishiyama, H., & Iwasaki, H. (2012). Identifying driver’s cognitive load using inductive logic programming. In International Conference on Inductive Logic Programming, pp. 166177. Berlin/Heidelberg: Springer.Google Scholar
Möller, R., & Haarslev, V. (2003). Tableau-based reasoning. In Baader, F. et al. (Eds.), The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge: Cambridge University Press.Google Scholar
Montague, R. (1973). The proper treatment of quantification in ordinary English. In Suppes, P., Moravcsik, J., & Hintikka, J. (Eds.), Approaches to Natural Language (pp. 221242). Amsterdam: Dordrecht.Google Scholar
Montague, R. (1974). Formal Philosophy: Selected Papers of Richard Montague, edited and with an introduction by Richmond H. Thomason. New Haven, CT: Yale University Press.Google Scholar
Mota, T., & Sridharan, M. (2019). Commonsense reasoning and knowledge acquisition to guide deep learning on robots. In Bicchi, A. et al. (Eds.), Robotics: Science and Systems Proceedings, Volume 15.Google Scholar
Muggleton, S. (1991). Inductive logic programming. New Generation Computing, 8(4), 295318.Google Scholar
Muggleton, S., Schmid, U., Zeller, C., Tamaddoni-Nezhad, A., & Besold, T. (2018). Ultra-strong machine learning: comprehensibility of programs learned with ILP. Machine Learning, 107(7), 11191140.CrossRefGoogle Scholar
Murray, W. R. (2011). Statistical relational learning in student modeling for intelligent tutoring systems. In International Conference on Artificial Intelligence in Education (pp. 516518). Berlin/Heidelberg: Springer.Google Scholar
Newell, A., Shaw, J. C., & Simon, H. A. (1958). Elements of a theory of human problem solving. Psychological Review, 65(3), 151.Google Scholar
Olsson, R. (1995). Inductive functional programming using incremental program transformation. Artificial Intelligence, 74(1), 5581.Google Scholar
Ovchinnikova, E. (2012). Integration of World Knowledge for Natural Language Understanding. Berlin: Springer.CrossRefGoogle Scholar
Paulheim, H. (2017). Knowledge graph refinement: a survey of approaches and evaluation methods. Semantic Web, 8, 489508.Google Scholar
Plotkin, G. (1969). A note on inductive generalization. Machine Intelligence, 5, 153163.Google Scholar
Plunkett, K., & Elman, J. (1996). Rethinking Innateness: A Handbook for Connectionist Simulations. Cambridge, MA: MIT Press.Google Scholar
Pollard, C., & Sag, I. (1994). Head-Driven Phrase Structure Grammar. Chicago, IL: University of Chicago Press.Google Scholar
Quillian, M. (1968). Semantic memory. In Minsky, M. (Ed.), Semantic Information Processing (pp. 227270). Cambridge, MA: MIT Press.Google Scholar
Quinlan, J. (1986). Induction of decision trees. Machine Learning, 1(1), 81106.Google Scholar
Quinlan, J. (1993). C4.5: Programs for Machine Learning. Burlington, MA: Morgan Kaufmann Publishers.Google Scholar
Quinlan, J. R. (1983). Learning efficient classification procedures and their application to chess end games. In Michalski, R., Carbonell, J., & Mitchell, T. (Eds.), Machine Learning. An Artificial Intelligence Approach, Volume 1 (pp. 463482). Berlin/Heidelberg: Springer.Google Scholar
Quinlan, J. R. (1987). Simplifying decision trees. International Journal of Man-Machine Studies, 27(3), 221234.Google Scholar
Rao, A., & Georgeff, M. (1991). Modeling rational agents within a BDI-architecture. In Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning (pp. 473484).Google Scholar
Richardson, M., & Domingos, P. (2006). Markov logic networks. Machine Learning, 62(1–2), 107136.Google Scholar
Robinson, J. (1965). A machine-oriented logic based on the resolution principle. Journal of the Association for Computing Machinery, 12(1), 2341. https://doi.org/10.1145/321250.321253Google Scholar
Robinson, J. (1971). Computational logic: the unification computation. Machine Intelligence, 6, 6372.Google Scholar
Ruppenhofer, J., Ellsworth, M., Petruck, M., Johnson, C., & Scheffczyk, J. (2010). FrameNet II: Extended Theory and Practice. Technical report, Berkeley, CA.Google Scholar
Schank, R. (1975). Conceptual Information Processing. New York, NY: Elsevier.Google Scholar
Schank, R., Abelsohn, R. (1977). Scripts, Plans, Goals, and Understanding. Hillsdale, NJ: Earlbaum Associates.Google Scholar
Schank, R. C., Goldman, N. M., RiegerIII, C. J., & Riesbeck, C. (1973). MARGIE: memory analysis response generation, and inference on English. In Proceedings of the Second International Joint Conference on Artificial Intelligence, Stanford, CA.Google Scholar
Schmid, U., & Kitzelmann, E. (2011). Inductive rule learning on the knowledge level. Cognitive Systems Research, 12(3–4), 237248.Google Scholar
Schmidt, M., Krumnack, U., Gust, H., & Kühnberger, K.-U. (2014). Heuristic-driven theory projection: an overview. In Prade, H. & Richard, G. (Eds.), Computational Approaches to Analogical Reasoning: Current Trends. Studies in Computational Intelligence (vol. 548). Berlin/Heidelberg: Springer. https://doi.org/10.1007/978-3-642-54516-0_7Google Scholar
Schöning, U. (1989). Logic for Computer Scientists. Boston, MA: Birkhäuser. https://doi.org/10.1007/978-0-8176-4763-6Google Scholar
Siegelmann, H. T. (1999). Neural Networks and Analog Computation: Beyond the Turing Limit. Berlin: Springer Science & Business Media.Google Scholar
Simmons, R. (1963). Synthetic language behavior. Data Processing Management, 5(12), 1118.Google Scholar
Skinner, B. (1957). Verbal Behavior. Acton: Copley Publishing Group.Google Scholar
Smolensky, P. (1988). On the proper treatment of connnectionism. Behavioral and Brain Sciences, 11(1), 174.CrossRefGoogle Scholar
Solway, A., & Botvinick, M. M. (2015). Evidence integration in model-based tree search. Proceedings of the National Academy of Sciences, 112(37), 1170811713.Google Scholar
Sowa, J. (1976). Conceptual graphs for a data base interface. IBM Journal of Research and Development, 20(4), 336357. https://doi.org/10.1147/rd.204.0336Google Scholar
Sowa, J. (2000). Knowledge Representation: Logical, Philosophical, and Computational Foundations. Pacific Grove, CA: Brooks Cole Publishing Co.Google Scholar
Spelke, E. S., & Kinzler, K. D. (2007). Core knowledge. Developmental Science, 10(1), 8996.Google Scholar
Staab, S., & Studer, R. (Eds.) (2009). Handbook on Ontologies. Berlin: Springer.Google Scholar
Steedman, M. (1996). Surface Structure and Interpretation. Cambridge, MA: MIT Press.Google Scholar
Stumme, G., & Maedche, A. (2001). Ontology merging for federated ontologies for the semantic web. In Gruniger, M. (Ed.), Ontologies and Information Sharing. 17th International Joint Conference on Artificial Intelligence Workshop on Ontologies and Information Sharing, Seattle, WA.Google Scholar
Suchan, J., Bhatt, M., & Schultz, C. (2016). Deeply semantic inductive spatio-temporal learning. In the 26th International Conference on Inductive Logic Programming. London, UK.Google Scholar
Sun, R. (2002). Hybrid systems and connectionist implementationalism. In Encyclopedia of Cognitive Science (pp. 697703). London: Nature Publishing Group (MacMillan).Google Scholar
Sun, R. (2016). Anatomy of the Mind. New York, NY: Oxford University Press.Google Scholar
Turing, A. (1936). On computable numbers, with an application to the entscheidungsproblem. Proceedings of the London Mathematical Society, Series 2, Volume 42.Google Scholar
Turing, A. (1950). Computing machinery and intelligence. Mind, LIX(236), 433460. https://doi.org/10.1093/mind/LIX.236.433Google Scholar
Urbani, J., Kotoulas, S., Maassen, J., van Harmelen, F., & Bal, H. (2010). OWL reasoning with WebPIE: calculating the closure of 100 billion triples. In Proceedings of the ESWC 2010, Heraklion, Greece.Google Scholar
Vanlehn, K., & Ball, W. (1987). A version space approach to learning context-free grammars. Machine Learning, 2(1), 3974.Google Scholar
Van Opheusden, B., Galbiati, G., Bnaya, Z., Li, Y., & Ma, W. J. (2017). A computational model for decision tree search. In Proceedings of the Thirty-ninth Annual Conference of the Cognitive Science Society. London, UK.Google Scholar
Vernon, D. (2022). Cognitive architectures. In Cangelosi, A. & Asada, M. (Eds.), Cognitive Robotics. Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/13780.003.0015.Google Scholar
Vu, M. H., Zehfroosh, A., Strother-Garcia, K., Sebok, M., Heinz, J., & Tanner, H. G. (2018). Statistical relational learning with unconventional string models. Frontiers in Robotics and AI, 5. https://doi.org/10.3389/frobt.2018.00076Google Scholar
Wermter, S., & Sun, R. (2000). An overview of hybrid neural systems. In S. Wermter & R. Sun (Eds.), Hybrid Neural Systems (pp. 113). Berlin/Heidelberg: Springer.Google Scholar
Wooldridge, M. (2000). Reasoning about Rational Agents. Cambridge, MA: MIT Press.Google Scholar
Wooldridge, M. (2009). An Introduction to Multi-Agent Systems (2nd ed.). Oxford: John Wiley & Sons.Google Scholar
Zellers, R., Bisk, Y., Farhadi, A., & Choi, Y. (2019). From recognition to cognition: visual commonsense reasoning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Amsterdam: IEEE Press.Google Scholar
Zhang, S., & Stone, P. (2015). CORPP: commonsense reasoning and probabilistic planning as applied to dialog with a mobile robot. In Proceedings of the 2015 AAAI Conference on Artificial Intelligence.Google Scholar
Zilke, J. R., Mencía, E. L., & Janssen, F. (2016). Deepred–rule extraction from deep neural networks. In International Conference on Discovery Science (pp. 457473). Cham: Springer.Google Scholar

References

Adam, C., Herzig, A., & Longin, D. (2009). A logical formalization of the OCC theory of emotions. Synthese, 168(2), 201248.Google Scholar
Andréka, H., Madarász, J. X., Németi, I., & Székely, G. (2011). A logic road from special relativity to general relativity. Synthese, 186, 117. https://doi.org/10.1007/s11229–011-9914-8Google Scholar
Arkoudas, K., & Bringsjord, S. (2009). Vivid: an AI framework for heterogeneous problem solving. Artificial Intelligence, 173(15), 13671405. http://kryten.mm.rpi.edu/KA_SB_Vivid_offprint_AIJ.pdfGoogle Scholar
Arora, S., & Barak, B. (2009). Computational Complexity: A Modern Approach. Cambridge: Cambridge University Press.Google Scholar
Ashcraft, M., & Radvansky, G. (2013). Cognition (6th ed.). London: Pearson.Google Scholar
Barwise, J., & Etchemendy, J. (1994). Hyperproof. Stanford, CA: CSLI.Google Scholar
Barwise, J., & Etchemendy, J. (1995). Heterogeneous logic. In Glasgow, J., Narayanan, N., & Chandrasekaran, B. (Eds.), Diagrammatic Reasoning: Cognitive and Computational Perspectives (pp. 211–234). Cambridge, MA: MIT Press.Google Scholar
Boolos, G. S., Burgess, J. P., & Jeffrey, R. C. (2003). Computability and Logic (4th ed.). Cambridge: Cambridge University Press.Google Scholar
Bringsjord, S. (2008), Declarative/logic-based cognitive modeling. In Sun, R., (Ed.), The Handbook of Computational Psychology. Cambridge: Cambridge University Press, pp. 127169. http://kryten.mm.rpi.edu/sb_lccm_ab-toc_031607.pdfGoogle Scholar
Bringsjord, S. (2014). Review of P. Thagard’s The Brain and the Meaning of Life. Religion & Theology, 21, 421425. http://kryten.mm.rpi.edu/SBringsjord_review_PThagard_TBTMOL.pdfGoogle Scholar
Bringsjord, S., Govindarajulu, N., & Giancola, M. (2021). Automated argument adjudication to solve ethical problems in multi-agent environments. Paladyn, Journal of Behavioral Robotics, 12, 310335.Google Scholar
Bringsjord, S., & Govindarajulu, N. S. (2018). Artificial intelligence. In Zalta, E., (Ed.), The Stanford Encyclopedia of Philosophy. Available at: https://plato.stanford.edu/entries/artificial-intelligenceGoogle Scholar
Bringsjord, S., Licato, J., & Bringsjord, A. (2016). The contemporary craft of creating characters meets today’s cognitive architectures: a case study in expressivity. In Turner, J., Nixon, M., Bernardet, U., & DiPaola, S. (Eds.), Integrating Cognitive Architectures into Virtual Character Design. Hershey, PA: IGI Global, pp. 151180.Google Scholar
Byrne, R. (1989). Suppressing valid inferences with conditionals. Journal of Memory and Language, 31, 6183.Google Scholar
Charniak, E., & McDermott, D. (1985). Introduction to Artificial Intelligence. Reading, MA: Addison-Wesley.Google Scholar
Chisholm, R. (1966). Theory of Knowledge. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Davis, M., Sigal, R., & Weyuker, E. (1994). Computability, Complexity, and Languages: Fundamentals of Theoretical Computer Science. New York, NY: Academic Press.Google Scholar
Dickmann, M. A. (1975). Large Infinitary Languages. Amsterdam: North-Holland.Google Scholar
Dietz, E.-A., Hölldobler, S., & Ragni, M. (2012). A computational logic approach to the suppression task. In Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 34.Google Scholar
Dietz, E.-A., Hölldobler, S., & Wernhard, C. (2014). Modeling the suppression task under weak completion and well-founded semantics. Journal of Applied Non-Classical Logics, 24(1–2), 6185.Google Scholar
Ebbinghaus, H. D., Flum, J., & Thomas, W. (1994). Mathematical Logic (2nd ed.). New York, NY: Springer-Verlag.Google Scholar
Feferman, S. (1995). Turing in the Land of O(Z). In Herken, R. (Ed.), The Universal Turing Machine (2nd ed.). Secaucus, NJ: Springer-Verlag, pp. 103134.Google Scholar
Francez, N. (2015). Proof-Theoretic Semantics. London: College Publications.Google Scholar
Genesereth, M., & Nilsson, N. (1987). Logical Foundations of Artificial Intelligence. Los Altos, CA: Morgan Kaufmann.Google Scholar
Giancola, M., Bringsjord, S., Govindarajulu, N. S., & Varela, C. (2020). Ethical reasoning for autonomous agents under uncertainty. In Tokhi, M., Ferreira, M., Govindarajulu, N., Silva, M., Kadar, E., Wang, J. Kaur, A. (Eds.), Smart Living and Quality Health with Robots. Proceedings of ICRES 2020, CLAWAR, London, pp. 26–41. The ShadowAdjudicator system can be obtained from: https://github.com/RAIRLab/ShadowAdjudicator; http://kryten.mm.rpi.edu/MG_SB_NSG_CV_LogicizationMiracleOnHudson.pdfGoogle Scholar
Glymour, C. (1992). Thinking Things Through. Cambridge, MA: MIT Press.Google Scholar
Govindarajulu, N., Bringsjord, S., & Peveler, M. (2019). On quantified modal theorem proving for modeling ethics. In Suda, M. & Winkler, S. (Eds.), Proceedings of the Second International Workshop on Automated Reasoning: Challenges, Applications, Directions, Exemplary Achievements (ARCADE 2019), vol. 311 of Electronic Proceedings in Theoretical Computer Science, Open Publishing Association, Waterloo, Australia, pp. 43–49. The ShadowProver system can be obtained here: https://naveensundarg.github.io/prover/; http://eptcs.web.cse.unsw.edu.au/paper.cgi?ARCADE2019.7.pdfGoogle Scholar
Govindarajulu, N. S., & Bringsjord, S. (2017). Strength factors: an uncertainty system for quantified modal logic. In V. Belle, J. Cussens, M. Finger, L. Godo, H. Prade, & G. Qi (Eds.), Proceedings of the IJCAI Workshop on “Logical Foundations for Uncertainty and Machine Learning.” Melbourne, Australia, pp. 34–40. http://homepages.inf.ed.ac.uk/vbelle/workshops/lfu17/proc.pdfGoogle Scholar
Groarke, L. (1996/2017). Informal logic. In Zalta, E. (Ed.), The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/entries/logic-informalGoogle Scholar
Hájek, P. (1998). Metamathematics of Fuzzy Logic: Trends in Logic (vol. 4). Dordrecht: Kluwer.Google Scholar
Hayes, P. (1978). The naïve physics manifesto. In Mitchie, D. (Ed.), Expert Systems in the Microelectronics Age. Edinburgh: Edinburgh University Press, pp. 242270.Google Scholar
Hayes, P. J. (1985). The second naïve physics manifesto. In Hobbs, J. R., & Moore, B. (Eds.), Formal Theories of the Commonsense World (pp. 1–36). Norwood, NJ: Ablex.Google Scholar
Heil, C. (2019). Introduction to Real Analysis. Cham: Springer.Google Scholar
Hendricks, V., & Symons, J. (2006). Epistemic logic. In Zalta, E. (Ed.), The Stanford Encyclopedia of Philosophy. http://plato.stanford.edu/entries/logic-epistemicGoogle Scholar
Hummel, J. (2010). Symbolic versus associative learning. Cognitive Science, 34(6), 958965.Google Scholar
Hummel, J. E., & Holyoak, K. J. (2003). A symbolic-connectionist theory of relational inference and generalization. Psychological Review, 110, 220264.Google Scholar
Johnson, G. (2016). Argument & Inference: An Introduction to Inductive Logic. Cambridge, MA: MIT Press.Google Scholar
Kemp, C. (2009). Quantification and the language of thought. In Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C., & Culotta, A. (Eds.), Advances in Neural Information Processing Systems, vol. 22. Red Hook, NY: Curran Associates. Available from: https://proceedings.neurips.cc/paper/2009/file/82161242827b703e6acf9c726942a1e4-Paper.pdfGoogle Scholar
Kleene, S. (1967). Mathematical Logic. New York, NY: Wiley & Sons.Google Scholar
Konyndyk, K. (1986). Introductory Modal Logic. Notre Dame, IN: University of Notre Dame Press.Google Scholar
Markman, A., & Gentner, D. (2001). Thinking. Annual Review of Psychology, 52, 223247.Google Scholar
McCarthy, J. (1980). Circumscription: a form of non-monotonic reasoning. Artificial Intelligence, 13, 2739.Google Scholar
McKeon, R. (Ed.). (1941). The Basic Works of Aristotle. New York, NY: Random House.Google Scholar
McKinsey, J., Sugar, A., & Suppes, P. (1953). Axiomatic foundations of classical particle mechanics. Journal of Rational Mechanics and Analysis, 2, 253272.Google Scholar
Nelson, M. (2015). Propositional attitude reports. In E. Zalta (Ed.), The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/entries/prop-attitude-reportsGoogle Scholar
Newell, A., & Simon, H. (1956). The logic theory machine: a complex information processing system. P-868 The RAND Corporation, pp. 25–63. Available from: http://shelf1.library.cmu.edu/IMLS/BACKUP/MindModels.pre_Oct1/logictheorymachine.pdfGoogle Scholar
Núñez, R., Murthi, M., Premaratine, K., Scheutz, M., & Bueno, O. (2018). Uncertain logic processing: logic-based inference and reasoning using Dempster-Shafer models. International Journal of Approximate Reasoning, 95, 121.Google Scholar
Paris, J., & Vencovská, A. (2015). Pure Inductive Logic. Cambridge: Cambridge University Press.Google Scholar
Partee, B. (2013). The starring role of quantifiers in the history of formal semantics. In Punčochář, V. & Švarný, P. (Eds.), The Logica Yearbook 2012. London: College Publications.Google Scholar
Pollock, J. (1995). Cognitive Carpentry: A Blueprint for How to Build a Person. Cambridge, MA: MIT Press.Google Scholar
Pollock, J. L. (1992). How to reason defeasibly. Artificial Intelligence, 57(1), 142.Google Scholar
Prakken, H., & Vreeswijk, G. (2001). Logics for defeasible argumentation. In Gabbay, D. & Guenthner, F. (Eds.), Handbook of Philosophical Logic (pp. 219–318). Dordrecht: Springer.Google Scholar
Reiter, R. (1980). A logic for default reasoning. Artificial Intelligence, 13, 81132.Google Scholar
Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). New York, NY: Pearson.Google Scholar
Saldanha, E.-A. D., & Kakas, A. (2020). Cognitive argumentation and the suppression task. arXiv:2002.10149Google Scholar
Simpson, S. (2010). Subsystems of Second Order Arithmetic (2nd ed.). Cambridge: Cambridge University Press.Google Scholar
Sloman, A. (1971). Interactions between philosophy and AI: the role of intuition and non-logical reasoning in intelligence. Artificial Intelligence, 2, 209225.Google Scholar
Smith, P. (2013). An Introduction to Gödel’s Theorems (2nd ed.). Cambridge: Cambridge University Press.Google Scholar
Stenning, K., & van Lambalgen, M. (2008). Human Reasoning and Cognitive Science. Cambridge, MA: MIT Press.Google Scholar
Sun, R. (2002). Duality of the Mind. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Sun, R., & Bringsjord, S. (2009). Cognitive systems and cognitive architectures. In Wah, B. W. (Ed.), The Wiley Encyclopedia of Computer Science and Engineering, Vol. 1 (pp. 420–428). New York, NY: Wiley. http://kryten.mm.rpi.edu/rs_sb_wileyency_pp.pdfGoogle Scholar
Szymanik, J., & Zajenkowski, M. (2009). Understanding Quantifiers in Language. Proceedings of the Annual Meeting of the Cognitive Science Society, 31, 11091114. Available from: https://escholarship.org/uc/item/6j17t373Google Scholar
Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338353.Google Scholar

References

Amari, S. (1977). Dynamics of pattern formation in lateral-inhibition type neural fields. Biological Cybernetics, 27, 7787.Google Scholar
Andersen, R. A., Essick, G. K., & Siegel, R. M. (1985). Encoding of spatial location by posterior parietal neurons. Science, 230(4724), 456458.Google Scholar
Ashby, R. W. (1956). An Introduction to Cybernetics. London: Chapman & Hall Ltd.Google Scholar
Bastian, A., Riehle, A., Erlhagen, W., & Schöner, G. (1998). Prior information preshapes the population representation of movement direction in motor cortex. Neuroreports, 9, 315319.Google Scholar
Bastian, A., Schöner, G., & Riehle, A. (2003). Preshaping and continuous evolution of motor cortical representations during movement preparation. European Journal of Neuroscience, 18, 20472058.Google Scholar
Beer, R. D. (2000). Dynamical approaches to cognitive science. Trends in Cognitive Sciences, 4(3), 9199.Google Scholar
Bicho, E., Louro, L., & Erlhagen, W. (2010). Integrating verbal and nonverbal communication in a dynamic neural field architecture for human-robot interaction. Frontiers in Neurorobotics, 4(5), 113.Google Scholar
Bicho, E., Mallet, P., & Schöner, G. (2000). Target representation on an autonomous vehicle with low-level sensors. The International Journal of Robotics Research, 19, 424447.Google Scholar
Botvinick, M. M., & Plaut, D. C. (2006). Short-term memory for serial order: a recurrent neural network model. Psychological Review, 113(2), 201233.Google Scholar
Bowers, J. S. (2017). Grandmother cells and localist representations: a review of current thinking. Language, Cognition and Neuroscience, 32(3), 257273.Google Scholar
Braitenberg, V. (1984). Vehicles: Experiments in Synthetic Psychology. Cambridge, MA: MIT Press.Google Scholar
Buonomano, D. V., & Laje, R. (2010 ). Population clocks: motor timing with neural dynamics. Trends in Cognitive Sciences, 14(12), 520527.Google Scholar
Buss, A. T., & Spencer, J. P. (2014). The emergent executive: a dynamic field theory of the development of executive function. Monographs of the Society for Research in Child Development, 79(2), 1103.Google Scholar
Chrysikou, E. G., Casasanto, D., & Thompson-Schill, S. L. (2017). Motor experience influences object knowledge. Journal of Experimental Psychology: General, 146(3), 395408.Google Scholar
Clearfield, M. W., Dineva, E., Smith, L. B., Diedrich, F. J., & Thelen, E. (2009). Cue salience and infant perseverative reaching: tests of the dynamic field theory. Developmental Science, 12(1), 2640.Google Scholar
Compte, A., Brunel, N., Goldman-Rakic, P. S., & Wang, X.-J. (2000). Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cerebral Cortex, 10, 910923.Google Scholar
Coombes, S., beim Graben, P., Potthast, R., & Wright, J. (Eds.). (2014). Neural Fields: Theory and Applications. New York, NY: Springer Verlag.Google Scholar
Desimone, R. (1998). Visual attention mediated by biased competition in extrastriate visual cortex. Philosophical Transactions of the Royal Society B: Biological Sciences, 353(1373), 12451255.Google Scholar
Dineva, E., & Schöner, G. (2018). How infants’ reaches reveal principles of sensorimotor decision making. Connection Science, 30(1), 5380.Google Scholar
Douglas, R. J., Martin, K. A. C., & Whitteridge, D. (1989). Microcircuit for neocortex. Neural Computation, 1, 480488.Google Scholar
Durstewitz, D., Seamans, J. K., & Sejnowski, T. J. (2000). Neurocomputational models of working memory. Nature Neuroscience Supplement, 3, 11841191.Google Scholar
Eliasmith, C. (2005). A unified approach to building and controlling spiking attractor networks. Neural Computation, 17, 12761314.CrossRefGoogle ScholarPubMed
Eliasmith, C., Stewart, T. C., Choo, X., et al. (2012). A large-scale model of the functioning brain. Science, 338(6111), 12021205.Google Scholar
Eliasmith, C., & Trujillo, O. (2014). The use and abuse of large-scale brain models. Current Opinion in Neurobiology, 25, 16.Google Scholar
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 179211.Google Scholar
Erlhagen, W., Bastian, A., Jancke, D., Riehle, A., & Schöner, G. (1999). The distribution of neuronal population activation (DPA) as a tool to study interaction and integration in cortical representations. Journal of Neuroscience Methods, 94(1), 5366.Google Scholar
Erlhagen, W., & Schöner, G. (2002). Dynamic field theory of movement preparation. Psychological Review, 109(3), 545572.Google Scholar
Ermentrout, B. (1998). Neural networks as spatio-temporal pattern-forming systems. Reports on Progress in Physics, 61, 353430.Google Scholar
Fauconnier, G., & Turner, M. (2002). The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities. New York, NY: Basic Books.Google Scholar
Fuster, J. M. (1995). Memory in the Cerebral Cortex: An Empirical Approach to Neural Networks in the Human and Nonhuman Primate. Cambridge, MA: MIT Press.Google Scholar
Gardenfors, P. (2000). Conceptual Spaces: The Geometry of Thought. Boston, MA: MIT Press.Google Scholar
Gayler, R. (2003). Vector symbolic architectures answer Jackendoff’s challenges for cognitive neuroscience. In Slezak, P. (Ed.), ICCS/ASCS International Conference on Cognitive Science (pp. 133138). Sydney, Australia: University of New South Wales.Google Scholar
Georgopoulos, A. P., Taira, M., & Lukashin, A. (1993). Cognitive neurophysiology of the motor cortex. Science, 260(5104), 4752.Google Scholar
Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition. Cambridge: Cambridge University Press.Google Scholar
Gibson, J. J. (1966). The Senses Considered as Perceptual Systems. Boston, MA: Houghton Mifflin Co.Google Scholar
Grabska-Barwińska, A., Distler, C., Hoffmann, K. P., & Jancke, D. (2009). Contrast independence of cardinal preference: stable oblique effect in orientation maps of ferret visual cortex. European Journal of Neuroscience, 29(6), 12581270.Google Scholar
Grieben, R., Tekülve, J., Zibner, S. K. U., Lins, J., Schneegans, S., & Schöner, G. (2020). Scene memory and spatial inhibition in visual search. Attention, Perception, and Psychophysics, 82, 775798.Google Scholar
Grossberg, S. (1970). Some networks that can learn, remember, and reproduce any number of complicated space-time patterns, II. Studies in Applied Mathematics, XLIX,(2), 135166.Google Scholar
Grossberg, S. (2021). Conscious Mind, Resonant Brain: How Each Brain Makes a Mind. Oxford: Oxford University Press.Google Scholar
Henson, R. N. A., & Burgess, N. (1997). Representations of serial order. In Bullinaria, J. A., Glasspool, D. W., & Houghton, G. (Eds.), Connectionist Representations (pp. 283300). New York, NY: Springer Verlag.Google Scholar
Hopfield, J. J., & Tank, D. W. (1986). Computing with neural circuits: a model. Science, 233, 625633.Google Scholar
Jancke, D., Erlhagen, W., Dinse, H. R., et al. (1999). Parametric population representation of retinal location: neuronal interaction dynamics in cat primary visual cortex. Journal of Neuroscience, 19, 90169028.Google Scholar
Johnson, J., Spencer, J., Luck, S., & Schöner, G. (2009). A dynamic neural field model of visual working memory and change detection. Psychological Science, 20(5) 568–577.Google Scholar
Johnson, J. S., Simmering, V. R., & Buss, A. T. (2014). Beyond slots and resources: grounding cognitive concepts in neural dynamics. Attention, Perception, and Psychophysics, 76(6), 16301654.Google Scholar
Klaes, C., Schneegans, S., Schöner, G., & Gail, A. (2012). Sensorimotor learning biases choice behavior: a learning neural field model for decision making. PLoS Computational Biology, 8(11), e1002774.Google Scholar
Knierim, J. J., & Zhang, K. (2012). Attractor dynamics of spatially correlated neural activity in the limbic system. Annual Review of Neuroscience, 35(1), 267285.Google Scholar
Knips, G., Zibner, S. K. U., Reimann, H., & Schöner, G. (2017). A neural dynamic architecture for reaching and grasping integrates perception and movement generation and enables on-line updating. Frontiers in Neurorobotics, 11(9), 114.Google Scholar
Kounatidou, P., Richter, M., & Schöner, G. (2018). A neural dynamic architecture that autonomously builds mental models. In Proceedings of the 40th Annual Conference of the Cognitive Science Society (pp. 16).Google Scholar
Kreiser, R., Aathmani, D., Quio, N., Indiveri, G., & Sandamirskaya, Y. (2018). Organizing sequential memory in a neuromorphic device using dynamic neural fields. Frontiers in Neuroscience, 12(717), 117.Google Scholar
Lakoff, G. J., & Johnson, M. (1999). Philosophy in the Flesh: The Embodied Mind and Its Challenge to Western Thought. New York, NY: Basic Books.Google Scholar
Latash, M. L. (2008). Synergy. New York, NY: Oxford University Press.Google Scholar
Lipinski, J., Schneegans, S., Sandamirskaya, Y., Spencer, J. P., & Schöner, G. (2012). A neuro-behavioral model of flexible spatial language behaviors. Journal of Experimental Psychology: Learning, Memory and Cognition, 38(6), 14901511.Google Scholar
Marino, R. A., Trappenberg, T. P., Dorris, M., & Munoz, D. P. (2012). Spatial interactions in the superior colliculus predict saccade behavior in a neural field model. Journal of Cognitive Neuroscience, 24(2), 315336.Google Scholar
Markounikau, V., Igel, C., Grinvald, A., & Jancke, D. (2010). A dynamic neural field model of mesoscopic cortical activity captured with voltage-sensitive dye imaging. PLoS Computational Biology, 6(9), e1000919.Google Scholar
Milde, M. B., Blum, H., Dietmüller, A., et al. (2017). Obstacle avoidance and target acquisition for robot navigation using a mixed signal analog/digital neuromorphic processing system. Frontiers in Neurorobotics, 11(28), 117.Google Scholar
Moran, D. W., & Schwartz, A. B. (1999). Motor cortical representation of speed and direction during reaching movement. Journal of Neurophysiology, 82, 26762692.Google Scholar
Oksendal, B. (2013). Stochastic Differential Equations: An Introduction with Applications (6th ed.). Berlin and Heidelberg: Springer.Google Scholar
O’Reilly, R. C. (2006). Biologically based computational models of high-level cognition. Science, 314, 9194.Google Scholar
Perko, L. (2001). Differential Equations and Dynamical Systems (3rd ed.). Berlin: Springer Verlag.Google Scholar
Perone, S., & Spencer, J. P. (2013). Autonomy in action: linking the act of looking to memory formation in infancy via dynamic neural fields. Cognitive Science, 37(1), 160.Google Scholar
Perone, S., & Spencer, J. P. (2014). The co-development of looking dynamics and discrimination performance. Developmental Psychology, 50(3), 837852.Google Scholar
Port, R., & van Gelder, R. (Eds.). (1995). Mind as Motion: Explorations in the Dynamics of Cognition. Cambridge, MA: MIT Press.Google Scholar
Pouget, A., & Snyder, L. H. (2000). Computational approaches to sensorimotor transformations. Nature Neuroscience Supplement, 3, 11921198.Google Scholar
Ramsey, W. M. (2007). Representation Reconsidered. Cambridge: Cambridge University Press.Google Scholar
Richter, M., Lins, J., & Schöner, G. (2017). A neural dynamic model generates descriptions of object-oriented actions. Topics in Cognitive Science, 9, 3547.Google Scholar
Richter, M., Lins, J., & Schöner, G. (2021). A neural dynamic model for the perceptual grounding of spatial and movement relations. Cognitive Science, 45, e13405.Google Scholar
Rolls, E. T., Stringer, S. M., & Trappenberg, T. P. (2002). A unified model of spatial and episodic memory. Proceedings of the Royal Society B: Biological Sciences, 269(1496), 10871093. https://doi.org/10.1098/rspb.2002.2009Google Scholar
Sabinasz, D., Richter, M., Lins, J., Richter, M., & Schöner, G. (2020). Grounding spatial language in perception by combining concepts in a neural dynamic architecture. In Proceedings of the 42nd Annual Conference of the Cognitive Science Society.Google Scholar
Samuelson, L. K., Smith, L. B., Perry, L. K., & Spencer, J. P. (2011 ). Grounding word learning in space. PloS One, 6(12), e28095.Google Scholar
Sandamirskaya, Y. (2014). Dynamic neural fields as a step toward cognitive neuromorphic architectures. Frontiers in Neuroscience, 7(276), 113.Google Scholar
Sandamirskaya, Y. (2016). Autonomous sequence generation in dynamic field theory. In Schöner, G., Spencer, J. P., & Research Group, T. DFT (Eds.), Dynamic Thinking: A Primer on Dynamic Field Theory (pp. 353368). New York, NY: Oxford University Press.Google Scholar
Sandamirskaya, Y., & Schöner, G. (2010). An embodied account of serial order: how instabilities drive sequence generation. Neural Networks, 10, 11641179.Google Scholar
Sandamirskaya, Y., & Storck, T. (2015). Learning to look and looking to remember: a neural-dynamic embodied model for generation of saccadic gaze shifts and memory formation. In Koprinkova-Hristova, P., Mladenov, V., & Kasabov, N. K. (Eds.), Artificial Neural Networks, vol. 4 (pp. 175200). New York, NY: Springer International Publishing.Google Scholar
Schneegans, S. (2016). Sensori-motor and cognitive transformation. In Schöner, G., Spencer, J. P., & Research Group, T. DFT (Eds.), Dynamic Thinking: A Primer on Dynamic Field Theory (pp. 169196). New York, NY: Oxford University Press.Google Scholar
Schneegans, S., & Bays, P. M. (2016). No fixed item limit in visuospatial working memory. Cortex, 83, 181193.Google Scholar
Schneegans, S., & Schöner, G. (2012). A neural mechanism for coordinate transformation predicts pre-saccadic remapping. Biological Cybernetics, 106(2), 89109.Google Scholar
Schneegans, S., Spencer, J. P., & Schöner, G. (2016). Integrating ‘what’ and ‘where’: visual working memory for objects in a scene. In Schöner, G., Spencer, J. P., & Research Group, T. DFT (Eds.), Dynamic Thinking: A Primer on Dynamic Field Theory (chap. 8). New York, NY: Oxford University Press.Google Scholar
Schöner, G. (2014). Dynamical systems thinking: from metaphor to neural theory. In Molenaar, P. C. M., Lerner, R. M., & Newell, K. M. (Eds.), Handbook of Developmental Systems Theory and Methodology (pp. 188219). New York, NY: Guilford Publications.Google Scholar
Schöner, G., Faubel, C., Dineva, E., & Bicho, E. (2016). Embodied neural dynamics. In Schöner, G., Spencer, J., & Research Group, T. DFT (Eds.), Dynamic Thinking: A Primer on Dynamic Field Theory (pp. 95118). New York, NY: Oxford University Press.Google Scholar
Schöner, G., & Kelso, J. A. (1988). Dynamic pattern generation in behavioral and neural systems. Science, 239(4847), 15131520.Google Scholar
Schöner, G., Spencer, J. P., & DFT Research Group, T. (2016). Dynamic Thinking: A Primer on Dynamic Field Theory. New York, NY: Oxford University Press.Google Scholar
Schöner, G., Tekülve, J., & Zibner, S. (2019). Reaching for objects : a neural process account in a developmental perspective. In Corbetta, D. & Santello, M. (Eds.), Reach-to-Grasp Behavior: Brain, Behavior and Modelling Across the Life Span (pp. 281318). Abingdon: Taylor & Francis.Google Scholar
Schöner, G., & Thelen, E. (2006). Using dynamic field theory to rethink infant habituation. Psychological Review, 113(2), 273299.Google Scholar
Schutte, A. R., & Spencer, J. P. (2009). Tests of the dynamic field theory and the spatial precision hypothesis: capturing a qualitative developmental transition in spatial working memory. Journal of Experimental Psychology. Human Perception and Performance, 35(6), 16981725.Google Scholar
Schutte, A. R., Spencer, J. P., & Schöner, G. (2003). Testing the dynamic field theory : working memory for locations becomes more spatially precise over development. Child Development, 74(5), 13931417.Google Scholar
Schwartz, A. B., Kettner, R. E., & Georgopoulos, A. P. (1988). Primate motor cortex and free arm movements to visual targets in three-dimensional space. I. Relations between single cell discharge and direction of movement. Journal of Neuroscience, 8(8), 29132927.Google Scholar
Searle, J. R. (1983). Intentionality: An Essay in the Philosophy of Mind. Cambridge: Cambridge University Press.Google Scholar
Shapiro, L. (Ed.). (2019). Embodied Cognition (2nd ed.). London: Routledge.Google Scholar
Simmering, V. (2016). Working memory capacity in context: modeling dynamic processes of behavior, memory and development. Monographs of the Society for Research in Child Development, 81(3), 1158.Google Scholar
Smolensky, P. (1990). Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence, 46(1–2), 159216.Google Scholar
Spencer, J. P., & Schöner, G. (2003). Bridging the representational gap in the dynamic systems approach to development. Developmental Science, 6, 392412.Google Scholar
Spencer, J. P., Simmering, V. R., & Schutte, A. R. (2006). Toward a formal theory of flexible spatial behavior: geometric category biases generalize across pointing and verbal response types. Journal of Experimental Psychology: Human Perception and Performance, 32(2), 473490.Google Scholar
Stewart, T. C., Tang, Y., & Eliasmith, C. (2011). A biologically realistic cleanup memory: autoassociation in spiking neurons. Cognitive Systems Research, 12(2), 8492.Google Scholar
Strauss, S., Woodgate, P. J., Sami, S. A., & Heinke, D. (2015). Choice reaching with a LEGO arm robot (CoRLEGO): the motor system guides visual attention to movement-relevant information. Neural Networks, 72, 312.Google Scholar
Sussillo, D., Churchland, M. M., Kaufman, M. T., & Shenoy, K. V. (2015). A neural network that finds a naturalistic solution for the production of muscle activity. Nature Neuroscience, 18(7), 10251033.Google Scholar
Tekülve, J., Fois, A., Sandamirskaya, Y., & Schöner, G. (2019). Autonomous sequence generation for a neural dynamic robot: scene perception, serial order, and object-oriented movement. Frontiers in Neurorobotics, 13, 208014669.Google Scholar
Tekülve, J., & Schöner, G. (2020). A neural dynamic network drives an intentional agent that autonomously learns beliefs in continuous time. IEEE Transactions on Cognitive and Developmental Systems, 99, 112.Google Scholar
Thelen, E., Schöner, G., Scheier, C., & Smith, L. (2001). The dynamics of embodiment: a field theory of infant perseverative reaching. Brain and Behavioral Sciences, 24, 133.Google Scholar
Thelen, E., & Smith, L. B. (1994). A Dynamic Systems Approach to the Development of Cognition and Action. Cambridge, MA: MIT Press.Google Scholar
Thompson, R. F., & Spencer, W. A. (1966). Habituation: a model phenomenon for the study of neuronal substrates of behavior. Psychological Review, 73(1), 1643.Google Scholar
Trappenberg, T. P. (2010). Fundamentals of Computational Neuroscience (2nd ed.). Oxford: Oxford University Press.Google Scholar
Trappenberg, T. P., Dorris, M. C., Munoz, D. P., & Klein, R. M. (2001). A model of saccade initiation based on the competitive integration of exogenous and endogenous signals in the superior colliculus. Journal of Cognitive Neuroscience, 13(2), 256271.Google Scholar
Treisman, A. M. (1980). A feature-integration theory of attention. Cognitive Psychology, 12, 97136.Google Scholar
Tripp, B., & Eliasmith, C. (2016). Function approximation in inhibitory networks. Neural Networks, 77, 95106.Google Scholar
Van Gelder, T. (1998). The dynamical hypothesis in cognitive science. Brain and Behavioral Sciences, 21, 615665.Google Scholar
Wilimzig, C., Schneider, S., & Schöner, G. (2006). The time course of saccadic decision making: dynamic field theory. Neural Networks, 19(8), 10591074.Google Scholar
Wilson, H. R., & Cowan, J. D. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical Journal, 12, 124.Google Scholar
Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9(4), 625636.Google Scholar

References

Aerts, D. (2009). Quantum structure in cognition. Journal of Mathematical Psychology, 53(5), 314348.Google Scholar
Aerts, D., Gabora, L., & Sozzo, S. (2013). Concepts and their dynamics: a quantum-theoretic modeling of human thought. Topics in Cognitive Science, 5(4), 737772.Google Scholar
Aerts, D., Sozzo, S., & Veloz, T. (2015). Quantum structure of negation and conjunction in human thought. Frontiers in Psychology, 6, 1447.Google Scholar
Asano, M., Basieva, I., Khrennikov, A., Ohya, M., & Tanaka, Y. (2017). A quantum-like model of selection behavior. Journal of Mathematical Psychology, 78, 212.Google Scholar
Asano, M., Ohya, M., Tanaka, Y., Basieva, I., & Khrennikov, A. (2011). Quantum-like model of brain’s functioning: decision making from decoherence. Journal of Theoretical Biology, 281(1), 5664.Google Scholar
Ashtiani, M., & Azgomi, M. A. (2015). A survey of quantum-like approaches to decision making and cognition. Mathematical Social Sciences, 75, 4980.Google Scholar
Atmanspacher, H., & Filk, T. (2013). The necker–zeno model for bistable perception. Topics in Cognitive Science, 5(4), 800817.Google Scholar
Atmanspacher, H., Filk, T., & Romer, H. (2004). Quantum zero features of bistable perception. Biological Cybernetics, 90, 3340.Google Scholar
Basieva, I., Pothos, E., Trueblood, J., Khrennikov, A., & Busemeyer, J. (2017). Quantum probability updating from zero priors (by-passing cromwells rule). Journal of Mathematical Psychology, 77, 5869.Google Scholar
Birnbaum, M. (2008). New paradoxes of risky decision making. Psychological Review, 115, 463501.Google Scholar
Boyer-Kassem, T., Duchêne, S., & Guerci, E. (2016). Testing quantum-like models of judgment for question order effect. Mathematical Social Sciences, 80, 3346.Google Scholar
Brainerd, C. J., Wang, Z., & Reyna, V. (2013). Superposition of episodic memories: overdistribution and quantum models.Topics in Cognitive Science, 5(4), 773799.Google Scholar
Broekaert, J. B., & Busemeyer, J. R. (2017). A hamiltonian driven quantum-like model for overdistribution in episodic memory recollection. Frontiers in Physics, 5, 23.Google Scholar
Broekaert, J. B., Busemeyer, J. R., & Pothos, E. M. (2020). The disjunction effect in two-stage simulated gambles. An experimental study and comparison of a heuristic logistic, Markov and quantum-like model. Cognitive Psychology, 117, 101262.Google Scholar
Bruza, P. D., Kitto, K., Ramm, B. J., & Sitbon, L. (2015). A probabilistic framework for analysing the compositionality of conceptual combinations. Journal of Mathematical Psychology, 67, 2638.Google Scholar
Bruza, P. D., Wang, Z., & Busemeyer, J. R. (2015). Quantum cognition: a new theoretical approach to psychology. Trends in Cognitive Sciences, 19(7), 383393.Google Scholar
Busemeyer, J. R., & Bruza, P. D. (2012). Quantum Models of Cognition and Decision. Cambridge: Cambridge University Press.Google Scholar
Busemeyer, J. R., Kvam, P. D., & Pleskac, T. J. (2020). Comparison of Markov versus quantum dynamical models of human decision making. WIREs Cognitive Science, 11(4), e1576.Google Scholar
Busemeyer, J. R., Pothos, E. M., Franco, R., & Trueblood, J. S. (2011). A quantum theoretical explanation for probability judgment errors. Psychological Review, 118(2), 193218.Google Scholar
Busemeyer, J. R., & Wang, Z. (2015). What is quantum cognition, and how is it applied to psychology? Current Directions in Psychological Science, 24(3), 163169.Google Scholar
Busemeyer, J. R., & Wang, Z. (2017). Is there a problem with quantum models of psychological measurements? PLoS One, 12(11), e0187733.Google Scholar
Busemeyer, J. R., & Wang, Z. (2018). Hilbert space multidimensional theory. Psychological Review, 125(4), 572591.Google Scholar
Busemeyer, J. R., Wang, Z., & Pothos, E. M. (2015). Quantum models of cognition and decision. In Busemeyer, J. R. (Ed.), Oxford Handbook of Computational and Mathematical Psychology. Oxford: Oxford University Press.Google Scholar
Busemeyer, J. R., Wang, Z., & Shiffrin, R. S. (2015). Bayesian model comparison favors quantum over standard decision theory account of dynamic inconsistency. Decision, 2, 112.Google Scholar
Busemeyer, J. R., Wang, Z., & Townsend, J. (2006). Quantum dynamics of human decision making. Journal of Mathematical Psychology, 50(3), 220241.Google Scholar
Cervantes, V. H., & Dzhafarov, E. (2018). Snow queen is evil and beautiful: experimental evidence for probabilistic contextuality in human choices. Decision, 5, 193204.Google Scholar
Costello, F., & Watts, P. (2018). Invariants in probabilistic reasoning. Cognitive Psychology, 100, 116.Google Scholar
Costello, F., Watts, P., & Fisher, C. (2017). Surprising rationality in probability judgment: assessing two competing models. Cognition, 170, 280297.Google Scholar
Denolf, J., & Lambert-Mogiliansky, A. (2016). Bohr complementarity in memory retrieval. Journal of Mathematical Psychology, 73, 2836.Google Scholar
Denolf, J., Martínez-Martínez, I., Josephy, H., & Barque-Duran, A. (2016). A quantum-like model for complementarity of preferences and beliefs in dilemma games. Journal of Mathematical Psychology, 78, 96106.Google Scholar
Diederich, A., & Trueblood, J. S. (2018). A dynamic dual process model of risky decision making. Psychological Review, 125(2), 270292.Google Scholar
Dirac, P. A. M. (1930/1958). The Principles of Quantum Mechanics. Oxford: Oxford University Press.Google Scholar
Dzhafarov, E. N., Zhang, R., & Kujala, J. (2016). Is there contextuality in behavioural and social systems? Philosophical Transactions of the Royal Society A, 374(2058), 20150099.Google Scholar
Fantino, E., Kulik, J., & Stolarz-Fantino, S. (1997). The conjunction fallacy: a test of averaging hypotheses. Psychonomic Bulletin and Review, 1, 96101.Google Scholar
Favre, M., Wittwer, A., Heinimann, H. R., Yukalov, V. I., & Sornette, D. (2016). Quantum decision theory in simple risky choices. PLoS One, 11(12), e0168045.Google Scholar
Fuss, I. G., & Navarro, D. J. (2013). Open parallel cooperative and competitive decision processes: a potential provenance for quantum probability decision models. Topics in Cognitive Science, 5(4), 818843.Google Scholar
Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: models of bounded rationality. Psychological Review, 103(4), 650669.Google Scholar
Gleason, A. M. (1957). Measures on the closed subspaces of a Hilbert space. Journal of Mathematical Mechanics, 6, 885893.Google Scholar
Hameroff, S. R. (2013). Quantum mechanical cognition requires quantum brain biology. Behavioral and Brain Sciences, 36(3), 287288.Google Scholar
Hampton, J. A. (1988a). Disjunction of natural concepts. Memory and Cognition, 16, 579591.Google Scholar
Hampton, J. A. (1988b). Overextension of conjunctive concepts: evidence for a unitary model for concept typicality and class inclusion. Journal of Experimental Psychology: Learning Memory and Cognition, 14, 1232.Google Scholar
He, Z., & Jiang, W. (2018). An evidential Markov decision-making model. Information Sciences, 467, 357372.Google Scholar
Hogarth, R., & Einhorn, H. J. (1992). Order effects in belief updating: the belief adjustment modeling. Cognitive Psychology, 24, 155.Google Scholar
Kellen, D., Singmann, H., & Batchelder, W. H. (2018). Classic-probability accounts of mirrored (quantum-like) order effects in human judgments. Decision, 5(4), 323338.Google Scholar
Khrennikov, A. Y. (2010). Ubiquitous Quantum Structure: From Psychology to Finance. New York, NY: Springer.Google Scholar
Khrennikov, A. Y., Basieva, I., Dzhafarov, E. N., & Busemeyer, J. R. (2014). Quantum models for psychological measurements: an unsolved problem. PloS One, 9(10), e110909.Google Scholar
Khrennikov, A. Y., Basieva, I., Pothos, E. M., & Yamato, I. (2018). Quantum probability in decision making from quantum information representation of neuronal states. Scientific Reports, 8 (1), 18.Google Scholar
Kintsch, W. (2014). Similarity as a function of semantic distance and amount of knowledge. Psychological Review, 121(3), 559561.Google Scholar
Kolmogorov, A. N. (1933/1950). Foundations of the Theory of Probability. New York, NY: Chelsea Publishing Co.Google Scholar
Kvam, P., Busemeyer, J. R., & Pleskac, T. (2021). Temporal oscillations in preference strength provide evidence for an open system model of constructed preference. Scientific Reports, 11, 8169.Google Scholar
Kvam, P. D., & Busemeyer, J. R. (2018). Quantum models of cognition and decision. In Batchelder, W. H., Colonius, H., Dzhafarov, E. N., & Myung, J. (Eds.), New Handbook of Mathematical Psychology, Vol. II. Cambridge: Cambridge University Press.Google Scholar
Kvam, P. D., & Pleskac, T. J. (2017). A quantum information architecture for cue-based heuristics. Decision, 4(4), 197233.Google Scholar
Kvam, P. D., Pleskac, T. J., Yu, S., & Busemeyer, J. R. (2015). Interference effects of choice on confidence. Proceedings of the National Academy of Science, 112(34), 1064510650.Google Scholar
La Mura, P. (2009). Projective expected utility. Journal of Mathematical Psychology, 53(5), 408414.Google Scholar
Manousakis, E. (2009). Quantum formalism to describe binocular rivalry. Biosystems, 98(2), 5766.Google Scholar
Martínez-Martínez, I. (2014). A connection between quantum decision theory and quantum games: the hamiltonian of strategic interaction. Journal of Mathematical Psychology, 58, 3344.Google Scholar
Martínez-Martínez, I., & Sánchez-Burillo, E. (2016). Quantum stochastic walks on networks for decision-making. Scientific reports, 6, 23812. https://doi.org/10.1038/srep23812Google Scholar
Mistry, P. K., Pothos, E. M., Vandekerckhove, J., & Trueblood, J. S. (2018). A quantum probability account of individual differences in causal reasoning. Journal of Mathematical Psychology, 87, 7697.Google Scholar
Moreira, C., & Wichert, A. (2016). Quantum-like Bayesian networks for modeling decision making. Frontiers in Psychology, 7, 11.Google Scholar
Nielsen, M. A., & Chuang, I. L. (2000). Quantum Computation and Quantum Information. Cambridge: Cambridge University Press.Google Scholar
Nilsson, H. (2008). Exploring the conjunction fallacy within a category learning framework. Journal of Behavioral Decision Making, 21, 471490.Google Scholar
Peres, A. (1998). Quantum Theory: Concepts and Methods. Norwell, MA: Kluwer Academic.Google Scholar
Pothos, E. M., & Busemeyer, J. R. (2022). Quantum cognition. Annual Review of Psychology, 73, 749778.Google Scholar
Pothos, E. M., & Busemeyer, J. R. (2009). A quantum probability model explanation for violations of ‘rational’ decision making. Proceedings of the Royal Society B, 276(1665), 21712178.Google Scholar
Pothos, E. M., & Busemeyer, J. R. (2013). Can quantum probability provide a new direction for cognitive modeling? Behavioral and Brain Sciences, 36, 255274.Google Scholar
Pothos, E. M., Busemeyer, J. R., & Trueblood, J. S. (2013). A quantum geometric model of similarity. Psychological Review, 120(3), 679696.Google Scholar
Pothos, E. M., & Trueblood, J. S. (2015). Structured representations in a quantum probability model of similarity. Journal of Mathematical Psychology, 64, 3543.Google Scholar
Ratcliff, R., Smith, P. L., Brown, S. L., & McCoon, G. (2016). Diffusion decision model: current history and issues. Trends in Cognitive Science, 20, 260281.Google Scholar
Rosner, A., Basieva, I., Barque-Duran, A., et al. (2022). Ambivalence in cognition. Cognitive Psychology, 134, 101464.Google Scholar
Sanborn, A. N., Griffiths, T. L., & Shiffrin, R. M. (2010). Uncovering mental representations with Markov chain monte carlo. Cognitive Psychology, 60(2), 63106.Google Scholar
Savage, L. J. (1954). The Foundations of Statistics. Chichester: John Wiley & Sons.Google Scholar
Scheibehenne, B., Rieskamp, J., & Wagenmakers, E.-J. (2013). Testing adaptive toolbox models: a Bayesian hierarchical approach. Psychological Review, 120(1), 39.Google Scholar
Shafir, E., & Tversky, A. (1992). Thinking through uncertainty: nonconsequential reasoning and choice. Cognitive Psychology, 24, 449474.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
Tesar, J. (2020). A quantum model of strategic decision-making explains the disjunction effect in the prisoner’s dilemma game. Decision, 7 (1), 4354.Google Scholar
Townsend, J. T., Silva, K. M., Spencer-Smith, J., & Wenger, M. (2000). Exploring the relations between categorization and decision making with regard to realistic face stimuli. Pragmatics and Cognition, 8, 83105.Google Scholar
Trueblood, J. S., & Busemeyer, J. R. (2010). A quantum probability account for order effects on inference. Cognitive Science, 35, 15181552.Google Scholar
Trueblood, J. S., & Hemmer, P. (2017). The generalized quantum episodic memory model. Cognitive Science, 41(8), 20892125.Google Scholar
Trueblood, J. S., Yearsley, J. M., & Pothos, E. M. (2017). A quantum probability framework for human probabilistic inference. Journal of Experimental Psychology: General, 146(9), 13071341.Google Scholar
Tversky, A. (1977). Features of similarity. Psychological Review, 84(4), 327.Google Scholar
Tversky, A., & Kahneman, D. (1983). Extensional versus intuitive reasoning: the conjunctive fallacy in probability judgment. Psychological Review, 90, 293315.Google Scholar
Tversky, A., & Kahneman, D. (1990). Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297323.Google Scholar
Tversky, A., & Shafir, E. (1992). The disjunction effect in choice under uncertainty. Psychological Science, 3, 305309.Google Scholar
Von Neumann, J. (1932/1955). Mathematical Foundations of Quantum Theory. Princeton, NJ: Princeton University Press.Google Scholar
Wang, Z., & Busemeyer, J. (2016a). Comparing quantum versus Markov random walk models of judgements measured by rating scales. Philosophical Transactions of the Royal Society A, 374(2058), 20150098.Google Scholar
Wang, Z., & Busemeyer, J. R. (2016b). Interference effects of categorization on decision making. Cognition, 150, 133149.Google Scholar
Wang, Z., Solloway, T., Shiffrin, R. M., & Busemeyer, J. R. (2014). Context effects produced by question orders reveal quantum nature of human judgments. Proceedings of the National Academy of Sciences, 111(26), 94319436.Google Scholar
White, L. C., Pothos, E., & Busemeyer, J. (2014). Sometimes it does hurt to ask: the constructive role of articulating impressions. Cognition, 1, 4864.Google Scholar
Yearsley, J. M., & Busemeyer, J. R. (2016). Quantum cognition and decision theories. Journal of Mathematical Psychology, 74, 99116.Google Scholar
Yearsley, J. M., & Pothos, E. M. (2016). Zeno’s paradox in decision-making. Proceedings of the Royal Society B: Biological Sciences, 283(1828), 20160291.Google Scholar
Yearsley, J. M., & Trueblood, J. (2018). A quantum theory account of order effects and conjunction fallacies in political judgments. Psychonomic Bulletin & Review, 25, 15171525.Google Scholar
Yukalov, V. I., & Sornette, D. (2011). Decision theory with prospect interference and entanglement. Theory and Decision, 70, 283328.Google Scholar

References

Anderson, J. R. (1976). Language, Memory and Thought. Mahwah, NJ: Erlbaum.Google Scholar
Anderson, J. R. (1990). The Adaptive Character of Thought. Mahwah, NJ: Erlbaum.Google Scholar
Anderson, J. R. (2005). Human symbol manipulation within an integrated cognitive architecture. Cognitive Science, 29(3), 313341.Google Scholar
Anderson, J. R. (2007). How Can the Human Mind Occur in the Physical Universe. Oxford: Oxford University Press.Google Scholar
Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of mind. Psychological Review, 111(4), 10361060.Google Scholar
Anderson, J. R., & Lebiere, C. L. (1998). The Atomic Components of Thought. Mahwah, NJ: Erlbaum.Google Scholar
Anderson, J. R., & Lebiere, C. L. (2003). The Newell test for a theory of cognition. Behavioral & Brain Sciences, 26, 587637.Google Scholar
Anderson, J. R., & Matessa, M. P. (1997). A production system theory of serial memory. Psychological Review, 104, 728748.Google Scholar
Anderson, J. R., Taatgen, N. A., & Byrne, M. D. (2005). Learning to achieve perfect time sharing: architectural implications of Hazeltine, Teague, & Ivry (2002). Journal of Experimental Psychology: Human Perception and Performance, 31(4), 749761.Google Scholar
Berry, D. C., & Broadbent, D. E. (1984). On the relationship between task performance and associated verbalizable knowledge. The Quarterly Journal of Experimental Psychology, 36A, 209231.Google Scholar
Borst, J. P., & Anderson, J. R. (2013). Using model-based functional MRI to locate working memory updates and declarative memory retrievals in the fronto-parietal network. Proceedings of the National Academy of Sciences, 110(5), 16281633.Google Scholar
Byrne, M. D., & Anderson, J. R. (2001). Serial modules in parallel: the psychological refractory period and perfect time-sharing. Psychological Review, 108, 847869.Google Scholar
Chater, N., & Vitányi, P. (2003). Simplicity: a unifying principle in cognitive science? Trends in Cognitive Sciences, 7(1), 1922.Google Scholar
Choi, H., Chang, L. H., Shibata, K., Sasaki, Y., & Watanabe, T. (2012). Resetting capacity limitations revealed by long-lasting elimination of attentional blink through training. Proceedings of the National Academy of Sciences, 109(30), 1224212247.Google Scholar
Chong, R. S. (1999). Modeling dual-task performance improvement: casting executive process knowledge acquisition as strategy refinement. Unpublished dissertation. University of Michigan.Google Scholar
Cooper, R., & Fox, J. (1998). COGENT: a visual design environment for cognitive modelling. Behavior Research Methods, Instruments, & Computers, 30, 553564.Google Scholar
Eliasmith, C. (2013). How to Build a Brain: A Neural Architecture for Biological Cognition. Oxford: Oxford University Press.Google Scholar
Eliasmith, C., Stewart, T. C., Choo, X., et al. (2012). A large-scale model of the functioning brain. Science, 338(6111), 12021205.Google Scholar
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 179211.Google Scholar
Ferlazzo, F., Lucido, S., Di Nocera, F., Fagioli, S., & Sdoia, S. (2007). Switching between goals mediates the attentional blink effect. Experimental Psychology, 54(2), 8998.Google Scholar
Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: a critical analysis. Cognition, 28, 371.Google Scholar
Forgy, C. L. (1982). Rete: a fast algorithm for the many object pattern match problem. Artificial Intelligence, 19, 1737.Google Scholar
Hoekstra, C., Martens, S., & Taatgen, N. A. (2020). A skill-based approach to modeling the attentional blink. Topics in Cognitive Science, 12(3), 10301045.Google Scholar
Hornof, A. J., & Kieras, D. E. (1997). Cognitive modeling reveals menu search is both random and systematic. Proceedings of CHI-97 (pp. 107114). New York, NY: Association for Computing Machinery.Google Scholar
Huffman, S. B., & Laird, J. E. (1995). Flexibly instructable agents. Journal of Artificial Intelligence Research, 3, 271324.Google Scholar
Huijser, S., van Vugt, M. K., & Taatgen, N. A. (2018). The wandering self: tracking distracting self-generated thought in a cognitively demanding context. Consciousness and Cognition, 58, 170185.Google Scholar
Just, M. A., & Carpenter, P. A. (1992). A capacity theory of comprehension: individual differences in working memory. Psychological Review, 99, 122149.Google Scholar
Kieras, D. E., Meyer, D. E., Mueller, S. T., & Seymour, T. L. (1999). Insights into working memory from the perspective of The EPIC Architecture for modeling skilled perceptual-motor and cognitive human performance. In: Miyaki, A. & Shah, P. (Eds.), Models of Working Memory. New York, NY: Cambridge University Press.Google Scholar
Kirk, J. R., & Laird, J. E. (2019). Learning hierarchical symbolic representations to support interactive task learning and knowledge transfer. Proceedings of IJCAI-19 (pp. 60956102).Google Scholar
Laird, J. E. (2012). The Soar Cognitive Architecture. Cambridge, MA: MIT Press.Google Scholar
Laird, J. E., Newell, A., & Rosenbloom, P. S. (1987). SOAR: an architecture for general intelligence. Artificial Intelligence, 33, 164.Google Scholar
Laird, J. E., Lebiere, C., & Rosenbloom, P. S. (2017). A standard model of the mind: toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics. AI Magazine, 38(4), 1326.Google Scholar
Lewis, R. L. (1996) Interference in short-term memory: the magical number two (or three) in sentence processing. Journal of Psycholinguistic Research, 25, 93115.Google Scholar
Lovett, M. C., Reder, L. M., & Lebiere, C. (1999). Modeling working memory in a unified architecture: an ACT-R perspective. In Miyake, A. & Shah, P. (Eds.), Models of Working Memory (pp. 135182). Cambridge: Cambridge University Press.Google Scholar
Marinier, R. P., & Laird, J. E. (2004). Toward a comprehensive computational model of emotions and feelings. Proceedings of the Sixth International Conference on Cognitive Modeling (pp. 172177). Mahwah, NJ: Erlbaum.Google 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, 419457.Google Scholar
McCloskey, M., & Cohen, N. J. (1989). Catastrophic interference in connectionist networks: the sequential learning problem. In Bower, G. H. (Ed.), The Psychology of Learning and Motivation (vol. 24, pp. 109164). San Diego, CA: Academic Press.Google Scholar
Meyer, D. E., & Kieras, D. E. (1997). A computational theory of executive cognitive processes and multiple-task performance. Part 1. Basic mechanisms Psychological Review, 104, 265.Google Scholar
Miller, G. A. (1956). The magic number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 63, 8197.Google Scholar
Nason, S., & Laird, J. E. (2004). Soar-RL: integrating reinforcement learning with Soar. Proceedings of the Sixth International Conference on Cognitive Modeling (pp. 208213). Mahwah, NJ: Erlbaum.Google Scholar
Newell, A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Newell, A., & Simon, H. A. (1963). GPS, a program that simulates human thought. In Feigenbaum, E. A. & Feldman, J. (Eds.), Computers and Thought. New York, NY: McGraw-Hill.Google Scholar
O’Reilly, R. C., & Munakata, Y. (2000). Computational Explorations in Cognitive Neuroscience. Cambridge, MA: MIT Press.Google Scholar
Pashler, H. (1994). Dual-task interference in simple tasks: data and theory. Psychological Bulletin, 116, 220244.Google Scholar
Penrose, R. (1989). The Emperor’s New Mind. Oxford: Oxford University Press.Google Scholar
Popper, K. R. (1962). Conjectures and Refutations: The Growth of Scientific Knowledge. New York, NY: Basic Books.Google Scholar
Roberts, S., & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107, 358367.Google Scholar
Salvucci, D. D., & Macuga, K. L. (2002). Predicting the effects of cellular-phone dialing on driver performance. Cognitive Systems Research, 3, 95102.Google Scholar
Salvucci, D. D., & Taatgen, N. A. (2008). Threaded cognition: an integrated theory of concurrent multitasking. Psychological Review, 114(1), 101130.Google Scholar
Schumacher, E. H., Seymour, T. L., Glass, J. M., et al. (2001). Virtually perfect time sharing in dual-task performance: uncorking the central cognitive bottleneck. Psychological Science, 12(2), 101108.Google Scholar
Singley, M. K., & Anderson, J. R. (1985). The transfer of text-editing skill. International Journal of Man-Machine Studies, 22(4), 403423.Google Scholar
Smolensky, P. (1990). Tensor product variable binding and the representation of symbolic structures in connectionist networks. Artificial Intelligence, 46, 159216.Google Scholar
Stocco, A., Lebiere, C., & Anderson, J. R. (2010). Conditional routing of information to the cortex: a model of the basal ganglia’s role in cognitive coordination. Psychological Review, 117(2), 541.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
Sun, R., Merrill, E., & Peterson, T. (2001). From implicit skills to explicit knowledge: a bottom-up model of skill learning. Cognitive Science, 25(2), 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(1), 159192.Google Scholar
Sun, R., & Zhang, X. (2004). Top-down versus bottom-up learning in cognitive skill acquisition. Cognitive Systems Research, 5(1), 6389.Google Scholar
Taatgen, N. A. (2005). Modeling parallelization and speed improvement in skill acquisition: from dual tasks to complex dynamic skills. Cognitive Science, 29, 421455.Google Scholar
Taatgen, N. A. (2007). The minimal control principle. In: Gray, W. (Ed.), Integrated Models of Cognitive Systems. Oxford: Oxford University Press.Google Scholar
Taatgen, N. A. (2013). The nature and transfer of cognitive skills. Psychological Review, 120(3), 439471.Google Scholar
Taatgen, N. A. (2018). The representation of task knowledge at multiple levels of abstraction. In Gluck, K. A. & Laird, J. E. (Eds.), Interactive Task Learning: Humans, Robots, and Agents Acquiring New Tasks Through Natural Interactions (pp. 7590), Cambridge, MA: MIT Press.Google Scholar
Taatgen, N. A. (2019). A spiking neural architecture that learns tasks. In Stewart, T. (Ed.), Proceedings of the 17th International Conference on Cognitive Modeling.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(2), 123155.Google Scholar
Taatgen, N. A., Huss, D., Dickison, D., & Anderson, J. R. (2008). The acquisition of robust and flexible cognitive skills. Journal of Experimental Psychology: General, 137(3), 548.Google Scholar
Taatgen, N. A., & Lee, F. J. (2003). Production compilation: a simple mechanism to model complex skill acquisition. Human Factors, 45(1), 6176.Google Scholar
Taatgen, N. A., van Rijn, D. H., & Anderson, J. R. (2007). An integrated theory of prospective time interval estimation: the role of cognition, attention and learning. Psychological Review, 114(3), 577598.Google Scholar
Taatgen, N. A., & Wallach, D. (2002). Whether skill acquisition is rule or instance based is determined by the structure of the task. Cognitive Science Quarterly, 2(2), 163204.Google Scholar
Tambe, M., Newell, A., & Rosenbloom, P. S. (1990). The problem of expensive chunks and its solution by restricting expressiveness. Machine Learning, 5, 299348.Google Scholar
Turing, A. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 2nd series, 42, 230265.Google Scholar
Turing, A. (1950). Computing machinery and intelligence. Mind, 59, 433460.Google Scholar
Young, R. M., & Lewis, R. L. (1999). The Soar cognitive architecture and human working memory. In Miyake, A. & Shah, P. (Eds.) Models of Working Memory (pp. 224256). Cambridge: Cambridge University Press.Google Scholar

References

Abadi, M., Barham, P., Chen, J., et al. (2016). Tensorflow: a system for large-scale machine learning. In Keeton, K., & Roscoe, T., (Eds.), In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (pp. 265283). USENIX Association.Google Scholar
Agostinelli, F., Hoffman, M. D., Sadowski, P. J., & Baldi, P. (2015). Learning activation functions to improve deep neural networks. In Bengio, Y. & LeCun, Y., (Eds.), 3rd International Conference on Learning Representations, ICLR 2015, Workshop Track Proceedings.Google Scholar
Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017). Deep reinforcement learning: a brief survey. IEEE Signal Processing Magazine, 34(6), 2638.Google Scholar
Bellman, R. (1961). Adaptive Control Processes: A Guided Tour. Princeton, NJ: Princeton University Press.Google Scholar
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007). Greedy layer-wise training of deep networks. In Schölkopf, B., Platt, J., & Hoffman, T., (Eds.), Advances in Neural Information Processing Systems 19 (pp. 153160). Cambridge, MA: MIT Press.Google Scholar
Bengio, Y., & Lecun, Y. (2007). Scaling Learning Algorithms Towards AI. Cambridge, MA: MIT Press.Google Scholar
Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157166.Google Scholar
Bianchini, M., Frasconi, P., & Gori, M. (1995). Learning in multilayered networks used as autoassociators. IEEE Transactions on Neural Networks, 6(2), 512515.Google Scholar
Bodyanskiy, Y., Deineko, A., Pliss, I., & Slepanska, V. (2019). Formal neuron based on adaptive parametric rectified linear activation function and its learning. In Kryvinska, N., Izonin, I., Gregus, M., Poniszewska-Maranda, A., & Dronyuk, I., (Eds.), Proceedings of the 1st International Workshop on Digital Content & Smart Multimedia (DCSMart 2019), vol. 2533 of CEUR Workshop Proceedings (pp. 14–22). CEUR-WS.org.Google Scholar
Bohn, B., Griebel, M., & Rieger, C. (2019). A representer theorem for deep kernel learning. Journal of Machine Learning Research, 20, 132.Google Scholar
Boring, E. (1950). A History of Experimental Psychology. New York, NY: Appleton-Century-Crofts.Google Scholar
Castelli, I., & Trentin, E. (2011). Supervised and unsupervised co-training of adaptive activation functions in neural nets. In Schwenker, F., & Trentin, E., (Eds.), Partially Supervised Learning – First IAPR TC3 Workshop, PSL 2011, Revised Selected Papers, vol. 7081 of Lecture Notes in Computer Science (pp. 5261). New York, NY: Springer.Google Scholar
Castelli, I., & Trentin, E. (2014). Combination of supervised and unsupervised learning for training the activation functions of neural networks. Pattern Recognition Letters, 37, 178191.Google Scholar
Cho, K., Courville, A., & Bengio, Y. (2015). Describing multimedia content using attention-based encoder-decoder networks. IEEE Transactions on Multimedia, 17(11), 18751886.Google Scholar
Clevert, D., Unterthiner, T., & Hochreiter, S. (2016). Fast and accurate deep network learning by exponential linear units (elus). In Bengio, Y., & LeCun, Y., (Eds.), Proceedings of the 4th International Conference on Learning Representations (ICLR, 2016).Google Scholar
Cortes, C., Gonzalvo, X., Kuznetsov, V., Mohri, M., & Yang, S. (2017). AdaNet: adaptive structural learning of artificial neural networks. In Precup, D., & Teh, Y. W., (Eds.), Proceedings of the 34th International Conference on Machine Learning (vol. 70, pp. 874–883).Google Scholar
Cover, T. M. (1965). Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. Electronic Computers, IEEE Transactions on, 14 (3), 326–334.Google Scholar
Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems, 2, 303314.Google Scholar
Dasgupta, S., Stevens, C. F., & Navlakha, S. (2017). A neural algorithm for a fundamental computing problem. Science, 358(6364), 793796.Google Scholar
Dauphin, Y. N., Pascanu, R., Gulcehre, C., Cho, K., Ganguli, S., & Bengio, Y. (2014). Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. In Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., & Weinberger, K. Q., (Eds.), Advances in Neural Information Processing Systems, vol. 27. New York, NY: Curran Associates, Inc.Google Scholar
Dechter, R. (1986). Learning while searching in constraint-satisfaction-problems. In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 178183.Google Scholar
Delahunt, C. B., Riffell, J. A., & Kutz, J. N. (2018). Biological mechanisms for learning: a computational model of olfactory learning in the manduca sexta moth, with applications to neural nets. Frontiers in Computational Neuroscience, 12, 102.Google Scholar
Ducoffe, M., & Precioso, F. (2018). Adversarial active learning for deep networks: a margin based approach. arXiv:1802.09841Google Scholar
Duda, R. O., & Hart, P. E. (1973). Pattern Classification and Scene Analysis. New York, NY: Wiley.Google Scholar
Dushkoff, M., & Ptucha, R. (2016). Adaptive activation functions for deep networks. Electronic Imaging, XVI(5), 15.Google Scholar
Elsayed, G. F., Shankar, S., Cheung, B., et al. (2018). Adversarial examples that fool both computer vision and time-limited humans. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 3914–3924. Red Hook, NY: Curran Associates.Google Scholar
Fiori, S. (2000). Blind signal processing by the adaptive activation function neurons. Neural Networks, 13, 597611.Google Scholar
Flennerhag, S., Yin, H., Keane, J., & Elliot, M. (2018). Breaking the activation function bottleneck through adaptive parameterization. In Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., & Garnett, R., (Eds.), Advances in Neural Information Processing Systems 31 (pp. 77397750). New York, NY: Curran Associates.Google Scholar
Fuchs, E., & Flügge, G. (2014). Adult neuroplasticity: more than 40 years of research. Neural Plasticity, 541870, 110.Google Scholar
Fukushima, K. (1975). Cognitron: a self-organizing multilayered neural network. Biological Cybernetics, 20(3–4), 121136.Google Scholar
Fukushima, K. (1980). Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4), 193202.Google Scholar
Fukushima, K. (2019). Recent advances in the deep CNN neocognitron. Nonlinear Theory and Its Applications, IEICE, 10(4), 304321.Google Scholar
Godfrey, L. B. (2019). An evaluation of parametric activation functions for deep learning. In Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics, pp. 30063011.Google Scholar
Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. (2014a). Generative adversarial nets. In Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., & Weinberger, K. Q., (Eds.), Advances in Neural Information Processing Systems, vol. 27. New York, NY: Curran Associates.Google Scholar
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., et al. (2014b). Generative adversarial nets. In Ghahramani, Z. et al., (Eds.), Advances in Neural Information Processing Systems, 27, 26722680.Google Scholar
Gori, M., & Scarselli, F. (1998). Are multilayer perceptrons adequate for pattern recognition and verification? IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 11211132.Google Scholar
Håstad, J. (1987). Computational Limitations of Small-Depth Circuits. Cambridge, MA: MIT Press.Google Scholar
Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. Hoboken, NJ: Prentice Hall.Google Scholar
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In Proceedings of the 2015 IEEE International Conference on Computer Vision, (pp. 1026–1034). IEEE Computer Society, USA.Google Scholar
He, X., Zhao, K., & Chu, X. (2021). Automl: a survey of the state-of-the-art. Knowledge-Based Systems, 212, 106622.Google Scholar
Hebb, D. O. (1949). The Organization of Behavior: A Neuropsychological Theory. New York, NY: Wiley.Google Scholar
Hinton, G. E., & Osindero, S. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18, 2006.Google Scholar
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 17351780.Google Scholar
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (pp. 22612269).Google Scholar
Hubel, D. H., & Wiesel, T. N. (1959). Receptive fields of single neurones in the cat’s striate cortex. The Journal of Physiology, 148(3), 574.Google Scholar
Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology, 160(1), 106.Google Scholar
Hubel, D. H., & Wiesel, T. N. (1977). Ferrier lecture-functional architecture of macaque monkey visual cortex. Proceedings of the Royal Society of London. Series B. Biological Sciences, 198(1130), 159.Google Scholar
Ivakhnenko, A. G. (1971). Polynomial theory of complex systems. IEEE Transactions on Systems, Man, and Cybernetics, 1(4), 364378.Google Scholar
Ivakhnenko, A. G., & Lapa, V. G. (1965). Cybernetic Predicting Devices. New York, NY: CCM Information Corporation.Google Scholar
Jagtap, A. D., Kawaguchi, K., & Karniadakis, G. E. (2020). Adaptive activation functions accelerate convergence in deep and physics-informed neural networks. Journal of Computational Physics, 404, 109136.Google Scholar
Kell, A. J., Yamins, D. L., Shook, E. N., Norman-Haignere, S. V., & McDermott, J. H. (2018). A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy. Neuron, 98(3), 630644.Google Scholar
Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations. OpenReview.net.Google Scholar
Klambauer, G., Unterthiner, T., Mayr, A., & Hochreiter, S. (2017). Self-normalizing neural networks. In Guyon, I. et al., (Eds.), Advances in Neural Information Processing Systems 30 (pp. 971980).Google Scholar
Kriegeskorte, N. (2015). Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual Review of Vision Science, 1(1), 417446.Google Scholar
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097–1105).Google Scholar
Kunc, V., & Kléma, J. (2019). On transformative adaptive activation functions in neural networks for gene expression inference. bioRxivGoogle Scholar
LeCun, Y., Boser, B., Denker, J. S., et al. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4), 541551.Google Scholar
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. In Proceedings of the IEEE, pp. 2278–2324.Google Scholar
Lee, H., & Fu, K. (1974). Grammatical inference for syntactic pattern recognition. In Tou, J., (Ed.), Information Systems (pp. 425449). Boston, MA: Springer.Google Scholar
Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009). Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th Annual International Conference on Machine Learning (pp. 609–616).Google Scholar
LeNail, A. (2019). NN-SVG: publication-ready neural network architecture schematics. The Journal of Open Source Software, 4(33), 747.Google Scholar
Li, D., Chen, X., Becchi, M., & Zong, Z. (2016). Evaluating the energy efficiency of deep convolutional neural networks on cpus and gpus. In the 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (pp. 477484).Google Scholar
Lippmann, R. P., & Gold, B. (1987). Neural classifiers useful for speech recognition. In IEEE Proceedings of the First International Conference on Neural Networks, vol. IV (pp. 417422). San Diego, CA.Google Scholar
Liu, B., Yu, X., Yu, A., Zhang, P., Wan, G., & Wang, R. (2018). Deep few-shot learning for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 57(4), 22902304.Google Scholar
Marra, G., Zanca, D., Betti, A., & Gori, M. (2018). Learning neuron non-linearities with kernel-based deep neural networks. CoRR, abs/1807.06302Google Scholar
Michels, F., Uelwer, T., Upschulte, E., & Harmeling, S. (2019). On the vulnerability of capsule networks to adversarial attacks. arXiv:1906.03612Google Scholar
Minsky, M., & Papert, S. A. (1969). Perceptrons: An Introduction to Computational Geometry. Cambridge, MA: MIT Press.Google Scholar
Mozzachiodi, R., & Byrne, J. (2010). More than synaptic plasticity: role of nonsynaptic plasticity in learning and memory. Trends in Neurosciences, 33(1), 1726.Google Scholar
Oléron, P. (1963). Les activités intellectuelles. In P. Oléron, J. Piaget, B. Inhelder, & P. Gréco, , (Eds.), Traité de psychologie expérimentale VII. L’Intelligence (pp. 170). Paris: Presses Universitaires de France.Google Scholar
Oléron, P., Piaget, J., Inhelder, B., & Gréco, P. (1963). Traité de psychologie expérimentale VII. L’Intelligence. Paris: Presses Universitaires de France.Google Scholar
Olson, R. S., Cava, W. G. L., Orzechowski, P., Urbanowicz, R. J., & Moore, J. H. (2017). PMLB: a large benchmark suite for machine learning evaluation and comparison. BioData Mining, 10(1), 36:1–36:13.Google Scholar
Paszke, A., Gross, S., Massa, F., et al. (2019). PyTorch: an imperative style, high-performance deep learning library. In Wallach, H. et al. (Eds.), Advances in Neural Information Processing Systems 32, (pp. 80248035). New York, NY: Curran Associates.Google Scholar
Peterson, J. C., Abbott, J. T., & Griffiths, T. L. (2018). Evaluating (and improving) the correspondence between deep neural networks and human representations. Cognitive Science, 42(8), 26482669.Google Scholar
Qian, S., Liu, H., Liu, C., Wu, S., & Wong, H.-S. (2018). Adaptive activation functions in convolutional neural networks. Neurocomputing, 272, 204212.Google Scholar
Roy, S., Unmesh, A., & Namboodiri, V. P. (2018). Deep active learning for object detection. In 29th British Machine Vision Conference (p. 91).Google Scholar
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986a). Learning representations by back-propagating errors. Nature, 323, 533536.Google Scholar
Rumelhart, D. E., McClelland, J. L., & Group, P. R. (1986b). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge, MA: MIT Press.Google Scholar
Sabour, S., Frosst, N., & Hinton, G. E. (2017). Dynamic routing between capsules. In Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 38593869).Google Scholar
Scardapane, S., Vaerenbergh, S. V., & Uncini, A. (2019). Kafnets: kernel-based non-parametric activation functions for neural networks. Neural Networks, 110, 1932.Google Scholar
Shawahna, A., Sait, S. M., & El-Maleh, A. (2019). FPGA-based accelerators of deep learning networks for learning and classification: a review. IEEE Access, 7, 78237859.Google Scholar
Shen, Y., Dasgupta, S., & Navlakha, S. (2020). Habituation as a neural algorithm for online odor discrimination. Proceedings of the National Academy of Sciences, 117(22), 1240212410.Google Scholar
Siddoway, B., Hou, H., & Xia, H. (2014). Molecular mechanisms of homeostatic synaptic downscaling. Neuropharmacology, 78, 3844.Google Scholar
Siu, K.-Y., Roychowdhury, V., & Kailath, T. (1995). Discrete Neural Networks. Hoboken, NJ: Prentice Hall.Google Scholar
Solazzi, M., & Uncini, A. (2004). Regularising neural networks using flexible multivariate activation function. Neural Networks, 17(2), 247260.Google Scholar
Steinkrau, D., Simard, P. Y., & Buck, I. (2005). Using GPUs for machine learning algorithms. In Proceedings of the 8th International Conference on Document Analysis and Recognition (pp. 11151119). IEEE Computer Society.Google Scholar
Szegedy, C., Zaremba, W., Sutskever, I., et al. (2014). Intriguing properties of neural networks. In 2nd International Conference on Learning Representations.Google Scholar
Tanay, T., & Griffin, L. (2016). A boundary tilting perspective on the phenomenon of adversarial examples. arXiv e-prints arXiv–1608Google Scholar
Tramèr, F., Papernot, N., Goodfellow, I., Boneh, D., & McDaniel, P. (2017). The space of transferable adversarial examples. arXiv:1704.03453Google Scholar
Trentin, E. (1998). Learning the amplitude of activation functions in layered networks. In Marinaro, M., & Tagliaferri, R. (Eds.), Neural Nets - WIRN Vietri 98, vol. 7081 of Lecture Notes in Computer Science, (pp. 138–144). Berlin: Springer.Google Scholar
Trentin, E. (2001). Networks with trainable amplitude of activation functions. Neural Networks, 14(4–5), 471493.Google Scholar
Turrigiano, G. G., & Nelson, S. B. (2000). Hebb and homeostasis in neuronal plasticity. Current Opinion in Neurobiology, 10(3), 358364.Google Scholar
Vanschoren, J., van Rijn, J. N., Bischl, B., & Torgo, L. (2013). OpenML: networked science in machine learning. SIGKDD Explorations, 15(2), 4960.Google Scholar
Vecci, L., Piazza, F., & Uncini, A. (1998). Learning and approximation capabilities of adaptive spline activation function neural networks. Neural Networks, 11(2), 259270.Google Scholar
Viroli, C., & Mclachlan, G. J. (2019). Deep Gaussian mixture models. Statistics and Computing, 29(1), 4351.Google Scholar
WardJr., J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236244.Google Scholar
Werbos, P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Science. Ph.D. Thesis, Department of Applied Mathematics, Harvard University.Google Scholar
Werbos, P. J. (1988). Generalization of backpropagation with application to a recurrent gas market model. Neural Networks, 1(4), 339356.Google Scholar
Wiener, N. (1958). Nonlinear Problems in Random Theory. New York, NY: John Wiley.Google Scholar
Xian, Y., Lampert, C. H., Schiele, B., & Akata, Z. (2018). Zero-shot learning: a comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(9), 22512265.Google Scholar
Xu, B., Wang, N., Chen, T., and Li, M. (2015). Empirical evaluation of rectified activations in convolutional network. arXiv:1505.00853v2Google Scholar
Yang, M., Sheth, S. A., Schevon, C. A., McKhann, G. M., & Mesgarani, N. (2015). Speech reconstruction from human auditory cortex with deep neural networks. In Proceedings of INTERSPEECH 2015, ISCA (pp. 1121–1125).Google Scholar
Zhang, L., Xiang, T., & Gong, S. (2017). Learning a deep embedding model for zero-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2021–2030).Google Scholar

References

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
Balleine, B. W., Delgado, M. R., & Hikosaka, O. (2007). The role of the dorsal striatum in reward and decision-making. Journal of Neuroscience, 27(31), 81618165. https://doi.org/10.1523/JNEUROSCI.1554-07.2007Google Scholar
Barto, A. G. (1995). Adaptive critics and the basal ganglia. In Houk, J. C., Davis, J. L., & Beiser, D. G. (Eds.), Models of Information Processing in the Basal Ganglia, (pp. 215232). Cambridge, MA: MIT Press.Google Scholar
Barto, A. G., Sutton, R. S., & Andersen, C. W. (1983). Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man, and Cybernetics, 13(5), 834846. https://doi.org/10.1109/TSMC.1983.6313077Google 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
Bendesky, A., Tsunozaki, M., Rockman, M. V., Kruglyak, L., & Bargmann, C. I. (2011). Catecholamine receptor polymorphisms affect decision-making in C. elegans. Nature, 472(7343), 313318. https://doi.org/10.1038/nature09821Google Scholar
Bengio, Y. (2017). The consciousness prior. arXiv(1709.08568)Google Scholar
Boyan, J. A., & Moore, A. W. (1995). Generalization in reinforcement learning: safely approximating the value function. In Leen, T. K. (Ed.), Advances in Neural Information Processing Systems 7 (pp. 369376). Cambridge, MA: MIT Press.Google 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
Cisek, P. (2007). Cortical mechanisms of action selection: the affordance competition hypothesis. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1485), 15851599. https://doi.org/10.1098/rstb.2007.2054Google Scholar
Coulom, R. (2006). Efficient selectivity and backup operators in Monte-Carlo tree search. 5th International Conference on Computer and Games. Turin, Italy. https://hal.inria.fr/inria-00116992Google 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
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., 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. (2009). Goal-directed control and its antipodes. Neural Networks, 22(3), 213219. https://doi.org/10.1016/j.neunet.2009.03.004Google Scholar
Delong, M. R. (1990). Primate models of movement disorders of basal ganglia origin. Trends in Neurosciences, 13, 281285.Google Scholar
Dorris, M. C., & Glimcher, P. W. (2004). Activity in posterior parietal cortex is correlated with the relative subjective desirability of action. Neuron, 44(2), 365378. https://doi.org/10.1016/j.neuron.2004.09.009Google 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. (2007). Reinforcement learning: computational theory and biological mechanisms. Frontiers in Life Science, 1(1), 3040. https://doi.org/10.2976/1.2732246/10.2976/1Google Scholar
Fermin, A. S., Yoshida, T., Yoshimoto, J., Ito, M., Tanaka, S. C., & Doya, K. (2016). Model-based action planning involves cortico-cerebellar and basal ganglia networks. Scientific Reports, 6, 31378. https://doi.org/10.1038/srep31378Google 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
Freund, T. F., Powell, J. F., & Smith, A. D. (1984). Tyrosine hydroxylase-immunoreactive boutons in synaptic contact with identified striatonigral neurons, with particular reference to dendritic spines. Neuroscience, 13(4), 11891215. https://doi.org/10.1016/0306-4522(84)90294-xGoogle Scholar
Geddes, C. E., Li, H., & Jin, X. (2018). Optogenetic editing reveals the hierarchical organization of learned action sequences. Cell, 174(1), 3243, e15. https://doi.org/10.1016/j.cell.2018.06.012Google Scholar
Gerfen, C. R. (1992). The neostriatal mosaic: multiple levels of compartmental organization in the basal ganglia. Annual Review of Neuroscience, 15, 285320.Google Scholar
Glascher, J., Daw, N., Dayan, P., & O’Doherty, J. P. (2010). States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning. Neuron, 66(4), 585595. https://doi.org/10.1016/j.neuron.2010.04.016Google Scholar
Glimcher, P. W., & Fehr, E. (2013). Neuroeconomics: Decision Making and the Brain (2nd ed.). London: Elsevier Academic Press.Google Scholar
Graybiel, A. M., & Ragsdale, C. W., Jr. (1978). Histochemically distinct compartments in the striatum of humans, monkeys, and cats demonstrated by acetylthiocholinesterase staining. Proceedings of the National Academy of Sciences, 75(11), 57235726. https://doi.org/10.1073/pnas.75.11.5723Google Scholar
Gu, S., Holly, E., Lillicrap, T., & Levine, S. (2017). Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. In IEEE International Conference on Robotics and Automation (ICRA 2017).Google Scholar
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245258. https://doi.org/10.1016/j.neuron.2017.06.011Google 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
Houk, J. C., Adams, J. L., & Barto, A. G. (1995a). 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
Houk, J. C., Adams, J. L., & Barto, A. G. (1995b). Models of Information Processing in the Basal Ganglia. Cambridge, MA: MIT Press.Google Scholar
Iino, Y., Sawada, T., Yamaguchi, K., et al. (2020). Dopamine D2 receptors in discrimination learning and spine enlargement. Nature (online). https://doi.org/10.1038/s41586–020-2115-1Google Scholar
Ito, M., & Doya, K. (2015). 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
Kahneman, D. (2011). Thinking, Fast and Slow. New York, NY: Farrar, Straus and Giroux.Google Scholar
Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47(2), 263291.Google 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
Matsumoto, K., Suzuki, W., & Tanaka, K. (2003). Neuronal correlates of goal-based motor selection in the prefrontal cortex. Science, 301(5630), 229232. https://doi.org/10.1126/science.1084204Google 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., Dayan, P., Person, C., & Sejnowski, T. J. (1995). Bee foraging in uncertain environments using predictive Hebbian learning. Nature, 377, 725728.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
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
Moore, A. W., & Atkeson, C. G. (1993). Prioritized sweeping: reinforcement learning with less data and less time. Machine Learning, 13(1), 103130. https://doi.org/10.1007/BF00993104Google 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
Nambu, A., Tokuno, H., & Takada, M. (2002). Functional significance of the cortico–subthalamo–pallidal ‘hyperdirect’ pathway. Neuroscience Research, 43(2), 111117. https://doi.org/10.1016/s0168–0102(02)00027-5Google Scholar
Peters, J., & Schaal, S. (2008). Reinforcement learning of motor skills with policy gradients. Neural Networks, 21(4), 682697. https://doi.org/10.1016/j.neunet.2008.02.003Google Scholar
Platt, M. L., & Glimcher, P. W. (1999). Neural correlates of decision variables in parietal cortex. Nature, 400, 233238.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
Reynolds, J. N., Hyland, B. I., & Wickens, J. R. (2001). A cellular mechanism of reward-related learning. Nature, 413(6851), 6770. https://doi.org/10.1038/35092560Google Scholar
Reynolds, J. N. J., & Wickens, J. R. (2002). Dopamine-dependent plasticity of corticostriatal synapses. Neural Networks, 15, 507521.Google Scholar
Samejima, K., Ueda, Y., Doya, K., & Kimura, M. (2005). Representation of action-specific reward values in the striatum. Science, 310(5752), 13371340. https://doi.org/10.1126/science.1115270Google Scholar
Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3, 210229.Google Scholar
Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80, 127.Google Scholar
Schultz, W., Apicella, P., & Ljungberg, T. (1993). Responses of monkey dopamine neurons to reward and conditioned stimuli during successive steps of learning a delayed response task. Journal of Neuroscience, 13, 900913.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
Schultz, W., Tremblay, L., & Hollerman, J. R. (2000). Reward processing in primate orbitofrontal cortex and basal ganglia. Cerebral Cortex, 10(3), 272284. https://doi.org/10.1093/cercor/10.3.272Google Scholar
Silver, D., Huang, A., Maddison, C. J., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484489. https://doi.org/10.1038/nature16961Google Scholar
Silver, D., Hubert, T., Schrittwieser, J., et al. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), 11401144. https://doi.org/10.1126/science.aar6404Google Scholar
Silver, D., Schrittwieser, J., Simonyan, K., et al. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354359. https://doi.org/10.1038/nature24270Google 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
Sugrue, L. P., Corrado, G. S., & Newsome, W. T. (2004). Matching behavior and the representation of value in the parietal cortex. Science, 304(5678), 17821787. https://doi.org/10.1126/science.1094765Google Scholar
Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). Cambridge, MA: MIT Press.Google Scholar
Tanaka, S. C., Doya, K., Okada, G., Ueda, K., Okamoto, Y., & Yamawaki, S. (2004). Prediction of immediate and future rewards differentially recruits cortico-basal ganglia loops. Nature Neuroscience, 7(8), 887893. https://doi.org/10.1038/nn1279Google Scholar
Tesauro, G. (1994). TD-Gammon, a self-teaching backgammon program, achieves master-level play. Neural Computation, 6, 215219.Google Scholar
Thorndike, E. L. (1898). Animal intelligence: an experimental study of the associate processes in animals. Psychological Review, Monograph Supplements, 2(8), 1109.Google Scholar
Tsitsiklis, J. N., & Roy, B. V. (1997). An analysis of temporal-difference learning with function approximation. IEEE Transactions on Automatic Control, 42, 674690.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 Neurosciences, 27(8), 468474. https://doi.org/10.1016/j.tins.2004.06.006Google Scholar
Watanabe, M. (1996). Reward expectancy in primate prefrontal neurons. Nature, 382, 629632.Google Scholar
Watkins, C. J. C. H. (1989). Learning from delayed rewards. Ph.D. Thesis, University of Cambridge.Google Scholar
Watkins, C. J. C. H., & Dayan, P. (1992). Q-Learning. Machine Learning, 8(3–4), 279292. https://doi.org/Doi10.1023/A:1022676722315Google Scholar
Wickens, J. R., Begg, A. J., & Arbuthnott, G. W. (1996). Dopamine reverses the depression of rat corticostriatal synapses which normally follows high-frequency stimulation of cortex in vitro. Neuroscience, 70(1), 15. https://doi.org/10.1016/0306-4522(95)00436-mGoogle 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. https://doi.org/10.1126/science.1255514Google 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 (online). https://doi.org/10.1073/pnas.1421930112Google 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.

  • Cognitive Modeling Paradigms
  • Edited by Ron Sun, Rensselaer Polytechnic Institute, New York
  • Book: The Cambridge Handbook of Computational Cognitive Sciences
  • Online publication: 21 April 2023
  • Chapter DOI: https://doi.org/10.1017/9781108755610.004
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.

  • Cognitive Modeling Paradigms
  • Edited by Ron Sun, Rensselaer Polytechnic Institute, New York
  • Book: The Cambridge Handbook of Computational Cognitive Sciences
  • Online publication: 21 April 2023
  • Chapter DOI: https://doi.org/10.1017/9781108755610.004
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.

  • Cognitive Modeling Paradigms
  • Edited by Ron Sun, Rensselaer Polytechnic Institute, New York
  • Book: The Cambridge Handbook of Computational Cognitive Sciences
  • Online publication: 21 April 2023
  • Chapter DOI: https://doi.org/10.1017/9781108755610.004
Available formats
×