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Part V - General Discussion

Published online by Cambridge University Press:  21 April 2023

Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
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References

References

Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In Petrov, B. N. & Csáki, F. (Eds.), 2nd International Symposium on Information Theory (pp. 267281). Budapest: Akadémiai Kiadó.Google Scholar
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19 (6), 716723.Google Scholar
Anderson, J. R. (1990). The Adaptive Character of Thought. Hillsdale, NJ: Erlbaum.Google Scholar
Anderson, J. R. (2007). How Can the Human Mind Occur in the Physical Universe? New York, NY: Oxford University Press.CrossRefGoogle Scholar
Ashby, F. G., & Townsend, J. T. (1980). Decomposing the reaction time distribution: pure insertion and selective influence revisited. Journal of Mathematical Psychology, 21 (2), 93123.CrossRefGoogle Scholar
Bakan, D. (1966). The test of significance in psychological research. Psychological Bulletin, 66 (6), 423437.CrossRefGoogle ScholarPubMed
Bamber, D., & Van Santen, J. P. (1985). How many parameters can a model have and still be testable? Journal of Mathematical Psychology, 29 (4), 443473.CrossRefGoogle Scholar
Bamber, D., & Van Santen, J. P. (2000). How to assess a model’s testability and identifiability. Journal of Mathematical Psychology, 44 (1), 2040.Google Scholar
Blaha, L. M. (2019). We have not looked at our results until we have displayed them effectively: a comment on robust modeling in cognitive science. Computational Brain & Behavior, 2 (3), 247250.Google Scholar
Blaha, L. M., Fisher, C. R., Walsh, M. M., Veksler, B. Z., & Gunzelmann, G. (2016) Real-time fatigue monitoring with computational cognitive models. In Proceedings of Human-Computer Interaction International 2016, Toronto, Canada.CrossRefGoogle Scholar
Blokpoel, M. & van Rooij, I. (2021). Theoretical modeling for cognitive science and psychology. Retrieved from: https://computationalcognitivescience.github.io/lovelace/home [last accessed August 2, 2022].Google Scholar
Bozdogan, H. (1990). On the information-based measure of covariance complexity and its application to the evaluation of multivariate linear models. Communications in Statistics – Theory and Methods, 19 (1), 221278.Google Scholar
Bozdogan, H. (2000). Akaike’s information criterion and recent developments in information complexity. Journal of Mathematical Psychology, 44 (1), 6291.Google Scholar
Broomell, S. B., Budescu, D. V., & Por, H.-H. (2011). Pair-wise comparisons of multiple models. Judgment and Decision Making, 6 (8), 821831.Google Scholar
Broomell, S. B., Sloman, S. J., Blaha, L. M., & Chelen, J. (2019). Interpreting model comparison requires understanding model-stimulus relationships. Computational Brain & Behavior, 2 (3), 233238.Google Scholar
Burnham, K. P., & Anderson, D. R. (2002) Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed.). New York, NY: Springer Verlag.Google Scholar
Busemeyer, J. R., & Diederich, A. (2010). Cognitive Modeling. Los Angeles, CA: Sage.Google Scholar
Campbell, G. E., & Bolton, A. E. (2005). HBR validation: integrating lessons learned from multiple academic disciplines, applied communities, and the AMBR project. In Gluck, K. A. & Pew, R. W. (Eds.), Modeling Human Behavior with Integrated Cognitive Architectures: Comparison, Evaluation, and Validation (pp. 365395), Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Chechile, R. A. (2010). A novel Bayesian parameter mapping method for estimating the parameters of an underlying scientific model. Communications in Statistics – Theory and Methods, 39 , 11901201.Google Scholar
Cohen, A. L., Sanborn, A. N., & Shiffrin, R. M. (2008). Model evaluation using grouped or individual data. Psychonomic Bulletin & Review, 15 (4), 692712.CrossRefGoogle ScholarPubMed
Colonius, H., & Vorberg, D. (1994). Distribution inequalities for parallel models with unlimited capacity. Journal of Mathematical Psychology, 38, 3558.CrossRefGoogle Scholar
Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52, 281302.Google Scholar
Dawid, A. P. (1984). Statistical theory: the prequential approach. Journal of the Royal Statistical Society A, 147, 278292.CrossRefGoogle Scholar
Devezer, B., Navarro, D. J., Vandekerckhove, J., & Buzbas, E. O. (2020). The case for formal methodology in scientific reform. Royal Society Open Science, 8 (3), 200805.Google Scholar
Dutton, J. M., & Starbuck, W. H. (1971). Computer Simulation of Human Behavior. New York, NY: Wiley.Google Scholar
Dzhafarov, E. N. (2003). Selective influence through conditional independence. Psychometrika, 68 (1), 725.CrossRefGoogle Scholar
Dzhafarov, E. N., Schweickert, R., & Sung, K. (2004). Mental architectures with selectively influenced but stochastically interdependent components. Journal of Mathematical Psychology, 48 (1), 5164.Google Scholar
Erev, I., Ert, E., Roth, A. E., et al. (2010). A choice prediction competition: choices from experience and from description. Journal of Behavioral Decision Making, 23 (1), 1547.Google Scholar
Estes, W. K. (2002). Traps in the route to models of memory and decision. Psychonomic Bulletin & Review, 9 (1), 325.Google Scholar
Farrell, S., & Lewandowsky, S. (2018). Computational Modeling of Cognition and Behavior. Cambridge: Cambridge University Press.Google Scholar
Fisher, C. R., Houpt, J. W., & Gunzelmann, G. (2020). Developing memory-based models of ACT-R within a statistical framework. Journal of Mathematical Psychology, 98, 102416.Google Scholar
Fum, D., Del Missier, F., & Stocco, A. (2007). The cognitive modeling of human behavior: why a model is (sometimes) better than 10,000 words. Cognitive Systems Research, 8, 135142.Google Scholar
Gallant, A. R. (1987). Nonlinear Statistical Models. New York, NY: Wiley.Google Scholar
Geisser, S. (1975). The predictive sample reuse method with applications. Journal of the American Statistical Association, 70 (350), 320328.CrossRefGoogle Scholar
Gluck, K. A., Bello, P., & Busemeyer, J. (2008). Introduction to the special issue. Cognitive Science, 32, 12451247.Google Scholar
Gluck, K. A., & Pew, R. W. (2005). Modeling Human Behavior with Integrated Cognitive Architectures: Comparison, Evaluation, and Validation. Mahwah, NJ: Erlbaum.Google Scholar
Gluck, K. A., Stanley, C. T., Moore, L. R., Reitter, D., & Halbrügge, M. (2010). Exploration for understanding in cognitive modeling. Journal of Artificial General Intelligence, 2 (2), 88107.CrossRefGoogle Scholar
Gronau, Q. F., & Wagenmakers, E. J. (2019). Limitations of Bayesian leave-one-out cross-validation for model selection. Computational Brain & Behavior, 2 (1), 111.Google Scholar
Grünwald, P. (2000). Model selection based on minimum description length. Journal of Mathematical Psychology, 44 (1), 133152.CrossRefGoogle ScholarPubMed
Gunzelmann, G. (2019). Promoting cumulation in models of the human mind. Computational Brain & Behavior, 2 (34), 157159.Google Scholar
Harding, B., Goulet, M. A., Jolin, S., Tremblay, C., Villeneuve, S. P., & Durand, G. (2016). Systems factorial technology explained to humans. Tutorials in Quantitative Methods for Psychology, 12 (1), 3956.Google Scholar
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, NY: Springer.Google Scholar
Hough, A. R., & Gluck, K. A. (2019). The understanding problem in cognitive science. Advances in Cognitive Systems, 8, 1332.Google Scholar
Houpt, J. W., Blaha, L. M., McIntire, J. P., Havig, P. R., & Townsend, J. T. (2014). Systems factorial technology with R. Behavior Research Methods, 46 (2), 307330.Google Scholar
Jeffreys, H. (1961). Theory of Probability (3rd ed.). Oxford: Oxford University Press.Google Scholar
Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90 (430), 773795.CrossRefGoogle Scholar
Kieras, D. E., & Meyer, D. E. (1997). An overview of the EPIC architecture for cognition and performance with application to human–computer interaction. Human–Computer Interaction, 12 (4), 391438.Google Scholar
Kim, W., Pitt, M. A., Lu, Z. L., Steyvers, M., & Myung, J. I. (2014). A hierarchical adaptive approach to optimal experimental design. Neural Computation, 26(11), 24652492.Google Scholar
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220 (4598), 671680.CrossRefGoogle ScholarPubMed
Kujala, J. V., & Dzhafarov, E. N. (2008). Testing for selectivity in the dependence of random variables on external factors. Journal of Mathematical Psychology, 52 (2), 128144.Google Scholar
Laird, J. E. (2012). The SOAR Cognitive Architecture. Cambridge, MA: MIT Press.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.CrossRefGoogle Scholar
Lebiere, C., Gonzalez, C., & Warwick, W. (2010). Editorial: cognitive architectures, model comparison, and AGI. Journal of Artificial General Intelligence, 2 (2), 119.CrossRefGoogle Scholar
Lee, M. D., Criss, A. H., Devezer, B., et al. (2019). Robust modeling in cognitive science. Computational Brain & Behavior, 2, 141153.Google Scholar
Little, D., Altieri, N., Fific, M., & Yang, C. T. (Eds.). (2017). Systems Factorial Technology: A Theory Driven Methodology for the Identification of Perceptual and Cognitive Mechanisms. New York, NY: Academic Press.Google Scholar
Macmillan, N. A., & Creelman, C. D. (2005). Detection Theory: A User’s Guide (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
McClelland, J. L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1 (1), 1138.Google Scholar
Miller, J. (1982). Divided attention: evidence for coactivation with redundant signals. Cognitive Psychology, 14, 247279.Google Scholar
Mosier, C. I. (1947). A critical examination of the concepts of face validity. Educational and Psychological Measurement, 7, 191205.Google Scholar
Myung, I. J., Balasubramanian, V., & Pitt, M. A. (2000). Counting probability distributions: differential geometry and model selection. Proceedings of the National Academy of Sciences, 97 (21), 1117011175.Google Scholar
Myung, I. J., Kim, C., & Pitt, M. A. (2000). Toward an explanation of the power law artifact: insights from response surface analysis. Memory & Cognition, 28 (5), 832840.Google Scholar
Myung, I. J., Navarro, D. J., & Pitt, M. A. (2006). Model selection by normalized maximum likelihood. Journal of Mathematical Psychology, 50 , 167179.Google Scholar
Myung, J. I., & Pitt, M. A. (2009). Optimal experimental design for model discrimination. Psychological Review, 116 (3), 499518.Google Scholar
Navarro, D. J. (2019). Between the devil and the deep blue sea: tensions between scientific judgement and statistical model selection. Computational Brain & Behavior, 2 (1), 2834.Google Scholar
Navarro, D. J. (2021). If mathematical psychology did not exist we might need to invent it: a comment on theory building in psychology. Perspectives on Psychological Science, 16 (4), 707716.Google Scholar
Navarro, D. J., Pitt, M. A., & Myung, I. J. (2004). Assessing the distinguishability of models and the informativeness of data. Cognitive Psychology, 49 (1), 4784.Google Scholar
Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. Computer Journal, 7 , 308313.Google Scholar
Newell, A., Shaw, J. C., & Simon, H. A. (1958). Elements of a theory of human problem solving. Psychological Review, 65 (3), 151166.Google Scholar
Peressini, A. L., Sullivan, F. E., & Uhl Jr., J. J. (1988). The Mathematics of Nonlinear Programming. New York, NY: Springer-Verlag.Google Scholar
Pitt, M. A., Kim, W., Navarro, D. J., & Myung, J. I. (2006). Global model analysis by parameter space partitioning. Psychological Review, 113 (1), 5783.Google Scholar
Pitt, M. A., & Myung, I. J. (2002). When a good fit can be bad. Trends in Cognitive Sciences, 6 (10), 421425.Google Scholar
Pitt, M. A., Myung, I. J., Montenegro, M., & Pooley, J. (2008). Measuring model flexibility with parameter space partitioning: an introduction and application example. Cognitive Science, 32, 12851303.CrossRefGoogle ScholarPubMed
Pitt, M. A., Myung, I. J., & Zhang, S. (2002). Toward a method of selecting among computational models of cognition. Psychological Review, 109 (3), 472491.Google Scholar
Rissanen, J. J. (1996). Fisher information and stochastic complexity. IEEE Transactions on Information Theory, 42 (1), 4047.CrossRefGoogle Scholar
Rissanen, J. J. (2001). Strong optimality of the normalized ML models as universal codes and information in data. IEEE Transactions on Information Theory, 47 , 17121717.Google Scholar
Roach, P. J. (2009). Fundamentals of Validation and Verification. Soccorro, NM: Hermosa Publishers.Google Scholar
Roberts, S., & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107 (2), 358367.Google Scholar
Rodgers, J. L., & Rowe, D. C. (2002). Theory development should begin (but not end) with good empirical fits: a comment on Roberts and Pashler (2000). Psychological Review, 109 (3), 599603.Google Scholar
Rosenbloom, P. S. (2013). On Computing: The Fourth Great Scientific Domain. Cambridge, MA: MIT Press.Google Scholar
Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In Tack, W. (Ed.), Psychologie der Kognition: Reden und Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115154). Saarbruken: University of Saarland Press.Google Scholar
Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6 (2), 461464.Google Scholar
Shiffrin, R. M., Lee, M. D., Kim, W., & Wagenmakers, E. J. (2008). A survey of model evaluation approaches with a tutorial on hierarchical Bayesian methods. Cognitive Science, 32 (8), 12481284.Google Scholar
Simon, H. A. (1992). What is an “explanation” of behavior? Psychological Science, 3 (3), 150161.Google Scholar
Simon, H. A. (1996). Models of My Life. Cambridge, MA: MIT Press.Google Scholar
Slaney, K. (2017). Validating Psychological Constructs: Historical, Philosophical, and Practical Dimensions. London: Palgrave Macmillan.Google Scholar
Smaldino, P. (2019). Better methods can’t make up for mediocre theory. Nature, 575 (7781), 910.Google Scholar
Stewart, T. (2006). Tools and techniques for quantitative and predictive cognitive science. In Sun, R. & Miyake, N. (Eds.), Proceedings of the 28th Annual Meeting of the Cognitive Science Society (pp. 816821). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Stokes, D. E. (1997). Pasteur’s Quadrant: Basic Science and Technological Innovation. Washington, DC: Brookings Institution Press.Google Scholar
Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36 (2), 111133.Google Scholar
Stone, M. (1977). An asymptotic equivalence of choice of model by cross‐validation and Akaike’s criterion. Journal of the Royal Statistical Society: Series B (Methodological), 39 (1), 4447.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
Thomas, R. D. (2001). Perceptual interactions of facial dimensions in speeded classification and identification. Perception & Psychophysics, 63 (4), 625650.Google Scholar
Townsend, J. T. (1990). Serial vs. parallel processing: sometimes they look like Tweedledum and Tweedledee but they can (and should) be distinguished. Psychological Science, 1 (1), 4654.Google Scholar
Townsend, J. T., & Ashby, F. G. (1983). Stochastic Modeling of Elementary Psychological Processes. Cambridge: Cambridge University Press.Google Scholar
Townsend, J. T., & Eidels, A. (2011). Workload capacity spaces: a unified methodology for response time measures of efficiency as workload is varied. Psychonomic Bulletin & Review, 18 (4), 659681.Google Scholar
Townsend, J. T., & Nozawa, G. (1995). Spatio-temporal properties of elementary perception: an investigation of parallel, serial, and coactive theories. Journal of Mathematical Psychology, 39 (4), 321359.Google Scholar
Tukey, J. W. (1977). Exploratory Data Analysis. Reading: Addison-Wesley Publishing.Google Scholar
U.S. Department of Defense. (2011). VV&A Recommended Practices Guide. Washington, DC: Defense Modeling and Simulation Coordination Office. Retrieved from: https://vva.msco.mil [last accessed August 2, 2022].Google Scholar
van Zandt, T. (2000). How to fit a response time distribution. Psychonomic Bulletin & Review, 7 (3), 424465.Google Scholar
Vandekerckhove, J., Matzke, D., & Wagenmakers, E.-J. (2015). Model comparison and the principle of parsimony. In Busemeyer, J. R., Wang, Z., Townsend, J. T., & Eidels, A. (Eds.), The Oxford Handbook of Computational and Mathematical Psychology (pp. 300319). Oxford: Oxford University Press.Google Scholar
Veksler, V. D., Myers, C. W., & Gluck, K. A. (2015). Model flexibility analysis. Psychological Review, 122 (4), 755769.CrossRefGoogle ScholarPubMed
Vitányi, P. M., & Li, M. (2000). Minimum description length induction, Bayesianism, and Kolmogorov complexity. IEEE Transactions on Information Theory, 46 (2), 446464.Google Scholar
Wagenmakers, E. J., Ratcliff, R., Gomez, P., & Iverson, G. J. (2004). Assessing model mimicry using the parametric bootstrap. Journal of Mathematical Psychology, 48 (1), 2850.CrossRefGoogle Scholar
Walsh, M. M., Gunzelmann, G., & Van Dongen, H. P. A. (2017). Computational cognitive models of the temporal dynamics of fatigue from sleep loss. Psychonomic Bulletin & Review, 24, 17851807.Google Scholar
Weaver, R. (2008). Parameters, predictions, and evidence in computational modeling: a statistical view informed by ACT–R. Cognitive Science 32 (8), 13491375.Google Scholar
Yang, J., Pitt, M. A., Ahn, W. Y., & Myung, J. I. (2021). ADOpy: a python package for adaptive design optimization. Behavior Research Methods, 53 (2), 874897.Google Scholar

References

Adams, F., & Aizawa, K. (2021). Causal theories of mental content. In Zalta, E. N. (Ed.), The Stanford Encyclopedia of Philosophy. Redwood City, CA: Stanford University Press.Google Scholar
Anderson, J. R. (2007). How Can the Human Mind Occur in a Physical Universe? Oxford: Oxford University Press.Google Scholar
Anderson, M. L. (2014). After Phrenology: Neural Reuse and the Interactive Brain. Cambridge, MA: MIT Press.Google Scholar
Apperly, I. A., & Butterfill, S. A. (2009). Do humans have two systems to track belief and belief-like states? Psychological Review, 116, 953970.Google Scholar
Baars, B. (1988). A Cognitive Theory of Consciousness. Cambridge: Cambridge University Press.Google Scholar
Baars, B., & Franklin, S. (2003). How conscious experience and working memory interact. Trends in Cognitive Sciences, 7, 166172.CrossRefGoogle ScholarPubMed
Barrett, H. C. (2020). Towards a cognitive science of the human: cross-cultural approaches and their urgency. Trends in Cognitive Sciences, 24, 620638.Google Scholar
Barrett, H. C., & Kurzban, R. (2006). Modularity in cognition: framing the debate. Psychological Review, 113, 628647.Google Scholar
Block, N. (1978). Troubles with functionalism. In Savage, C. W. (Ed.), Perception and Cognition: Issues in the Foundations of Psychology (Minnesota Studies in the Philosophy of Science, Vol. 9, pp. 261325). Minneapolis, MN: University of Minnesota Press.Google Scholar
Block, N. (1980). What intuitions about homunculi don’t show. Behavioral and Brain Sciences, 3, 425426.Google Scholar
Block, N. (1986). Advertisement for a semantics for psychology. Midwest Studies in Philosophy, 10, 615678.Google Scholar
Block, N. (1990). Consciousness and accessibility. Behavioral and Brain Sciences, 13, 596598.Google Scholar
Block, N. (2007). Consciousness, accessibility, and the mesh between psychology and neuroscience. Behavioral and Brain Sciences, 30, 481548.CrossRefGoogle ScholarPubMed
Boden, M. A. (1989). Escaping from the Chinese Room. In Artificial Intelligence in Psychology (pp. 82100). Cambridge, MA: MIT Press.Google Scholar
Boolos, G., Burgess, J. P., & Jeffrey, R. C. (2002). Computability and Logic (4th ed.). Cambridge: Cambridge University Press.Google Scholar
Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47, 139159.Google Scholar
Buckner, C. (2021). Black boxes or unflattering mirrors? Comparative bias in the science of machine behaviour. The British Journal for the Philosophy of Science (online). https://doi.org/10.1086/714960Google Scholar
Burge, T. (1986). Individualism and psychology. Philosophical Review, 95, 345.Google Scholar
Carruthers, P. (2006). The Architecture of the Mind. Oxford: Oxford University Press.Google Scholar
Chalmers, D. J. (1996). The Conscious Mind. Oxford: Oxford University Press.Google Scholar
Chalmers, D. J. (2010a). Facing up to the problem of consciousness. In Chalmers, D. J. (Ed.), The Character of Consciousness (pp. 34). Oxford: Oxford University Press.Google Scholar
Chalmers, D. J. (2010b). The two‐dimensional argument against materialism. In Chalmers, D. J. (Ed.), The Character of Consciousness (pp. 141205). Oxford: Oxford University Press.Google Scholar
Chalmers, D. J. (2010c). Consciousness and its place in nature. In Chalmers, D. J. (Ed.), The Character of Consciousness (pp. 103139). Oxford: Oxford University Press.CrossRefGoogle Scholar
Chalmers, D. J. (2010d). How can we construct a science of consciousness? In Chalmers, D. J. (Ed.), The Character of Consciousness (pp. 3758). Oxford: Oxford University Press.Google Scholar
Chalmers, D. J. (2012). A computational foundation for the study of cognition. Journal of Cognitive Science, 12, 323357.Google Scholar
Chirimuuta, M. (forthcoming). How to Simplify the Brain. Cambridge, MA: MIT Press.Google Scholar
Chomsky, N. (1995). Language and nature. Mind, 104, 161.Google Scholar
Chow, S. J. (2013). What’s the problem with the frame problem? Review of Philosophy and Psychology, 4, 309331.Google Scholar
Clark, A. (2000). A case where access implies qualia? Analysis, 60, 3038.Google Scholar
Clark, A. (2002). Global abductive inference and authoritative sources, or, how search engines can save cognitive science. Cognitive Science Quarterly, 2, 115140.Google Scholar
Clark, A. (2016). Surfing Uncertainty: Prediction, Action, and the Embodied Mind. Oxford: Oxford University Press.Google Scholar
Cohen, M. A., & Dennett, D. C. (2011). Consciousness cannot be separated from function. Trends in Cognitive Sciences, 15, 358364.Google Scholar
Cole, D. (2020). The Chinese room argument. In Zalta, E. N. (Ed.), The Stanford Encyclopedia of Philosophy. Redwood City, CA: Stanford University Press.Google Scholar
Colombo, M., & Hartmann, S. (2017). Bayesian cognitive science, unification, and explanation. The British Journal for the Philosophy of Science, 68, 451484.Google Scholar
Colombo, M., & Seriès, P. (2012). Bayes on the brain – on Bayesian modelling in neuroscience. The British Journal for the Philosophy of Science, 63, 697723.Google Scholar
Danks, D. (2014). Unifying the Mind: Cognitive Representations as Graphical Models. Cambridge, MA: MIT Press.Google Scholar
Davis, E., & Morgenstern, L. (2004). Introduction: progress in formal commonsense reasoning. Artificial Intelligence, 153, 12.Google Scholar
Dehaene, S., & Changeux, J.-P. (2004). Neural mechanisms for access to consciousness. In Gazzaniga, M. (Ed.), The Cognitive Neurosciences III (pp. 11451157). Cambridge, MA: MIT Press.Google Scholar
Dehaene, S., & Changeux, J.-P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70, 200227.Google Scholar
Dehaene, S., Changeux, J.-P., Naccache, L., Sackur, J., & Sergent, C. (2006). Conscious, preconscious, and subliminal processing: a testable taxonomy. Trends in Cognitive Sciences, 10, 204211.Google Scholar
Dennett, D. C. (1978). Why you can’t make a computer that feels pain. Synthese, 38, 415456.Google Scholar
Dennett, D. C. (1987). The Intentional Stance. Cambridge, MA: MIT Press.Google Scholar
Dennett, D. C. (1991). Consciousness Explained. Boston, MA: Little, Brown & Company.Google Scholar
Dennett, D. C. (1995). The unimagined preposterousness of zombies. Journal of Consciousness Studies, 2, 322326.Google Scholar
Dennett, D. C. (2001). The zombic hunch: extinction of an intuition? Royal Institute of Philosophy Supplement, 48, 2743.Google Scholar
Dennett, D. C. (2013). Intuition Pumps and Other Tools for Thinking. New York, NY: W. W. Norton.Google Scholar
Dennett, D. C. (2017). From Bacteria to Bach and Back: The Evolution of Minds. New York, NY: W. W. Norton.Google Scholar
Dewhurst, J. (2018). Individuation without representation. The British Journal for the Philosophy of Science, 69, 103116.Google Scholar
Dretske, F. (1981). Knowledge and the Flow of Information. Cambridge, MA: MIT Press.Google Scholar
Dretske, F. (1995). Naturalizing the Mind. Cambridge, MA: MIT Press.Google Scholar
Dreyfus, H. L. (1972). What Computers Can’t Do. New York, NY: Harper & Row.Google Scholar
Dreyfus, H. L. (1991). Being-in-the-World: A Commentary on Heidegger’s Being and Time, Division I. Cambridge, MA: MIT Press.Google Scholar
Dreyfus, H. L. (1992). What Computers Still Can’t Do. Cambridge, MA: MIT Press.Google Scholar
Dreyfus, H. L. (2007). Why Heideggerian AI failed and how fixing it would require making it more Heideggerian. Artificial Intelligence, 171, 11371160.Google Scholar
Dreyfus, H. L., & Dreyfus, S. E. (1988). Making a mind versus modeling the brain: artificial intelligence back at a branchpoint. Daedalus, 117, 1544.Google Scholar
Egan, F. (2003). Naturalistic inquiry: where does mental representation fit in? In Antony, L. M. & Hornstein, N. (Eds.), Chomsky and His Critics. Oxford: Blackwell.Google Scholar
Egan, F. (2010). Computational models: a modest role for content. Studies in History and Philosophy of Science, 41, 253259.Google Scholar
Egan, F. (2014). How to think about mental content. Philosophical Studies, 170, 115135.Google Scholar
Elgin, C. Z. (2017). True Enough. Cambridge, MA: MIT Press.Google Scholar
Eliasmith, C. (2003). Moving beyond metaphors: understanding the mind for what it is. The Journal of Philosophy, 10, 493520.Google Scholar
Eliasmith, C. (2005). Neurosemantics and categories. In Cohen, H. & Lefebvre, C. (Eds.), Handbook of Categorization in Cognitive Science (pp. 10351055). Amsterdam: Elsevier.Google Scholar
Eliasmith, C. (2013). How to Build a Brain: A Neural Architecture for Biological Cognition. Oxford: Oxford University Press.Google Scholar
Fodor, J. A. (1978). Tom Swift and his procedural grandmother. Cognition, 6, 229247.Google Scholar
Fodor, J. A. (1980). Searle on what only brains can do. Behavioral and Brain Sciences, 3, 431432.Google Scholar
Fodor, J. A. (1983). The Modularity of Mind. Cambridge, MA: MIT Press.Google Scholar
Fodor, J. A. (1990). A Theory of Content and Other Essays. Cambridge, MA: MIT Press.Google Scholar
Fodor, J. A. (1998). Concepts. Oxford: Blackwell.Google Scholar
Fodor, J. A. (2000). The Mind Doesn’t Work That Way. Cambridge, MA: MIT Press.Google Scholar
Fodor, J. A. (2008). LOT2: The Language of Thought Revisited. Oxford: Oxford University Press.Google Scholar
Fodor, J. A., & Lepore, E. (1992). Holism: A Shopper’s Guide. Oxford: Blackwell.Google Scholar
Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture. Cognition, 28, 371.Google Scholar
Ford, K. M., & Pylyshyn, Z. W. (Eds.) (1996). The Robot’s Dilemma Revisited. Norwood, NJ: Ablex.Google Scholar
Frankish, K. (2016). Illusionism as a theory of consciousness. Journal of Consciousness Studies, 23, 1139.Google Scholar
Freeman, W. J. (2000). How Brains Make Up Their Minds. New York, NY: Columbia University Press.Google Scholar
Gelder, T. van. (1995). What might cognition be, if not computation? The Journal of Philosophy, 91, 345381.Google Scholar
Gigerenzer, G., Todd, P. M., & ABC Research Group (Eds.) (1999). Simple Heuristics That Make Us Smart. New York, NY: Oxford University Press.Google Scholar
Glymour, C. (2001). The Mind’s Arrows: Bayes Nets and Graphical Causal Models in Psychology. Cambridge, MA: MIT Press.Google Scholar
Godfrey-Smith, P. (2016). Mind, matter, and metabolism. The Journal of Philosophy, 113, 481506.Google Scholar
Goyal, A., Didolkar, A., Lamb, A., et al. (2021). Coordination among neural modules through a shared global workspace. arXiv:2103.01197.Google Scholar
Graziano, M. S. A. (2016). Consciousness engineered. Journal of Consciousness Studies, 23, 98115.Google Scholar
Gulick, R. van. (2018). Consciousness. In Zalta, E. N. (Ed.), The Stanford Encyclopedia of Philosophy. Redwood City, CA: Stanford University Press.Google Scholar
Harman, G. (1987). (Nonsolipsistic) conceptual role semantics. In Lepore, E. (Ed.), New Directions in Semantics (pp. 5581). London: Academic Press.Google Scholar
Harnad, S. (1990). The symbol grounding problem. Physica D, 42, 335346.Google Scholar
Haugeland, J. (1998). Mind embodied and embedded. In Haugeland, J. (Ed.), Having Thought: Essays in the Metaphysics of Mind (pp. 207240). Cambridge, MA: Harvard University Press.Google Scholar
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33, 61135.Google Scholar
Irvine, E., & Sprevak, M. (2020). Eliminativism about consciousness. In Kriegel, U. (Ed.), The Oxford Handbook of the Philosophy of Consciousness (pp. 348370). Oxford: Oxford University Press.Google Scholar
Isaac, A. M. C. (2019). The semantics latent in Shannon information. The British Journal for the Philosophy of Science, 70, 103125.Google Scholar
Johnson-Laird, P. N. (1978). What’s wrong with Grandma’s guide to procedural semantics: a reply to Jerry Fodor. Cognition, 6, 249261.Google Scholar
Kripke, S. A. (1980). Naming and Necessity. Cambridge, MA: Harvard University Press.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
Lee, J. (2018). Mechanisms, wide functions and content: towards a computational pluralism. The British Journal for the Philosophy of Science (online). https://doi.org/10.1093/bjps/axy061Google Scholar
Lenat, D. B., & Feigenbaum, E. A. (1991). On the thresholds of knowledge. Artificial Intelligence, 47, 185250.Google Scholar
Lifschitz, V. (2015). The dramatic true story of the frame default. Journal of Philosophical Logic, 44, 163196.Google Scholar
Loewer, B. (2017). A guide to naturalizing semantics. In Hale, B., Wright, C., & Miller, A. (Eds.), Companion to the Philosophy of Language (2nd ed., pp. 174196). New York, NY: John Wiley & Sons.Google Scholar
Lormand, E. (1990). Framing the frame problem. Synthese, 82, 353374.Google Scholar
Ludwig, K., & Schneider, S. (2008). Fodor’s challenge to the classical computational theory of mind. Mind and Language, 23 (3), 123143.Google Scholar
Machery, E. (2013). In defense of reverse inference. The British Journal for the Philosophy of Science, 65, 251267.Google Scholar
Machery, E. (forthcoming). Discovery and confirmation in evolutionary psychology. In Prinz, J. (Ed.), The Oxford Handbook of Philosophy of Psychology. Oxford: Oxford University Press.Google Scholar
Marcus, G., & Davis, E. (2019). Rebooting AI: Building Artificial Intelligence We Can Trust. New York, NY: Penguin Books.Google Scholar
Mashour, G. A., Roelfsema, P. R., Changeux, J.-P., & Dehaene, S. (2020). Conscious processing and the Global Neuronal Workspace hypothesis. Neuron, 105, 776798.Google Scholar
Maudlin, T. (1989). Computation and consciousness. The Journal of Philosophy, 86, 407432.Google Scholar
McCarthy, J. (1990). Formalizing Common Sense: Papers by John McCarthy. (Lifschitz, V. L., Ed.). Norwood, NJ: Ablex.Google Scholar
McCarthy, J., & Hayes, P. J. (1969). Some philosophical problems from the standpoint of artificial intelligence. In Meltzer, B. & Michie, D. (Eds.), Machine Intelligence 4 (pp. 463502). Edinburgh: Edinburgh University Press.Google Scholar
Millikan, R. G. (2004). The Varieties of Meaning. Cambridge, MA: MIT Press.Google Scholar
Mollo, D. C. (2018). Functional individuation, mechanistic implementation: the proper way of seeing the mechanistic view of concrete computation. Synthese, 195, 34773497.Google Scholar
Mollo, D. C. (2021). Deflationary realism: representation and idealization in cognitive science. Mind and Language (online). https://doi.org/10.1111/mila.12364Google Scholar
Morrison, M. (2014). Reconstructing Reality: Models, Mathematics, and Simulations. Oxford: Oxford University Press.Google Scholar
Nagel, T. (1974). What is it like to be a bat? Philosophical Review, 83, 435450.Google Scholar
Neander, K., & Schulte, P. (2021). Teleological theories of mental content. In Zalta, E. N. (Ed.), The Stanford Encyclopedia of Philosophy. Redwood City, CA: Stanford University Press.Google Scholar
Newell, A., & Simon, H. A. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Nisbett, R. E. (2003). The Geography of Thought. New York, NY: The Free Press.Google Scholar
Papineau, D. (1987). Reality and Representation. Oxford: Blackwell.Google Scholar
Piccinini, G. (2015). The Nature of Computation. Oxford: Oxford University Press.Google Scholar
Potochnik, A. (2017). Idealization and the Aims of Science. Chicago, IL: University of Chicago Press.Google Scholar
Prinz, J. (2016). Against illusionism. Journal of Consciousness Studies, 23, 186196.Google Scholar
Putnam, H. (1981). Reason, Truth and History. Cambridge: Cambridge University Press.Google Scholar
Pylyshyn, Z. W. (Ed.). (1987). The Robot’s Dilemma. Norwood, NJ: Ablex.Google Scholar
Ramsey, W. M. (2007). Representation Reconsidered. Cambridge: Cambridge University Press.Google Scholar
Rescorla, M. (2013). Against structuralist theories of computational implementation. The British Journal for the Philosophy of Science, 64, 681707.Google Scholar
Rescorla, M. (2016). Bayesian sensorimotor psychology. Mind and Language, 31, 36.Google Scholar
Rolls, E. T., & Treves, A. (2011). The neural encoding of information in the brain. Progress in Neurobiology, 95, 448490.Google Scholar
Ryder, D. (2004). SINBAD neurosemantics: a theory of mental representation. Mind and Language, 19, 211240.Google Scholar
Samuels, R. (1998). Evolutionary psychology and the massive modularity hypothesis. The British Journal for the Philosophy of Science, 49, 575602.Google Scholar
Samuels, R. (2005). The complexity of cognition: tractability arguments for massive modularity. In Carruthers, P., Laurence, S., & Stich, S. P. (Eds.), The Innate Mind: Vol. I, Structure and Contents (pp. 107121). Oxford: Oxford University Press.Google Scholar
Samuels, R. (2010). Classical computationalism and the many problems of cognitive relevance. Studies in History and Philosophy of Science, 41, 280293.Google Scholar
Schank, R. C., & Abelson, R. P. (1977). Scripts, Plans, Goals, and Understanding. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Schneider, S. (2011). The Language of Thought: A New Philosophical Direction. Cambridge, MA: MIT Press.Google Scholar
Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3, 417424.Google Scholar
Searle, J. R. (1984). Minds, Brains and Science. Cambridge, MA: Harvard University Press.Google Scholar
Searle, J. R. (1990). Is the brain’s mind a computer program? Scientific American, 262, 2025.Google Scholar
Searle, J. R. (1992). The Rediscovery of the Mind. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Sellars, W. (1962). Philosophy and the scientific image of man. In Colodny, R. (Ed.), Frontiers of Science and Philosophy (pp. 3578). Pittsburgh, PA: University of Pittsburgh Press.Google Scholar
Shagrir, O. (2012). Structural representations and the brain. The British Journal for the Philosophy of Science, 63, 519545.Google Scholar
Shagrir, O. (2020). In defense of the semantic view of computation. Synthese, 197, 40834108.Google Scholar
Shanahan, M. (1997). Solving the Frame Problem. Cambridge, MA: Bradford Books/MIT Press.Google Scholar
Shanahan, M. (2016). The frame problem. In Zalta, E. N. (Ed.), The Stanford Encyclopedia of Philosophy. Redwood City, CA: Stanford University Press.Google Scholar
Shanahan, M., & Baars, B. (2005). Applying global workspace theory to the frame problem. Cognition, 98, 157176.Google Scholar
Shea, N. (2013). Naturalising representational content. Philosophy Compass, 8, 496509.CrossRefGoogle ScholarPubMed
Shea, N. (2018). Representation in Cognitive Science. Oxford: Oxford University Press.Google Scholar
Shea, N., & Bayne, T. (2010). The vegetative state and the science of consciousness. The British Journal for the Philosophy of Science, 61, 459484.Google Scholar
Skyrms, B. (2010). Signals. Oxford: Oxford University Press.Google Scholar
Sprevak, M. (2010). Computation, individuation, and the received view on representation. Studies in History and Philosophy of Science, 41, 260270.Google Scholar
Sprevak, M. (2013). Fictionalism about neural representations. The Monist, 96, 539560.Google Scholar
Sprevak, M. (2016). Philosophy of the psychological and cognitive sciences. In Humphreys, P. (Ed.), Oxford Handbook for the Philosophy of Science (pp. 92114). Oxford: Oxford University Press.Google Scholar
Sprevak, M. (2019). Review of Susan Schneider, The Language of Thought: A New Philosophical Direction. Mind, 128, 555564.Google Scholar
Sterelny, K. (2003). Thought in a Hostile World. Oxford: Blackwell.Google Scholar
Strawson, G. (2010). Mental Reality (2nd ed.). Cambridge, MA: MIT Press.Google Scholar
Strawson, G. (2018). The consciousness deniers. The New York Review of Books.Google Scholar
Sullivan, E. (2019). Understanding from machine learning models. The British Journal for the Philosophy of Science (online). https://doi.org/10.1093/bjps/axz035Google Scholar
Swoyer, C. (1991). Structural representation and surrogative reasoning. Synthese, 87, 449508.Google Scholar
Tye, M. (2018). Qualia. In Zalta, E. N. (Ed.), The Stanford Encyclopedia of Philosophy. Redwood City, CA: Stanford University Press.Google Scholar
Usher, M. (2001). A statistical referential theory of content: using information theory to account for misrepresentation. Mind and Language, 16, 311334.Google Scholar
Wakefield, J. C. (2003). The Chinese room argument reconsidered: essentialism, indeterminacy, and Strong AI. Minds and Machines, 13, 285319.Google Scholar
Wheeler, M. (2005). Reconstructing the Cognitive World. Cambridge, MA: MIT Press.Google Scholar
Wheeler, M. (2008). Cognition in context: phenomenology, situated robotics and the frame problem. International Journal of Philosophical Studies, 16, 323349.Google Scholar
Winograd, T. (1972). Understanding natural language. Cognitive Psychology, 3, 191.Google Scholar

References

Abelson, R. P. (1973). The structure of belief systems. In Schank, R. C. & Colby, K. M. (Eds.), Computer Models of Thought and Language (pp. 287339). San Francisco, CA: Freeman.Google Scholar
Agre, P. E., & Chapman, D. (1987). Pengi: an implementation of a theory of activity. In Proceedings of AAAI-87, Seattle (pp. 268–272).Google Scholar
Agre, P. E., & Chapman, D. (1991). What are plans for? In Maes, P. (Ed.), Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back (pp. 1734). Cambridge, MA: MIT Press.Google Scholar
Aleksander, I. (2000). How to Build a Man: Dreams and Diaries. London: Weidenfeld & Nicolson.Google Scholar
Anderson, J. R. (1983). The Architecture of Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Anderson, J. R. (1996). ACT: a simple theory of complex cognition. American Psychologist, 5, 355365.Google Scholar
Arbib, M. A., & Hesse, M. B. (1986). The Construction of Reality. Cambridge: Cambridge University Press.Google Scholar
Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22, 577609.Google Scholar
Blake, D. V., & Uttley, A. M. (Eds.). (1959). The Mechanization of Thought Processes (2 vols.) National Physical Laboratory Symposium No. 10. London: Her Majesty’s Stationery Office.Google Scholar
Boden, M. A. (1972). Purposive Explanation in Psychology. Cambridge, MA: Harvard University Press.Google Scholar
Boden, M. A. (1977/1987). Artificial Intelligence and Natural Man. New York, NY: Basic Books.Google Scholar
Boden, M. A. (2006). Mind as Machine: A History of Cognitive Science. Oxford: The Clarendon Press.Google Scholar
Bransford, J. D., & Johnson, M. K. (1972). Contextual prerequisites for understanding: some investigations of comprehension and recall. Journal of Verbal Learning and Verbal Behaviour, 11, 717726.Google Scholar
Broadbent, D. E. (1952a). Listening to one of two synchronous messages. Journal of Experimental Psychology, 44, 5155.Google Scholar
Broadbent, D. E. (1952b). Failures of attention in selective listening. Journal of Experimental Psychology, 44, 428433.Google Scholar
Broadbent, D. E. (1958). Perception and Communication. Oxford: Pergamon Press.Google Scholar
Brooks, R. A. (1991a). Intelligence without representation. Artificial Intelligence, 47, 139159.Google Scholar
Brooks, R. A. (1991b). Intelligence without reason. In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, Sydney.Google Scholar
Bruner, J. S. (1957). Going beyond the information given. In Gruber, H., Hammond, K. R., & Jessor, R. (Eds.), Contemporary Approaches to Cognition (pp. 4169). Cambridge, MA: Harvard University Press.Google Scholar
Bruner, J. S., Goodnow, J., & Austin, G. (1956). A Study of Thinking. New York, NY: Wiley.Google Scholar
Changeux, J.-P. (1985). Neuronal Man: The Biology of Mind. Trans. L. Garey. New York, NY: Pantheon.Google Scholar
Chomsky, A. N. (1957). Syntactic Structures. S-Gravenhage: Mouton.Google Scholar
Chrisley, R. L. (1999). Transparent computationalism. In Scheutz, M. (Ed.), Proceedings of the Workshop “New Trends in Cognitive Science 1999: Computationalism – The Next Generation”. Vienna: Conceptus-Studien.Google Scholar
Clark, A. J. (1997). Being There: Putting Brain, Body, and World Together Again. Cambridge, MA: MIT Press.Google Scholar
Clark, A. J., & Karmiloff-Smith, A. (1993). The cognizer’s innards: a psychological and philosophical perspective on the development of thought. Mind and Language, 8, 487519.Google Scholar
Clippinger, J. H. (1977). Meaning and Discourse: A Computer Model of Psychoanalytic Discourse and Cognition. London: Johns Hopkins University Press.Google Scholar
Cohen, P. R., Morgan, J., & Pollack, M. E. (Eds.). (1990). Intentions in Communication. Cambridge, MA: MIT Press.Google Scholar
Cohen, P. R., & Perrault, C. R. (1979). Elements of a plan-based theory of speech acts. Cognitive Science, 3 (3), 177212.Google Scholar
Colby, K. M. (1964). Experimental treatment of neurotic computer programs. Archives of General Psychiatry, 10, 220227.Google Scholar
Colby, K. M. (1967). Computer simulation of change in personal belief systems. Behavioral Science, 12, 248253.Google Scholar
Colby, K. M. (1975). Artificial Paranoia: A Computer Simulation of Paranoid Processes. New York, NY: Pergamon.Google Scholar
Copeland, B. J. (2002). Effective computation by humans and machines. Minds and Machines (Special Issue on Hypercomputing), 13, 281300.Google Scholar
Damasio, A. R. (1994). Descartes’ Error: Emotion, Reason and the Human Brain. New York, NY: Putnam.Google Scholar
Dennett, D. C. (1984). Elbow Room: The Varieties of Free Will Worth Wanting. Cambridge, MA: MIT Press.Google Scholar
Dennett, D. C. (1991). Consciousness Explained. London: Allen Lane.Google Scholar
Dienes, Z., & Perner, J. (2007). The cold control theory of hypnosis. In Jamieson, G. (Ed.), Hypnosis and Conscious States: The Cognitive Neuroscience Perspective. Oxford: Oxford University Press.Google Scholar
Di Paolo, E. A. (1998). An investigation into the evolution of communication. Adaptive Behavior, 6, 285324.Google Scholar
Di Paolo, E. A. (1999). On the evolutionary and behavioral dynamics of social coordination: models and theoretical aspects. D.Phil. Thesis, School of Cognitive and Computing Sciences, University of Sussex.Google Scholar
Dreyfus, H. L. (1965). Alchemy and artificial intelligence. Research Report P-3244, December 1965. Santa Monica, CA: Rand Corporation.Google Scholar
Dreyfus, H. L. (1972). What Computers Can’t Do: A Critique of Artificial Reason. New York, NY: Harper & Row.Google Scholar
Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: a critical analysis. Cognition, 28, 371.Google Scholar
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 179212.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., 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
Evans, D. (2001). Emotion: The Science of Sentiment. Oxford: Oxford University Press.Google Scholar
Evans, D., & Cruse, P. (Eds.). (2004). Emotion, Evolution, and Rationality. Oxford: Oxford University Press.Google Scholar
Fauconnier, G. R., & Turner, M. (2002). The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities. New York, NY: Basic Books.Google Scholar
Feigenbaum, E. A., & Feldman, J. A. (Eds.). (1963). Computers and Thought. New York, NY: McGraw-Hill.Google Scholar
Fodor, J. A. (1983). The Modularity of Mind: An Essay in Faculty Psychology. Cambridge, MA: MIT Press.Google Scholar
Gazdar, G. J. M., Klein, E., Pullum, G., & Sag, I. A. (1985). Generalized Phrase Structure Grammar. Oxford: Blackwell.Google Scholar
Gigerenzer, G. (2004). Fast and frugal heuristics: the tools of bounded rationality. In Koehler, D. J. & Harvey, N. (Eds.), Blackwell Handbook of Judgment and Decision Making (pp. 6288). Oxford: Blackwell.Google Scholar
Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: models of bounded rationality. Psychological Review, 103, 650669.Google Scholar
Goldberg, D., & Mataric, M. J. (1999). Coordinating mobile robot group behavior using a model of interaction dynamics. In Proceedings of Third International Conference on Autonomous Agents (Agents-99), Seattle, WA (pp. 100107). Washington, DC: ACM Press.Google Scholar
Goodale, M. A., & Milner, A. D. (1992). Separate visual pathways for perception and action. Trends in Neuroscience, 13, 2023.Google Scholar
Gregory, R. L. (1966). Eye and Brain: The Psychology of Seeing. London: Weidenfeld & Nicolson.Google Scholar
Gregory, R. L. (1967). Will seeing machines have illusions? In Collins, N. L. & Michie, D. M. (Eds.), Machine Intelligence 1 (pp. 169180). Edinburgh: Edinburgh University Press.Google Scholar
Grosz, B. (1977). The representation and use of focus in a system for understanding dialogs. In Proceedings of the Fifth International Joint Conference on Artificial Intelligence (pp. 6776). Cambridge, MA.Google Scholar
Haugeland, J. (1985). Artificial Intelligence: The Very Idea. Cambridge, MA: MIT Press.Google Scholar
Haugeland, J. (1996). Body and world: a review of What Computers Still Can’t Do (Hubert L. Dreyfus). Artificial Intelligence, 80, 119128.Google Scholar
Hebb, D. O. (1949). The Organization of Behavior: A Neuropsychological Theory. New York, NY: Wiley.Google Scholar
Hofstadter, D. R. (1979). Godel, Escher, Bach: An Eternal Golden Braid. New York, NY: Basic Books.Google Scholar
Hofstadter, D. R. (1983/1985). “Waking up from the Boolean dream, or subcognition as computation” and “Post scriptum”. (The first item was originally published in Machlup, F. & Mansfield, U. (Eds.), The Study of Information: Interdisciplinary Messages. New York, NY: Wiley, 1983, pp. 263285.)Google Scholar
Hogg, D. C. (1996). Machine vision. In Boden, M. A. (Ed.), Artificial Intelligence (pp. 183228). London: Academic Press.Google Scholar
Hollis, M. (1977). Models of Man: Philosophical Thoughts on Social Action. Cambridge: Cambridge University Press.Google Scholar
Hutchins, E. L. (1995). Cognition in the Wild. Cambridge, MA: MIT Press.Google Scholar
Johnson, M. H. (Ed.). (1993). Brain Development and Cognition: A Reader. Oxford: Blackwell.Google Scholar
Johnson-Laird, P. N. (1983). Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness. Cambridge: Cambridge University Press.Google Scholar
Karmiloff-Smith, A. (1979). Micro- and macro-developmental changes in language acquisition and other representational systems. Cognitive Science, 3, 81118.Google Scholar
Karmiloff-Smith, A. (1986). From meta-processes to conscious access: evidence from children’s metalinguistic and repair data. Cognition, 23, 95147.Google Scholar
Karmiloff-Smith, A. (1992). Beyond Modularity: A Developmental Perspective on Cognitive Science. London: MIT Press.Google Scholar
Kirsh, D. (1991). Today the earwig, tomorrow man?. Artificial Intelligence, 47, 161184.Google Scholar
Laird, J. E., Newell, A., & Rosenbloom, P. (1987). Soar: an architecture for general intelligence. Artificial Intelligence, 33, 164.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
McDowell, J. (1994). Mind and World. Cambridge, MA: Harvard University Press.Google Scholar
Marcus, M. (1979). A theory of syntactic recognition for natural language. In Winston, P. H. & Brown, R. H. (Eds.), Artificial Intelligence: An MIT Perspective (Vol. 1, pp. 193230). Cambridge, MA: MIT Press.Google Scholar
Marr, D. C. (1976). Early processing of visual information. Philosophical Transactions of the Royal Society B, 275, 483524.Google Scholar
Marr, D. C. (1977). Artificial intelligence: a personal view. Artificial Intelligence, 9, 3748.Google Scholar
Marr, D. C. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. San Francisco, CA: Freeman.Google Scholar
Marr, D. C., & Hildreth, E. (1980). Theory of edge-detection. Proceedings of the Royal Society B, 207, 187217.Google Scholar
Mead, G. H. (1934). Mind, Self, and Society: From the Standpoint of a Social Behaviorist. Chicago, IL: Chicago University Press.Google Scholar
Meehl, P. E. (1954). Clinical versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence. Minneapolis, MN: University of Minnesota Press.Google Scholar
Miller, G. A., Galanter, E., & Pribram, K. H. (1960). Plans and the Structure of Behavior. New York, NY: Holt.Google Scholar
Miller, G. A., & Johnson-Laird, P. N. (1976). Language and Perception. Cambridge: Cambridge University Press.Google Scholar
Milner, A. D., & Goodale, M. A. (1993). Visual pathways to perception and action. In Hicks, T. P., Molotchnikoff, S., & Ono, T. (Eds.), Progress in Brain Research (Vol. 95, pp. 317337). Amsterdam: Elsevier.Google Scholar
Minsky, M. L. (1965). Matter, mind, and models. In Proceedings of the International Federation of Information Processing Congress (Vol. 1, pp. 4549). Washington, DC: Spartan.Google Scholar
Minsky, M. L. (1985). The Society of Mind. New York, NY: Simon & Schuster.Google Scholar
Minsky, M. L., & Papert, S. A. (1969). Perceptrons: An Introduction to Computational Geometry. Cambridge, MA: MIT Press.Google Scholar
Minsky, M. L., & Papert, S. A. (1988). “Prologue: a view from 1988” and “Epilogue: the new connectionism.” In Perceptrons: An Introduction to Computational Geometry (2nd ed., pp. viii–xv, 247280). Cambridge, MA: MIT Press.Google Scholar
Newell, A., Shaw, J. C., & Simon, H. A. (1957). Empirical explorations with the logic theory machine. In Proceedings of the Western Joint Computer Conference (Vol. 15, pp. 218239).Google Scholar
Newell, A., Shaw, J. C., & Simon, H. A. (1958). Elements of a theory of human problem-solving. Psychological Review, 65, 151166.Google Scholar
Newell, A., Shaw, J. C., & Simon, H. A. (1959). A general problem-solving program for a computer. In Proceedings of the International Conference on Information Processing, Paris (pp. 256264).Google Scholar
Norman, D. A., & Shallice, T. (1980). Attention to action: willed and automatic control of behavior. CHIP Report 99, University of California San Diego. (Officially published in Davidson, R., Schwartz, G., & Shapiro, D. (Eds.), Consciousness and Self-Regulation: Advances in Research and Theory (Vol. 4, pp. 118). New York, NY: Plenum, 1986.)Google Scholar
Philippides, A., Husbands, P., & O’Shea, M. (1998). Neural signalling – it’s a gas! In Niklasson, L., Boden, M., & Ziemke, T. (Eds.), ICANN98: Proceedings of the 8th International Conference on Artificial Neural Networks (pp. 5163). London: Springer-Verlag.Google Scholar
Philippides, A., Ott, S. R., Husbands, P. N., Lovick, T. A., & O’Shea, M. (2005). Modeling cooperative volume signaling in a plexus of nitric oxide synthase-expressing neurons. Journal of Neuroscience, 25 (28), 65206532.Google Scholar
Picard, R. W. (1997). Affective Computing. Cambridge, MA: MIT Press.Google Scholar
Pinker, S., & Prince, A. (1988). On language and connectionism: analysis of a parallel distributed model of language acquisition. Cognition, 28, 73193.Google Scholar
Popper, K. R. (1957). The Poverty of Historicism. London: Routledge & Kegan Paul.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
Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386408.Google Scholar
Rosenbloom, P. S., Laird, J. E., & Newell, A. (Eds.). (1993). The SOAR Papers: Research on Integrated Intelligence (2 vols.). Cambridge, MA: MIT Press.Google Scholar
Rumelhart, D. E., & McClelland, J. L. (1986). On learning the past tenses of English verbs. In Rumelhart, D. E., McClelland, J. L., & the PDP Research Group, (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition (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
Sahota, M., & Mackworth, A. K. (1994). Can situated robots play soccer? In Proceedings of the Canadian Conference on Artificial Intelligence, Banff, Alberta (pp. 249254).Google Scholar
Schank, R. C. (1973). Identification of conceptualizations underlying natural language. In Schank, R. C. & Colby, K. M. (Eds.), Computer Models of Thought and Language (pp. 187247). San Francisco, CA: Freeman.Google Scholar
Schank, R. C., & Abelson, R. P. (1977). Scripts, Plans, Goals, and Understanding. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Scheutz, M. (Ed.). (2002). Computationalism: New Directions. Cambridge, MA: MIT Press.Google Scholar
Selfridge, O. G. (1959). Pandemonium: a paradigm for learning. In Blake, D. V. & Uttley, A. M. (Eds.), The Mechanization of Thought Processes (vol. 1, pp. 511529). London: Her Majesty’s Stationery Office.Google Scholar
Simon, H. A. (1967). Motivational and emotional controls of cognition. Psychological Review, 74, 2939.Google Scholar
Simon, H. A. (1969). The Sciences of the Artificial. Cambridge, MA: MIT Press.Google Scholar
Sloman, A. (1974). Physicalism and the bogey of determinism. In Brown, S. C. (Ed.), Philosophy of Psychology (pp. 283304). London: Macmillan.Google Scholar
Sloman, A. (1978). The Computer Revolution in Philosophy: Philosophy, Science, and Models of Mind. Brighton, UK: Harvester Press. Online at: www.cs.bham.ac.uk/research/cogaff/crp/ [last accessed August 8, 2022].Google Scholar
Sloman, A. (1989). On designing a visual system: towards a Gibsonian computational model of vision. Journal of Experimental and Theoretical AI, 1, 289337.Google Scholar
Sloman, A. (2000). Architectural requirements for human-like agents both natural and artificial. In Dautenhahn, K. (Ed.), Human Cognition and Social Agent Technology: Advances in Consciousness Research (pp. 163195). Amsterdam: John Benjamins.Google Scholar
Sloman, A. (2002). The irrelevance of Turing machines to artificial intelligence. In Scheutz, M., (Ed.), Computationalism: New Directions (pp. 87127). Cambridge, MA: MIT Press.Google Scholar
Sloman, A. (2003). How many separately evolved emotional beasties live within us? In Trappl, R., Petta, P., & Payr, S. (Eds.), Emotions in Humans and Artifacts (pp. 2996). Cambridge, MA: MIT Press.Google Scholar
Smith, B. C. (1996). On the Origin of Objects. Cambridge, MA: MIT Press.Google Scholar
Sperber, D., & Wilson, D. (1986). Relevance: Communication and Cognition. Oxford: Blackwell.Google Scholar
Sun, R. (2001). Hybrid systems and connectionist implementationalism. In Nadel, L. (Ed.), Encyclopedia of Cognitive Science (Vol. 2, pp. 697703). New York, NY: Macmillan.Google Scholar
Sun, R. (2006). The CLARION cognitive architecture: extending cognitive modeling to social simulation. In Sun, R. (Ed.), Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation (pp. 79102). New York, NY: Cambridge University Press.Google Scholar
Sun, R., & Bookman, L. (Eds.). (1994). Computational Architectures Integrating Neural and Symbolic Processes. Needham, MA: Kluwer Academic.Google Scholar
Sun, R., Peterson, T., & Merrill, E. (2001). From implicit skills to explicit knowledge: a bottom-up model of skill learning. Cognitive Science, 25 (2), 203244.Google Scholar
Sun, R., & Qi, D. (2000). Rationality assumptions and optimality of co-learning. In Zhang, C. & Soo, V. (Eds.), Design and Application of Intelligent Agents (pp. 6175). Heidelberg: Springer-Verlag.Google Scholar
Tomkins, S. S., & Messick, S. (Eds.). (1963). Computer Simulation of Personality: Frontier of Psychological Research. New York, NY: Wiley.Google Scholar
Vera, A. H., & Simon, H. A. (1993). Situated action: a symbolic interpretation. Cognitive Science, 17, 748.Google Scholar
Wheeler, M. W. (2005). Reconstructing the Cognitive World: The Next Step. Cambridge, MA: MIT Press.Google Scholar
Winograd, T. (1972). Understanding Natural Language. Edinburgh: Edinburgh University Press.Google Scholar
Woods, W. A. (1973). An experimental parsing system for transition network grammars. In Rustin, R. (Ed.), Natural Language Processing (pp. 111154). New York, NY: Algorithmics Press.Google Scholar
Wright, I. P., & Sloman, A. (1997). MINDER1: an implementation of a proto-emotional agent architecture. Technical Report CSRP-97-1, School of Computer Science, University of Birmingham.Google Scholar
Wright, I. P., Sloman, A., & Beaudoin, L. P. (1996). Towards a design-based analysis of emotional episodes. Philosophy, Psychiatry, and Psychology, 3, 101137.Google Scholar
Young, R. M. (1976). Seriation by Children: An Artificial Intelligence Analysis of a Piagetian Task. Basel: Birkhauser.Google Scholar
Zelazo, P. D., Moscovitch, M., & Thompson, E. (2007). The Cambridge Handbook of Consciousness. Cambridge: Cambridge University Press.Google Scholar

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  • General Discussion
  • 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/10.1017/9781108755610.041
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  • General Discussion
  • 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/10.1017/9781108755610.041
Available formats
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  • General Discussion
  • 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/10.1017/9781108755610.041
Available formats
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