Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cc8bf7c57-pd9xq Total loading time: 0 Render date: 2024-12-10T17:01:42.035Z Has data issue: false hasContentIssue false
Series:   Elements in Philosophy of Mind

The Computational Theory of Mind

Published online by Cambridge University Press:  13 November 2023

Matteo Colombo  and
Gualtiero Piccinini
Matteo Colombo
Affiliation:
Universiteit van Tilburg, The Netherlands
Gualtiero Piccinini
Affiliation:
University of Missouri, St Louis

Summary

The Computational Theory of Mind says that the mind is a computing system. It has a long history going back to the idea that thought is a kind of computation. Its modern incarnation relies on analogies with contemporary computing technology and the use of computational models. It comes in many versions, some more plausible than others. This Element supports the theory primarily by its contribution to solving the mind-body problem, its ability to explain mental phenomena, and the success of computational modelling and artificial intelligence. To be turned into an adequate theory, it needs to be made compatible with the tractability of cognition, the situatedness and dynamical aspects of the mind, the way the brain works, intentionality, and consciousness.
Get access

Keywords

mindcomputationrepresentationbraincognition
Type
Element
Information
Series: Elements in Philosophy of Mind
Online ISBN: 9781009183734
Publisher: Cambridge University Press
Print publication: 07 December 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

Aaronson, D., Grupsmith, E., & Aaronson, M. (1976). The impact of computers on cognitive psychology. Behavioral Research Methods & Instrumentation, 8: 129–38.Google Scholar
Aaronson, S. (2013). Why philosophers should care about computational complexity. In Copeland, B. J., Posy, C. J., & Shagrir, O. (eds.), Computability: Turing, Gödel, Church, and Beyond. Cambridge, MA: MIT Press, pp. 261–328.CrossRefGoogle Scholar
Abraham, T. H. (2018). Cybernetics. In Sprevak, M. & Colombo, M. (eds.), The Routledge handbook of the computational mind. New York: Routledge, pp. 52–64.CrossRefGoogle Scholar
Adamatzky, A. (2021). Handbook of Unconventional Computing. Singapore: World Scientific.Google Scholar
Adrian, E. D., & Zotterman, Y. (1926). The impulses produced by sensory nerve endings: Part 3. Impulses set up by touch and pressure. The Journal of Physiology, 61(4): 465–93.Google ScholarPubMed
Anderson, N. G., & Piccinini, G. (forthcoming). The Physical Signature of Computation: A Robust Mapping Account. Oxford: Oxford University Press.Google Scholar
Ashby, W. R. (1952). Design for a Brain. London: Chapman and Hall.Google Scholar
Baars, B. J. (1993). A Cognitive Theory of Consciousness. Cambridge: Cambridge University Press.Google Scholar
Baker, B., Lansdell, B., & Kording, K. P. (2022). Three aspects of representation in neuroscience. Trends in Cognitive Sciences, 26(11): 942–58.CrossRefGoogle ScholarPubMed
Barlow, H. B. (1961). Possible Principles Underlying the Transformation of Sensory Messages. In Rosenblith, W. A. (ed.), Sensory Communication. Cambridge, MA: MIT Press.Google Scholar
Bechtel, W., & Shagrir, O. (2015). The non-redundant contributions of Marr’s three levels of analysis for explaining information-processing mechanisms. Topics in Cognitive Science, 7(2): 312–22.CrossRefGoogle ScholarPubMed
Beer, R. D. (2000). Dynamical approaches to cognitive science. Trends in Cognitive Sciences, 4(3): 91–9.CrossRefGoogle ScholarPubMed
Beer, R. D., & Williams, P. L. (2015). Information processing and dynamics in minimally cognitive agents. Cognitive Science, 39(1): 1–38.CrossRefGoogle ScholarPubMed
Bell, A. J. (1999). Levels and loops: The future of artificial intelligence and neuroscience. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 354(1392): 2013–20.CrossRefGoogle ScholarPubMed
Bengio, Y., Lecun, Y., & Hinton, G. (2021). Deep learning for AI. Communications of the ACM, 64(7): 58–65.CrossRefGoogle Scholar
Bennett, M. R., & Hacker, P. M. S. (2022). Philosophical Foundations of Neuroscience. 2nd ed. Hoboken: John Wiley & Sons.Google Scholar
Bickhard, M. H., & Terveen, L. (1995). Foundational Issues in Artificial Intelligence and Cognitive Science: Impasse and Solution. Amsterdam: North-Holland.Google Scholar
Birhane, A. (2021). The impossibility of automating ambiguity. Artificial Life, 27(1): 44–61.CrossRefGoogle ScholarPubMed
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York: Springer.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. Minneapolis: University of Minnesota Press, pp. 261–325.Google Scholar
Block, N. (1995). On a confusion about a function of consciousness. The Behavioral and Brain Sciences, 18(2): 227–87.CrossRefGoogle Scholar
Block, N., & Fodor, J. A. (1972). What psychological states are not. The Philosophical Review, 81(2): 159–81.CrossRefGoogle Scholar
Bourdillon, P., Hermann, B., Guénot, M., et al. (2020). Brain-scale cortico-cortical functional connectivity in the delta-theta band is a robust signature of conscious states: An intracranial and scalp EEG study. Scientific Reports, 10: 14037. https://doi.org/10.1038/s41598-020-70447-7.CrossRefGoogle ScholarPubMed
Bowers, J. S., Malhotra, G., Dujmović, M., et al. (2022). Deep problems with neural network models of human vision. Behavioral and Brain Sciences, 1–74. https://doi.org/10.1017/S0140525X22002813.CrossRefGoogle Scholar
Brentano, F. (1874/1973). Psychology from an Empirical Standpoint. Trans. Rancurello, A. C., Terrell, D. B., & McAlister, L. L. London: Routledge and Kegan Paul.Google Scholar
Brette, R. (2019). Is coding a relevant metaphor for the brain? Behavioral and Brain Sciences, 42: e215.CrossRefGoogle Scholar
Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47(1–3): 139–59.CrossRefGoogle Scholar
Buckner, C. (forthcoming). Deeply Rational Machines. Oxford: Oxford University Press.Google Scholar
Calvo, P., & Symons, J. (eds.). (2014). The Architecture of Cognition: Rethinking Fodor and Pylyshyn’s Systematicity Challenge. Cambridge: MIT Press.CrossRefGoogle Scholar
Camp, E. (2007). Thinking with maps. Philosophical Perspectives, 21: 145–82.CrossRefGoogle Scholar
Campbell, D. I., & Yang, Y. (2021). Does the solar system compute the laws of motion? Synthese, 198: 3203–20.CrossRefGoogle Scholar
Cao, R. (2018). Computational explanations and neural coding. In Sprevak, M., & Colombo, M. (eds.), The Routledge Handbook of the Computational Mind. Routledge: New York, pp. 283–96.Google Scholar
Cao, R. (2022). Multiple realizability and the spirit of functionalism. Synthese, 200: 506. https://doi.org/10.1007/s11229-022-03524-1.CrossRefGoogle Scholar
Chalmers, D. J. (1994). On implementing a computation. Minds and Machines, 4(4): 391–402.CrossRefGoogle Scholar
Chalmers, D. J. (1996). The Conscious Mind: In Search of a Fundamental Theory. Oxford: Oxford University Press.Google Scholar
Chalmers, D. J. (2011). A computational foundation for the study of cognition. The Journal of Cognitive Science, 12: 323–57.Google Scholar
Chater, N., Tenenbaum, J. B., & Yuille, A. (2006). Probabilistic models of cognition. Trends in Cognitive Science, 10(7): 287–93.Google ScholarPubMed
Chemero, A. (2011). Radical Embodied Cognitive Science. Cambridge: MIT Press.Google Scholar
Chirimuuta, M. (2018). Explanation in computational neuroscience: Causal and non-causal. The British Journal for the Philosophy of Science, 69: 849–80.CrossRefGoogle Scholar
Chirimuuta, M. (2021). Your brain is like a computer: Function, analogy, simplification. In Calzavarini, F., & Viola, M. (eds.), Neural Mechanisms: Studies in Brain and Mind, vol. 17. Cham: Springer, pp. 235–61.CrossRefGoogle Scholar
Church, A. (1936a). A note on the Entscheidungsproblem. Journal of Symbolic Logic, 1: 40–1. https://doi.org/10.2307/2269326.Google Scholar
Church, A. (1936b). An unsolvable problem of elementary number theory. American Journal of Mathematics, 58: 345–63. https://doi.org/10.2307/2371045.CrossRefGoogle Scholar
Churchland, P. M. (1992). A Neurocomputational Perspective: The Nature of Mind and the Structure of Science. Cambridge: MIT Press.CrossRefGoogle Scholar
Churchland, P. S., & Sejnowski, T. J. (1992). The Computational Brain. Cambridge: MIT Press.CrossRefGoogle Scholar
Cisek, P. (2019). Resynthesizing behavior through phylogenetic refinement. Attention, Perception, & Psychophysics, 81(7): 2265–87.CrossRefGoogle ScholarPubMed
Clark, A. (1993). Associative Engines: Connectionism, Concepts, and Representational Change. Cambridge: MIT Press.CrossRefGoogle Scholar
Clark, A. (1997). The dynamical challenge. Cognitive Science, 21(4): 461–81.CrossRefGoogle Scholar
Clark, A. (1998). Being There: Putting Brain, Body, and World Together Again. Cambridge: MIT Press.Google Scholar
Clark, A. (2008). Supersizing the Mind: Embodiment, Action, and Cognitive Extension. New York: Oxford University Press.CrossRefGoogle Scholar
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3): 181–204.CrossRefGoogle ScholarPubMed
Coelho Mollo, D. (2018). Functional individuation, mechanistic implementation: The proper way of seeing the mechanistic view of concrete computation. Synthese, 195(8): 3477–97.CrossRefGoogle Scholar
Collins, A. G. E., & Cockburn, J. (2020). Beyond dichotomies in reinforcement learning. Nature Reviews Neuroscience, 21, 576–86.CrossRefGoogle ScholarPubMed
Colombo, M. (2009). Does embeddedness tell against computationalism? A tale of bees and sea hares. AISB09 Proceedings of the 2nd Symposium on Computing and Philosophy. Edinburgh: Society for the Study of Artificial Intelligence and the Simulation of Behaviour, pp. 16–21.Google Scholar
Colombo, M. (2010). How ‘authentic intentionality’ can be enabled: A neurocomputational hypothesis. Minds & Machines, 20: 183–202.CrossRefGoogle Scholar
Colombo, M. (2014a). Deep and beautiful: The reward prediction error hypothesis of dopamine. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 45: 57–67.CrossRefGoogle ScholarPubMed
Colombo, M. (2014b). Explaining social norm compliance: A plea for neural representations. Phenomenology and the Cognitive Sciences, 13(2): 217–38.CrossRefGoogle Scholar
Colombo, M. (2017). Why build a virtual brain? Large-scale neural simulations as jump start for cognitive computing. Journal of Experimental & Theoretical Artificial Intelligence, 29(2): 361–70.CrossRefGoogle Scholar
Colombo, M. (2021). (Mis) computation in computational psychiatry. In Calzavarini, F., & Viola, M. (eds.), Neural Mechanisms: Studies in Brain and Mind, vol. 17. Cham: Springer, pp. 427–48.CrossRefGoogle Scholar
Colombo, M. (2022). Computational modelling for alcohol use disorder. Erkenntnis. https://doi.org/10.1007/s10670-022-00533-x.CrossRefGoogle Scholar
Colombo, M., & Heinz, A. (2019). Explanatory integration, computational phenotypes, and dimensional psychiatry: The case of alcohol use disorder. Theory & Psychology, 29(5): 697–718.CrossRefGoogle Scholar
Copeland, B. J. (1996). What is computation? Synthese, 108: 335–59.CrossRefGoogle Scholar
Copeland, B. J. (2000). Narrow versus wide mechanism: Including a re-examination of Turing’s views on the mind–machine issue. The Journal of Philosophy, 97: 5–32.Google Scholar
Copeland, B. J., & Proudfoot, D. (1996). On Alan Turing’s anticipation of connectionism. Synthese, 108(3): 361–77.CrossRefGoogle Scholar
Corabi, J., & Schneider, S. (2012). The metaphysics of uploading. Journal of Consciousness Studies, 19(7): 26–44.Google Scholar
Crick, F. (1989). The recent excitement about neural networks. Nature, 337: 129–32.CrossRefGoogle ScholarPubMed
D’Angelo, E., & Jirsa, V. (2022). The quest for multiscale brain modeling. Trends in Neurosciences, 45(10): 777–90.CrossRefGoogle ScholarPubMed
Cummins, R. (1983) The Nature of Psychological Explanation. Cambridge: MIT Press.Google Scholar
Daston, L. (1994). Enlightenment calculations. Critical Inquiry, 21(1): 182–202.CrossRefGoogle Scholar
Davis, E., & Marcus, G. (2015). Commonsense reasoning and commonsense knowledge in artificial intelligence. Communications of the ACM, 58(9): 92–103.CrossRefGoogle Scholar
Dabney, W., Kurth-Nelson, Z., Uchida, N., et al. (2020). A distributional code for value in dopamine-based reinforcement learning. Nature, 577(7792): 671–75.CrossRefGoogle ScholarPubMed
Daw, N. D., Niv, Y., & Dayan, P. (2005). Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature Neuroscience, 8(12): 1704–11.CrossRefGoogle ScholarPubMed
Daw, N. D. & Frank, M. J. (2009). Reinforcement learning and higher level cognition: introduction to the special issue. Cognition, 113: 259–61.CrossRefGoogle Scholar
Dayan, P. (1994). Computational modelling. Current Opinion in Neurobiology, 4(2): 212–17.CrossRefGoogle ScholarPubMed
Dayan, P. (2001). Levels of Analysis in Neural Modeling. Encyclopedia of Cognitive Science. London: MacMillan Press.Google Scholar
Dayan, P., & Abbott, L. F. (2005). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge: MIT Press.Google Scholar
Dehaene, S., Lau, H., & Kouider, S. (2017). What is consciousness, and could machines have it? Science, 358(6362): 486–92.CrossRefGoogle Scholar
Dennett, D. C. (1969). Content and Consciousness. London: Routledge & Kegan Paul.Google Scholar
Dennett, D. C. (1978). Brainstorms: Philosophical Essays on Mind and Psychology. Montgomery: Bradford.Google Scholar
Dennett, D. C. (1991a). Real patterns. Journal of Philosophy, 88(1): 27–51.CrossRefGoogle Scholar
Dennett, D. C. (1991b). Consciousness Explained. Boston: Little, Brown.Google Scholar
Dewhurst, J. (2018). Individuation without representation. The British Journal for the Philosophy of Science, 69(1): 103–16.CrossRefGoogle Scholar
Dickinson, A. (1985). Actions and habits: the development of behavioural autonomy. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 308, 67–78.Google Scholar
Dickinson, A., & Balleine, B. (2002). The role of learning in the operation of motivational systems. In: Gallistel, C. R. (ed) Stevens’ handbook of experimental psychology: learning, motivation, and emotion. New York: Wiley, pp. 497–534.Google Scholar
Dolan, R. J., & Dayan, P. (2013). Goals and habits in the brain. Neuron, 80(2): 312–25.CrossRefGoogle ScholarPubMed
Dretske, F. (1981). Knowledge and the Flow of Information. Cambridge, MA: MIT Press.Google Scholar
Dreyfus, H. (1992). What Computers Still Can’t Do: A Critique of Artificial Reason. New York: MIT Press.Google Scholar
Dreyfus, H. (2002a). Intelligence without representation: Merleau-Ponty’s critique of mental representation. Phenomenology and the Cognitive Sciences, 1(4): 413–25.Google Scholar
Dreyfus, H. (2002b). Refocusing the question: Can there be skillful coping without propositional representations or brain representations? Phenomenology and the Cognitive Sciences, 1: 413–25.Google Scholar
Edelman, G. (1992). Bright Air, Brilliant Fire. New York: Basic Books.Google Scholar
Egan, F. (2014). How to think about mental content. Philosophical Studies, 170(1): 115–35.CrossRefGoogle Scholar
Egan, F. (2018). The nature and function of content in computational models. In Sprevak, M., & Colombo, M. (eds.),The Routledge Handbook of the Computational Mind. New York: Routledge, pp. 247–58.Google Scholar
Eliasmith, C. (1996). The third contender: A critical examination of the dynamicist theory of cognition. Philosophical Psychology, 9(4): 441–63.CrossRefGoogle Scholar
Eliasmith, C. (2009). How we ought to understand computation in the brain. Studies in History and Philosophy of Science, 41: 313–20.Google Scholar
Eliasmith, C., Stewart, T. C., Choo, X., et al. (2012). A large-scale model of the functioning brain. Science, 338(6111): 1202–5.CrossRefGoogle ScholarPubMed
Evans, J. S. B., & Stanovich, K. E. (2013). Dual-process theories of higher cognition advancing the debate. Perspectives on Psychological Science, 8(3): 223–41.CrossRefGoogle ScholarPubMed
Faisal, A. A., Selen, L. P., & Wolpert, D. M. (2008). Noise in the nervous system. Nature Reviews Neuroscience, 9(4): 292–303.CrossRefGoogle ScholarPubMed
Feynman, R., Leighton, R. B., & Sands, M. L. (1989). Lectures on Physics, vol. 1, retrieved from Caltech, HTML Edition. www.FeynmanLectures.caltech.edu/.Google Scholar
Figdor, C. (2018). Pieces of Mind: The Proper Domain of Psychological Predicates. New York: Oxford University Press.CrossRefGoogle Scholar
Fodor, J. A. (1965). Explanations in psychology. In Black, M. (ed.), Philosophy in America. London: Routledge & Kegan Paul, pp. 161–79.Google Scholar
Fodor, J. A. (1968). Psychological Explanation. New York: Random House.Google Scholar
Fodor, J. A. (1975). The Language of Thought. New York: Thomas Y. Crowell.Google Scholar
Fodor, J. A. (1987). Psychosemantics. Cambridge: MIT Press.CrossRefGoogle Scholar
Fodor, J. A. (1996). Deconstructing Dennett’s Darwin, Mind and Language, 11: 246–62.CrossRefGoogle Scholar
Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1–2): 3–71.CrossRefGoogle ScholarPubMed
Frankish, K. (ed.). (2017). Illusionism: As a Theory of Consciousness. Exeter: Imprint Academic.Google Scholar
Freeman, W. J. (1991). The physiology of perception. Scientific American, 264: 78–85.CrossRefGoogle ScholarPubMed
Fresco, N. (2014). Physical Computation and Cognitive Science. Heidelberg: Springer.CrossRefGoogle Scholar
Fresco, N. (2022). Information in explaining cognition: How to evaluate it? Philosophies, 7(2): 28.CrossRefGoogle Scholar
Fresco, N., Copeland, B. J., & Wolf, M. J. (2021). The indeterminacy of computation. Synthese, 199(5): 12753–75.CrossRefGoogle Scholar
Fresco, N., & Primiero, G. (2013). Miscomputation. Philosophy & Technology, 26(3): 253–72.CrossRefGoogle Scholar
Friston, K. (2018). Am I self-conscious?(Or does self-organization entail self-consciousness?). Frontiers in Psychology, 9: 579.CrossRefGoogle Scholar
Gabor, D. (1946). Theory of communication. Part 1: The analysis of information. Journal of the Institution of Electrical Engineers-part III: Radio and Communication Engineering, 93(26): 429–41.Google Scholar
Gallagher, S. (2006). How the Body Shapes the Mind. New York: Oxford University Press.Google Scholar
Gallistel, C. R. (1990). The Organization of Learning. Cambridge: The MIT Press.Google Scholar
Gallistel, C., & King, A. (2010). Memory and the Computational Brain. Oxford: Wiley-Blackwell.Google Scholar
Garey, M. R., & Johnson, D. S. (1979). Computers and Intractability: A Guide to the Theory of NP-completeness. San Francisco, CA: W. H. Freeman.Google Scholar
Gerard, R. W. (1951). Some of the problems concerning digital notions in the central nervous system: Cybernetics. In Foerster, H. V., Mead, M., & Teuber, H. L. (eds.), Circular Causal and Feedback Mechanisms in Biological and Social Systems. Transactions of the Seventh Conference. New York: Macy Foundation, pp. 11–57.Google Scholar
Gibson, J. J. (1979). The Ecological Approach to Visual Perception. Boston: Houghton Mifflin.Google Scholar
Gigerenzer, G., & Goldstein, D. G. (1996a). Mind as computer: Birth of a metaphor. Creativity Research Journal, 9(2–3): 131–44.CrossRefGoogle Scholar
Gigerenzer, G., & Goldstein, D. G. (1996b). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103(4): 650–69. https://doi.org/10.1037/0033-295X.103.4.650.CrossRefGoogle ScholarPubMed
Gilson, M., Tagliazucchi, E., & Cofré, R. (2023). Entropy production of multivariate Ornstein-Uhlenbeck processes correlates with consciousness levels in the human brain. Physical Review, E107: 024121.Google Scholar
Gillett, C. (2007). A mechanist manifesto for the philosophy of mind: A third way for functionalists. Journal of Philosophical Research, 32: 21–42.CrossRefGoogle Scholar
Gładziejewski, P., & Miłkowski, M. (2017). Structural representations: Causally relevant and different from detectors. Biology and Philosophy, 32: 337–55.CrossRefGoogle ScholarPubMed
Globus, G. G. (1992). Toward a noncomputational cognitive neuroscience. Journal of Cognitive Neuroscience, 4(4): 299–300.CrossRefGoogle Scholar
Gödel, K. (1931). Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme, I, Monatshefte für Mathematik und Physik, 38: 173–98. Reprinted in Feferman, S., Kleene, S., Moore, G., Solovay, R., & van Heijenoort, J. (eds.). (1986). Collected Works. I: Publications 1929–1936. Oxford: Oxford University Press, pp. 144–95.Google Scholar
François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018). An Introduction to Deep Reinforcement Learning. Foundations and Trends in Machine Learning, 11(3–4): 219–354.CrossRefGoogle Scholar
Gödel, K. (1951). Some basic theorems on the foundations of mathematics and their implications, lecture manuscript. Feferman, S., Dawson, J., Kleene, S., et al. (eds.). (1995). Collected Works. III: Unpublished Essays and Lectures. Oxford: Oxford University Press, pp. 304–23.Google Scholar
Godfrey-Smith, P. (2009). Triviality arguments against functionalism. Philosophical Studies, 145: 273–95.CrossRefGoogle Scholar
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge: MIT Press.Google Scholar
Graves, A., Wayne, G., & Danihelka, I. (2014). Neural Turing Machines. arXiv preprint arXiv: 1410.5401.Google Scholar
Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010). Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences, 14(8): 357–64.CrossRefGoogle ScholarPubMed
Guest, O., & Martin, A. E. (2021). How computational modeling can force theory building in psychological science. Perspectives on Psychological Science, 16(4): 789–802.CrossRefGoogle ScholarPubMed
Harman, G. (1973). Thought. Princeton: Princeton University Press.Google Scholar
Harnad, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42(1-3): 335–46.CrossRefGoogle Scholar
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2): 245–58.CrossRefGoogle ScholarPubMed
Haugeland, J. (1985). Artificial Intelligence: The Very Idea. Cambridge, MA: MIT Press.Google Scholar
Haugeland, J. (1998). Having Thought: Essays in the Metaphysics of Mind. Cambridge: Harvard University Press.Google Scholar
Haugeland, J. (2002). Authentic intentionality. In Scheutz, M. (ed.), Computationalism: New Directions. Cambridge, MA: MIT Press, pp. 159–74.Google Scholar
Hebb, D. (1949). The Organization of Behavior. New York: Wiley & Sons.Google Scholar
Hesse, M. (1966). Models and Analogies in Science. Notre Dame: University of Notre Dame Press.Google Scholar
Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4): 500–44.CrossRefGoogle ScholarPubMed
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79: 2554–8.CrossRefGoogle ScholarPubMed
Horsman, D., Kendon, V., & Stepney, S. (2018). Abstraction/representation theory and the natural science of computation. In Cuffaro, M. E. & Fletcher, S. C. (eds.), Physical Perspectives on Computation, Computational Perspectives on Physics. Cambridge: Cambridge University Press, pp. 127–52.Google Scholar
Houk, J. C., Adams, J. L., & Barto, A. G. (1995). A Model of How the Basal Ganglia Generate and Use Neural Signals that Predict Reinforcement. In Houk, J. C., Davis, J. L., Beiser, D. G. (eds.), Models of Information Processing in the Basal Ganglia. Cambridge: MIT Press, pp. 249–70.Google Scholar
Huang, Z., Mashour, G. A., & Hudetz, A. G. (2023). Functional geometry of the cortex encodes dimensions of consciousness. Nature Communications, 14, 72. https://doi.org/10.1038/s41467-022-35764-7.CrossRefGoogle ScholarPubMed
Huys, Q. J., Maia, T.V., & Frank, M.J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19(3), 404–13.CrossRefGoogle ScholarPubMed
Isaac, A. M. C. (2017). The semantics latent in shannon information. The British Journal for the Philosophy of Science, 70(1): 103–25.Google Scholar
Isaac, A. M. C. (2018a). Computational thought from Descartes to Lovelace. In Sprevak, M., & Colombo, M. (eds.), The Routledge Handbook of the Computational Mind. New York: Routledge, pp. 9–22.CrossRefGoogle Scholar
Isaac, A. M. C. (2018b). Embodied cognition as analog computation. Reti, Saperi, Linguaggi: Italian Journal of Cognitive Sciences, 2: 239–62.Google Scholar
Isaac, A. M. C. 2019. The semantics latent in Shannon information. The British Journal for the Philosophy of Science, 70: 103–25.CrossRefGoogle Scholar
Izhikevich, E. (2007). Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. Cambridge, MA: The MIT Press.Google Scholar
Jeffress, L. A. (ed.). (1951). Cerebral Mechanisms in Behavior. New York: Wiley.Google Scholar
Jonas, E., & Körding, K. P. (2017). Could a neuroscientist understand a microprocessor? PLoS Computational Biology, 13(1): e1005268.CrossRefGoogle ScholarPubMed
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of artificial intelligence research, 4, 237–85.CrossRefGoogle Scholar
Kahneman, D. (2011). Thinking, fast and slow. MacmillanGoogle Scholar
Kaplan, D. M. (2011). Explanation and description in computational neuroscience. Synthese, 183(3): 339–73.CrossRefGoogle Scholar
Kiela, D., Bartolo, M., Nie, Y., et al. (2021). Dynabench: Rethinking Benchmarking in NLP. arXiv preprint arXiv: 2104.14337.Google Scholar
Kirkpatrick, K. L. (2022). Biological computation: Hearts and flytraps. Journal of Biological Physics, 48(1): 55–78.CrossRefGoogle ScholarPubMed
Kleene, S. C. (1936). General recursive functions of natural numbers. Mathematische Annelen, 112: 727–42.Google Scholar
Kleene, S. C. (1956). Representation of events in nerve nets and finite automata. In Shannon, C. E., & McCarthy, J. (eds.), Automata Studies. Princeton, NJ: Princeton University Press, pp. 3–42.Google Scholar
Klein, C. (2008). Dispositional implementation solves the superfluous structure problem. Synthese, 165: 141–53.CrossRefGoogle Scholar
Knill, D. C., & Pouget, A. (2004). The Bayesian brain: The role of uncertainty in neural coding and computation. TRENDS in Neurosciences, 27(12): 712–19.CrossRefGoogle ScholarPubMed
Koch, C., & Segev, I. (eds.). (1998). Methods in Neuronal Modeling: From Synapses to Networks. Cambridge: MIT Press.Google Scholar
Koch, C. (1999). Biophysics of computation: information processing in single neurons. New York: Oxford University Press.Google Scholar
Konishi, M. (2003). Coding of auditory space. Annual Review of Neuroscience, 26: 31–55.CrossRefGoogle ScholarPubMed
Körding, K. P., Blohm, G., Schrater, P., & Kay, K. (2018). Appreciating Diversity of Goals in Computational Neuroscience. https://doi.org/10.31219/osf.io/3vy69.CrossRefGoogle Scholar
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2016). Building machines that learn and think like people. Behavioral and Brain Sciences, 40: 1–101. https://doi.org/10.1017/S0140525X16001837.Google ScholarPubMed
Langdon, A. J., Sharpe, M. J., Schoenbaum, G., & Niv, Y. (2018). Model-based predictions for dopamine. Current Opinion in Neurobiology, 49: 1–7.CrossRefGoogle ScholarPubMed
Landgrebe, J., & Smith, B. (2022). Why machines will never rule the world: artificial intelligence without fear. New York: Taylor & Francis.CrossRefGoogle Scholar
Lashley, K. S. (1958). Cerebral organization and behavior. Research Publications, Association for Research in Nervous and Mental Diseases, 36: 1–18.Google ScholarPubMed
Lau, H. (2022). In Consciousness We Trust: The Cognitive Neuroscience of Subjective Experience. Oxford: Oxford University Press.CrossRefGoogle Scholar
Laughlin, S. B., de van Steveninck, Ruyter, R. R., & Anderson, J. C. (1998). The metabolic cost of neural information. Nature neuroscience, 1(1): 36–41.CrossRefGoogle ScholarPubMed
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553): 436–44.CrossRefGoogle ScholarPubMed
Lee, J. (2018). Structural representation and the two problems of content. Mind and Language, 34: 606–26.Google Scholar
Lee, J. (2021). Rise of the swamp creatures: Reflections on a mechanistic approach to content. Philosophical Psychology, 34: 805–28.CrossRefGoogle Scholar
Leibniz, G. W. (1714). The Monadology. Monadology and Other Philosophical Essays (1965) translated and edited by Schrecker, P., & Schrecker, A. M. New York: Bobbs-Merrill.Google Scholar
Lem, S. (1964). Summa Technologiae. Electronic Mediations Series. Trans. Zylinska, J. (2013). Minneapolis, MN: University of Minnesota Press.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: 1–60.CrossRefGoogle Scholar
Light, J. S. (1999). When computers were women. Technology and Culture, 40: 455–83.CrossRefGoogle Scholar
Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. (2020). Backpropagation and the brain. Nature Reviews Neuroscience, 21(6): 335–46.CrossRefGoogle ScholarPubMed
Lovelace, A. A. (1843). Translation of, and notes to, Luigi F. Menabrea’s sketch of the analytical engine invented by Charles Babbage. Scientific Memoirs, 3: 691–731.Google Scholar
Lucas, J. R. (1961). Minds, Machines, and Gödel. Philosophy, 36: 112–37.CrossRefGoogle Scholar
Luppi, A. I., Vohryzek, J., Kringelbach, M. L., et al. (2023). Distributed harmonic patterns of structure-function dependence orchestrate human consciousness. Communications Biology, 6: 117. https://doi.org/10.1038/s42003-023-04474-1.CrossRefGoogle ScholarPubMed
Lyon, P., Keijzer, F., Arendt, D., & Levin, M. (2021). Reframing cognition: Getting down to biological basics. Philosophical Transactions of the Royal Society B, 376(1820): 20190750.CrossRefGoogle ScholarPubMed
MacKay, D. M. (1969). Information, mechanism and meaning. Cambridge: MIT Press.CrossRefGoogle Scholar
Maia, T. V., & Frank, M. J. (2011). From reinforcement learning models to psychiatric and neurological disorders. Nature Neuroscience, 14(2): 154–62.CrossRefGoogle ScholarPubMed
Maley, C. J. (2023). Analogue computation and representation. The British Journal for the Philosophy of Science, 271–7. https://doi.org/10.1086/715031.CrossRefGoogle Scholar
Maley, C. J., & Piccinini, G. (2016). Closed loops and computation in neuroscience: What it means and why it matters. In El Hady, A. (ed.), Closed Loop Neuroscience. London: Elsevier, pp. 271–7.Google Scholar
Maley, C. J., & Piccinini, G. (2017). A unified mechanistic account of teleological functions for psychology and neuroscience. In Kaplan, D. M. (ed.), Explanation and Integration in Mind and Brain Science. Oxford: Oxford University Press, 236–56.Google Scholar
Mandelbaum, E. (2022). Everything and more: The prospects of whole brain emulation. The Journal of Philosophy, 119(8): 444–59.CrossRefGoogle Scholar
Marcus, G. (2018). Deep Learning: A Critical Appraisal. arXiv preprint arXiv: 1801.00631.Google Scholar
Marcus, G. F. (2001). The Algebraic Mind: Integrating Connectionism and Cognitive Science. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Marcus, G., & Davis, E. (2019). Rebooting AI: Building Artificial Intelligence We Can Trust. New York: Vintage.Google Scholar
Marder, E., & Goaillard, J. M. (2006). Variability, compensation and homeostasis in neuron and network function. Nature Reviews Neuroscience, 7(7): 563–74.CrossRefGoogle ScholarPubMed
Markram, H. (2006). The blue brain project. Nature Reviews Neuroscience, 7(2): 153–60.CrossRefGoogle ScholarPubMed
Marr, D., & Poggio, T. (1976). From understanding computation to understanding neural circuitry [AI Memo 357]. MIT Artificial Intelligence Laboratory. https://dspace.mit.edu/bitstream/handle/1721.1/5782/AIM-357.pdf.Google Scholar
Marr, D. (1982) Vision. San Francisco: W.H. Freeman.Google Scholar
Mashour, G. A., Roelfsema, P., Changeux, J. P., & Dehaene, S. (2020). Conscious processing and the global neuronal workspace hypothesis. Neuron, 105(5): 776–98.CrossRefGoogle ScholarPubMed
Maudlin, T. (1989). Computation and consciousness. The Journal of Philosophy, 86(8): 407–32.CrossRefGoogle Scholar
McCarthy, J. (1959). Programs with common sense. In Proceedings of the Teddington Conference on the Mechanization of Thought Processes. London: Her Majesty’s Stationary Office, pp. 75–91.Google Scholar
McClelland, J. L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1(1): 11–38.CrossRefGoogle ScholarPubMed
McClelland, J. L., Botvinick, M. M., Noelle, D. C., et al. (2010). Letting structure emerge: Connectionist and dynamical systems approaches to cognition. Trends in cognitive sciences, 14(8): 348–56.CrossRefGoogle ScholarPubMed
McCulloch, W. S. (1949). The brain computing machine. Electrical Engineering, 68(6): 492–97.CrossRefGoogle Scholar
McCulloch, W. S., & Pitts, W. H. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 7: 115–33.Google Scholar
Michel, M., Beck, D., Block, N., et al. (2019). Opportunities and challenges for a maturing science of consciousness. Nature Human Behaviour, 3(2): 104–7.CrossRefGoogle ScholarPubMed
Mickevich, A. (1961). The Game. Translation of Dneprov (1961). Moscow: Moscow State University.Google Scholar
Miłkowski, M. (2013). Explaining the Computational Mind. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Miłkowski, M. (2017). Situatedness and embodiment of computational systems. Entropy, 19(4): 1–15. https://doi.org/10.3390/e19040162.CrossRefGoogle Scholar
Miłkowski, M. (2018). From computer metaphor to computational modeling: The evolution of computationalism. Minds and Machines, 28(3): 515–41.CrossRefGoogle Scholar
Miller, G. (2003). The cognitive revolution: A historical perspective. Trends in Cognitive Sciences, 7(3): 141–44.CrossRefGoogle ScholarPubMed
Millikan, R. G. (2023). Teleosemantics and the frogs. Mind & Language, 1–9. https://doi.org/10.1111/mila.12456.CrossRefGoogle Scholar
Minsky, M., & Seymour, P. (1969). Perceptrons. Cambridge: MIT Press.Google Scholar
Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540): 529.CrossRefGoogle ScholarPubMed
Montague, P. R., Dayan, P, Person, C, & Sejnowski, T. J. (1995). Bee foraging in uncertain environments using predictive Hebbian learning. Nature, 377: 725–8.CrossRefGoogle ScholarPubMed
Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012). Computational psychiatry. Trends in Cognitive Sciences, 16(1): 72–80.CrossRefGoogle ScholarPubMed
Montague, R. (2007). Your Brain Is (Almost) Perfect: How We Make Decisions. London: Penguin.Google Scholar
Morgan, A. (2022). Against neuroclassicism: On the perils of armchair neuroscience. Mind & Language, 37(3): 329–55.CrossRefGoogle Scholar
Morgan, A., & Piccinini, G. (2018). Towards a cognitive neuroscience of intentionality. Minds and Machines, 28: 119–39.CrossRefGoogle Scholar
Morillo, C. (1992). Reward event systems: Reconceptualizing the explanatory roles of motivation, desire and pleasure. Philosophical Psychology, 5: 7–32.CrossRefGoogle Scholar
Moutoussis, M., Shahar, N., Hauser, T. U., & Dolan, R. J. (2019). Computation in psychotherapy, or how computational psychiatry can aid learning-based psychological therapies. Computational Psychiatry, 2: 50–73.CrossRefGoogle Scholar
Murphy, R. R. (2019). Introduction to AI Robotics. Cambridge: MIT Press.Google Scholar
Neander, K. (1991). Functions as selected effects: The conceptual analyst’s defense. Philosophy of Science, 58(2): 168–84.CrossRefGoogle Scholar
Neander, K. (2017). A Mark of the Mental: In Defense of Informational Teleosemantics. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Newell, A. (1982). The knowledge level. Artificial intelligence, 18(1): 87–127.CrossRefGoogle Scholar
Newell, A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Newell, A., & Simon, H. A. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the Association for Computing Machinery, 19: 113–26.CrossRefGoogle Scholar
Niv, Y. (2009). Reinforcement learning in the brain. Journal of Mathematical Psychology, 53(3): 139–54.CrossRefGoogle Scholar
Niv, Y., & Montague, P. R. (2009) Theoretical and empirical studies of learning. In Glimcher, P. W., et al. (eds.), Neuroeconomics: Decision Making and the Brain. New York: Academic Press, pp. 249–329.Google Scholar
O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. London: Broadway Books.Google Scholar
Papayannopoulos, P., Fresco, N., & Shagrir, O. (2022). On two different kinds of computational indeterminacy. The Monist, 105(2): 229–46.CrossRefGoogle Scholar
Patzelt, E. H., Hartley, C. A., & Gershman, S. J. (2018). Computational phenotyping: using models to understand individual differences in personality, development, and mental illness. Personality Neuroscience, 1: e18.CrossRefGoogle ScholarPubMed
Pavlick, E. (2022). Semantic Structure in Deep Learning. Annual Review of Linguistics, 8(1): 447–71.CrossRefGoogle Scholar
Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge: Cambridge University Press.Google Scholar
Penrose, R. (1989). The Emperor’s New Mind: Concerning Computers, Minds, and the Laws of Physics. Oxford: Oxford University Press.CrossRefGoogle Scholar
Piccinini, G. (2004). The First computational theory of mind and brain: A close look at McCulloch and Pitts’s ‘logical calculus of ideas immanent in nervous activity’. Synthese, 141: 175–215.CrossRefGoogle Scholar
Piccinini, G. (2010). The mind as neural software? Understanding functionalism, computationalism, and computational functionalism. Philosophy and Phenomenological Research, 81(2): 269–311.CrossRefGoogle Scholar
Piccinini, G. (2015). Physical Computation: A Mechanistic Account. Oxford: Oxford University Press.CrossRefGoogle Scholar
Piccinini, G., & Bahar, S. (2013). Neural Computation and the Computational Theory of Cognition. Cognitive Science, 34: 453–88.Google Scholar
Piccinini, G. (2020). Neurocognitive Mechanisms: Explaining Biological Cognition. Oxford: Oxford University Press.CrossRefGoogle Scholar
Piccinini, G. (2021). The myth of mind uploading. In Clowes, R. W., Gärtner, K., & Hipólito, I. (eds.), The Mind-Technology Problem. Studies in Brain and Mind, vol. 18. Cham: Springer.CrossRefGoogle Scholar
Piccinini, G. (2022). Situated neural representations: Solving the problems of content. Frontiers in Neurorobotics, 16: 1–13.CrossRefGoogle ScholarPubMed
Piccinini, G., and Ritchie, B. J. (forthcoming). Cognitive Computational Neuroscience. In Heinzelmann, N. (ed.), Advances in Neurophilosophy. Bloomsbury.Google Scholar
Poldrack, R. A. (2021). The physics of representation. Synthese, 199(1): 1307–25.CrossRefGoogle Scholar
Polger, T. W., & Shapiro, L. A. (2016). The Multiple Realization Book. New York: Oxford University Press.CrossRefGoogle Scholar
Potochnik, A. (2017). Idealization and the Aims of Science. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Pouget, A., Beck, J. M., Ma, W. J., & Latham, P. E. (2013). Probabilistic brains: Knowns and unknowns. Nature Neuroscience, 16(9): 1170–8.CrossRefGoogle Scholar
Price, C. J., & Friston, K. J. (2002). Degeneracy and cognitive anatomy. Trends in Cognitive Sciences, 6(10): 416–21.CrossRefGoogle ScholarPubMed
Psillos, S. (2011). Living with the abstract: Realism and models. Synthese, 180(1): 3–17.CrossRefGoogle Scholar
Putnam, H. (1960). Minds and machines. In Hook, S. (ed.), Dimensions of Mind. New York: New York University Press, pp. 57–80.Google Scholar
Putnam, H. (1967). Psychological predicates. In Capitan, W. H., & Merrill, D. D. (eds.), Art, Philosophy, and Religion. Pittsburgh: University of Pittsburgh Press. Reprinted as The nature of mental states. In Lycan, W. (ed.). (1999). Mind and Cognition: An Anthology. 2nd ed. Malden: Blackwell, pp. 27–34.Google Scholar
Putnam, H. (1975). The mental life of some machines. In Mind, Language and Reality: Philosophical Papers, vol. 2. Cambridge: Cambridge University Press, pp. 408–28.CrossRefGoogle Scholar
Putnam, H. (1988). Representation and Reality. Cambridge, MA: MIT Press.Google Scholar
Pylyshyn, Z. W. (1980). Computation and cognition: Issues in the foundations of cognitive science. Behavioral and Brain Sciences, 3(1): 111–32.CrossRefGoogle Scholar
Pylyshyn, Z. W. (1984). Computation and Cognition. Cambridge: MIT Press.Google Scholar
Quilty-Dunn, J., Porot, N., & Mandelbaum, E. (2022). The best game in town: The re-emergence of the language of thought hypothesis across the cognitive sciences. Behavioral and Brain Sciences, 1–55. https://doi.org/10.1017/S0140525X22002849.CrossRefGoogle Scholar
Quiroga, R. Q., & Panzeri, S. (eds.). (2013). Principles of Neural Coding. Boca Raton: CRC Press.CrossRefGoogle Scholar
Rahwan, I., Cebrian, M., Obradovich, N., et al. (2019). Machine behaviour. Nature, 568(7753): 477–86.CrossRefGoogle ScholarPubMed
Ramsey, W. M. (2016). Untangling two questions about mental representation. New Ideas in Psychology, 40: 3–12.CrossRefGoogle Scholar
Rangel, A., Camerer, C., & Montague, P. R. (2008). A framework for studying the neurobiology of value-based decision making. Nature Reviews Neuroscience, 9(7): 545–56.CrossRefGoogle ScholarPubMed
Rashevsky, N. (1938). Mathematical Biophysics: Physicomathematical Foundations of Biology. Chicago: University of Chicago Press.Google Scholar
Rescorla, M. (2014). A theory of computational implementation. Synthese, 191(6): 1277–307.CrossRefGoogle Scholar
Richards, B. A., Lillicrap, T. P., Beaudoin, P., et al. (2019). A deep learning framework for neuroscience. Nature Neuroscience, 22(11): 1761–70.CrossRefGoogle ScholarPubMed
Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6): 386–408.CrossRefGoogle ScholarPubMed
Rumelhart, D., McClelland, J., & the PDP Research Group. (1986). Parallel Distributed Processing, vol. 1. Cambridge: MIT Press.CrossRefGoogle Scholar
Russell, S. J. (1997). Rationality and intelligence. Artificial intelligence, 94(1–2): 57–77.CrossRefGoogle Scholar
Russell, S. J., & Norvig, P. (2009). Artificial Intelligence: A Modern Approach. Upper Saddle River: Prentice Hall.Google Scholar
Santoro, A., Lampinen, A., Mathewson, K., Lillicrap, T., & Raposo, D. (2021). Symbolic Behaviour in Artificial Intelligence. arXiv preprint arXiv: 2102.03406.Google Scholar
Schneider, S. (2011). The Language of Thought: A New Philosophical Direction. Cambridge: MIT Press.CrossRefGoogle Scholar
Schroeder, T. (2004). Three Faces of Desire, New York: Oxford University Press.CrossRefGoogle Scholar
Schultz, W., Dayan, P., & Montague, P.R. (1997). A neural substrate of prediction and reward. Science, 275: 1593–9.CrossRefGoogle ScholarPubMed
Schwartz, E. L. (ed.). (1990). Computational Neuroscience. Cambridge: MIT Press.Google Scholar
Schweizer, P. (2019). Computation in physical systems: A normative mapping account. In Berkich, D., d’Alfonso, M. (eds.), On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence. Cham: Springer, pp. 27–47. https://doi.org/10.1007/978-3-030-01800-9_2.CrossRefGoogle Scholar
Searle, J. R. (1980). Minds, brains and programs. Behavioral and Brain Sciences, 3(3): 417–57.CrossRefGoogle Scholar
Segundo Ortín, M., & Calvo, P. (2022). Consciousness and cognition in plants. Wiley Interdisciplinary Reviews: Cognitive Science, 13(2): e1578.Google ScholarPubMed
Sejnowski, T. J., Koch, C., & Churchland, P. S. (1988). Computational neuroscience. Science, 241(4871): 1299–306.CrossRefGoogle ScholarPubMed
Sellars, W. (1963). Science, Perception, and Reality. Atascadero, CA: Ridgeview.Google Scholar
Seth, A. K., & Bayne, T. (2022). Theories of consciousness. Nature Reviews Neuroscience, 23: 439–52.CrossRefGoogle ScholarPubMed
Shagrir, O. (2022). The Nature of Physical Computation. New York: Oxford University Press.CrossRefGoogle Scholar
Shah, A. (2012). Psychological and neuroscientific connections with reinforcement learning. In Wiering, M., & Otterlo, M., (eds). Reinforcement Learning. Berlin: Springer, pp. 507–37.Google Scholar
Shannon, C. (1948). A mathematical theory of communication. Bell Systems Technical Journal, 27: 279–423, 623–56.CrossRefGoogle Scholar
Shapiro, L. (2010). Embodied Cognition. New York: Routledge.CrossRefGoogle Scholar
Shea, N. (2018). Representation in Cognitive Science. Oxford: Oxford University Press.CrossRefGoogle Scholar
Silberstein, M., & Chemero, A. (2012). Complexity and extended phenomenological-cognitive systems. Topics in Cognitive Science, 4(1): 35–50.CrossRefGoogle ScholarPubMed
Simon, H. A. (1969). Sciences of the Artificial. Cambridge: MIT Press.Google Scholar
Simon, H. A. (1979). Information processing models of cognition. Annual Review Psychology, 30: 363–96.CrossRefGoogle ScholarPubMed
Skyrms, B. (2010). Signals: Evolution, Learning, & Information. New York: Oxford University Press.CrossRefGoogle Scholar
Smith, L. B., & Thelen, E. (2003). Development as a dynamic system. Trends in Cognitive Sciences, 7(8): 343–48.CrossRefGoogle ScholarPubMed
Spirtes, P., Glymour, C. N., Scheines, R., & Heckerman, D. (2000). Causation, Prediction, and Search. Cambridge: MIT Press.Google Scholar
Spivey, M. (2007). The Continuity of Mind. Oxford: Oxford University Press.Google Scholar
Sporns, O. (2016). Networks of the Brain. Cambridge: MIT Press.Google Scholar
Sprevak, M. (2010). Computation, individuation, and the received view on representation. Studies in History and Philosophy of Science Part A, 41(3): 260–70.CrossRefGoogle Scholar
Sprevak, M. (2018). Triviality arguments about computational implementation. In Sprevak, M., & Colombo, M. (eds.), The Routledge Handbook of the Computational Mind. Routledge: New York, pp. 175-91.CrossRefGoogle Scholar
Sprevak, M., & Colombo, M. (eds.). (2018). The Routledge Handbook of the Computational Mind. Routledge: New York.CrossRefGoogle Scholar
Sterling, P., & Laughlin, S. (2015). Principles of Neural Design. Cambridge: MIT Press.Google Scholar
Stich, S. (1983). From Folk Psychology to Cognitive Science: The Case Against Belief. Cambridge, MA: MIT Press.Google Scholar
Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning. An Introduction. 2nd ed. Cambridge: MIT Press.Google Scholar
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: Statistics, structure, and abstraction. Science, 331(6022): 1279–85.CrossRefGoogle Scholar
Thorndike, E. L. (1932). The fundamentals of learning. New York: Teachers College Press.CrossRefGoogle Scholar
Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016). Integrated information theory: From consciousness to its physical substrate. Nature Reviews Neuroscience, 17(7): 450–61.CrossRefGoogle ScholarPubMed
Tucker, C. (2018). How to explain miscomputation. Philosophers’ Imprint, 18(24): 1–17.Google Scholar
Turing, A. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 42: 230–65.Google Scholar
Turing, A. (1948). Intelligent Machinery: A Report. London: National Physical Laboratory.Google Scholar
Turing, A. (1950). Computing Machinery and Intelligence. Mind, 49: 433–60.Google Scholar
Uckelman, S. (2018). Computation in mediaeval Western Europe. In Hansson, S. O. (ed.), Technology and Mathematics: Philosophical and Historical Investigations. Berlin: Springer, pp. 33–46.CrossRefGoogle Scholar
van Gelder, T. (1995). What might cognition be, if not computation? The Journal of Philosophy, 92(7): 345–81.CrossRefGoogle Scholar
van Rooij, I. (2008). The tractable cognition thesis. Cognitive Science, 32(6): 939–84.CrossRefGoogle ScholarPubMed
van Rooij, I., Wright, C. D., & Wareham, T. (2012). Intractability and the use of heuristics in psychological explanations. Synthese, 187(2): 471–87.CrossRefGoogle Scholar
Varela, F. J., Thompson, E., & Rosch, E. (2016). The Embodied Mind, Revised Edition: Cognitive Science and Human Experience. Cambridge: MIT Press.Google Scholar
Vendler, Z. (1972). Res cogitans. Ithaca: Cornell University Press.Google Scholar
Vilas, M. G., Auksztulewicz, R., & Melloni, L. (2022). Active inference as a computational framework for consciousness. Review of Philosophy and Psychology, 13(4): 859–78. https://doi.org/10.1007/s13164-021-00579-w.CrossRefGoogle Scholar
Villalobos, M., & Dewhurst, J. (2017). Why post-cognitivism does not (necessarily) entail anti-computationalism. Adaptive Behavior, 25(3): 117–28.CrossRefGoogle Scholar
Villalobos, M., & Dewhurst, J. (2018). Enactive autonomy in computational systems. Synthese, 195(5): 1891–908.CrossRefGoogle Scholar
von Neumann, J. (1951). The general and logical theory of automata. In Jeffress, L. A. (ed.), Cerebral Mechanisms in Behavior: The Hixon Symposium. New York: John Wiley & Sons, pp. 1–31.Google Scholar
von Neumann, J. (1958). The Computer and the Brain. New Haven: Yale University Press.Google Scholar
von Neumann, J. (1966). Theory of Self-Reproducing Automata, Burks, A. W. (ed.), Urbana: University of Illinois Press.Google Scholar
von Neumann, J. (1981). First draft report on the EDVAC. Report prepared for the U.S. Army Ordnance Department under contract W-670-ORD-4926, 1945. In Stern, N. (ed.), From ENIAC to UNIVAC. Bedford: Digital Press, pp. 177–246.Google Scholar
Weinberger, N., & Allen, C. (2022). Static-dynamic hybridity in dynamical models of cognition. Philosophy of Science, 89: 283–301.CrossRefGoogle Scholar
Weisberg, M. (2013). Simulation and Similarity: Using Models to Understand the World. Oxford: Oxford University Press.CrossRefGoogle Scholar
Weiskopf, D. A. (2018). The explanatory autonomy of cognitive models. In Kaplan, D. M. (ed.), Explanation and Integration in Mind and Brain Science. Oxford: Oxford University Press, pp. 44–69.Google Scholar
Werbos., P. (1974). Beyond regression: New tools for prediction and analysis in the behavioral sciences. Ph.D. thesis, Committee on Applied Mathematics. Cambridge: Harvard University.Google Scholar
Wiener, N. (1948). Cybernetics. New York: John Wiley.Google ScholarPubMed
Wiese, W. (forthcoming). Could large language models be conscious? A perspective from the free energy principle. In Hipolito, I., Hesp, C., & Friston, K. (eds.), The Free Energy Principle: Science, Technology, and Philosophy. London: Routledge.Google Scholar
Wilson, M. (2006). Wandering Significance: An Essay on Conceptual Behaviour. Oxford: Oxford University Press.CrossRefGoogle Scholar
Wilson, M. (2022). Imitation of Rigor. Oxford: Oxford University Press.Google Scholar
Wilson, R. A. (1994). Wide computationalism. Mind, 103(411): 351–72.CrossRefGoogle Scholar
Woodward, J. (2003). Making things happen: A theory of causal explanation. New York: Oxford University Press.Google Scholar
Wright, C., Colombo, M., & Beard, A. (2017). HIT and brain reward function: A case of mistaken identity (theory). Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 64: 28–40.CrossRefGoogle ScholarPubMed
Wright, L. G., Onodera, T., Stein, M. M., et al. (2022). Deep physical neural networks trained with backpropagation. Nature, 601(7894): 549–55.CrossRefGoogle ScholarPubMed
Zuboff, A. (1981). The story of a brain. In Dennett, D., & Hofstadter, D. (eds.), The Mind’s I. New York: Basic Books, pp. 202–11.Google Scholar

Save element to Kindle

To save this element to your Kindle, first ensure no-reply@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.

The Computational Theory of Mind
Available formats
×

Save element 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.

The Computational Theory of Mind
Available formats
×

Save element 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.

The Computational Theory of Mind
Available formats
×