Hostname: page-component-848d4c4894-x5gtn Total loading time: 0 Render date: 2024-06-02T10:24:08.564Z Has data issue: false hasContentIssue false
  • English
  • Français

Cognitive Models Are Distinguished by Content, Not Format

Published online by Cambridge University Press:  01 January 2022

Patrick Butlin
Get access Rights & Permissions [Opens in a new window]

Abstract

Cognitive scientists often describe the mind as constructing and using models of aspects of the environment, but it is not obvious what makes something a model as opposed to a mere representation. The leading proposal among philosophers is that models are structural representations and are therefore distinguished by their format. However, an alternative conception is suggested by recent work in artificial intelligence, on which models are distinguished by their content. This article outlines the two conceptions and argues for the content conception, against the standard philosophical view.

Type
Research Article
Information
Philosophy of Science , Volume 88 , Issue 1 , January 2021 , pp. 83 - 102
Copyright
Copyright © The Philosophy of Science Association

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.)

Footnotes

†

Many thanks to Nick Shea, James Stazicker, Matt Crosby, Henry Shevlin, and the other members of the Kinds of Intelligence reading group at Cambridge for comments and discussion and to Eivinas Butkus for getting me started on this topic. Many thanks also to two anonymous reviewers for this journal, for exceptionally helpful comments that resulted in a much-improved final version.

References

Balleine, Bernard, and Dickinson, Anthony. 1998. “Goal-Directed Instrumental Action: Contingency and Incentive Learning and Their Cortical Substrates.” Neuropharmacology 37 (4–5): 407–19.CrossRefGoogle ScholarPubMed
Block, Ned. 2014. “Seeing-as in the Light of Vision Science.” Philosophy and Phenomenological Research 89 (3): 560–72..CrossRefGoogle Scholar
Boyan, Justin, and Moore, Andrew W.. 1995. “Generalization in Reinforcement Learning: Safely Approximating the Value Function.” In Advances in Neural Information Processing Systems 7, ed. Leen, Todd, Tesauro, Gerald, and Touretzky, David, 369–76. Cambridge, MA: MIT Press.Google Scholar
Bradley, Francis H. 1893. Appearance and Reality. London: Allen & Unwin.Google Scholar
Burge, Tyler. 2010. The Origins of Objectivity. Oxford: Oxford University Press.CrossRefGoogle Scholar
Carey, Susan. 2009. The Origin of Concepts. Oxford: Oxford University Press.CrossRefGoogle Scholar
Clark, Andy. 2013. “Whatever Next? Predictive Brains, Situated Agents, and the Future of Cognitive Science.” Behavioral and Brain Sciences 36 (3): 181–204..CrossRefGoogle ScholarPubMed
Clark, Andy. 2016. Surfing Uncertainty: Prediction, Action and the Embodied Mind. New York: Oxford University Press.CrossRefGoogle Scholar
Craik, Kenneth. 1943. The Nature of Explanation. Cambridge: Cambridge University Press.Google Scholar
Cummins, Robert. 1989. Meaning and Mental Representation. Cambridge, MA: MIT Press.Google Scholar
Danks, David. 2014. Unifying the Mind: Cognitive Representations as Graphical Models. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Eliasmith, Chris. 2007. “How to Build a Brain: From Function to Implementation.” Synthese 153 (3): 373–88..Google Scholar
Garnelo, Marta, Arulkumaran, Kai, and Shanahan, Murray. 2016. “Towards Deep Symbolic Reinforcement Learning.” arXiv preprint.Google Scholar
Giere, Ronald. 1988. Explaining Science: A Cognitive Approach. Chicago: Cambridge University Press.CrossRefGoogle Scholar
Gładziejewski, Paweł. 2016. “Predictive Coding and Representationalism.” Synthese 193 (2): 559–82..CrossRefGoogle Scholar
Gładziejewski, Paweł, and Miłkowski, Marcin. 2017. “Structural Representations: Causally Relevant and Different from Detectors.” Biology and Philosophy 32 (3): 337–55..CrossRefGoogle ScholarPubMed
Gläscher, Jan, Daw, Nathaniel, Dayan, Peter, and O’Doherty, John P.. 2010. “States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning.” Neuron 66:585–95.CrossRefGoogle ScholarPubMed
Godfrey-Smith, Peter. 2006a. “Mental Representation, Naturalism and Teleosemantics.” In Teleosemantics, ed. MacDonald, Graham and Papineau, David, 42–68. Oxford: Oxford University Press.Google Scholar
Godfrey-Smith, Peter. 2006b. “The Strategy of Model-Based Science.” Biology and Philosophy 21:725–40.Google Scholar
Grush, Rick. 2004. “The Emulation Theory of Representation: Motor Control, Imagery and Perception.” Behavioral and Brain Sciences 27:377–442.CrossRefGoogle ScholarPubMed
Hohwy, Jakob. 2013. The Predictive Mind. Oxford: Oxford University Press.CrossRefGoogle Scholar
Jebara, Tony. 2004. Machine Learning: Discriminative and Generative. New York: Springer.CrossRefGoogle Scholar
Johnson-Laird, Philip. 1983. Mental Models: Towards a Cognitive Science of Language, Inference and Consciousness. Cambridge, MA: Harvard University Press.Google Scholar
Johnson-Laird, Philip. 2006. How We Reason. New York: Oxford University Press.Google Scholar
Kiefer, Alex, and Hohwy, Jakob. 2018. “Content and Misrepresentation in Hierarchical Generative Models.” Synthese 195 (6): 2387–415..CrossRefGoogle Scholar
Kuvayev, Leonid, and Sutton, Rich. 1996. “Model-Based Reinforcement Learning with an Approximate, Learned Model.” In Proceedings of the Ninth Yale Workshop on Adaptive and Learning Systems, 101–5. New Haven, CT: Yale University.Google Scholar
Lake, Brendan, Ullman, Tomer, Tenenbaum, Joshua, and Gershman, Samuel. 2017. “Building Machines That Learn and Think like People.” Behavioral and Brain Sciences 40:e253.CrossRefGoogle ScholarPubMed
Lee, Sang Wan, Shimojo, Shinsuke, and O’Doherty, John P.. 2014. “Neural Computations Underlying Arbitration between Model-Based and Model-Free Learning.” Neuron 81 (3): 687–99..CrossRefGoogle ScholarPubMed
Magnani, Lorenzo, and Nersessian, Nancy. 2002. Model-Based Reasoning: Science, Technology, Values. New York: Springer.CrossRefGoogle Scholar
Mnih, Volodymyr, et al. 2015. “Human-Level Control through Deep Reinforcement Learning.” Nature 518 (7540): 529–33..CrossRefGoogle ScholarPubMed
O’Brien, Gerard, and Opie, Jon. 2004. “Notes towards a Structuralist Theory of Mental Representation.” In Representation in Mind: New Approaches to Mental Representation, ed. Clapin, Hugh, Staines, Phillip, and Slezak, Peter, 1–20. Oxford: Elsevier.Google Scholar
Quilty-Dunn, Jake. 2016. “Iconicity and the Format of Perception.” Journal of Consciousness Studies 23 (3–4): 255–63.Google Scholar
Quilty-Dunn, Jake. 2020. “Perceptual Pluralism.” Noûs 54 (4): 807–38..CrossRefGoogle Scholar
Ramsey, W. 2007. Representation Reconsidered. New York: Cambridge University Press.CrossRefGoogle Scholar
Rusu, Andrei, Rabinowitz, Neil, Desjardins, Guillaume, Soyer, Hubert, Kirkpatrick, James, Kavukcuoglu, Koray, Pascanu, Razvan, and Hadsell, Raia. 2016. “Progressive Neural Networks.” arXiv preprint.Google Scholar
Ryder, Dan. 2004. “SINBAD Neurosemantics: A Theory of Mental Representation.” Mind and Language 19:211–40.CrossRefGoogle Scholar
Shagrir, Oron. 2012. “Structural Representations and the Brain.” British Journal for the Philosophy of Science 63:519–45.CrossRefGoogle Scholar
Shea, Nicholas. 2014. “Exploited Isomorphism and Structural Representation.” Proceedings of the Aristotelian Society 64:123–44.Google Scholar
Shea, Nicholas. 2018. Representation in Cognitive Science. Oxford: Oxford University Press.CrossRefGoogle Scholar
Suárez, Mauricio. 2003. “Scientific Representation: Against Similarity and Isomorphism.” International Studies in the Philosophy of Science 17:225–44.CrossRefGoogle Scholar
Sutton, Richard. 1991. “Dyna, an Integrated Architecture for Learning, Planning and Reacting.” SIGART Bulletin 2 (4): 160–63..CrossRefGoogle Scholar
Sutton, Richard, and Barto, Andrew. 2018. Reinforcement Learning: An Introduction. 2nd ed. Cambridge, MA: MIT Press.Google Scholar
Swoyer, Chris. 1991. “Structural Representation and Surrogative Reasoning.” Synthese 87:449–508.CrossRefGoogle Scholar
Tenenbaum, Joshua, Kemp, Charles, Griffiths, Thomas, and Goodman, Noah. 2011. “How to Grow a Mind: Statistics, Structure and Abstraction.” Science 331 (6022):1279–85..CrossRefGoogle Scholar
Von Eckardt, Barbara. 1993. What Is Cognitive Science? Cambridge, MA: MIT Press.Google Scholar
Webb, Taylor, and Graziano, Michael. 2015. “The Attention Schema Theory: A Mechanistic Account of Subjective Awareness.” Frontiers in Psychology 6.Google Scholar
Weisberg, Michael. 2013. Simulation and Similarity: Using Models to Understand the World. New York: Oxford University Press.CrossRefGoogle Scholar
Williams, Daniel, and Colling, Lincoln. 2018. “From Symbols to Icons: The Return of Resemblance in the Cognitive Neuroscience Revolution.” Synthese 195 (5): 1941–67..CrossRefGoogle Scholar
Wolpert, Daniel M., Ghahramani, Zoubin, and Jordan, Michael I.. 1995. “An Internal Model for Sensorimotor Integration.” Science 269:1880–82.CrossRefGoogle ScholarPubMed