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On Cognitive Modeling and Other Minds

Published online by Cambridge University Press:  14 December 2023

J. P. Gamboa Open the ORCID record for J. P. Gamboa [Opens in a new window]
J. P. Gamboa*
Affiliation:
Department of History and Philosophy of Science, University of Pittsburgh, Pittsburgh, PA, USA
*
Email: jog122@pitt.edu
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Abstract

Scientists and philosophers alike debate whether various systems such as plants and bacteria exercise cognition. One strategy for resolving such debates is to ground claims about nonhuman cognition in evidence from mathematical models of cognitive capacities. In this article, I show that proponents of this strategy face two major challenges: demarcating phenomenological models from process models and overcoming underdetermination by model fit. I argue that even if the demarcation problem is resolved, fitting a process model to behavioral data is, on its own, not strong evidence for any cognitive process, let alone processes shared with humans.

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Philosophy of Science , First View , pp. 1 - 19
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© The Author(s), 2024. Published by Cambridge University Press on behalf of the Philosophy of Science Association

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References

Adams, Fred. 2018. “Cognition Wars.” Studies in History and Philosophy of Science Part A 68: 20–30. https://doi.org/10.1016/j.shpsa.2017.11.007 and https://www.sciencedirect.com/science/article/pii/S0039368117302741.CrossRefGoogle ScholarPubMed
Adams, Fred, and Garrison, Rebecca. 2013. “The Mark of the Cognitive.” Minds and Machines 23 (3):339–52. https://doi.org/10.1007/s11023-012-9291-1.CrossRefGoogle Scholar
Allen, Colin. 2014. “Models, Mechanisms, and Animal Minds.” Southern Journal of Philosophy 52 (S1):75–97.CrossRefGoogle Scholar
Alpi, Amedeo, Amrhein, Nikolaus, Bertl, Adam, Blatt, Michael R., Blumwald, Eduardo, Cervone, Felice, Dainty, Jack, Maria Ida De Michelis, Epstein, Emanuel, Galston, Arthur W., Mary Helen, M. Goldsmith, Hawes, Chris, Hell, Rüdiger, Alistair Hetherington, Hofte, Herman, Juergens, Gerd, Leaver, Chris J., Moroni, Anna, Murphy, Angus, Oparka, Karl, Perata, Pierdomenico, Quader, Hartmut, Rausch, Thomas, Christophe Ritzenthaler, Rivetta, Alberto, Robinson, David G., Sanders, Dale, Scheres, Ben, Schumacher, Karin, Sentenac, Hervé, Slayman, Clifford L., Soave, Carlo, Somerville, Chris, Taiz, Lincoln, Thiel, Gerhard, and Wagner, Richard. 2007. “Plant Neurobiology: No Brain, No Gain?” Trends in Plant Science 12 (4):135–36. https://doi.org/10.1016/j.tplants.2007.03.002.CrossRefGoogle ScholarPubMed
Andrews, Mel. 2021. “The Math Is Not the Territory: Navigating the Free Energy Principle.” Biology & Philosophy 36 (3):30. https://doi.org/10.1007/s10539-021-09807-0.CrossRefGoogle Scholar
Bassett, Danielle S., Zurn, Perry, and Gold, Joshua I.. 2018. “On the Nature and Use of Models in Network Neuroscience.” Nature Reviews Neuroscience 19 (9):566–78. https://doi.org/10.1038/s41583-018-0038-8.CrossRefGoogle ScholarPubMed
Bickle, John. 2008. “Real Reduction in Real Neuroscience: Metascience, Not Philosophy of Science (and certainly not metaphysics!).” In Being Reduced: New Essays on Reduction, Explanation, and Causation, edited by Hohwy, Jakob and Kallestrup, Jesper, 34–51. Oxford: Oxford University Press.CrossRefGoogle Scholar
Bitzer, Sebastian, Park, Hame, Blankenburg, Felix, and Kiebel, Stefan. 2014. “Perceptual Decision Making: Drift-Diffusion Model Is Equivalent to a Bayesian Model.” Frontiers in Human Neuroscience 8: 1–17. https://doi.org/10.3389/fnhum.2014.00102.CrossRefGoogle ScholarPubMed
Bokulich, Alisa. 2011. “How Scientific Models Can Explain.” Synthese 180 (1):33–45. https://doi.org/10.1007/s11229-009-9565-1.CrossRefGoogle Scholar
Brunswik, Egon. 1952. The Conceptual Framework of Psychology. Chicago: The University of Chicago Press.Google Scholar
Buckner, Cameron. 2013. “Morgan’s Canon, Meet Hume’s Dictum: Avoiding Anthropofabulation in Cross-species Comparisons.” Biology & Philosophy 28 (5):853–71. https://doi.org/10.1007/s10539-013-9376-0.CrossRefGoogle Scholar
Busemeyer, Jerome R., and Stout, Julie C.. 2002. “A Contribution of Cognitive Decision Models to Clinical Assessment: Decomposing Performance on the Bechara Gambling Task.” Psychological Assessment 14 (3):253–62. https://doi.org/10.1037/1040-3590.14.3.253.CrossRefGoogle ScholarPubMed
Busemeyer, Jerome R., and Wang, Yi-Min. 2000. “Model Comparisons and Model Selections Based on Generalization Criterion Methodology.” Journal of Mathematical Psychology 44 (1):171–89. https://doi.org/10.1006/jmps.1999.1282 and https://www.sciencedirect.com/science/article/pii/S0022249699912828.CrossRefGoogle ScholarPubMed
Callender, Craig, and Cohen, Jonathan. 2010. “There Is No Special Problem about Scientific Representation.” Theoria: An International Journal for Theory, History and Fundations of Science 21 (1):67–85.CrossRefGoogle Scholar
Carruthers, Peter, and Williams, David M.. 2022. “Model-free Metacognition.” Cognition 225: 105117. https://doi.org/10.1016/j.cognition.2022.105117 and https://www.sciencedirect.com/science/article/pii/S0010027722001056.CrossRefGoogle ScholarPubMed
Cartwright, Nancy. 1983. How the Laws of Physics Lie. Oxford: Oxford University Press.CrossRefGoogle Scholar
Chirimuuta, Mazviita. 2014. “Minimal Models and Canonical Neural Computations: The Distinctness of Computational Explanation in Neuroscience.” Synthese 191 (2):127–53. https://doi.org/10.1007/s11229-013-0369-y.CrossRefGoogle Scholar
Chirimuuta, Mazviita. 2018. “Explanation in Computational Neuroscience: Causal and Non-causal.” British Journal for the Philosophy of Science 69 (3):849–80.CrossRefGoogle Scholar
Colombo, Michael, and Scarf, Damian. 2020. “Are There Differences in “Intelligence” between Nonhuman Species? The Role of Contextual Variables.” Frontiers in Psychology 11, 2072. https://doi.org/10.3389/fpsyg.2020.02072 and https://www.frontiersin.org/articles/10.3389/fpsyg.2020.02072.CrossRefGoogle ScholarPubMed
Craver, Carl F., and Michael Kaplan, David. 2020. “Are More Details Better? On the Norms of Completeness for Mechanistic Explanations.” British Journal for the Philosophy of Science 71 (1):287–319.CrossRefGoogle Scholar
DasGupta, Shamik, Ferreira, Clara Howcroft, and Miesenböck, Gero. 2014. “FoxP Influences the Speed and Accuracy of a Perceptual Decision in Drosophila.” Science 344 (6186): 901–4. https://doi.org/10.1126/science.1252114 and https://www.science.org/doi/abs/10.1126/science.1252114.CrossRefGoogle ScholarPubMed
Dawes, Robyn M. 2018. “The Mind, the Model, and the Task.” In Cognitive theory, edited by Restle, Frank, Shiffrin, Richard M., John Castellan, N., Lindman, Harold R., and Pisoni, David B., 119–29. New York: Psychology Press.CrossRefGoogle Scholar
Dawes, Robyn M., and Corrigan, Bernard. 1974. “Linear Models in Decision Making.” Psychological Bulletin 81 (2):95–106.CrossRefGoogle Scholar
Drayson, Zoe. 2020. “Why I Am Not a Literalist.” Mind and Language 35 (5):661–70.CrossRefGoogle Scholar
Egan, Frances. 2017. “Function-Theoretic Explanation and the Search for Neural Mechanisms.” In Explanation and Integration in Mind and Brain Science, edited by David M. Kaplan, 145–63. New York: Oxford University Press.Google Scholar
Einhorn, Hillel J., Kleinmuntz, Don N., and Kleinmuntz, Benjamin. 1979. “Linear Regression and Process-Tracing Models of Judgment.” Psychological Review 86 (5):465–85. https://doi.org/10.1037/0033-295X.86.5.465 and https://psycnet.apa.org/doi/10.1037/0033-295X.86.5.465.CrossRefGoogle Scholar
Evans, Nathan J. 2019. “Assessing the Practical Differences between Model Selection Methods in Inferences about Choice Response Time Tasks.” Psychonomic Bulletin & Review 26 (4): 1070–98. https://doi.org/10.3758/s13423-018-01563-9.CrossRefGoogle ScholarPubMed
Fard, Pouyan R., Park, Hame, Warkentin, Andrej, Kiebel, Stefan, and Bitzer, Sebastian. 2017. “A Bayesian Reformulation of the Extended Drift-Diffusion Model in Perceptual Decision Making.” Frontiers in Computational Neuroscience 11, 29. https://doi.org/10.3389/fncom.2017.00029 and https://www.frontiersin.org/articles/10.3389/fncom.2017.00029.CrossRefGoogle ScholarPubMed
Farrar, Benjamin, and Ostojic, Ljerka. 2019. “The Illusion of Science in Comparative Cognition.” PsyArXiv. https://doi.org/10.31234/osf.io/hduyx.CrossRefGoogle Scholar
Figdor, Carrie. 2018. Pieces of Mind: The Proper Domain of Psychological Predicates. Oxford: Oxford University Press.CrossRefGoogle Scholar
Figdor, Carrie. 2020. “Why Literalism Is Still the Best Game in Town: Replies to Drayson, Machery, and Schwitzgebel.” Mind and Language 35 (5):687–93.CrossRefGoogle Scholar
Frigg, Roman, and Hartmann, Stephan. 2020. “Models in Science.” In The Stanford Encyclopedia of Philosophy (Spring 2020 edition), edited by Edward N. Zalta. https://plato.stanford.edu/archives/spr2020/entries/models-science/.Google Scholar
Gagliano, Monica, Renton, Michael, Depczynski, Martial, and Mancuso, Stefano. 2014. “Experience Teaches Plants to Learn Faster and Forget Slower in Environments Where It Matters.” Oecologia 175 (1):63–72. https://doi.org/10.1007/s00442-013-2873-7.CrossRefGoogle ScholarPubMed
Giere, Ronald N. 2010. “An Agent-Based Conception of Models and Scientific Representation.” Synthese 172 (2):269–81.CrossRefGoogle Scholar
Gigerenzer, Gerd, and Goldstein, Daniel G.. 1999. “Betting on One Good Reason: The Take the Best Heuristic.” In Simple Heuristics That Make Us Smart, 75–95. New York: Oxford University Press.Google Scholar
Gigerenzer, Gerd, Hoffrage, Ulrich, and Goldstein, Daniel G.. 2008. “Fast and Frugal Heuristics Are Plausible Models of Cognition: Reply to Dougherty, Franco-Watkins, and Thomas (2008).” Psychological Review 115 (1):230–39. https://doi.org/10.1037/0033-295X.115.1.230.CrossRefGoogle Scholar
Glöckner, Andreas, and Betsch, Tilmann. 2011. “The Empirical Content of Theories in Judgment and Decision Making: Shortcomings and Remedies.” Judgment and Decision Making 6 (8):711–21.CrossRefGoogle Scholar
Goldberg, Lewis R. 1971. “Five Models of Clinical Judgment: An Empirical Comparison between Linear and Nonlinear Representations of the Human Inference Process.” Organizational Behavior and Human Performance 6 (4):458–79. https://doi.org/10.1016/0030-5073(71)90028-6 and https://www.sciencedirect.com/science/article/pii/0030507371900286.CrossRefGoogle Scholar
Hartmann, Stephan. 1999. “Models and Stories in Hadron Physics.” In Models as Mediators: Perspectives on Natural and Social Science, edited by Morrison, Margaret and Morgan, Mary. New York: Cambridge University Press.Google Scholar
Hoffman, Paul J. 1960. “The Paramorphic Representation of Clinical Judgment.” Psychological Bulletin 57 (2):116–31. https://doi.org/10.1037/h0047807.CrossRefGoogle ScholarPubMed
Hogarth, Robin M., and Karelaia, Natalia. 2007. “Heuristic and Linear Models of Judgment: Matching Rules and Environments.” Psychological Review 114 (3):733–58. https://doi.org/10.1037/0033-295X.114.3.733.CrossRefGoogle ScholarPubMed
Jarecki, Jana B., Tan, Jolene H., and Jenny, Mirjam A.. 2020. “A Framework for Building Cognitive Process Models.” Psychonomic Bulletin & Review 27 (6):1218–29. https://doi.org/10.3758/s13423-020-01747-2.CrossRefGoogle ScholarPubMed
Johnson, Eric J., Schulte-Mecklenbeck, Michael, and Willemsen, Martijn C.. 2008. “Process Models Deserve Process Data: Comment on Brandstätter, Gigerenzer, and Hertwig (2006).” Psychological Review 115 (1): 263–72. https://doi.org/10.1037/0033-295X.115.1.263.CrossRefGoogle ScholarPubMed
Joo, Hannah R., Liang, Hexin, Chung, Jason E., Charlotte Geaghan-Breiner, Jiang Lan Fan, Nachman, Benjamin P., Kepecs, Adam, and Frank, Loren M.. 2021. “Rats Use Memory Confidence to Guide Decisions.” Current Biology 31 (20):4571–83.e4. https://doi.org/10.1016/j.cub.2021.08.013 and https://www.sciencedirect.com/science/article/pii/S0960982221011179.CrossRefGoogle ScholarPubMed
Kaplan, David Michael. 2011. “Explanation and Description in Computational Neuroscience.” Synthese 183 (3):339. https://doi.org/10.1007/s11229-011-9970-0.CrossRefGoogle Scholar
Kaplan, David Michael, and Bechtel, William. 2011. “Dynamical Models: An Alternative or Complement to Mechanistic Explanations?” Topics in Cognitive Science 3 (2):438–44. https://doi.org/10.1111/j.1756-8765.2011.01147.x and https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1756-8765.2011.01147.x.CrossRefGoogle ScholarPubMed
Kaplan, David Michael, and Craver, Carl F.. 2011. “The Explanatory Force of Dynamical and Mathematical Models in Neuroscience: A Mechanistic Perspective.” Philosophy of Science 78 (4):601–27. https://doi.org/10.1086/661755.CrossRefGoogle Scholar
Karelaia, Natalia, and Hogarth, Robin M.. 2008. “Determinants of Linear Judgment: A Meta-analysis of Lens Model Studies.” Psychology Bulletin 134 (3):404–26. https://doi.org/10.1037/0033-2909.134.3.404.CrossRefGoogle ScholarPubMed
Kellen, David. 2019. “A Model Hierarchy for Psychological Science.” Computational Brain & Behavior 2 (3):160–65. https://doi.org/10.1007/s42113-019-00037-y.CrossRefGoogle Scholar
Kostić, Daniel. 2018. “The Topological Realization.” Synthese 195 (1):79–98. https://doi.org/10.1007/s11229-016-1248-0.CrossRefGoogle Scholar
Kostić, Daniel, and Khalifa, Kareem. 2023. “Decoupling Topological Explanations from Mechanisms.” Philosophy of Science 90 (2):245–68. https://doi.org/10.1017/psa.2022.29 and https://www.cambridge.org/core/article/decoupling-topological-explanations-from-mechanisms/4801B5E2E1B920E818D5F27CEABA84BE.CrossRefGoogle Scholar
Kruschke, John K. 2011. “Bayesian Assessment of Null Values via Parameter Estimation and Model Comparison.” Perspectives on Psychological Science 6 (3):299–312. https://doi.org/10.1177/1745691611406925.CrossRefGoogle ScholarPubMed
Kruschke, John K., and Liddell, Torrin M.. 2018. “The Bayesian New Statistics: Hypothesis Testing, Estimation, Meta-analysis, and Power Analysis from a Bayesian Perspective.” Psychonomic Bulletin & Review 25 (1):178–206. https://doi.org/10.3758/s13423-016-1221-4.CrossRefGoogle ScholarPubMed
Lee, Michael D., Gluck, Kevin A., and Walsh, Matthew M.. 2019. “Understanding the Complexity of Simple Decisions: Modeling Multiple Behaviors and Switching Strategies.” Decision 6 (4):335–68. https://doi.org/10.1037/dec0000105.CrossRefGoogle Scholar
Leng, Mary. 2010. Mathematics and Reality. Oxford: Oxford University Press.CrossRefGoogle Scholar
Levy, Arnon. 2015. “Modeling without Models.” Philosophical Studies 172 (3):781–98. https://doi.org/10.1007/s11098-014-0333-9.CrossRefGoogle Scholar
Luan, Shenghua, Schooler, Lael J., and Tan, Jolene H.. 2020. “Improving Judgment Accuracy by Sequential Adjustment.” Psychonomic Bulletin & Review 27 (1):170–77. https://doi.org/10.3758/s13423-019-01696-5.CrossRefGoogle ScholarPubMed
Luce, R. Duncan. 1995. “Four Tensions Concerning Mathematical Modeling in Psychology.” Annual Review of Psychology 46 (1):1–27. https://doi.org/10.1146/annurev.ps.46.020195.000245.CrossRefGoogle ScholarPubMed
Ludvig, Elliot A., Sutton, Richard S., and James Kehoe, E.. 2012. “Evaluating the TD Model of Classical Conditioning.” Learning & Behavior 40 (3):305–19. https://doi.org/10.3758/s13420-012-0082-6.CrossRefGoogle Scholar
Machery, Edouard. 2020. “What Do Plants and Bacteria Want? Commentary on Carrie Figdor’s Pieces of Mind.” Mind and Language 35 (5):677–86.CrossRefGoogle Scholar
Marr, David. 2010. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Mauk, Michael D. 2000. “The Potential Effectiveness of Simulations Versus Phenomenological Models.” Nature Neuroscience 3 (7):649–51. https://doi.org/10.1038/76606.CrossRefGoogle ScholarPubMed
Mikhalevich, Irina. 2017. “Simplicity and Cognitive Models: Avoiding Old Mistakes in New Experimental Contexts.” In The Routledge Handbook of Philosophy of Animal Minds, edited by Andrews, K. and Beck, J., 427–36. New York: Routledge.CrossRefGoogle Scholar
Morrison, Margaret. 1999. “Models as Autonomous Agents.” In Models as Mediators: Perspectives on Natural and Social Science, edited by Morrison, Margaret and Mary, S. Morgan, In Ideas in Context (52), 38–65. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Myung, Jay. 2000. “The Importance of Complexity in Model Selection.” Journal of Mathematical Psychology 44 (1):190–204. https://doi.org/10.1006/jmps.1999.1283 and https://www.sciencedirect.com/science/article/pii/S002224969991283X.CrossRefGoogle ScholarPubMed
Myung, Jay, Cavagnaro, Daniel R., and Pitt, Mark A.. 2016. “Model Evaluation and Selection.” In New Handbook of Mathematical Psychology: Volume 1: Foundations and Methodology, edited by Ehtibar, N. Dzhafarov, Colonius, Hans, Myung, Jay and William, H. Batchelder, In Cambridge Handbooks in Psychology, 552–98. Cambridge: Cambridge University Press.Google Scholar
Navarro, Danielle J. 2019. “Between the Devil and the Deep Blue Sea: Tensions between Scientific Judgement and Statistical Model Selection.” Computational Brain & Behavior 2 (1):28–34. https://doi.org/10.1007/s42113-018-0019-z.CrossRefGoogle Scholar
Newell, Ben R., Weston, Nicola J., and Shanks, David R.. 2003. “Empirical Tests of a Fast-and-Frugal Heuristic: Not Everyone “Takes-the-Best.” Organizational Behavior and Human Decision Processes 91 (1):82–96. https://doi.org/10.1016/S0749-5978(02)00525-3.CrossRefGoogle Scholar
Pachur, Thorsten, Hertwig, Ralph, Gigerenzer, Gerd, and Brandstätter, Eduard. 2013. “Testing Process Predictions of Models of Risky Choice: A Quantitative Model Comparison Approach.” Frontiers in Psychology 4, 646. https://doi.org/10.3389/fpsyg.2013.00646.CrossRefGoogle Scholar
Payne, John W., Bettman, James R., and Johnson, Eric J.. 1993. The Adaptive Decision Maker. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Piccinini, Gualtiero, and Craver, Carl. 2011. “Integrating Psychology and Neuroscience: Functional Analyses as Mechanism Sketches.” Synthese 183 (3):283–311. https://doi.org/10.1007/s11229-011-9898-4.CrossRefGoogle Scholar
Pincock, Christopher. 2012. Mathematics and Scientific Representation. New York: Oxford University Press.CrossRefGoogle Scholar
Pitt, Mark A., and Myung, Jay. 2002. “When a Good Fit Can Be Bad.” Trends in Cognitive Sciences 6 (10):421–25. https://doi.org/10.1016/S1364-6613(02)01964-2 and https://www.sciencedirect.com/science/article/pii/S1364661302019642.CrossRefGoogle Scholar
Potochnik, Angela. 2017. Idealization and the Aims of Science. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Ratcliff, Roger, and McKoon, Gail. 2008. “The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks.” Neural Computation 20 (4):873–922. https://doi.org/10.1162/neco.2008.12-06-420.CrossRefGoogle ScholarPubMed
Rice, Collin. 2015. “Moving beyond Causes: Optimality Models and Scientific Explanation.” Noûs 49 (3):589–615. https://doi.org/10.1111/nous.12042 and https://onlinelibrary.wiley.com/doi/abs/10.1111/nous.12042.CrossRefGoogle Scholar
Roberts, Seth, and Pashler, Harold. 2000. “How Persuasive Is a Good Fit? A Comment on Theory Testing.” Psychological Review 107 (2):358–67. https://doi.org/10.1037/0033-295X.107.2.358.CrossRefGoogle Scholar
Ross, Lauren N. 2015. “Dynamical Models and Explanation in Neuroscience.” Philosophy of Science 82 (1):32–54.CrossRefGoogle Scholar
Schultz, Wolfram. 2016. “Dopamine Reward Prediction Error Coding.” Dialogues Clinical Neuroscience 18 (1):23–32. https://doi.org/10.31887/DCNS.2016.18.1/wschultz.CrossRefGoogle ScholarPubMed
Schultz, Wolfram, Dayan, Peter, and Read Montague, P.. 1997. “A Neural Substrate of Prediction and Reward.” Science 275 (5306):1593–9. https://doi.org/10.1126/science.275.5306.1593.CrossRefGoogle ScholarPubMed
Serban, Maria. 2015. “The Scope and Limits of a Mechanistic View of Computational Explanation.” Synthese 192 (10):3371–96. https://doi.org/10.1007/s11229-015-0709-1.CrossRefGoogle Scholar
Shapiro, Stewart. 1997. Philosophy of Mathematics: Structure and Ontology. New York: Oxford University Press.Google Scholar
Silberstein, Michael, and Chemero, Anthony. 2013. “Constraints on Localization and Decomposition as Explanatory Strategies in the Biological Sciences.” Philosophy of Science 80 (5):958–70. https://doi.org/10.1086/674533.CrossRefGoogle Scholar
Smith, J. David, Couchman, Justin J., and Beran, Michael J.. 2014. “Animal Metacognition: A Tale of Two Comparative Psychologies.” Journal of Comparative Psychology 128 (2):115–31. https://doi.org/10.1037/a0033105.CrossRefGoogle ScholarPubMed
Smith, J. David, Zakrzewski, Alexandria C., and Church, Barbara A.. 2016. “Formal Models in Animal-Metacognition Research: The Problem of Interpreting Animals’ Behavior.” Psychonomic Bulletin & Review 23 (5):1341–53. https://doi.org/10.3758/s13423-015-0985-2.CrossRefGoogle ScholarPubMed
Sun, Ron. 2008. “Introduction to Computational Cognitive Modeling.” In Cambridge Handbook of Computational Psychology, edited by Ron Sun, 3–19. New York: Cambridge University Press.Google Scholar
Suri, Roland E, and Schultz, Wolfram. 2001. “Temporal Difference Model Reproduces Anticipatory Neural Activity.” Neural Computation 13 (4):841–62.CrossRefGoogle ScholarPubMed
Sutton, Richard S, and Andrew G Barto. 1987. “A Temporal-Difference Model of Classical Conditioning.” In Proceedings of the Ninth Annual Conference of the Cognitive Science Society. 355–78.Google Scholar
Taiz, Lincoln, Alkon, Daniel, Draguhn, Andreas, Murphy, Angus, Blatt, Michael, Hawes, Chris, Thiel, Gerhard, and Robinson, David G.. 2019. “Plants neither Possess nor Require Consciousness.” Trends in Plant Science 24 (8):677–87. https://doi.org/10.1016/j.tplants.2019.05.008 and https://www.sciencedirect.com/science/article/pii/S1360138519301268.CrossRefGoogle ScholarPubMed
Taylor, Alex H., Bastos, Amalia P. M., Brown, Rachael L., and Allen, Colin. 2022. “The signature-Testing Approach to Mapping Biological and Artificial Intelligences.” Trends in Cognitive Sciences 26 (9):738–50. https://doi.org/10.1016/j.tics.2022.06.002.CrossRefGoogle ScholarPubMed
Toon, Adam. 2012. Models as Make-Believe: Imagination, Fiction and Scientific Representation. London: Palgrave Macmillan.CrossRefGoogle Scholar
Turnbull, Margaret Greta. 2018. “Underdetermination in Science: What It Is and Why We Should Care.” Philosophy Compass 13 (2):e12475. https://doi.org/10.1111/phc3.12475 and https://compass.onlinelibrary.wiley.com/doi/abs/10.1111/phc3.12475.CrossRefGoogle Scholar
van Rooij, Iris, Wright, Cory D., Kwisthout, Johan, and Wareham, Todd. 2018. “Rational Analysis, Intractability, and the Prospects of ‘as if'-Explanations.” Synthese 195 (2):491–510. http://www.jstor.org/stable/26748857.CrossRefGoogle Scholar
Weisberg, Michael. 2013. Simulation and Similarity: Using Models to Understand the World. New York: Oxford University Press.CrossRefGoogle Scholar
Weiskopf, Daniel A. 2011. “Models and Mechanisms in Psychological Explanation.” Synthese 183 (3):313–38.CrossRefGoogle Scholar