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From Implausible Artificial Neurons to Idealized Cognitive Models: Rebooting Philosophy of Artificial Intelligence

Published online by Cambridge University Press:  01 January 2022

Abstract

There is a vast literature within philosophy of mind that focuses on artificial intelligence but hardly mentions methodological questions. There is also a growing body of work in philosophy of science about modeling methodology that hardly mentions examples from cognitive science. Here these discussions are connected. Insights developed in the philosophy of science literature about the importance of idealization provide a way of understanding the neural implausibility of connectionist networks. Insights from neurocognitive science illuminate how relevant similarities between models and targets are picked out, how modeling inferences are justified, and the metaphysical status of models.

Type
Research Article
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

On its very long journey to publication, this article benefited from discussions with many colleagues, including Ken Schaffner, Peter Machamer, Jim Bogen, Floh Thiels, Dave Touretzky, Jackie Sullivan, Liz Irvine, Hong Yu Wong, Eva Engels, Gregor Hochstetter, Boris Hennig, Tim Bayne, Cameron Buckner, and Mikio Akagi, as well as audiences in Barcelona, Cambridge, Berlin, London (Ontario), and Toronto.

References

Andersen, Holly K. 2017. “Patterns, Information, and Causation.” Journal of Philosophy 114 (11): 592622..CrossRefGoogle Scholar
Anderson, James A., and Rosenfeld, Edward, eds. 2000. Talking Nets: An Oral History of Neural Networks. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Babadi, Baktash, and Sompolinsky, Haim. 2014. “Sparseness and Expansion in Sensory Representations.” Neuron 83 (5): 1213–26..CrossRefGoogle ScholarPubMed
Batterman, Robert W. 2001. The Devil in the Details: Asymptotic Reasoning in Explanation, Reduction, and Emergence. New York: Oxford University Press.CrossRefGoogle Scholar
Batterman, Robert W.. 2002. “Asymptotics and the Role of Minimal Models.” British Journal for the Philosophy of Science 53:2138.CrossRefGoogle Scholar
Baxter, Donald. 2001. “Instantiation as Partial Identity.” Australasian Journal of Philosophy 79 (4): 449–64..Google Scholar
Billings, Guy, Piasini, Eugenio, Lőrincz, Andrea, Nusser, Zoltan, and Silver, R. Angus. 2014. “Network Structure within the Cerebellar Input Layer Enables Lossless Sparse Encoding.” Neuron 83 (4): 960–74..CrossRefGoogle ScholarPubMed
Boden, Margaret. 2006. Mind as Machine: A History of Cognitive Science. Oxford: Clarendon.Google Scholar
Broadbent, Donald. 1985. “A Question of Levels: Comment on McClelland and Rumelhart.” Journal of Experimental Psychology: General 114 (2): 189–92..Google Scholar
Buckner, Cameron. 2018. “Empiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks.” Synthese 195 (12): 5339–72..CrossRefGoogle Scholar
Cartwright, Nancy. 1989. “Capacities and Abstractions.” In Scientific Explanation, ed. Kitcher, Philip and Salmon, Wesley C., 349–56. Minneapolis: University of Minnesota Press.Google 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
Churchland, Patricia S., and Sejnowski, Terrence J.. 1990. “Neural Representation and Neural Computation.” Philosophical Perspectives 4:343–82.CrossRefGoogle Scholar
Craver, Carl F. 2007. Explaining the Brain: Mechanisms and the Mosaic Unity of Neuroscience. Oxford: Clarendon.CrossRefGoogle Scholar
Dennett, Daniel C. 1991. “Real Patterns.” Journal of Philosophy 88 (1): 2751..CrossRefGoogle Scholar
Fodor, Jerry A, and Pylyshyn, Zenon W.. 1988. “Connectionism and Cognitive Architecture: A Critical Analysis.” Cognition 28:371.CrossRefGoogle ScholarPubMed
Frigg, Roman, and Nguyen, James. 2016. “The Fiction View of Models Reloaded.” Monist 99 (3): 225–42..CrossRefGoogle Scholar
Fuhs, Mark C., and Touretzky, David S.. 2006. “A Spin Glass Model of Path Integration in Rat Medial Entorhinal Cortex.” Journal of Neuroscience 26 (16): 4266–76..CrossRefGoogle ScholarPubMed
Garson, James. 2015. “Connectionism.” In Stanford Encyclopedia of Philosophy, ed. Zalta, Edward N.. Stanford, CA: Stanford University. https://plato.stanford.edu/entries/connectionism/.Google Scholar
Giere, Ronald N. 1988. Explaining Science: A Cognitive Approach. Chicago: Cambridge University Press.CrossRefGoogle Scholar
Giere, Ronald N.. 2004. “How Models Are Used to Represent Reality.” Philosophy of Science 71 (5): 742–52..CrossRefGoogle Scholar
Godfrey-Smith, Peter. 2006. “The Strategy of Model-Based Science.” Biology and Philosophy 21:725–40.Google Scholar
Godfrey-Smith, Peter. 2009. “Models and Fictions in Science.” Philosophical Studies 143:101–16.CrossRefGoogle Scholar
Green, Christopher D. 1998. “Are Connectionist Models Theories of Cognition?Psycoloquy 9 (4). http://www.cogsci.ecs.soton.ac.uk/cgi/psyc/newpsy?9.04.Google Scholar
Han, Chihye, Yoon, Wonjun, Kwon, Gihyun, Nam, Seungkyu, and Kim, Daeshik. 2019. “Representation of White- and Black-Box Adversarial Examples in Deep Neural Networks and Humans: A Functional Magnetic Resonance Imaging Study.” arXiv:1905.02422, Cornell University.CrossRefGoogle Scholar
Hempel, Carl G. 1958. “The Theoretician’s Dilemma: A Study in the Logic of Theory Construction.” In Minnesota Studies in the Philosophy of Science, Vol. 2, ed. Herbert Feigl, Michael Scriven, and Grover Maxwell. Minneapolis: University of Minnesota Press.Google Scholar
Hennig, Boris. 2015. “Instance Is the Converse of Aspect.” Australasian Journal of Philosophy 93 (1): 320..CrossRefGoogle Scholar
Hinton, Geoffrey E. 1984. “Distributed Representations.” CMU-CS-84-157, Computer Science Department, Carnegie Mellon University.Google Scholar
Hinton, Geoffrey E., ed. 1990. “Connectionist Symbol Processing.” Special issue, Artificial Intelligence 46 (1–2).CrossRefGoogle Scholar
Irvine, Elizabeth. 2014. “Model-Based Theorizing in Cognitive Neuroscience.” British Journal for the Philosophy of Science 67 (1): 143–68..Google Scholar
Kaplan, David M. 2011. “Explanation and Description in Computational Neuroscience.” Synthese 183 (3): 339–73..CrossRefGoogle Scholar
Khalidi, Muhammad Ali. 1998. “Natural Kinds and Crosscutting Categories.” Journal of Philosophy 95 (1): 3350..CrossRefGoogle Scholar
Khalidi, Muhammad Ali. 2013. Natural Categories and Human Kinds: Classification in the Natural and Social Sciences. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Günter, Küppers, and Lenhard, Johannes. 2004. “The Controversial Status of Simulations.” In 18th European Simulation Multiconference, ed. Horton, Graham, 271–75. Erlangen: SCS.Google Scholar
Mäki, Uskali. 2012. “The Truth of False Idealizations in Modeling.” In Models, Simulations, and Representations, ed. Humphreys, Paul and Imbert, Cyrille, 216–33. London: Routledge.Google Scholar
Marcus, G. 2018. “Deep Learning: A Critical Appraisal.” arXiv:1801.00631 [cs.AI], Cornell University.Google Scholar
Marr, David. 1969. “A Theory of Cerebellar Cortex.” Journal of Physiology 202 (2): 437–70..CrossRefGoogle ScholarPubMed
Marr, David. 1982. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. New York: Freeman.Google Scholar
McClelland, James L. 1981. “Retrieving General and Specific Information from Stored Knowledge of Specifics.” In Proceedings of the Third Annual Conference of the Cognitive Science Society, 170–72. Hillsdale, NJ: Cognitive Science Society.Google Scholar
McClelland, James L.. 2009. “The Place of Modeling in Cognitive Science.” Topics in Cognitive Science 1 (1): 1138..CrossRefGoogle ScholarPubMed
McClelland, James L. and Rumelhart, David E.. 1985. “Distributed Memory and the Representation of General and Specific Information.” Journal of Experimental Psychology: General 114 (2): 159–97..Google ScholarPubMed
McClelland, James L. and Rumelhart, David E.. 1986. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 2, Psychological and Biological Models. Cambridge, MA: MIT Press.Google Scholar
Miłkowski, Marcin. 2013. Explaining the Computational Mind. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Morgan, Mary S. 2002. “Model Experiments and Models in Experiments.” In Model-Based Reasoning: Science, Technology, Values, ed. Magnani, Lorenzo and Nersessian, Nancy J., 4158. New York: Kluwer.CrossRefGoogle Scholar
Morgan, Mary S.. 2003. “Experiments without Material Intervention: Model Experiments, Virtual Experiments and Virtually Experiments.” In The Philosophy of Scientific Experimentation, ed. Radder, Hans, 216–35. Pittsburgh: University of Pittsburgh Press.Google Scholar
Newell, Allen, and Simon, Herbert A.. 1961. “Computer Simulation of Human Thinking.” Science 134 (3495): 2011–17..CrossRefGoogle ScholarPubMed
Newell, Allen, and Simon, Herbert A.. 1976. “Computer Science as Empirical Inquiry: Symbols and Search.” Communications of the ACM 19 (3): 113–26..CrossRefGoogle Scholar
Norton, Stephen, and Suppe, Frederick. 2001. “Why Atmospheric Modeling Is Good Science.” In Changing the Atmosphere, ed. Miller, Clark A. and Edwards, Paul N., 67105. Cambridge, MA: MIT Press.Google Scholar
Parker, Wendy. 2009. “Does Matter Really Matter? Computer Simulations, Experiments, and Materiality.” Synthese 169 (3): 483–96..CrossRefGoogle Scholar
Plaut, David C. 1995. “Double Dissociation without Modularity: Evidence from Connectionist Neuropsychology.” Journal of Clinical and Experimental Neuropsychology 17 (2): 291321..CrossRefGoogle ScholarPubMed
Rumelhart, David E., and McClelland, James L.. 1985. “Levels Indeed! A Response to Broadbent.” Journal of Experimental Psychology: General 114 (2): 193–97..Google Scholar
Rumelhart, David E., and McClelland, James L.. 1986a. “On Learning the Past Tenses of English Verbs.” In McClelland and Rumelhart 1986, 216–71.Google Scholar
Rumelhart, David E., and McClelland, James L.. 1986b. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, Foundations. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Rumelhart, David E., and McClelland, James L.. 1986c. “PDP Models and General Issues in Cognitive Science.” In Rumelhart and McClelland 1986b, 110–46.Google Scholar
Russakovsky, Olga, Deng, Jia, Su, Hao, Krause, Jonathan, Satheesh, Sanjeev, Ma, Sean, Huang, Zhiheng, Karpathy, Andrej, Khosla, Aditya, Bernstein, Michael, Berg, Alexander C., and Fei-Fei, Li. 2015. “ImageNet Large Scale Visual Recognition Challenge.” International Journal of Computer Vision 115 (3): 211–52..CrossRefGoogle Scholar
Sejnowski, Terrence J., and Rosenberg, Charles R.. 1986. “NETtalk: A Parallel Network That Learns to Read Aloud.” Technical Report JHU/EEC-86/01, Electrical Engineering and Computer Science, Johns Hopkins University.Google Scholar
Smolensky, Paul. 1988a. “The Constituent Structure of Connectionist Mental States: A Reply to Fodor and Pylyshyn.” Southern Journal of Philosophy 26 (S1): 137–61.Google Scholar
Smolensky, Paul. 1988b. “On the Proper Treatment of Connectionism.” Behavioral and Brain Sciences 11:174.CrossRefGoogle Scholar
Smolensky, Paul. 1991. “Connectionism, Constituency, and the Language of Thought.” In Meaning in Mind: Fodor and His Critics, ed. Loewer, Barry M and Rey, Georges, 201–27. Oxford: Blackwell.Google Scholar
Steinle, Friedrich. 1997. “Entering New Fields: Exploratory Uses of Experimentation.” Philosophy of Science 64:S65S74.CrossRefGoogle Scholar
Steinle, Friedrich. 2002. “Experiments in History and Philosophy of Science.” Perspectives on Science 10 (4): 408–32..Google Scholar
Stinson, Catherine. 2018. “Explanation and Connectionist Models.” In The Routledge Handbook of the Computational Mind, ed. Colombo, Matteo and Sprevak, Mark, 120–33. London: Routledge.Google Scholar
Suárez, Mauricio. 2003. “Scientific Representation: Against Similarity and Isomorphism.” International Studies in the Philosophy of Science 17 (3): 225–44..CrossRefGoogle Scholar
Suri, Roland E., and Schultz, Wolfram. 2001. “Temporal Difference Model Reproduces Anticipatory Neural Activity.” Neural Computation 13 (4): 841–62..CrossRefGoogle ScholarPubMed
Thomas, Michael S. C., and McClelland, James L.. 2008. “Connectionist Models of Cognition.” In Cambridge Handbook of Computational Psychology, ed. Sun, Ron, 2358. Cambridge: Cambridge University Press.Google Scholar
Touretzky, David S., and Hinton, Geoffrey E.. 1988. “A Distributed Connectionist Production System.” Cognitive Science 12 (3): 423–66..CrossRefGoogle Scholar
Weisberg, Michael. 2012. Simulation and Similarity: Using Models to Understand the World. Oxford: Oxford University Press.Google Scholar
Wilson, Hugh R., and Cowan, Jack D.. 1972. “Excitatory and Inhibitory Interactions in Localized Populations of Model Neurons.” Biophysical Journal 12 (1): 124..CrossRefGoogle ScholarPubMed
Winsberg, Eric. 2009. “A Tale of Two Methods.” Synthese 169 (3): 575–92..CrossRefGoogle Scholar
Winsberg, Eric. 2010. Science in the Age of Computer Simulation. Chicago: Cambridge University Press.CrossRefGoogle Scholar