Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-26T04:11:57.599Z Has data issue: false hasContentIssue false
  • English
  • Français

Structural Content: A Naturalistic Approach to Implicit Belief

Published online by Cambridge University Press:  01 January 2022

Paul Skokowski
Get access Rights & Permissions [Opens in a new window]

Abstract

Various systems that learn are examined to show how content is carried in connections installed by a learning history. Agents do not explicitly use the content of such states in practical reasoning, yet the content plays an important role in explaining behavior, and the physical state carrying that content plays a role in causing behavior, given other occurrent beliefs and desires. This leads to an understanding of the environmental reasons which are the determinate content of these states, and leads to a better grasp of how representational content can be carried by systems without an explicit representation.

Type
Research Article
Information
Philosophy of Science , Volume 71 , Issue 3 , July 2004 , pp. 362 - 379
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

‡

The author would like to thank Fred Dretske, John Perry, and Martin Davies for helpful comments on an earlier draft, and would also like to thank an anonymous reviewer for helpful comments and suggestions.

References

Bailey, Craig, and Kandel, Eric (1995), “Molecular and Structural Mechanisms Underlying Long-term Memory”, in Gazzaniga, Michael (ed.), The Cognitive Neurosciences. Cambridge, MA: MIT Press, 19–36.Google Scholar
Buller, David (ed.) (1999), Function, Selection, and Design. Albany: SUNY Press.Google Scholar
Buonomano, Dean, and Byrne, John (1990), “Long-Term Synaptic Changes Produced by Cellular Analog of Classical Conditioning in Aplysia”, Long-Term Synaptic Changes Produced by Cellular Analog of Classical Conditioning in Aplysia 249:420–423.Google ScholarPubMed
Churchland, Paul (1989), A Neurocomputational Perspective. Cambridge, MA: MIT Press.Google Scholar
Clark, Andy, and Toribio, Josefa (1994), “Doing Without Representing?”, Doing Without Representing? 101:401–431.Google Scholar
Cleeremans, Axel, and Jimenez, Luis (2002), “Implicit Learning and Consciousness: A Graded, Dynamic Perspective”, in French, Robert and Cleeremans, Axel (eds.), Implicit Learning and Consciousness. New York: Psychology Press.Google Scholar
Cotman, Carl, and Lynch, Gary (1989), “The Neurobiology of Learning and Memory”, The Neurobiology of Learning and Memory 33:211–241.Google ScholarPubMed
Draye, Jean-Phillipe (2001), “Recurrent Neural Networks: Properties and Models”, in Mastebroek and Vos 1991, 49–74.Google Scholar
Dretske, Fred (1981), Knowledge and the Flow of Information. Cambridge, MA: MIT Press.Google Scholar
Dretske, Fred (1988), Explaining Behavior. Cambridge, MA: MIT Press.Google Scholar
Dretske, Fred (1991), “Dretske's Replies”, in McLaughlin, Brian (ed.), Dretske and His Critics. Oxford: Blackwell, 180–221.Google Scholar
Gardner, Daniel (1993), “Static Determinants of Synaptic Strength”, in Gardner, Daniel (ed.), The Neurobiology of Neural Networks. Cambridge, MA: MIT Press, 21–70.CrossRefGoogle Scholar
Godfrey-Smith, Peter (1999), “A Modern History Theory of Functions”, in Buller 1999, 199–220.Google Scholar
Goldman, Alvin (1970), A Theory of Human Action. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Gorman, R. Paul, and Sejnowski, Terrence (1988), “Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets”, Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets 1:75–89.Google Scholar
Killackey, Herbert, (1986), “Morphology and Memory” in Lynch, Gary (ed.), Synapses, Circuits and the Beginnings of Memory. Cambridge, MA: MIT Press, 111–120.Google Scholar
Kim, Jaegwon (1973), “Causation, Nomic Subsumption, and the Concept of Event”, Causation, Nomic Subsumption, and the Concept of Event 70:217–236.Google Scholar
Kirsh, David (1991), “When is Information Explicitly Represented?”, in Hanson, Philip (ed), Information, Thought and Content. Vancouver: University of British Columbia Press, 340–365.Google Scholar
Kitcher, Philip (1999), “Function and Design”, in Buller 1999, 159–183.Google Scholar
Ledoux, Joseph (2002), Synaptic Self. New York: Viking Press.Google Scholar
Lynch, Gary (1986), Synapses, Circuits and the Beginnings of Memory. Cambridge, MA: MIT Press.Google Scholar
Mastebroek, Henk, and Vos, Johan (eds.) (2001), Plausible Neural Networks for Biological Modelling. Dordrecht: Kluwer Academic Publishers.CrossRefGoogle Scholar
Mazzoni, Pietro, Andersen, Richard, and Jordan, Michael (1991), “A More Biologically Plausible Learning Rule for Neural Networks”, A More Biologically Plausible Learning Rule for Neural Networks 88:4433–4437.Google ScholarPubMed
McCaugh, James, Weinberger, Norman, and Lynch, Gary (eds.) (1990), Brain Organization and Memory. Oxford: Oxford University Press.Google Scholar
McLeod, Peter, Plunkett, Kim, and Rolls, Edmund (1998), Introduction to Connectionist Modelling of Cognitive Processes. Oxford: Oxford University Press.Google Scholar
Muller, Dominique, Toni, Nicolas, and Buchs, Pierre-Alain (2000), “Long-Term Potentiation: From Molecular Mechanisms to Structural Changes”, in Baudry, Michael, Davis, Joel, and Thompson, Richard (eds.), Advances in Synaptic Plasticity. Cambridge, MA: MIT Press, 87–102.Google Scholar
Rolls, Edmund, and Treves, Alessandro (1998), Neural Networks and Brain Function. Oxford: Oxford University Press.Google Scholar
Rumelhart, David (1986), “A General Framework for Parallel Distributed Processing”, in Rumelhart, David and McClelland, James (eds.) Parallel Distributed Processing, Vol. 1. Cambridge, MA: MIT Press, 45–76.CrossRefGoogle Scholar
Schwartz, Daniel, Samalam, Vijay, Solla, Sara, and Denker, John (1990), “Exhaustive Learning”, Exhaustive Learning 2:374–385.Google Scholar
Searle, John (2002), Consciousness and Language. New York: Cambridge University Press.CrossRefGoogle Scholar
Sejnowski, Terrence, and Tesauro, Gerald (1988), “Building Network Learning Algorithms from Hebbian Synapses”, in McCaugh et al. 1998, 338–355.Google Scholar
Smolensky, Paul (1986), “Neural and Conceptual Interpretation of PDP Models”, in Rumelhart, David and McClelland, James (eds.) Parallel Distributed Processing, Vol. 2. Cambridge, MA: MIT Press, 390–431.Google Scholar
Swoyer, Chris (1991), “Structural Representations and Surrogative Reasoning”, Structural Representations and Surrogative Reasoning 87:449–508.Google Scholar
Thompson, Richard (1986), “The Neurobiology of Learning and Memory”, The Neurobiology of Learning and Memory 233:941–947.Google ScholarPubMed
van Gelder, Tim (1991), “What is the “D” in “PDP”?. A Survey of the Concept of Distribution”, in Ramsey, William, Rumelhart, David, and Stich, Stephen (eds.), Philosophy and Connectionist Theory. Hillsdale NJ: Lawrence Erlbaum and Associates, 33–60.Google Scholar
van Gelder, Tim (1995), “What Might Cognition Be, If Not Computation?” Journal of Philosophy, 91:345–381.CrossRefGoogle Scholar
Vos, Johan (2001), “Biological Evidence for Synapse Modification Relevant for Neural Network Modeling”, in Mastebroek and Vos 1991, 7–21.Google Scholar