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Tacit Representations and Artificial Intelligence: Hidden Lessons from an Embodied Perspective on Cognition

  • Elena SpitzerEmail author
Chapter
Part of the Synthese Library book series (SYLI, volume 376)

Abstract

In this paper, I explore how an embodied perspective on cognition might inform research on artificial intelligence. Many embodied cognition theorists object to the central role that representations play on the traditional view of cognition. Based on these objections, it may seem that the lesson from embodied cognition is that AI should abandon representation as a central component of intelligence. However, I argue that the lesson from embodied cognition is actually that AI research should shift its focus from how to utilize explicit representations to how to create and use tacit representations. To develop this suggestion, I provide an overview of the commitments of the classical view and distinguish three critiques of the role that representations play in that view. I provide further exploration and defense of Daniel Dennett’s distinction between explicit and tacit representations. I argue that we should understand the embodied cognition approach using a framework that includes tacit representations. Given this perspective, I will explore some AI research areas that may be recommended by an embodied perspective on cognition.

Keywords

Embodied cognition Artificial intelligence Representation Tacit representations 

References

  1. Adriaans, P. (2012). Information. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Winter 2012.). Retrieved from http://plato.stanford.edu/archives/win2012/entries/information/
  2. Aydede, M. (2010). The language of thought hypothesis. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Fall 2010). Retrieved from http://plato.stanford.edu/archives/fall2010/entries/language-thought/
  3. Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47(1), 139–159.CrossRefGoogle Scholar
  4. Clark, A. (2001). Reasons, robots and the extended mind. Mind & Language, 16(2), 121–145.CrossRefGoogle Scholar
  5. Dennett, D. C. (1982). Styles of mental representation. Proceedings of the Aristotelian Society, 83, 213–226. doi: 10.2307/4545000.CrossRefGoogle Scholar
  6. Dretske, F. I. (1983). The epistemology of belief. Synthese, 55(1), 3–19. doi: 10.2307/20115855.CrossRefGoogle Scholar
  7. Dreyfus, H. L. (1992). What computers still can’t do: A critique of artificial reason. Cambridge, MA: MIT Press.Google Scholar
  8. Dreyfus, H. L., & Dreyfus, S. E. (1988). Making a mind versus modeling the brain: Artificial intelligence back at a branchpoint. Daedalus, 117, 15–43.Google Scholar
  9. Floridi, L. (2013). Semantic conceptions of information. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Spring 2013.). Retrieved from http://plato.stanford.edu/archives/spr2013/entries/information-semantic/
  10. Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1–2), 3–71. doi: 10.1016/0010-0277(88)90031-5.CrossRefGoogle Scholar
  11. Prinz, J. J., & Barsalou, L. W. (2000). Steering a course for embodied representation. Cognitive dynamics: Conceptual change in humans and machines, 51–77. Cambridge, MA: MIT Press.Google Scholar
  12. Shapiro, L. (2010). Embodied cognition. Routledge: Taylor & Francis.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of WisconsinMadisonUSA

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