skip to main content
article

How "Authentic Intentionality" can be Enabled: a Neurocomputational Hypothesis

Authors Info & Claims
Published:01 July 2010Publication History
Skip Abstract Section

Abstract

According to John Haugeland, the capacity for "authentic intentionality" depends on a commitment to constitutive standards of objectivity. One of the consequences of Haugeland's view is that a neurocomputational explanation cannot be adequate to understand "authentic intentionality". This paper gives grounds to resist such a consequence. It provides the beginning of an account of authentic intentionality in terms of neurocomputational enabling conditions. It argues that the standards, which constitute the domain of objects that can be represented, reflect the statistical structure of the environments where brain sensory systems evolved and develop. The objection that I equivocate on what Haugeland means by "commitment to standards" is rebutted by introducing the notion of "florid, self-conscious representing". Were the hypothesis presented plausible, computational neuroscience would offer a promising framework for a better understanding of the conditions for meaningful representation.

References

  1. Churchland, P. S. (1986). Neurophilosophy. Toward a unified science of the mind-brain. Cambridge, MA: MIT Press. Google ScholarGoogle Scholar
  2. Churchland, P., & Grush, R. (1999). Computation and the brain. In F. Heil & R. Wilson (Eds.), The MIT Encyclopedia of Cognitive Sciences (pp. 155-158). Cambridge, MA: MIT Press.Google ScholarGoogle Scholar
  3. Churchland, P., & Sejnowski, T. (1992). The Computational Brain. Cambridge, MA: MIT Press. Google ScholarGoogle Scholar
  4. Clark, A. (1997a). Being There. Putting Brain, Body and World Together Again. Cambridge, MA: MIT Press. Google ScholarGoogle Scholar
  5. Clark, A. (1997b). The dynamical challenge. Cognitive Science, 21(4), 461-481.Google ScholarGoogle ScholarCross RefCross Ref
  6. Clark, A., & Grush, R. (1999). Towards a cognitive robotics. Adaptive Behavior, 7(1), 5-16. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Clifford, C., Webster, M., Stanley, G., Stocker, A., Kohn, A., Sharpee, T., et al. (2007). Visual adaptation: Neural, psychological and computational aspects. Vision Research, 47, 3125-3131.Google ScholarGoogle ScholarCross RefCross Ref
  8. Dayan, P. (1994). Computational modelling. Current Opinion in Neurobiology, 4, 212-217.Google ScholarGoogle ScholarCross RefCross Ref
  9. Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience: Computational and mathematical modeling of neural systems. Cambridge, MA: MIT Press. Google ScholarGoogle Scholar
  10. Deneve, S., Latham, P. E., & Pouget, A. (2001). Efficient computation and cue integration with noisy population codes. Nature Neuroscience, 4(8), 826-831.Google ScholarGoogle ScholarCross RefCross Ref
  11. Dennett, D. (1969). Content and Consciousness. Routledge: London.Google ScholarGoogle Scholar
  12. Dennett, D. (1998). Making tools for thinking. In D. Sperber (Ed.) (2000). Metarepresentation. New York: Oxford University Press.Google ScholarGoogle Scholar
  13. Eliasmith, C. (2003). Moving beyond metaphors: Understanding the mind for what it is. Journal of Philosophy, C(10), 493-520.Google ScholarGoogle ScholarCross RefCross Ref
  14. Fahle, M., & Poggio, T. (Eds.). (2002). Perceptual learning. Cambridge, MA: MIT Press.Google ScholarGoogle Scholar
  15. Fodor, J. (1981). Representations. Cambridge, MA: MIT Press.Google ScholarGoogle Scholar
  16. Gibson, J. J. (1937). Adaptation, after-effect, and contrast in the perception of tilted lines. II. Simultaneous contrast and the areal restriction of the after-effect. Journal of Experimental Psychology, 20, 553-569.Google ScholarGoogle ScholarCross RefCross Ref
  17. Grush, R. (2004). The emulation theory of representation: motor control, imagery, and perception. Behavioral and Brain Sciences, 27, 377-442.Google ScholarGoogle ScholarCross RefCross Ref
  18. Haugeland, J. (1992). Understanding Dennett and Searle. In A. Revonsuo & M. Kamppinen (Eds.), Consciousness in philosophy and cognitive neuroscience (pp. 115-128). Hillsdale, NJ: Lawrence Erlbaum.Google ScholarGoogle Scholar
  19. Haugeland, J. (1996). Objective Perception. In K. Akins (Ed.), Perception: Vancouver studies in cognitive science, Vol V (pp. 268-289). New York: Oxford University Press.Google ScholarGoogle Scholar
  20. Haugeland, J. (1998a). Having thought: Essays in the metaphysics of mind. Cambridge, MA: Harvard University Press.Google ScholarGoogle Scholar
  21. Haugeland, J. (Ed.). (1998b). Truth and rule following. In Having Thought (pp. 305-361). Cambridge, MA: Harvard University Press.Google ScholarGoogle Scholar
  22. Haugeland, J. (2002a). Authentic Intentionality. In Matthias. Scheutz (Ed.), Computationalism: New directions (pp. 159-174). Cambridge: MIT Press.Google ScholarGoogle Scholar
  23. Haugeland, J. (2002b). Reply to cummins on representation and intentionality. In H. caplin (Ed.). Philosophy of Mental Representation (pp. 138-144). Oxford: Oxford University Press.Google ScholarGoogle Scholar
  24. Hurley, S. (2008). The shared circuits model: How control, mirroring and simulation can enable imitation, deliberation, and mindreading. Behavioral and the Brain Sciences, 31(1), 1-21.Google ScholarGoogle ScholarCross RefCross Ref
  25. Jazayeri, M., & Movshon, J. A. (2006). Optimal representation of sensory information by neural populations. Nature Neuroscience, 9(5), 690-696.Google ScholarGoogle ScholarCross RefCross Ref
  26. Martinez-Conde, S., Macknik, S. L., & Hubel, D. H. (2004). The role of fixational eye movements in visual perception. Nature Reviews Neuroscience, 5, 229-240.Google ScholarGoogle ScholarCross RefCross Ref
  27. McCloskey, M. (1983). Intuitive physics. Scientific American, 284, 122-129.Google ScholarGoogle ScholarCross RefCross Ref
  28. McDowell, J. (1994). The content of perceptual experience. Philosophical Quarterly, 44, 190-205.Google ScholarGoogle ScholarCross RefCross Ref
  29. Montague, R. (2007). Your brain is almost perfect: How we make decisions. New York: Plume.Google ScholarGoogle Scholar
  30. Montague, R., & Berns, G. (2002). Neural economics and the biological substrates of valuation. Neuron, 36, 265-284.Google ScholarGoogle ScholarCross RefCross Ref
  31. Niv, Y., Joel, D., & Dayan, P. (2006). A normative perspective on motivation. Trends in Cognitive Sciences, 10(8), 375-381.Google ScholarGoogle ScholarCross RefCross Ref
  32. O'Reilly, R. C., & Munakata, Y. (2000). Computational explorations in cognitive neuroscience: Understanding the mind by simulating the brain. Cambridge, MA: MIT Press. Google ScholarGoogle Scholar
  33. Rolls, E. T. (2001). Representations in the brain. Synthèse 129, 153-171.Google ScholarGoogle ScholarCross RefCross Ref
  34. Schwartz, O., Hsu, A., & Dayan, P. (2007). Space and time in visual context. Nature Reviews Neuroscience, 8(7), 522-535.Google ScholarGoogle ScholarCross RefCross Ref
  35. Searle, J. R. (1980). Minds, brains and programs. Behavioral and Brain Sciences, 3(3), 417-457.Google ScholarGoogle ScholarCross RefCross Ref
  36. Seriès, P., Stocker, A., & Simoncelli, E. (2009). Is the Homunculus "aware" of sensory adaptation? Neural Computation, 21(12), 1-33. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Shagrir, O. (2006). Why we view the brain as a computer. Synthese, 153, 393-416.Google ScholarGoogle ScholarCross RefCross Ref
  38. Shannon, C. (1948). The mathematical theory of communication. Bell System Technical Journal, 27, 379-423.Google ScholarGoogle ScholarCross RefCross Ref
  39. Simoncelli, E. P., & Olshausen, B. A. (2001). Natural image statistics and neural representation. Annual Review of Neuroscience, 24, 1193-1216.Google ScholarGoogle ScholarCross RefCross Ref
  40. Spelke, E. S., & Kinzler, K. D. (2007). Core knowledge. Developmental Science, 10, 89-96.Google ScholarGoogle ScholarCross RefCross Ref
  41. Spelke, E. S., & Van de Walle, G. (1993). Perceiving and reasoning about objects: Insights from infants. In N. Eilan, B. Brewer, & R. McCarthy (Eds.), Spatial representation (pp. 132-161). London: Blackwell.Google ScholarGoogle Scholar
  42. Sutton, R., & Barto, A. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press. Google ScholarGoogle Scholar
  43. Teich, A., & Qian, N. (2003). Learning and adaptation in a recurrent model of v1 orientation selectivity. Journal of Neurophysiology, 89, 2086-2100.Google ScholarGoogle ScholarCross RefCross Ref

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in

Full Access

  • Article Metrics

    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0

    Other Metrics