Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Publisher: Elsevier
Languages: English
Types: Article
Subjects: BF0309, T1
Early agreement within cognitive science on the topic of representation has now given way to a combination of positions. Some question the significance of representation in cognition. Others continue to argue in favor, but the case has not been demonstrated in any formal way. The present paper sets out a framework in which the value of representation-use can be mathematically measured, albeit in a broadly sensory context rather than a specifically cognitive one. Key to the approach is the use of Bayesian networks for modeling the distal dimension of sensory processes. More relevant to cognitive science is the theoretical result obtained, which is that a certain type of representational architecture is *necessary* for achievement of sensory efficiency. While exhibiting few of the characteristics of traditional, symbolic encoding, this architecture corresponds quite closely to the forms of embedded representation now being explored in some embedded/embodied approaches. It becomes meaningful to view that type of representation-use as a form of information recovery. A formal basis then exists for viewing representation not so much as the substrate of reasoning and thought, but rather as a general medium for efficient, interpretive processing.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Brooks, R. (1991a). Intelligence without reason. In J. Mylopoulos and R. Reiter (Eds.), Proceedings of the Twelth International Joint Conference on Artificial Intelligence (pp. 569-595). San Mateo, California: Morgan Kaufman.
    • Brooks, R. (1991b). Intelligence without representation. Artificial Intelligence, 47 (pp. 139-159).
    • Clancey, W. (1997). Situated Cognition: On Human Knowledge and Computer Representations. Cambridge: Cambridge University Press.
    • Clark, A. (1996). Happy couplings: emergence and explanatory interlock. In M.A. Boden (Ed.), The Philosophy of Artificial Life (pp. 262-281). Oxford: Oxford University Press.
    • Clark, A. (1997). Being There: Putting Brain, Body and World Together Again. Cambridge, MA: MIT Press.
    • Clark, A. (2003). Natural Born Cyborgs: Minds, Technologies and the Future of Human Intelligence. Oxford University Press.
    • Clark, A. (2008). Supersizing the Mind: Embodiment, Action and Cognitive Extension. Oxford: Oxford University Press.
    • Clark, A. and Grush, R. (1999). Towards a cognitive robotics. Adaptive Behavior, 7, No. 1 (pp. 5-16).
    • Clark, A. and Thornton, C. (1997). Trading spaces: computation, representation and the limits of uninformed learning. Behaviour and Brain Sciences, 20 (pp. 57-90). Cambridge University Press.
    • Clark, A. and Toribio, J. (1994). Doing without representing. Synthese, 101 (pp. 401-431).
    • Cover, T. and Thomas, J. (1991). Elements of Information Theory. Hoboken, New Jersey: John Wiley & Sons, Inc.
    • Dean, P., Mayhew, J. and Langdon, P. (1994). Learning and maintaining saccadic accuracy: a model of brainstem-cerebellum interactions. Jounral of Cognitive Neuroscience, 6 (pp. 117-138).
    • Dreyfus, H. (1979). What Computers CanĀ“t Do (revised edition). New York: Harper and Row.
    • Gibson, J. (1966). The Senses Considered as Perceptual Systems. Oxford, England: Houghton-Mifflin.
    • Gibson, J. (1979). The Ecological Approach to Visual Perception. Boston: Houghton Miffin.
    • Grush, R. (2004). The emulation theory of representation. Behavioral and Brain Sciences, 27 (pp. 377-442).
    • Harnad, S. (1990). The symbol grounding problem. Physica D, 42 (pp. 335- 346).
    • Haselager, P., de Groot, A. and van Rappard, H. (2003). Representationalism vs. anti-representationalism: a debate for the sake of appearance. Philosophical Psychology, 16 (pp. 5-23).
    • Haugeland, J. (1991). Representational genera. In W. Ramsay, S.P. Stich and D.E. Rumelhart (Eds.), Philosophy and Connectionist Theory (pp. 61-89). Mahwah, NJ: Lawrence Erlbaum Associates.
    • Hesslow, G. (2002). Conscious thought as simulation of behaviour and perception. Trends in Cognitive Sciences, 6 (pp. 242-247).
    • Hillis, J., Ernst, M., Banks, M. and Landy, M. (2002). Combining sensory information: mandatory fusion within, but not between, senses. Science, 298 (pp. 1627-1630).
    • Kawato, M., Furukawa, K. and Suzuki, R. (1987). A hierarchical neural network model for the control and learning of voluntary movement. Biological Cybernetics, 57 (pp. 169-185).
    • Kirsh, D. (1996). Today the earwig, tomorrow man?. In M.A. Boden (Ed.), The Philosophy of Artificial Life (pp. 237-261). Oxford: Oxford University Press.
    • Korb, K. and Nicholson, A. (2003). Bayesian Artificial Intelligence. Boca Raton, Florida: CRC Press.
    • Kullback, S. (1987). The kullback-leibler distance. The American Statistician, 41 (pp. 340-341).
    • Maes, P. and Brooks, R. (1990). Learning to coordinate behaviors. Proceedings of the Eighth National Conference on Artificial Intelligence (pp. 796-802). San Francisco, CA: Morgan Kaufmann.
    • Maravita, A. and Iriki, A. (2004). Tools for the body (schema). Trends in Cognitive Sciences, 8, No. 2 (pp. 79-86).
    • Newell, A. and Simon, H. (1976). Computer science as empirical inquiry: symbols and search. Communications of the ACM, 19, No. 3 (pp. 113-126).
    • Searle, J. (1980). Minds, brains and programs [with peer commentaries]. Behavioural and Brain Sciences, 3 (pp. 417-57).
    • Sedgewick, R. (1988). Algorithmics (2nd edition). Addison-Wesley Publising Company, Inc.
    • Shannon, C. (1948). A mathematical theory of communication. Bell System Technical Journal, 27 (pp. 379-423 and 623-656).
    • Shannon, C. and Weaver, W. (1949). The Mathematical Theory of Communication. Urbana, Illinois: University of Illinois Press.
    • Smith, B. (1996). On the Origin of Objects. Cambridge, MA: MIT Press.
    • Smith, C. (2000). Biology of Sensory Systems. New York: John Wiley & Sons, Ltd.
    • Stewart, J. (1995). Cognition = life: implications for higher-level cognition. Behavioural Processes, 35 (pp. 311-326).
    • Svensson, H. and Ziemke, T. (2005). Embodied representation: what are the issues?. In B.G. Bara, L. Barsalou and M. Bucciarelli (Eds.), Proceedings of the 27th Annual Conference of the Cognitive Science Society (pp. 2116- 2121). Hillsdale, NJ: Lawrence Erlbaum Associates.
    • Tononi, G., Sporns, O. and Edelman, G. (1994). A measure for brain complexity: relating functional segregation and integration in the nervous system. Proceedings of the Nat. Academy of Science, 91 (pp. 5033-5037).
    • Tononi, G., Sporns, O. and Edelman, G. (1996). A complexity measure for selective matching of signals by the brain. Proceedings of the Nat. Academy of Science, 93 (pp. 3422-3427).
    • Usher, M. and Keating, D. (1996). Sensors and Transducers: Characteristics, Applications, Instrumentation, Interfacing. London: Macmillan Press Ltd.
    • van Gelder, T. (1995). What might cognition be, if not computation. Journal of Philosophy, XCII, No. 7 (pp. 345-381).
    • van Hateren, J. (1992). A theory of maximizing sensory information. Biological Cybernetics, 68, No. 1 (pp. 23-29). Berlin/Heidelberg: Springer.
    • Wheeler, M. and Clark, A. (1999). Genic representation: reconciling content and causal complexity. The British Journal for the Philosophy of Science, 50 (pp. 103-135). Br Soc Philosophy Sci.
    • Winograd, T. and Flores, F. (1986). Understanding Computers and Cognition: A New Foundation for Design. Norwood, New Jersea: Ablex Publishing Corporation.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article