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Empirically testable models are needed for understanding visual prediction

Published online by Cambridge University Press:  14 May 2008

Giuseppe Trautteur ,
Edoardo Datteri  and
Matteo Santoro
Giuseppe Trautteur
Affiliation:
Dipartimento di Scienze Fisiche, Università di Napoli Federico II, Complesso Universitario Monte Sant'Angelo, NA 80126 Napoli, Italy; trau@na.infn.it
Edoardo Datteri
Affiliation:
Dipartimento di Scienze Umane per la Formazione “Riccardo Massa”, Università degli Studi di Milano-Bicocca, MI 20126 Milano, Italy; edoardo.datteri@unimib.ithttp://ethicbots.na.infn.it/datteri/
Matteo Santoro
Affiliation:
DISI, Universita' degli Studi di Genova, GE 16146 Genova, Italy. matteo.santoro@disi.unige.it
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Abstract

Nijhawan argues convincingly that predictive mechanisms are pervasive in the central nervous system (CNS). However, scientific understanding of visual prediction requires one to formulate empirically testable neurophysiological models. The author's suggestions in this direction are to be evaluated on the basis of more realistic experimental methodologies and more plausible assumptions on the hierarchical character of the human visual cortex.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 31 , Issue 2 , April 2008 , pp. 217 - 218
Copyright
Copyright ©Cambridge University Press 2008

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References

Butz, M. V., Sigaud, O., Pezzulo, G. & Baldassarre, G., eds. (2007) Anticipatory behavior in adaptive learning systems: Advances in anticipatory processing. Springer.CrossRefGoogle Scholar
Craver, C. F. & Darden, L. (2001) Discovering mechanisms in neurobiology: The Case of spatial memory. In: Theory and method in neuroscience, ed. Machamer, P.K., Grush, R. & McLaughlin, P., pp. 112–37. University of Pittsburgh Press.CrossRefGoogle Scholar
Cummins, R. (1983) The nature of psychological explanation MIT Press/Bradford Books.Google Scholar
Glennan, S. (2005) Modeling mechanisms. Studies in History and Philosophy of Biological and Biomedical Sciences 36:443–64.CrossRefGoogle ScholarPubMed
Machamer, P., Darden, L. & Craver, C. F. (2000) Thinking about mechanisms. Philosophy of Science 67:1–25.CrossRefGoogle Scholar
Serre, T., Kouh, M., Cadieu, C., Knoblich, U., Kreiman, G. & Poggio, T. (2005) A theory of object recognition: Computations and circuits in the feedforward path of the ventral stream in primate visual cortex. CBCL Paper No. 259/AI Memo No. 2005-036, Massachusetts Institute of Technology, MA: Cambridge. Available at: http://cbcl.mit.edu/projects/cbcl/publications/ai-publications/2005/AIM-2005-036.pdf.Google Scholar
Wolpert, D. M., Ghahramani, Z. & Jordan, M. I. (1995) An internal model for sensorimotor integration. Science 269(5232):1880–82.CrossRefGoogle ScholarPubMed