2010/cognmodels10

We seek to understand cognitive behaviour at different levels of abstraction. Key to success in this endeavor is data, models and algorithms for extracting structure from data. Probabilistic models of cognition offers a rich mathematical foundation for neuromorphic cognition engineering. Here is a set of seminal papers both at various levels from tutorial to more advance.

Journal club reading list:

Chater, Tenenbaum, Yuille, Probabilistic models of cognition: Conceptual foundations, TRENDS in Cognitive Sciences Vol.10 No.7 July 2006

Chater, Tenenbaum, Yuille, Probabilistic models of cognition: Where Next, TRENDS in Cognitive Sciences Vol.10 No.7 July 2006

Kemp and Tenenbaum, The discovery of structural form, PNAS August 5, 2008 vol. 105 no. 31 10687–10692 (Matlab code to reproduce the results in this paper is also available)

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