Behavioral and Brain Sciences

Table of Contents - 2009 - Volume 32, Issue 02  

Cambridge Journals Online - CUP Full-Text Page
Behavioral and Brain Sciences (2009), 32:223-224 Cambridge University Press
Copyright © Cambridge University Press 2009
doi:10.1017/S0140525X09001125

Open Peer Commentary

The computational nature of associative learning

N. A. Schmajuka1 and G. M. Kutlua1

a1 Department of Psychology and Neuroscience, Duke University, Durham, NC 27516. nestor@duke.edu gunes.kutlu@duke.edu
Article author query
schmajuk na [PubMed]  [Google Scholar]
kutlu gm [PubMed]  [Google Scholar]

Abstract

An attentional-associative model (Schmajuk et al. 1996), previously evaluated against multiple sets of classical conditioning data, is applied to causal learning. In agreement with Mitchell et al.'s suggestion, according to the model associative learning can be a conscious, controlled process. However, whereas our model correctly predicts blocking following or preceding subadditive training, the propositional approach cannot account for those results.

The propositional nature of human associative learning Chris J. Mitchell, Jan De Houwer and Peter F. Lovibond School of Psychology, University of New South Wales, Kensington 2052, Australia chris.mitchell@unsw.edu.au http://www.psy.unsw.edu.au/profiles/cmitchell.html; Department of Psychology, Ghent University, Henri Dunantlaan 2, B-9000 Ghent, Belgium jan.dehouwer@ugent.be http://users.ugent.be/~jdhouwer/">; School of Psychology, University of New South Wales, Kensington 2052, Australia p.lovibond@unsw.edu.au http://www.psy.unsw.edu.au/profiles/plovibond.html">