Premium
How Do Simple Connectionist Networks Achieve a Shift From “Featural” to “Correlational” Processing in Categorization?
Author(s) -
Thomas Michael S. C.
Publication year - 2004
Publication title -
infancy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 69
eISSN - 1532-7078
pISSN - 1525-0008
DOI - 10.1207/s15327078in0502_5
Subject(s) - connectionism , categorization , psychology , cognitive psychology , replicate , mechanism (biology) , cognitive science , cognition , artificial intelligence , computer science , neuroscience , epistemology , statistics , mathematics , philosophy
Three developmental connectionist models simulate a purported shift from “featural” to “correlational” processing in infant categorization (models: Gureckis & Love, 2004/this issue; Shultz & Cohen, 2004/this issue; Westermann & Mareschal, 2004/this issue; empirical data: Cohen & Arthur, 2003; Younger, 1985; Younger & Cohen, 1986). In this article, the way in which the models are able to simulate the behavioral data is revealed, and their respective theoretical commitments are evaluated. Together the models argue that the shift from featural to correlational processing in infant categorization might be illusory, as these models are able to replicate the key behavioral features while processing correlations right from the start. As such, they claim the behavioral description of a shift is not reflected at the level of mechanism.