Premium
An Emerging Consensus: Younger and Cohen Revisited
Author(s) -
Younger Barbara A.,
Hollich George,
Furrer Stephanie D.
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_6
Subject(s) - psychology , jargon , categorization , pooling , set (abstract data type) , cognitive psychology , cognitive science , artificial intelligence , computer science , linguistics , philosophy , programming language
From Aesop to Sun Tzu, the importance of working together has long been acknowledged. Yet as long as cooperation has existed, so have the difficulties associated with it. Pooling two fields might mean twice the power, but this union also brings twice the jargon, twice the competing theories, and twice the head butting. Nonetheless, in this collection, researchers have made a heroic effort to set aside their theoretical differences to produce three computational models for the influential set of empirical data showing that young infants have difficulty detecting correlations among features (Younger & Cohen, 1983, 1986). Specifically, Gureckis and Love (2004/this issue) fit a well‐developed adult‐learning model to the infant work (down to the exact order and number of test trials) in support of a common mechanism underlying categorization. Shultz and Cohen (2004/this issue) vary depth of processing in a cascade correlation network to show how older infants learn more than younger infants from the same amount of exposure. And Westermann and Mareschal (2004/this issue) introduce a model for their representational acuity hypothesis, which explains qualitative shifts as a decrease in the size of neural receptive fields. This commentary takes stock of this attempt at unifying computational modeling and developmental data by identifying common themes, clarifying points of disagreement, and providing a synthesis for this work.