Comparing models of rule-based classification learning: A replication and extension of Shepard, . . .

Comparing models of rule-based classification learning: A replication and extension of Shepard, . . . (1994)

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by Robert M. Nosofsky , Mark A. Gluck , Thomas J Palmeri , Stephen C McKinley , PAUL GLAUTHIER

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Datum	Value	Source
TITLE	Comparing models of rule-based classification learning: A replication and extension of Shepard, . . .	user correction
AUTHOR NAME	Robert M. Nosofsky	user correction
AUTHOR AFFIL	Indiana University, Bloomington, Indiana	user correction
AUTHOR NAME	Mark A. Gluck	user correction
AUTHOR ADDR	Rutgers University, New Brunswick, New Jersey	user correction
AUTHOR NAME	Thomas J Palmeri	user correction
AUTHOR AFFIL	Indiana Uniuersaty, Bloomington, Indiana; and; PAUL GLAUTHIER	user correction
AUTHOR ADDR	Rutgers University New Brunswick, New Jersey	user correction
AUTHOR NAME	Stephen C McKinley	user correction
AUTHOR AFFIL	Indiana University	user correction
AUTHOR ADDR	Bloomington, Indiana	user correction
AUTHOR NAME	PAUL GLAUTHIER	user correction
AUTHOR AFFIL	Rutgers University	user correction
AUTHOR ADDR	New Brunswick, New Jersey	user correction
ABSTRACT	... difficulty for learning six fundamental types of rule-based categorization problems. Our main results mirrored those of Shepard et al., with the ordering of task difficulty being the same as in the original study. A much richer data set was collected, however, which enabled the generation of block-by-block learning curves suitable for quantitative fitting. Four current computational models of classification learning were fitted to the learning data: ALCOVE (Kruschke, 1992), the rational model (Anderson, 3991), the configural-cue model (cluck & Bower, 1988b), and an extended version of the conf`igural-cue model with dimensionalized, adaptive learning rate mechanisms. Although all of the models captured important qualitative aspects of the learning data, ALCOVE provided the best overall quantitative fit. The results suggest the need to incorporate same form of selective attention todimensions in category-learning models based on stimulus generalization and cue conditioning.	user correction
YEAR	1994	user correction
CITATIONS	27 found	ParsCit 1.0