Dimensional Relevance Shifts in Category Learning
Author:
John K. Kruschke
DOI:
10.1080/095400996116893
Publication Frequency:
4 issues per year
Subjects:
Cognitive Artificial Intelligence.;
Cognitive Psychology;
Cognitive Science;
Computational Linguistic & Language Recognition;
Connectionism/Neural Nets;
Cybernetics;
Formats available:
PDF
(English)
View Article:
View Article (PDF)
Abstract
A category learning experiment involving human participants compared the difficulties of four types of shift learning. Initial learning was of an exclusive-or (XOR) structure on two of three stimulus dimensions. One shift type was a reversal, a second shift was to a single previously relevant dimension, a third shift was to a single previously irrelevant dimension, and a fourth shift was to an XOR on one previously relevant dimension and one previously irrelevant dimension. Results showed that reversal shift was easiest, followed, in order, by shift to a single previously relevant dimension, shift to a single previously irrelevant dimension, and a shift to a new XOR. An extended version of the ALCOVE model, called AMBRY, qualitatively fits the data. The model incorporates two essential principles. First, internal category representations that can be quickly remapped to overt responses are important for accounting for the ease of reversal shift. Second, perseverating dimensional attention is important for accounting for the ease of shifting to a previously relevant dimension as opposed to a previously irrelevant dimension. It is suggested that any model of these effects will need to implement both of these principles.
|
| Keywords: Category Learning; Concept Learning; Discrimination Learning; Dimensional Shift; Relevance Shift; Reversal Shift |
| view citations (3) |

Download Citation

CiteULike
Del.icio.us
BibSonomy
Connotea