Dimensional Relevance Shifts in Category Learning

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 ...

[1]  W. R. Garner The Processing of Information and Structure , 1974 .

[2]  R. Shepard,et al.  Toward a universal law of generalization for psychological science. , 1987, Science.

[3]  J L Wolff,et al.  Concept-shift and discrimination-reversal learning in humans. , 1967, Psychological bulletin.

[4]  R. Nosofsky Attention, similarity, and the identification-categorization relationship. , 1986 .

[5]  J. Michael O'Malley,et al.  Learning Strategies in Second Language Acquisition: A cognitive theory of learning , 1990 .

[6]  H. Kendler,et al.  Vertical and horizontal processes in problem solving. , 1962, Psychological review.

[7]  G. Bower,et al.  Evaluating an adaptive network model of human learning , 1988 .

[8]  T. Wickens Multiway Contingency Tables Analysis for the Social Sciences , 1989 .

[9]  M. Levine A cognitive theory of learning: Research on hypothesis testing. , 1975 .

[10]  John K. Kruschke,et al.  Human Category Learning: Implications for Backpropagation Models , 1993 .

[11]  Roman Taraban,et al.  Categorization by humans and machines , 1993 .

[12]  John K. Kruschke,et al.  Investigations of an Exemplar-Based Connectionist Model of Category Learning , 1992 .

[13]  R. Nosofsky,et al.  Combining exemplar-based category representations and connectionist learning rules. , 1992, Journal of experimental psychology. Learning, memory, and cognition.

[14]  J. Kruschke,et al.  ALCOVE: an exemplar-based connectionist model of category learning. , 1992, Psychological review.

[15]  Sandro Ridella,et al.  Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithmCorrigenda for this article is available here , 1987, TOMS.

[16]  Michael Cole,et al.  Subproblem analysis of discrimination-shift learning , 1971 .

[17]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[18]  John K. Kruschke,et al.  Three principles for models of category learning , 1993 .

[19]  N. Mackintosh SELECTIVE ATTENTION IN ANIMAL DISCRIMINATION LEARNING. , 1965, Psychological bulletin.

[20]  Deborah G. Kemler,et al.  Selective attention and dimensional learning: A logical analysis of two-stage attention theories , 1973 .

[21]  Timothy R. Barnes,et al.  Transfer of compound and component solution modes , 1978 .

[22]  William L. Goffe,et al.  SIMANN: FORTRAN module to perform Global Optimization of Statistical Functions with Simulated Annealing , 1992 .

[23]  M. McDaniel,et al.  Incorporating prior biases innetwork models of conceptual rule learning , 1993, Memory & cognition.

[24]  R. Shepard Attention and the metric structure of the stimulus space. , 1964 .

[25]  R. Nosofsky Attention, similarity, and the identification-categorization relationship. , 1986, Journal of experimental psychology. General.