A further investigation of category learning by inference

Categories are learned in many ways besides by classification, for example, by making inferences about classified items. One hypothesis is that classifications lead to the learning of features that distinguish categories, whereas inferences promote the learning of the internal structure of categories, such as the typical features. Experiment 1 included single-feature and full-feature classification tests following either classification or inference learning. Consistent with predictions, inference learners did better on the single tests but worse on the full tests. Experiment 2 further showed that inference learners, unlike classification learners, were no better at classifying items that they had seen at study compared with equally typical items they had not seen at study. Experiment 3 showed that features queried about during inference learning were classified better than ones not queried about, although even the latter features showed some learning on single-feature tests. The discussion focuses on how different types of category learning lead to different category representations.

[1]  J. D. Smith,et al.  Prototypes in the Mist: The Early Epochs of Category Learning , 1998 .

[2]  A. Markman,et al.  Category Learning by Inference and Classification , 1998 .

[3]  E. Rosch,et al.  Family resemblances: Studies in the internal structure of categories , 1975, Cognitive Psychology.

[4]  Brian H. Ross,et al.  Postclassification category use : The effects of learning to use categories after learning to classify , 1999 .

[5]  Brian H. Ross,et al.  The Use of Categories Affects Classification , 1997 .

[6]  R. Nosofsky Similarity, frequency, and category representations. , 1988 .

[7]  J. Boster,et al.  Form or Function: A Comparison of Expert and Novice Judgments of Similarity Among Fish , 1989 .

[8]  Brian H. Ross,et al.  The effects of category use on learned categories , 2000, Memory & cognition.

[9]  D. Medin,et al.  Family resemblance, conceptual cohesiveness, and category construction , 1987, Cognitive Psychology.

[10]  Bradley C. Love,et al.  Learning nonlinearly separable categories by inference and classification. , 2002 .

[11]  D. Medin,et al.  Concepts do more than categorize , 1999, Trends in Cognitive Sciences.

[12]  Douglas L. Medin,et al.  Context theory of classification learning. , 1978 .

[13]  John R. Anderson,et al.  The Adaptive Nature of Human Categorization. , 1991 .

[14]  B. Ross,et al.  Predictions From Uncertain Categorizations , 1994, Cognitive Psychology.

[15]  Robert L. Goldstone,et al.  Two competing attentional mechanisms in category learning. , 1998 .

[16]  D. Medin,et al.  Categorization and Reasoning among Tree Experts: Do All Roads Lead to Rome? , 1997, Cognitive Psychology.

[17]  A. Markman,et al.  Inference using categories. , 2000, Journal of experimental psychology. Learning, memory, and cognition.

[18]  D. Medin,et al.  SUSTAIN: a network model of category learning. , 2004, Psychological review.

[19]  Dorrit Billman,et al.  Observational Learning From Internal Feedback: A Simulation of an Adaptive Learning Method , 1988, Cogn. Sci..

[20]  G. Murphy,et al.  Induction and category coherence , 1996, Psychonomic bulletin & review.

[21]  K. Holyoak,et al.  Induction of category distributions: a framework for classification learning. , 1984, Journal of experimental psychology. Learning, memory, and cognition.

[22]  R. Nosofsky,et al.  Rule-plus-exception model of classification learning. , 1994, Psychological review.

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

[24]  Seth Chin-Parker,et al.  The effect of category learning on sensitivity to within-category correlations , 2002, Memory & cognition.

[25]  B. Ross,et al.  Category-based predictions: influence of uncertainty and feature associations. , 1996, Journal of experimental psychology. Learning, memory, and cognition.