Competing strategies in categorization: expediency and resistance to knowledge restructuring.

The authors investigated people's ability to restructure their knowledge when additional information about a categorization task is revealed. In 2 experiments, people first learned to rely on a fairly accurate (but imperfect) predictor. At various points in training, a complex relationship between 2 other predictors was revealed in a schematic diagram that could support perfect performance. In Experiment 1, people adopted the complex strategy when it was revealed at the outset but were unable to restructure their knowledge after the expedient predictor had been learned. In Experiment 2, expedient knowledge persisted even with an adaptive display. The persistence of expedient knowledge is explained by associative blocking of potential alternative cues. A 3rd experiment analyzed the strategies people use with and without the diagram. The study confirmed that the diagram, when presented at the outset, significantly alters people's approach to the task.

[1]  L. Kamin Predictability, surprise, attention, and conditioning , 1967 .

[2]  R. Rescorla,et al.  A theory of Pavlovian conditioning : Variations in the effectiveness of reinforcement and nonreinforcement , 1972 .

[3]  Walter Schneider,et al.  Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. , 1977 .

[4]  Mark W. Altom,et al.  Correlated symptoms and simulated medical classification. , 1982, Journal of experimental psychology. Learning, memory, and cognition.

[5]  S. Edgell Delayed exposure to configural information in nonmetric multiple-cue probability learning , 1983 .

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

[7]  S. Edgell,et al.  Delayed exposure to additional relevant information in nonmetric multiple-cue probability learning , 1987 .

[8]  I. Biederman,et al.  Sexing Day-Old Chicks : A Case Study and Expert Systems Analysis of a Difficult Perceptual-Learning Task , 1987 .

[9]  Herbert A. Simon,et al.  Why a Diagram is (Sometimes) Worth Ten Thousand Words , 1987, Cogn. Sci..

[10]  Gregory Ashby,et al.  Decision rules in the perception and categorization of multidimensional stimuli. , 1988, Journal of experimental psychology. Learning, memory, and cognition.

[11]  G. Bower,et al.  From conditioning to category learning: an adaptive network model. , 1988, Journal of experimental psychology. General.

[12]  D. Medin,et al.  Problem structure and the use of base-rate information from experience. , 1988, Journal of experimental psychology. General.

[13]  R. Glaser,et al.  Expertise in a complex skill: Diagnosing x-ray pictures. , 1988 .

[14]  J. Staszewski Skilled memory and expert mental calculation. , 1988 .

[15]  R. Nosofsky,et al.  Rules and exemplars in categorization, identification, and recognition. , 1989, Journal of experimental psychology. Learning, memory, and cognition.

[16]  Joan K. Gallini,et al.  When Is an Illustration Worth Ten Thousand Words , 1990 .

[17]  David R. Shanks,et al.  CATEGORIZATION BY A CONNECTIONIST NETWORK , 1991 .

[18]  L. Brooks,et al.  Role of specific similarity in a medical diagnostic task. , 1991, Journal of experimental psychology. General.

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

[20]  David R. Shanks,et al.  Associative versus contingency accounts of category learning: Reply to Melz, Cheng, Holyoak, and Waldmann (1993). , 1993 .

[21]  Douglas A. Williams,et al.  Configural and elemental strategies in predictive learning , 1994 .

[22]  Simon P. Davies,et al.  Knowledge restructuring and the acquisition of programming expertise , 1994, Int. J. Hum. Comput. Stud..

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

[24]  R M Nosofsky,et al.  Recognition memory for exceptions to the category rule. , 1995, Journal of experimental psychology. Learning, memory, and cognition.

[25]  K. A. Ericsson,et al.  The Road To Excellence: The Acquisition of Expert Performance in the Arts and Sciences, Sports, and Games , 1996 .

[26]  P. Schyns,et al.  Categorization creates functional features , 1997 .

[27]  S. Macho Effect of relevance shifts in category acquisition: a test of neural networks. , 1997, Journal of experimental psychology. Learning, memory, and cognition.

[28]  J. Mintzes,et al.  Knowledge restructuring in the life sciences: A longitudinal study of conceptual change in biology , 1997 .

[29]  E. Wasserman,et al.  Backward Blocking and Recovery from Overshadowing in Human Causal Judgement: The Role of Within-compound Associations , 1998, The Quarterly journal of experimental psychology. B, Comparative and physiological psychology.

[30]  J. Kruschke,et al.  Rules and exemplars in category learning. , 1998, Journal of experimental psychology. General.

[31]  L. Reder,et al.  The Strategy-Specific Nature of Improvement: The Power Law Applies by Strategy Within Task , 1998 .

[32]  J. Kruschke,et al.  Rule and Exemplar Representation in Rule-defined Category Structures , 1999 .

[33]  J. Kruschke,et al.  Rule-based extrapolation in perceptual categorization , 2002, Psychonomic bulletin & review.