Cue Competition in Human Categorization: Contingency or the Rescorla-Wagner Learning Rule? Comment on Shanks (1991)

Shanks (1991) reported experiments that show selective-learning effects in a categorization task, and presented simulations of his data using a connectionist network model implementing the Rescorla-Wagner (R-W) theory of animal conditioning. He concluded that his results (a) support the application of the R-W theory to account for human categorization, and (b) contradict a particular variant of contingency-b ased theories of categorizatio n. We examine these conclusions. We show that the asymptotic weights produced by the R-W model actually predict systematic deviations from the observed human learning data. Shanks claimed that his simulations provided good qualitative fits to the observed data when the weights in the networks were allowed to reach their asymptotic values. However, analytic derivations of the asymptotic weights reveal that the final weights obtained in Shanks' Simulations 1 and 2 do not correspond to the actual asymptotic weights, apparently because the networks were not in fact run to asymptote. We show that a contingency-based theory that incorporates the notion of focal sets can provide a more adequate explanation of cue competition than does the R-W model. Shanks (1991) described three experiments in which subjects were asked to play the role of medical diagnosticians. After being presented with a series of case histories (patterns of patients' symptoms associated with various fictitious diseases), subjects were asked to rate how strongly they associated each symptom with each disease, using a 0-100 rating scale. Subjects' association ratings consistently varied with the relative predictiveness of each symptom for the disease as defined by the Rescorla and Wagner (1972) model (hereinafter the R-W model) rather than with the cue validity of the symptoms (i.e., the probability of the disease given a symptom; Shanks, 1991, Experiments 1-3) or with the contingency of the symptoms (i.e., the difference between the probability of the disease given the presence of a symptom and that probability given the absence of the symptom; Shanks, 1991, Experiments 2-3). For example, consider the design of Shanks's (1991) Experiment 2. (Summaries of the experimental designs of Experiments 1-3 are presented in Table 1.) In Shanks's (1991)

[1]  P. Cheng,et al.  Covariation in natural causal induction. , 1992 .

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

[3]  N. Cartwright.  Nature's Capacities and Their Measurement , 1995 .

[4]  G. Bower,et al.  From conditioning to category learning: an adaptive network model. , 1988 .

[5]  E. H. Simpson,et al.  The Interpretation of Interaction in Contingency Tables , 1951 .

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

[7]  Ralph R. Miller,et al.  The comparator hypothesis: A response rule for the expression of associations. , 1988 .

[8]  P. Cheng,et al.  Causes versus enabling conditions , 1991, Cognition.

[9]  C. Gallistel.  The organization of learning , 1990 .

[10]  G. Chapman,et al.  Cue interaction in human contingency judgment , 1990, Memory & cognition.

[11]  A. Dickinson,et al.  Judgement of Act-Outcome Contingency: The Role of Selective Attribution , 1984 .

[12]  M. Fishbein.  Progress in social psychology , 1980 .

[13]  D. Shanks.  Forward and Backward Blocking in Human Contingency Judgement , 1985 .

[14]  Knowledge-based assumptions in causal attribution , 1992 .

[15]  B. Campbell,et al.  Punishment and aversive behavior , 1969 .

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

[17]  H. Reichenbach,et al.  The Direction of Time , 1959 .

[18]  N. Cartwright.  How the laws of physics lie , 1984 .

[19]  John H. Holland,et al.  Induction: Processes of Inference, Learning, and Discovery , 1987, IEEE Expert.

[20]  P. Suppes.  A Probabilistic Theory Of Causality , 1970 .

[21]  D R Shanks,et al.  Connectionism and the Learning of Probabilistic Concepts , 1990, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[22]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[23]  G. Chapman,et al.  Trial order affects cue interaction in contingency judgment. , 1991, Journal of experimental psychology. Learning, memory, and cognition.

[24]  Laura R. Novick,et al.  A probabilistic contrast model of causal induction. , 1990 .

[25]  A G Barto,et al.  Toward a modern theory of adaptive networks: expectation and prediction. , 1981, Psychological review.

[26]  A. Tversky,et al.  Causal Schemata in Judgments under Uncertainty , 1982 .

[27]  J. Woodward,et al.  Scientific Explanation and the Causal Structure of the World , 1988 .

[28]  K. Holyoak,et al.  Predictive and diagnostic learning within causal models: asymmetries in cue competition. , 1992, Journal of experimental psychology. General.