Connectionism and Human Learning: Critique of Gluck and Bower (1988)


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Abstract

Gluck and Bower argued that their data supported a connectionist model of human learning. Their Ss judged the probabilities of two diseases, given a target symptom; these probabilities were in fact equal. Because one disease was more frequent than the other, the target symptom was a better predictor of the less frequent disease than of the other. The Ss rated the two probabilities as unequal. In addition, Gluck and Bower argued that a simple connectionist model would be able to simulate the judgment bias. They assumed that Ss estimated the probabilities of the two diseases across all trials on which the target symptom occurred, either alone or with other symptoms, rather than the probability of each disease given only the target symptom. In fact, it is possible that the Ss were estimating the latter, in which case the bias in probability judgments was justified. Last, even if the Ss were estimating the former, the connectionist model cannot account for the bias seen in the probability judgments.

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