Information processing of classification task
TLDR
The purpose of this paper is to analyze the processes of classification task by means of the application of the information processing models by Gregg and Simon to the stochastic process models and found that subjects adopted consistently the strategy which made them select a hypothesis from the subset of hypotheses that are consistent with the last stimulus pattern.Abstract:
The purpose of this paper is to analyze the processes of classification task by means of the application of the information processing models by Gregg and Simon to the stochastic process models. The result was as follows; 1) Subjects adopted consistently the strategy which made them select a hypothesis from the subset of hypotheses that are consistent with the last stimulus pattern presented to them regardless of the number of attribute-dimensions. 2) As the number of dimensions increased, the deviations between observations and predictions of any models tended to be great in spite of the stationarity of the series of responses and the statistical independence of the intermediate responses.read more
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Application of a model to paired-associate learning
TL;DR: The proposal is made to consider a paired-associate item as becoming conditioned to its correct response in all-or-none fashion, and that prior to this conditioning event the subject guesses responses at random to an unlearned item.
Journal ArticleDOI
Process models and stochastic theories of simple concept formation
Lee W. Gregg,Herbert A. Simon +1 more
TL;DR: A class of information-processing models is constructed for a concept attainment experiment previously studied by Bower and Trabasso, and the formal process models are shown to be useful in discovering inconsistencies and unstated assumptions in informal descriptions of the psychological processes underlying the stochastic theory.