An adaptive filter model for recognition memory

Abstract
A model for the adaptive formation of an associative memory structure based on a stochastic approximation algorithm is described. This model incorporates a feedback loop which allows the modification to memory to depend upon the novelty of the stimulus input. Predictions are derived for change in the memory structure across trials and these results are applied to the analysis of serial position effects in the Sternberg item‐recognition paradigm as well as familiarity judgements in a continuous recognition task. Illustrative applications of the model arc presented.

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