Mediated learning: A computational rendering of ketamine-induced symptoms.

A computational model of associative learning, the double error dynamic asymptote model, can help explain how spurious associations arise in schizophrenia. The model simulates mediated conditioning, mediated extinction, and a novel prediction of mediated enhancement of latent inhibition. Manipulating a parameter that controls memory retrieval and the decay of nonperceptual representations produces effects similar to those caused by ketamine on associative memories and mediated learning. The model offers a plausible error-correction mechanism that may account for associative deficits observed in animal models of schizophrenia.

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Abstract

This article explores the contribution of the double error dynamic asymptote computational associative learning model to understanding the role of mediated learning mechanisms in the generation of spurious associations, as those postulated to characterize schizophrenia. Three sets of simulations for mediated conditioning, mediated extinction, and a mediated enhancement of latent inhibition, a unique model prediction, are presented. For each set of simulations, a parameter that modulates the impact of associative memory retrieval and the dissipation of nonperceptual activated representations through the network was manipulated. The effect of this operation is analyzed and compared to ketamine-induced effects on associative memories and mediated learning. The model's potential to predict these effects and present a plausible error-correction associative mechanism is discussed in the context of animal models of schizophrenia. (PsycInfo Database Record (c) 2024 APA, all rights reserved).