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Mediated Learning: A Computational Rendering of Ketamine-induced Symptoms

Mondragón, E. ORCID: 0000-0003-4180-1261 (2024). Mediated Learning: A Computational Rendering of Ketamine-induced Symptoms. Behavioral Neuroscience,

Abstract

This paper explores the contribution of the DDA 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 non-perceptual 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.

Publication Type: Article
Additional Information: ©American Psychological Association, 2024. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. The final article will be available, upon publication, at: https://www.apa.org/pubs/journals/bne.
Publisher Keywords: associative learning, mediated learning, latent inhibition, computational modeling, associative memories
Departments: School of Science & Technology
School of Science & Technology > Computer Science
SWORD Depositor:
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