A Computational Associative Framework for Ketamine-induced Symptoms

What is it about?

This paper looks into how computational models of learning like the DDA model can potentially assist theory development and analysis of the role of associative memories in generating the kind of fake relationships present in schizophrenia. The study includes simulations for laboratory-controlled phenomena commonly used to explore these spurious associations and a new model prediction that can shed some light on the learning mechanisms. In these simulations, the strength and spread of memory retrieval were manipulated, and the resulting computational outputs were compared to known ketamine-induced effects on mediated learning.

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Why is it important?

The computational model’s operation offers a potential error correction mechanism capable of reproducing fake learned associations, bearing theoretical implications for animal models of schizophrenia.

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