Artificial Intelligence Research Centre, Department of Computer Science, City, University of London.
Behav Neurosci. 2024 Jun;138(3):178-194. doi: 10.1037/bne0000591. Epub 2024 Apr 18.
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).
本文探讨了双误差动态渐近计算联想学习模型在理解中介学习机制在产生虚假联想中的作用的贡献，这些联想被认为是精神分裂症的特征。本文提出了三组中介条件作用、中介消退和潜伏抑制的中介增强的模拟，这是一个独特的模型预测。对于每组模拟，都操纵了一个参数，该参数调节联想记忆检索的影响以及通过网络消散非知觉激活表示的程度。分析了这种操作的效果，并将其与氯胺酮对联想记忆和中介学习的影响进行了比较。本文还讨论了该模型在精神分裂症动物模型中预测这些影响并提出合理的错误校正联想机制的潜力。
