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Solving Pavlov's puzzle: Attentional, associative, and flexible configural mechanisms in classical conditioning

机译:解决帕夫洛夫的难题:经典条件中的注意,关联和灵活的配置机制

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This article introduces a new "real-time" model of classical conditioning that combines attentional, associative, and "flexible" configural mechanisms. In the model, attention to both conditioned (CS) and configural (CN) stimuli are modulated by the novelty detected in the environment. Novelty increases with the unpredicted presence or absence of any CS, unconditioned stimulus (US), or context. Attention regulates the magnitude of the associations CSs and CNs form with other CSs and the US. We incorporate a flexible configural mechanism in which attention to the CN stimuli increases only after the model has unsuccessfully attempted learn input-output combinations with CS-US associations. That is, CSs become associated with the US and other CSs on fewer trials than they do CNs. Because the CSs activate the CNs through unmodifiable connections, a CS can become directly and indirectly (through the CN) associated with the US or other CSs. In order to simulatetiming processes, we simply assume that a CS is formed by a temporal spectrum of short-duration CSs that are activated by the nominal CS trace. The model accurately describes 94 % of the basic properties of classical conditioning, using fixed model parameters and simulation values in all simulations
机译:本文介绍了一种新的经典条件的“实时”模型,该模型结合了注意力,联想和“灵活”的配置机制。在模型中,通过在环境中检测到的新颖性来调节对条件刺激(CS)和结构刺激(CN)的关注。随着CS的意外出现或缺失,无条件刺激(US)或环境的出现,新颖性会增加。注意规范了CS和CN与其他CS和美国之间建立的关联的程度。我们并入了一种灵活的配置机制,其中仅在模型尝试使用CS-US关联学习输入输出组合失败后,对CN刺激的注意才会增加。也就是说,CS与美国和其他CS的联系比CN更少。由于CS通过不可修改的连接激活CN,因此CS可以直接和间接(通过CN)与美国或其他CS关联。为了模拟时序过程,我们简单地假设CS是由短期CS的时间频谱形成的,这些CS由名义CS迹线激活。该模型在所有模拟中均使用固定的模型参数和模拟值,准确描述了经典空调的94%的基本特性

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