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A neural dynamic model of associative two-process theory: The differential outcomes effect and infant development

机译:关联两过程理论的神经动力学模型:结果差异效应与婴儿发育

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In animal and human learning, outcome expectancy is understood to control action under a number of learning paradigms. One such paradigm, the differential outcomes effect (DOE), entails faster learning when responses have differential, rather than non-differential, outcomes. The associative two-process theory has provided an increasingly accepted explanation as to how outcome expectancies influence action selection, though it is computationally not well understood. In this paper, we describe a neural-dynamic model of this theory implemented as an Actor-Critic like architecture. The model utilizes expectation-based, or prospective, action control that following differential outcomes training suppresses stimulus-based, or retrospective, action control (known as overshadowing in the learning literature). It thereby facilitates learning. The neural-dynamics of the model are evaluated in a simulation of experiments with young children (aged 4–8.6 years) that uses a differential outcomes procedure. We assess development parametrically in neural-dynamic terms.
机译:在动物和人类学习中,预期结果被理解为在许多学习范式下控制动作。一种这样的范式,即差异结果效应(DOE),需要在响应具有差异结果而不是非差异结果时更快地学习。尽管在计算上还没有很好的理解,但是结合两过程理论已经对结果期望如何影响行动选择提供了越来越多的接受的解释。在本文中,我们描述了一种作为Actor-Critic架构实现的神经动力学模型。该模型利用了基于期望的或前瞻性的动作控制,在不同的结果训练之后,该动作抑制了基于刺激的或回顾性的动作控制(在学习文献中被称为阴影)。因此,它促进了学习。该模型的神经动力学在模拟儿童实验(4–8.6岁)中使用了不同的结果程序进行了评估。我们用神经动力学的术语来评估开发。

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