We present a neural network implementation of central components of the iterative reprocessing (IR) model. The IR model argues that the evaluation of social stimuli (attitudes, stereotypes) is the result of the IR of stimuli in a hierarchy of neural systems: The evaluation of social stimuli develops and changes over processing. The network has a multilevel, bidirectional feedback evaluation system that integrates initial perceptual processing and later developing semantic processing. The network processes stimuli (e.g., an individual's appearance) over repeated iterations, with increasingly higher levels of semantic processing over time. As a result, the network's evaluations of stimuli evolve. We discuss the implications of the network for a number of different issues involved in attitudes and social evaluation. The success of the network supports the IR model framework and provides new insights into attitude theory.

中文翻译：

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对评估动力学进行建模：迭代再处理模型的多级神经网络实现。

我们介绍了迭代再处理（IR）模型的核心组件的神经网络实现。IR模型认为，社会刺激（态度，刻板印象）的评估是神经系统层次结构中刺激IR的结果：社会刺激的评估随着过程的发展而变化。该网络具有多级双向反馈评估系统，该系统集成了初始感知处理和后来开发的语义处理。网络在重复的迭代中处理刺激（例如，一个人的外表），并且随着时间的流逝语义处理水平越来越高。结果，网络对刺激的评估不断发展。我们讨论了网络对于态度和社会评估中涉及的许多不同问题的影响。