Two fundamental difficulties when learning novel categories are deciding (a) what information is relevant and (b) when to use that information. Although previous theories have specified how observers learn to attend to relevant dimensions over time, those theories have largely remained silent about how attention should be allocated on a within-trial basis, which dimensions of information should be sampled, and how the temporal order of information sampling influences learning. Here, we use the adaptive attention representation model (AARM) to demonstrate that a common set of mechanisms can be used to specify: (a) How the distribution of attention is updated between trials over the course of learning and (b) how attention dynamically shifts among dimensions within a trial. We validate our proposed set of mechanisms by comparing AARM’s predictions to observed behavior in four case studies, which collectively encompass different theoretical aspects of selective attention. We use both eye-tracking and choice response data to provide a stringent test of how attention and decision processes dynamically interact during category learning. Specifically, how does attention to selected stimulus dimensions gives rise to decision dynamics, and in turn, how do decision dynamics influence which dimensions are attended to via gaze fixations?

中文翻译：

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内部如此，外部如此，如上所述，如下：通用机制可以支持试验类别之间和内部的学习动态。

学习新类别时的两个基本困难是决定（a）哪些信息是相关的以及（b）何时使用该信息。尽管先前的理论已经具体说明了观察者如何随着时间的推移学会关注相关维度，但这些理论在很大程度上对于在试验内应如何分配注意力、应对信息的哪些维度进行采样以及信息的时间顺序如何进行保持沉默。抽样影响学习。在这里，我们使用自适应注意力表示模型（AARM）来证明可以使用一组通用机制来指定：（a）在学习过程中的试验之间如何更新注意力的分布以及（b）注意力如何动态更新试验中维度之间的转换。我们通过将 AARM 的预测与四个案例研究中观察到的行为进行比较来验证我们提出的一组机制，这些案例共同涵盖了选择性注意的不同理论方面。我们使用眼球追踪和选择响应数据来严格测试注意力和决策过程在类别学习期间如何动态交互。具体来说，对选定刺激维度的关注如何引起决策动态，反过来，决策动态如何影响通过注视关注哪些维度？