Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA.
Mem Cognit. 2023 May;51(4):915-929. doi: 10.3758/s13421-022-01365-4. Epub 2022 Oct 18.
A fundamental component of human categorization involves learning to attend selectively to relevant dimensions and ignore irrelevant ones. Past research has shown that humans can learn flexible strategies in which the attended dimensions vary depending on the region of feature space in which classification takes place. However, region-specific selective attention (RSA) is often challenging to learn. Here, we test the hypothesis that RSA is facilitated when individual categories are embedded within single regions of stimulus space rather than dispersed across multiple regions. We conduct an experiment that varies across conditions whether categories are embedded within regions, but in which the same RSA strategy would benefit performance across the conditions. To evaluate the hypothesis, we use measures of overall performance accuracy as well as comparisons among formal computational models that do and do not make allowance for RSA. We find strong support for the hypothesis among the upper-median-performing participants in the tested groups. However, even in the condition that promotes the learning of RSA, performance is considerably worse than in comparison conditions in which a single set of dimensions can be attended throughout the entire stimulus space.
人类分类的一个基本组成部分涉及学习有选择地关注相关维度和忽略不相关的维度。过去的研究表明，人类可以学习灵活的策略，其中关注的维度取决于分类发生的特征空间区域。然而，区域特定的选择性注意（RSA）通常很难学习。在这里，我们测试了这样一个假设，即当个体类别被嵌入到单个刺激空间区域内而不是分散在多个区域时，RSA 会更容易学习。我们进行了一项实验，在不同条件下，类别是否被嵌入到区域中，但在相同的 RSA 策略将有益于所有条件下的表现。为了评估这个假设，我们使用整体表现准确性的衡量标准，以及对是否允许 RSA 的正式计算模型的比较。我们在被测试组中表现中上的参与者中找到了对该假设的有力支持。然而，即使在促进 RSA 学习的条件下，表现也比在比较条件下差得多，在比较条件下，整个刺激空间都可以关注一组单一的维度。
