The task requirements during the course of category learning are critical for promoting within-category representations (e.g., correlational structure of the categories). Recent data suggest that for unidimensional rule-based structures, only inference training promotes the learning of within-category representations, and generalization across tasks is limited. It is unclear if this is a general feature of rule-based structures, or a limitation of unidimensional rule-based structures. The present work reports the results of three experiments further investigating this issue using an exclusive-or rule-based structure where successful performance depends upon attending to two stimulus dimensions. Participants were trained using classification or inference and were tested using inference. For both the classification and inference training conditions, within-category representations were learned and could be generalized at test (i.e., from classification to inference) and this result was dependent upon a congruence between local and global regions of the stimulus space. These data further support the idea that the task requirements during learning (i.e., a need to attend to multiple stimulus dimensions) are critical determinants of the category representations that are learned and the utility of these representations for supporting generalization in novel situations.

中文翻译：

---

基于规则的类别结构中类别内表示的学习和归纳。

类别学习过程中的任务要求对于促进类别内表示（例如类别的相关结构）至关重要。最近的数据表明，对于基于一维规则的结构，只有推理训练才能促进类别内表示的学习，并且跨任务的概括是有限的。尚不清楚这是基于规则的结构的一般特征还是一维基于规则的结构的限制。本工作报告了三个实验的结果，这些实验使用排他或基于规则的结构进一步研究了这个问题，其中成功的表现取决于参与两个刺激维度。使用分类或推论对参与者进行培训，并使用推论对参与者进行测试。对于分类和推理训练条件，可以学习类别内表示，并且可以在测试中将其概括化（即，从分类到推理），并且此结果取决于刺激空间的局部区域和全局区域之间的一致性。这些数据进一步支持这样的想法，即学习过程中的任务要求（即需要关注多个刺激维度）是所学习的类别表示的关键决定因素，也是这些表示在新颖情况下支持泛化的效用的关键。