Quantifying the Internal Structure of Categories Using a Neural Typicality Measure
Quantifying the Internal Structure of Categories Using a Neural Typicality Measure
复制标题
DOI：
10.1093/cercor/bht014
复制
发表时间：
2014-07-01
期刊：
影响因子：
3.7
通讯作者：
Poldrack, Russell A.
中科院分区：
文献类型：
--
作者：
Davis, Tyler;Poldrack, Russell A.
类别是如何被表示的，一直是神经科学和心理学的热门话题。范畴表征是认知研究的核心，但在神经生物学研究中却未得到充分探索，其中一个主题涉及范畴的内部结构。内部结构是指如何编码类别成员之间的自然变异，以便我们能够确定哪些成员是其类别的更典型或更好的示例。心理分类模型提供了预测内部结构的工具，并表明，典型性的看法产生于心理空间中类别成员的表征之间的相似性。受这些模型的启发，我们开发了一种神经典型性测量方法，该方法允许我们测量哪些类别成员引起与其类别的其他成员相似的激活模式，从而在神经空间中更重要。使用人工分类任务，我们测试了心理和身体的典型性如何有助于神经典型性，并发现枕叶和颞叶区域的神经典型性与被试的典型性感知显著相关。结果表明，心理和神经类别表征之间的收敛，并建议我们的神经典型性措施是一个有用的工具，连接心理和神经措施的内部类别结构。
How categories are represented continues to be hotly debated across neuroscience and psychology. One topic that is central to cognitive research on category representation but underexplored in neurobiological research concerns the internal structure of categories. Internal structure refers to how the natural variability between-category members is coded so that we are able to determine which members are more typical or better examples of their category. Psychological categorization models offer tools for predicting internal structure and suggest that perceptions of typicality arise from similarities between the representations of category members in a psychological space. Inspired by these models, we develop a neural typicality measure that allows us to measure which category members elicit patterns of activation that are similar to other members of their category and are thus more central in a neural space. Using an artificial categorization task, we test how psychological and physical typicality contribute to neural typicality, and find that neural typicality in occipital and temporal regions is significantly correlated with subjects' perceptions of typicality. The results reveal a convergence between psychological and neural category representations and suggest that our neural typicality measure is a useful tool for connecting psychological and neural measures of internal category structure.