Meeter M, Radics G, Myers C E, Gluck M A, Hopkins R O
Department of Cognitive Psychology, Vrije Universiteit Amsterdam, The Netherlands.
Neurosci Biobehav Rev. 2008;32(2):237-48. doi: 10.1016/j.neubiorev.2007.11.001. Epub 2007 Nov 19.
In probabilistic categorization tasks, various cues are probabilistically (but not perfectly) predictive of class membership. This means that a given combination of cues sometimes belongs to one class and sometimes to another. It is not yet clear how categorizers approach such tasks. Here, we review evidence in favor of two alternative conceptualizations of learning in probabilistic categorization: as rule-based learning, or as incremental learning. Each conceptualization forms the basis of a way of analyzing performance: strategy analysis assumes rule-based learning, while rolling regression analysis assumes incremental learning. Here, we contrasted the ability of each to predict performance of normal categorizers. Both turned out to predict responses about equally well. We then reviewed performance of patients with damage to regions deemed important for either rule-based or incremental learning. Evidence was again about equally compatible with either alternative conceptualization of learning, although neither predicted an involvement of the medial temporal lobe. We suggest that a new way of conceptualizing probabilistic categorization might be fruitful, in which the medial temporal lobe help set up representations that are then used by other regions to assign patterns to categories.
在概率分类任务中，各种线索对类别归属具有概率性（但并非完全准确）的预测作用。这意味着给定的线索组合有时属于一个类别，有时属于另一个类别。目前尚不清楚分类者如何处理此类任务。在此，我们回顾了支持概率分类学习的两种替代概念化方式的证据：基于规则的学习，或增量学习。每种概念化方式都构成了一种分析表现的方法的基础：策略分析假定基于规则的学习，而滚动回归分析假定增量学习。在此，我们对比了每种方式预测正常分类者表现的能力。结果发现两者对反应的预测效果大致相同。然后，我们回顾了脑部区域受损患者的表现，这些区域被认为对基于规则的学习或增量学习很重要。证据再次大致同等程度地支持学习的两种替代概念化方式中的任何一种，尽管两者均未预测到内侧颞叶会有参与。我们认为，一种新的概率分类概念化方式可能富有成效，即内侧颞叶有助于建立表征，然后其他区域利用这些表征将模式分配到类别中。
