Ell Shawn W, Ashby F Gregory
Cognition and Action Lab, Helen Wills Neuroscience Institute and Psychology Department, University of California, 3210 Tolman Hall 1650, Berkeley, CA 94720-1650, USA.
Percept Psychophys. 2006 Aug;68(6):1013-26. doi: 10.3758/bf03193362.
In three experiments, we investigated whether the amount of category overlap constrains the decision strategies used in category learning, and whether such constraints depend on the type of category structures used. Experiments 1 and 2 used a category-learning task requiring perceptual integration of information from multiple dimensions (an information-integration task) and Experiment 3 used a task requiring the application of an explicit strategy (a rule-based task). In the information-integration task, participants used perceptual-integration strategies at moderate levels of category overlap, but explicit strategies at extreme levels of overlap--even when such strategies were suboptimal. In contrast, in the rule-based task, participants used explicit strategies, regardless of the level of category overlap. These data are consistent with a multiple systems view of category learning, and suggest that categorization strategy depends on the type of task that is used, and on the degree to which each stimulus is probabilistically associated with the contrasting categories.
在三项实验中，我们研究了类别重叠量是否会限制类别学习中使用的决策策略，以及这种限制是否取决于所使用的类别结构类型。实验1和实验2使用了一个需要对来自多个维度的信息进行感知整合的类别学习任务（一个信息整合任务），实验3使用了一个需要应用明确策略的任务（一个基于规则的任务）。在信息整合任务中，参与者在中等程度的类别重叠时使用感知整合策略，但在极端重叠程度时使用明确策略——即使这些策略并非最优。相比之下，在基于规则的任务中，参与者无论类别重叠程度如何都使用明确策略。这些数据与类别学习的多系统观点一致，并表明分类策略取决于所使用的任务类型，以及每个刺激与对比类别概率相关的程度。
