Little Daniel R, Lewandowsky Stephan
School of Psychology, University of Western Australia, Crawley, W A 6009, Australia.
J Exp Psychol Hum Percept Perform. 2009 Apr;35(2):530-50. doi: 10.1037/0096-1523.35.2.530.
In probabilistic categorization, also known as multiple cue probability learning (MCPL), people learn to predict a discrete outcome on the basis of imperfectly valid cues. In MCPL, normatively irrelevant cues are usually ignored, which stands in apparent conflict with recent research in deterministic categorization that has shown that people sometimes use irrelevant cues to gate access to partial knowledge encapsulated in independent partitions. The authors report 2 experiments that sought support for the existence of such knowledge partitioning in probabilistic categorization. The results indicate that, as in other areas of concept acquisition (such as function learning and deterministic categorization), a significant proportion of participants partitioned their knowledge on the basis of an irrelevant cue. The authors show by computational modeling that knowledge partitioning cannot be accommodated by 2 exemplar models (Generalized Context Model and Rapid Attention Shifts 'N Learning), whereas a rule-based model (General Recognition Theory) can capture partitioned performance. The authors conclude by pointing to the necessity of a mixture-of-experts approach to capture performance in MCPL and by identifying reduction of complexity as a possible explanation for partitioning.
在概率分类中，也被称为多线索概率学习（MCPL），人们基于有效性不完美的线索来学习预测一个离散的结果。在MCPL中，通常规范性无关的线索会被忽略，这与确定性分类领域的近期研究明显冲突，后者表明人们有时会使用无关线索来控制对独立分区中封装的部分知识的访问。作者报告了两项实验，旨在为概率分类中存在这种知识分区寻求支持。结果表明，与概念习得的其他领域（如功能学习和确定性分类）一样，相当一部分参与者基于一个无关线索对他们的知识进行了分区。作者通过计算建模表明，两个范例模型（广义上下文模型和快速注意力转移与学习）无法解释知识分区，而基于规则的模型（广义识别理论）可以捕捉到分区表现。作者最后指出，需要采用专家混合方法来捕捉MCPL中的表现，并将复杂性的降低作为分区的一种可能解释。
