Information Processing Models for Attention, Categorization, and Multiple Choice Paradigms

本研究的目的是探讨在复杂刺激分类中使用的知觉和认知过程。分类决策有两种广义的解释：决策可能基于抽象规则，也可能基于记忆中特定示例的回忆。这些解释被实现为形式模型（如Nosofsky的GCM模型和Ashby的grt模型）。一系列的概率学习实验将有助于确定这两种解释中哪一种在各种刺激条件下提供了更好的解释。初步结果表明，当有几个待分类的刺激时，人们使用记忆范例，但当有几个待分类的刺激时，他们使用规则。工作假设是，人们是灵活的：根据任务的认知需求，使用基于规则或基于范例的策略。当任务的认知需求相对较轻时，使用基于范例的分类；当任务的认知需求相对较重时，使用基于规则的分类。这项研究将使我们更全面地了解分类过程的本质，这将有助于我们理解人类的决策，并对教育产生重要影响。

The goal of this research is to investigate the perceptual andcognitive processes used in the categorization of complex stimuli.There are two broadly-defined explanations of categorizationdecisions: decisions may be based on abstract rules, or they may be basedon recall of particular examples from memory. These explanations areimplemented as formal models (e.g., Nosofsky's GCM Model and Ashby'sGRT model). A series of probability-learning experiments will helpdetermine which of the two explanations provide a better accountacross various stimulus conditions. Preliminary results indicate thatpeople use memory exemplars when there are a few to-be-classifiedstimuli, but they use rules when there are several to-be-classifiedstimuli. The working hypothesis is that people are flexible: usingeither rule-based or exemplar-based strategies depending on thecognitive demands of the task. Exemplar-based categorization is usedwhen the cognitive demands of the task are relatively light whilerule-based categorization is used when the cognitive demands of thetask are relatively heavy. The research will allow for a fullerunderstanding of the nature of categorization processes which will aidour understanding of human decision-making and have important implications for education.