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

注意力、分类和多项选择范式的信息处理模型

• 批准号: 9817561
• 负责人: Jeffrey Rouder
• 金额: $ 4万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-07-01 至 1999-10-22
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9817561&HistoricalAwards=false
• 关键词: Information; Processing; Models; Attention; Categorization

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.

本研究的目的是探讨复杂刺激分类的知觉和认知过程，分类决策有两种广义的解释：一种是基于抽象规则的分类决策，另一种是基于对特定例子的回忆。 这些解释被实现为正式模型（例如，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的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，当有少量待分类的刺激时，人们使用记忆样本，但当有多个待分类的刺激时，人们使用规则。 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。 当任务的认知要求相对较轻时，使用基于范例的范畴化;当任务的认知要求相对较重时，使用基于规则的范畴化.这项研究将有助于我们更全面地理解分类过程的本质，这将有助于我们理解人类的决策过程，并对教育产生重要影响。