Attention, Memory, and Judgment

注意力、记忆力和判断力

• 批准号: 0620062
• 负责人: Michael Dougherty
• 金额: $ 35万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0620062&HistoricalAwards=false
• 关键词: Attention; Memory; Judgment

A fundamental component of human decision making is how people generate, assess, and test diagnostic hypotheses. Take the task of a clinical diagnostician as an example. The clinician's task is similar to that of a detective in that he or she is compelled to search for clues (i.e., data) that can be used to generate and test possible explanations of the presenting symptoms. The clinician presumably generates likely diagnoses (i.e., disease hypotheses) and actively seeks information to evaluate the generated diagnoses. The clinician's search for information in the environment likely is not random, but is instead guided by those diagnostic hypotheses he or she is presently entertaining. The information or data newly revealed through the search process is used to both evaluate the diagnoses currently under consideration as well as generate new diagnoses. The generation of new diagnoses may, in turn, lead to fresh information search threads where new hypotheses might be brought to mind. At some point, either the search space is exhausted, the clinician continually fails to generate additional plausible hypotheses, or one particular hypothesis gains enough evidential support that the clinician can render a diagnosis with confidence. The goal of the proposed research is two fold. One goal is to better understand the cognitive constraints that govern hypothesis generation, probability judgment, and information search in humans. Understanding these cognitive constraints hopefully will enable us to developed methodologies or technologies to improve diagnostic decision making in real-world decision tasks such as medical diagnosis or intelligence analysis. The second goal is to develop a cognitive model of human judgment that can take as input a piece of data (e.g., a symptom) and generate a set of diagnostic hypotheses to explain that datum, provide probability estimates of the generated hypotheses, and revise both the generated hypotheses and the probability judgments iteratively as new data are experienced. This type of system has natural implications both for understanding human decision making in dynamic tasks and for developing artificial intelligence systems that can outperform human decision makers.

人类决策的一个基本组成部分是人们如何生成、评估和测试诊断假设。以临床诊断医生的任务为例。临床医生的任务类似于侦探的任务，因为他或她被迫搜索线索(即数据)，这些线索可用于生成和测试对呈现症状的可能解释。临床医生大概会生成可能的诊断(即，疾病假设)，并主动寻找信息来评估生成的诊断。临床医生在环境中搜索信息可能不是随机的，而是由他或她目前正在考虑的诊断假设指导的。通过搜索过程新显示的信息或数据用于评估当前正在考虑的诊断以及生成新的诊断。新诊断的产生可能反过来导致新的信息搜索线索，其中可能会产生新的假设。在某一时刻，要么搜索空间耗尽，要么临床医生不断无法生成更多可信的假设，要么某个特定的假设获得了足够的证据支持，使得临床医生可以信心十足地做出诊断。这项拟议研究的目标有两个。一个目标是更好地理解支配人类假设生成、概率判断和信息搜索的认知约束。了解这些认知限制有望使我们能够开发方法或技术来改进现实世界决策任务中的诊断决策，如医疗诊断或情报分析。第二个目标是开发一种人类判断的认知模型，该模型可以将一段数据(例如，症状)作为输入，并生成一组诊断假设来解释该数据，提供所生成的假设的概率估计，并且随着新数据的体验而迭代地修改所生成的假设和概率判断。这种类型的系统对于理解人类在动态任务中的决策，以及开发能够超越人类决策者的人工智能系统，都具有自然的影响。