Collaborative Research: Determining the Fundamental Cognitive Properties of Decision Making

协作研究：确定决策的基本认知属性

• 批准号: 1854763
• 负责人: Mario Fific
• 金额: $ 14.76万
• 依托单位: Grand Valley State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1854763&HistoricalAwards=false
• 关键词: Collaborative; Research; Determining; Fundamental; Cognitive

Non-technical Description of the ProjectIn nearly every decision a person makes, they are required to combine multiple, sometimes conflicting sources information. Thus, understanding how people use these multiple sources of information is fundamental to understanding and predicting choices. Established approaches within psychology allow examination these processes in some situations. For example, to explore decision stages, a decision-maker may be asked to describe her thought process when making a choice, and that description is used to inform the researcher. In many situations, direct information about the choice processes is not accessible. For example, a decision maker may search her memory to find the information that could be used for choosing between options. When memory search is fast, people rarely have clear insight into how the memories were accessed and used. To allow for investigating decisions based on multiple sources of information across the widest range of situations, the principal investigators will integrate a powerful methodology based on mathematical cognitive modelling with standard decision-making research methodologies. The research will accomplish three objectives: (1) to gain an important understanding how multiple sources of information are combined during decision making, (2) conduct new empirical tests of the fundamental process across different decision making tasks, and (3) rigorously compare among the top decision making theories based on the new empirical findings. These results will inform decision makers, allow assessment of decision making efficiency, and enable better models of choice behavior.Technical DescriptionOver the years, several decision-making models were proposed that can account for many of the typical findings in human decision making, using both choice accuracy and response time output measures. However, no clear agreement about the underlying cognitive mechanism has been reached. These models can often be distinguished by strong implications about the processes that lead to a decision. Specifically, these models imply (a) how information is searched for, (b) when this information search is stopped, and (c) how the acquired information is integrated to reach the decision. Nonetheless, the disagreement remains. In part this is due to model mimicking: Without appropriate experimental design and analysis, different theories can make equivalent predictions. For example, inferences based on traditional mean response time (RT) analysis are limited due to model mimicking, as different decision-making models can imply exactly the same mean response time patterns. To overcome this limitation, we will integrate the powerful systems factorial technology (SFT) with traditional decision-making methods. SFT has been successfully applied in a wide range of perceptual and cognitive tasks to identify processing order, stopping rule and process dependency (a, b, and c from above), but has only been applied to decision making in very limited way. Our research will link SFT to the classical methods in judgment and decision making to improve our understanding of the cognitive processes underlying decision making. Our project will also contribute to improving the professional academic development of undergraduate and graduate students at two state universities that traditionally have fewer research opportunities for students. All aspects of this research project, from methodology to theoretical development, will be communicated at national and international professional Furthermore, the research will be submitted for publication in high-quality, peer-reviewed journals. One of the aims of the proposed research is to disseminate the findings to a broader audience through our respective university press offices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

项目的非技术性描述几乎在每个人做出的决策中，他们都需要结合联合收割机多个，有时是相互冲突的信息源。 因此，了解人们如何使用这些多种信息来源是理解和预测选择的基础。 心理学中的既定方法允许在某些情况下检查这些过程。例如，为了探索决策阶段，决策者可能会被要求描述她在做出选择时的思维过程，并且该描述用于通知研究人员。在许多情况下，关于选择过程的直接信息是无法获得的。例如，决策者可能会搜索她的记忆，以找到可以用于在选项之间进行选择的信息。当记忆搜索速度很快时，人们很少能清楚地了解记忆是如何被访问和使用的。为了能够在最广泛的情况下根据多个信息来源调查决策，主要研究人员将基于数学认知建模的强大方法与标准决策研究方法相结合。本研究将实现三个目标：（1）获得在决策过程中如何组合多种信息源的重要理解，（2）在不同的决策任务中对基本过程进行新的实证测试，以及（3）根据新的实证发现对顶级决策理论进行严格比较。 这些结果将通知决策者，决策效率的评估，并使更好的模型的选择behavior.Technical DescriptionOver多年来，提出了几个决策模型，可以考虑许多典型的结果，在人类决策，使用选择的准确性和响应时间输出的措施。然而，关于潜在的认知机制还没有达成明确的共识。这些模型通常可以通过对导致决策的过程的强烈暗示来区分。具体而言，这些模型意味着（a）如何搜索信息，（B）何时停止该信息搜索，以及（c）如何整合所获取的信息以达成决策。然而，分歧依然存在。 这部分是由于模型模仿：没有适当的实验设计和分析，不同的理论可以做出相同的预测。例如，基于传统的平均响应时间（RT）分析的推断由于模型模仿而受到限制，因为不同的决策模型可能意味着完全相同的平均响应时间模式。为了克服这一局限性，我们将整合强大的系统因子技术（SFT）与传统的决策方法。SFT已成功应用于广泛的知觉和认知任务，以识别加工顺序，停止规则和加工依赖性（a，B和c从上面），但仅以非常有限的方式应用于决策。 我们的研究将SFT与经典的判断和决策方法联系起来，以提高我们对决策背后的认知过程的理解。 我们的项目还将有助于提高两所州立大学的本科生和研究生的专业学术发展，这两所大学传统上为学生提供的研究机会较少。 该研究项目的各个方面，从方法论到理论发展，将在国家和国际专业领域进行交流。此外，该研究将提交高质量的同行评审期刊发表。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。