A person-by-situation approach to predicting behavior with implicit measures

通过隐式测量来预测行为的逐人方法

• 批准号: 2125944
• 负责人: Colin Smith
• 金额: $ 20.52万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125944&HistoricalAwards=false
• 关键词: person; situation; approach; predicting; behavior

The ability to accurately predict human behavior is a signature challenge of social science. Attitudes encapsulate specific thoughts and feelings that presumably should predict behavior. However, often people are unwilling or unable to provide accurate reports of their attitudes, which frequently is the case when their attitudes reflect socially controversial beliefs. It has now been roughly two decades since the invention and innovation of a class of attitude measures termed "implicit measures", which were designed to access mental content that a person is either unwilling or unable to report. This important methodological advance has had an immense impact on both scientific theory and public discourse. However, the evidence is mixed with respect to the utility of these measures in predicting behavior. The current project reframes decades of research to suggest that the mental content picked up by these measures varies in predictive ability 1) across people who differ from one another in important ways, and 2) as those people find themselves in a variety of different situations. The appropriate question may not be "Do implicit measures predict behavior?". Rather they are "Do implicit measures predict behavior better for some people rather than others and in some situations more than in others?" and "Do these two levels of inquiry interact so that prediction works particularly well for some people in some situations?"The project provides the most comprehensive test to date of factors affecting the utility of implicit measures. The 12 studies test predictions using three implicit measures across five attitude domains and examine eight individual differences within four different eliciting contexts. Specific methods test person factors (attitudinal accessibility, consistency) and situational factors (activation, control). The breadth of inquiry and large samples are essential to make confident and generalizable claims. The findings of this research have critical implications regarding the validity of attitudes that are difficult to measure, including those that contribute to the adverse treatment of others. Only by accurately assessing attitudes across people and contexts can their downstream consequences on societal problems be better understood and addressed.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.

准确预测人类行为的能力是社会科学的一个标志性挑战。态度包含了特定的想法和感受，这些想法和感受可能会预测行为。然而，人们往往不愿意或不能提供关于他们态度的准确报告，当他们的态度反映了社会上有争议的信念时，情况往往如此。自从发明和创新一类被称为“内隐测量”的态度测量以来，已经过去了大约二十年，这种测量旨在获取一个人不愿意或不能报告的心理内容。这一重要的方法论进步对科学理论和公共话语都产生了巨大的影响。然而，这些措施在预测行为方面的效用的证据是混合的。目前的项目重新构建了数十年的研究，表明这些测量方法所获得的心理内容在预测能力方面各不相同：1）在重要方面彼此不同的人之间，2）当这些人发现自己处于各种不同的情况下时。适当的问题可能不是“内隐测量能预测行为吗？".相反，他们是“内隐测量是否能更好地预测某些人的行为，而不是其他人，在某些情况下比在其他情况下更好？这两个层次的探究是否相互作用，使得预测在某些情况下对某些人特别有效？“该项目提供了迄今为止对影响隐性措施效用的因素的最全面的测试。这12项研究使用三种内隐测量方法在五个态度域中测试预测，并在四种不同的诱发背景下检查八种个体差异。具体方法测试人的因素（态度的可达性，一致性）和情境因素（激活，控制）。调查的广度和大量的样本对于提出有信心和可推广的主张至关重要。这项研究的结果对于难以衡量的态度的有效性具有重要意义，包括那些导致他人受到不利待遇的态度。只有通过准确评估不同人群和背景下的态度，才能更好地理解和解决其对社会问题的下游影响。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。