Fluency as a Substitute for Validity in Cue Selection

流畅性可以替代提示选择的有效性

• 批准号: 0518811
• 负责人: Daniel Oppenheimer
• 金额: $ 33.61万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0518811&HistoricalAwards=false
• 关键词: Fluency; Substitute; Validity; Cue; Selection

When making judgments, people typically use a variety of cues. For example, a legal case may involve a number of pieces of evidence that jurors could use to judge guilt or innocence. Similarly, a patient may exhibit multiple symptoms that can help a doctor diagnosis an ailment. Some cues are more likely than others to successfully predict an outcome and many previous models have assumed that people give more weight to more valid cues. However, it is not always obvious which cues are the most valid, and the use of cue validity for cue weighting is relatively implausible on both theoretical and empirical grounds. This project proposes that cue weighting may be based on fluency rather than validity. By fluency, we mean the ease with which information is processed. Fluency has been shown to be an important cognitive tool in many domains, because people always have access to their own fluency experiences, and fluency is correlated with so many things. In particular, cue fluency likely to be correlated with cue validity, because of the basic cognitive mechanisms of associative learning and priming. Therefore fluency is both a cognitively plausible and ecologically valid mechanism for cue weighting. This project uses a wide array of converging paradigms to explore this possibility.

在做出判断时，人们通常会使用各种线索。 例如，一个法律的案件可能涉及陪审员可以用来判断有罪或无罪的许多证据。 同样，患者可能会表现出多种症状，可以帮助医生诊断疾病。 有些线索比其他线索更有可能成功地预测结果，许多以前的模型都假设人们对更有效的线索给予更多的权重。然而，它并不总是很明显的线索是最有效的，使用线索有效性的线索加权是相对难以置信的理论和经验的理由。 这个项目建议，线索加权可能是基于流畅性，而不是有效性。我们所说的流畅性是指信息处理的容易程度。 流利性在许多领域都被证明是一种重要的认知工具，因为人们总是可以获得自己的流利性经验，而且流利性与许多事情相关。 特别是，线索流畅性可能与线索有效性相关，因为联想学习和启动的基本认知机制。因此，流畅性是一个认知上合理的和生态上有效的线索加权机制。 这个项目使用了广泛的聚合范式来探索这种可能性。