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利用信心和一致性来预测解决问题和在真实网络搜索中投入的时间。

Using confidence and consensuality to predict time invested in problem solving and in real-life web searching.

机构信息

Technion-Israel Institute of Technology, Israel.

Microsoft Research Israel, Israel.

出版信息

Cognition. 2020 Jun;199:104248. doi: 10.1016/j.cognition.2020.104248. Epub 2020 Mar 4.

Abstract

Understanding processes that lead people to invest a certain amount of time in challenging tasks is important for theory and practice. In particular, researchers often assume strong linear associations between confidence, consensuality (the degree to which an answer is independently given by multiple participants), and response time. The Diminishing Criterion Model (DCM; Ackerman, 2014) is a metacognitive model which explains the stopping rules people employ under uncertainty in terms of the confidence-time association. This model is unique in predicting a curvilinear rather than a linear confidence-time association. Using consensuality as an alternative to confidence for predicting response time offers theoretical and practical opportunities. In four experiments, including replications and variations, we examined confidence (where collected) and consensuality as predictors of the time people invest in three problem-solving tasks and in real-life web searching. The results using consensuality, like those for confidence, fitted the curvilinear time pattern predicted by the DCM, with one exception: at least 30% of the population must endorse a potential answer for consensuality to predict response time based on the stopping rules in the DCM. Beyond examining consensuality as a predictor, the study brings converging evidence supporting the DCM's curvilinear confidence-time association over alternative models. The methodology used for analyzing web searching offers new directions for metacognitive research in naturally-performed tasks.

摘要

理解导致人们在挑战性任务上投入一定时间的过程对于理论和实践都很重要。特别是,研究人员通常假设信心、一致性(答案由多个参与者独立给出的程度)和反应时间之间存在很强的线性关联。认知模型(DCM;Ackerman,2014)是一种元认知模型,它根据信心与时间的关联来解释人们在不确定条件下使用的停止规则。该模型的独特之处在于预测了一种曲线而不是线性的信心与时间的关联。使用一致性作为预测反应时间的信心替代物,为理论和实践提供了机会。在四项实验中,包括复制和变化,我们检验了信心(在收集的情况下)和一致性作为预测人们在三个解决问题任务和现实生活中的网络搜索中投入时间的指标。使用一致性的结果,与信心的结果一样,符合 DCM 预测的曲线时间模式,只有一个例外:一致性必须至少有 30%的人群支持潜在的答案,才能根据 DCM 中的停止规则预测反应时间。除了检验一致性作为预测指标之外,该研究还提供了支持 DCM 曲线置信时间关联的收敛证据,而不是替代模型。用于分析网络搜索的方法为自然执行任务中的元认知研究提供了新的方向。

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