Judgment errors in naturalistic numerical estimation
Judgment errors in naturalistic numerical estimation
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DOI：
10.1016/j.cognition.2021.104647
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发表时间：
2021-03
期刊：
影响因子：
3.4
通讯作者：
Wanling Zou;Sudeep Bhatia
中科院分区：
文献类型：
--
作者：
Wanling Zou;Sudeep Bhatia
人们以每天为基础估算数字量（如食物的卡路里）。尽管这些估计会影响行为并决定幸福感，但它们容易出现两种重要的错误。当人们在报告自己对某一特定数字的看法时犯了错误（例如，夸大了小数字），就会出现比例误差。当人们使用他们对判断目标的知识来形成他们对数字数量的信念时（例如，通过夸大某些线索），就会出现信念错误。在本文中，我们定量地模拟了日常判断任务中的数值估计，以及相应的尺度和信念误差。我们的方法在使用语义记忆研究的见解来指定自然判断目标的知识方面是独一无二的，允许我们的模型正式描述先前研究中未考虑的信念中的细微错误。在研究1和2中，我们发现信念误差模型预测参与者的估计和误差具有非常高的样本外准确率，显著优于尺度误差模型的预测。事实上，最佳拟合的信念误差模型可以非常接近地模仿缩放误差模型所捕获的倒s形模式，这表明先前归因于缩放误差的反应类型可以被视为信念误差。在研究3至8中，我们发现信念错误模型也能够预测人们在与数字判断相关的语义判断、自由联想和口头协议任务中的反应，从而很好地解释了判断的认知基础。
People estimate numerical quantities (such as the calories of foods) on a day-to-day basis. Although these estimates influence behavior and determine wellbeing, they are prone to two important types of errors. Scaling errors occur when people make mistakes reporting their beliefs about a particular numerical quantity (e.g. by inflating small numbers). Belief errors occur when people make mistakes using their knowledge of the judgment target to form their beliefs about the numerical quantity (e.g. by overweighting certain cues). In this paper, we quantitatively model numerical estimates, and in turn, scaling and belief errors, in everyday judgment tasks. Our approach is unique in using insights from semantic memory research to specify knowledge for naturalistic judgment targets, allowing our models to formally describe nuanced errors in belief not considered in prior research. In Studies 1 and 2, we find that belief error models predict participant estimates and errors with very high out-of-sample accuracy rates, significantly outperforming the predictions of scaling error models. In fact, the best-fitting belief error models can closely mimic the inverse-S shaped patterns captured by scaling error models, suggesting that the types of responses previously attributed to scaling errors can be seen as errors of belief. In Studies 3 to 8, we find that belief error models are also able to predict people's responses in semantic judgment, free association, and verbal protocol tasks related to numerical judgment, and thus provide a good account of the cognitive underpinnings of judgment.