Department of Psychology, Kingston University, Kingston-upon-Thames, KT1 2EE, UK.
Mem Cognit. 2021 Feb;49(2):389-399. doi: 10.3758/s13421-020-01084-8.
Covariation information can be used to infer whether a causal link plausibly exists between two dichotomous variables, and such judgments of contingency are central to many critical and everyday decisions. However, individuals do not always interpret and integrate covariation information effectively, an issue that may be compounded by limited numeracy skills, and they often resort to the use of heuristics, which can result in inaccurate judgments. This experiment investigated whether presenting covariation information in a composite bar chart increased accuracy of contingency judgments, and whether it can mitigate errors driven by low numeracy skills. Participants completed an online questionnaire, which consisted of an 11-item numeracy scale and three covariation problems that varied in level of difficulty, involving a fictitious fertilizer and its impact on whether a plant bloomed or not. Half received summary covariation information in a composite bar chart, and half in a 2 × 2 matrix that summarized event frequencies. Viewing the composite bar charts increased accuracy of individuals both high and low in numeracy, regardless of problem difficulty, resulted in more consistent judgments that were closer to the normatively correct value, and increased the likelihood of detecting the correct direction of association. Findings are consistent with prior work, suggesting that composite bar charts are an effective way to improve covariation judgment and have potential for use in the domain of health risk communication.
共变信息可用于推断两个二分变量之间是否存在因果关系，而这种对关联的判断是许多关键和日常决策的核心。然而，个体并不总是能够有效地解释和整合共变信息，这个问题可能会因为有限的计算能力而变得更加复杂，并且他们经常诉诸启发式方法，这可能导致不准确的判断。本实验研究了在复合条形图中呈现共变信息是否会提高关联判断的准确性，以及它是否可以减轻由低计算能力技能驱动的错误。参与者完成了一个在线问卷，其中包括一个 11 项的计算能力量表和三个共变问题，难度不同，涉及一种虚构的肥料及其对植物是否开花的影响。一半的参与者在复合条形图中收到了总结性的共变信息，另一半在总结事件频率的 2×2 矩阵中收到了信息。无论问题的难度如何，查看复合条形图都提高了高计算能力和低计算能力个体的准确性，使判断更加一致，更接近规范正确的值，并增加了检测正确关联方向的可能性。这些发现与先前的工作一致，表明复合条形图是一种改进共变判断的有效方法，并且有可能在健康风险沟通领域得到应用。
