How Do People Process Different Representations of Statistical Information? Insights into Cognitive Effort, Representational Inconsistencies, and Individual Differences.

IF 3.1 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Medical Decision Making Pub Date : 2023-10-01 Epub Date: 2023-10-16 DOI:10.1177/0272989X231202505
Kevin E Tiede, Wolfgang Gaissmaier
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引用次数: 0

Abstract

Background: Graphical representation formats (e.g., icon arrays) have been shown to lead to better understanding of the benefits and risks of treatments compared to numbers. We investigate the cognitive processes underlying the effects of format on understanding: how much cognitive effort is required to process numerical and graphical representations, how people process inconsistent representations, and how numeracy and graph literacy affect information processing.

Methods: In a preregistered between-participants experiment, 665 participants answered questions about the relative frequencies of benefits and side effects of 6 medications. First, we manipulated whether the medical information was represented numerically, graphically (as icon arrays), or inconsistently (numerically for 3 medications and graphically for the other 3). Second, to examine cognitive effort, we manipulated whether there was time pressure or not. In an additional intervention condition, participants translated graphical information into numerical information before answering questions. We also assessed numeracy and graph literacy.

Results: Processing icon arrays was more strongly affected by time pressure than processing numbers, suggesting that graphical formats required more cognitive effort. Understanding was lower when information was represented inconsistently (v. consistently) but not if there was a preceding intervention. Decisions based on inconsistent representations were biased toward graphically represented options. People with higher numeracy processed quantitative information more efficiently than people with lower numeracy did. Graph literacy was not related to processing efficiency.

Limitations: Our study was conducted with a nonpatient sample, and the medical information was hypothetical.

Conclusions: Although graphical (v. numerical) formats have previously been found to lead to better understanding, they may require more cognitive effort. Therefore, the goal of risk communication may play an important role when choosing how to communicate medical information.

Highlights: This article investigates the cognitive processes underlying the effects of representation format on the understanding of statistical information and individual differences therein.Processing icon arrays required more cognitive effort than processing numbers did.When information was represented inconsistently (i.e., partly numerically and partly graphically), understanding was lower than with consistent representation, and decisions were biased toward the graphically represented options.People with higher numeracy processed quantitative information more efficiently than people with lower numeracy did.

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人们如何处理统计信息的不同表示?认知努力、表征不一致和个体差异的见解。
背景:与数字相比,图形表示格式(如图标阵列)已被证明可以更好地理解治疗的益处和风险。我们研究了格式对理解影响的认知过程:处理数字和图形表示需要多少认知努力,人们如何处理不一致的表示,以及算术和图形素养如何影响信息处理。方法:在一项预先登记的参与者间实验中,665名参与者回答了关于6种药物的益处和副作用的相对频率的问题。首先,我们处理了医疗信息是以数字、图形(作为图标阵列)还是不一致(3种药物以数字表示,其他3种药物则以图形表示)。其次,为了检验认知努力,我们操纵是否存在时间压力。在另一种干预条件下,参与者在回答问题之前将图形信息转换为数字信息。我们还评估了算术和图形素养。结果:处理图标数组比处理数字更容易受到时间压力的影响,这表明图形格式需要更多的认知努力。当信息表现不一致(v.一致)时,理解力较低,但如果之前有干预,理解力则不会降低。基于不一致表示的决策偏向于图形表示的选项。计算能力较高的人比计算能力较低的人更有效地处理定量信息。图形素养与处理效率无关。局限性:我们的研究是用非患者样本进行的,医学信息是假设的。结论:尽管以前已经发现图形(v.数字)格式可以更好地理解,但它们可能需要更多的认知努力。因此,风险沟通的目标可能在选择如何沟通医疗信息时发挥重要作用。亮点:本文研究了表征形式对统计信息理解的影响及其个体差异的认知过程。处理图标数组比处理数字需要更多的认知努力。当信息表示不一致时(即,部分用数字表示,部分用图形表示),理解力低于一致表示,决策偏向于用图形表示的选项。计算能力较高的人比计算能力较低的人更有效地处理定量信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Medical Decision Making
Medical Decision Making 医学-卫生保健
CiteScore
6.50
自引率
5.60%
发文量
146
审稿时长
6-12 weeks
期刊介绍: Medical Decision Making offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health care policy development. Using the fundamentals of decision analysis and theory, economic evaluation, and evidence based quality assessment, Medical Decision Making presents both theoretical and practical statistical and modeling techniques and methods from a variety of disciplines.
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