Wu Charley M, Meder Björn, Filimon Flavia, Nelson Jonathan D
Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development.
J Exp Psychol Learn Mem Cogn. 2017 Aug;43(8):1274-1297. doi: 10.1037/xlm0000374. Epub 2017 Mar 20.
While the influence of presentation formats have been widely studied in Bayesian reasoning tasks, we present the first systematic investigation of how presentation formats influence information search decisions. Four experiments were conducted across different probabilistic environments, where subjects (N = 2,858) chose between 2 possible search queries, each with binary probabilistic outcomes, with the goal of maximizing classification accuracy. We studied 14 different numerical and visual formats for presenting information about the search environment, constructed across 6 design features that have been prominently related to improvements in Bayesian reasoning accuracy (natural frequencies, posteriors, complement, spatial extent, countability, and part-to-whole information). The posterior variants of the icon array and bar graph formats led to the highest proportion of correct responses, and were substantially better than the standard probability format. Results suggest that presenting information in terms of posterior probabilities and visualizing natural frequencies using spatial extent (a perceptual feature) were especially helpful in guiding search decisions, although environments with a mixture of probabilistic and certain outcomes were challenging across all formats. Subjects who made more accurate probability judgments did not perform better on the search task, suggesting that simple decision heuristics may be used to make search decisions without explicitly applying Bayesian inference to compute probabilities. We propose a new take-the-difference (TTD) heuristic that identifies the accuracy-maximizing query without explicit computation of posterior probabilities. (PsycINFO Database Record
虽然呈现格式在贝叶斯推理任务中的影响已得到广泛研究，但我们首次系统地调查了呈现格式如何影响信息搜索决策。在不同的概率环境中进行了四项实验，受试者（N = 2858）在两个可能的搜索查询之间进行选择，每个查询都有二元概率结果，目标是最大化分类准确性。我们研究了14种不同的数值和视觉格式来呈现有关搜索环境的信息，这些格式基于与贝叶斯推理准确性提高显著相关的6个设计特征构建（自然频率、后验概率、互补性、空间范围、可数性和部分与整体信息）。图标阵列和条形图格式的后验概率变体导致正确回答的比例最高，并且明显优于标准概率格式。结果表明，以后验概率的形式呈现信息并使用空间范围（一种感知特征）可视化自然频率在指导搜索决策方面特别有帮助，尽管具有概率和确定结果混合的环境在所有格式中都具有挑战性。做出更准确概率判断的受试者在搜索任务上表现并不更好，这表明可能使用简单的决策启发式方法来做出搜索决策，而无需明确应用贝叶斯推理来计算概率。我们提出了一种新的求差（TTD）启发式方法，该方法无需明确计算后验概率即可识别使准确性最大化的查询。（PsycINFO数据库记录）
