College of Education, Psychology, and Social Work.
School of Psychological Science.
J Exp Psychol Appl. 2019 Dec;25(4):716-732. doi: 10.1037/xap0000216. Epub 2019 Feb 28.
Signal detection theory provides models of information integration that allow researchers to predict and benchmark collaborative performance in a visual search task. Naturalistic stimuli, however, may not conform to the simplifying assumptions-specifically, assumptions of equal-variance signal and noise distributions and stochastically independent observers-that are often made to make collaborative signal detection models tractable. Here, we used Bayesian hierarchical modeling of receiver operating characteristics to circumvent this difficulty. Participants ( = 28-32 per experiment) performed a simulated baggage x-ray screening task, working alone or in teams of 2. Team performance was compared with the predictions of 2 versions of a uniform weighting model of information integration, 1 that assumed stochastically independent judgments from the 2 members of a team and 1 that allowed for correlated judgments. Across 4 experiments, teams fell short of the uncorrelated-judgment model's predictions, but outperformed predictions based on the observed correlations in individual judgments. Results imply motivational effects that improve individual searchers' effort under collaborative conditions, or collaborative strategies that effectively decorrelate the individual searchers' judgments. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
信号检测理论提供了信息整合的模型，使研究人员能够预测和基准化视觉搜索任务中的协作表现。然而，自然刺激可能不符合简化的假设——特别是信号和噪声分布的等方差假设，以及随机独立观察者的假设——这些假设通常是为了使协作信号检测模型易于处理而做出的。在这里，我们使用了接收者操作特性的贝叶斯层次建模来规避这一困难。参与者（每个实验 28-32 人）单独或 2 人一组完成模拟行李 X 光筛查任务。团队表现与信息整合的 2 种统一加权模型的预测进行了比较，1 种假设团队中 2 名成员的判断是随机独立的，1 种允许判断相关。在 4 项实验中，团队表现低于不相关判断模型的预测，但优于基于个体判断中观察到的相关性的预测。结果表明存在动机效应，即在协作条件下可以提高个体搜索者的努力程度，或者存在协作策略，可以有效地使个体搜索者的判断去相关。（PsycINFO 数据库记录（c）2019 APA，保留所有权利）。
