Two-person teams outperform individuals in search tasks, and even exceed expectations based on statistical limitations. Here, we aimed to replicate and extend this result. We used Bayesian hierarchical modelling of receiver operating characteristics to examine collaborative performance in a visual search task wherein top-down target information was constrained. Participants (N = 16 teams per experiment in Experiments 1 and 2; N = 24 teams in Experiment 3), working independently or collaboratively, performed a search task framed as a medical image reading task. Stimuli were polygons generated by randomly distorting a prototype shape. Observers judged whether an extreme distortion was present among a set of low-distortion distractor objects. Team members’ individual sensitivity levels were used to predict collaborative sensitivity using two versions of a uniform judgment-weighting (UW) model, one that assumed stochastically independent judgments and one that accounted for correlations in the team members’ judgments. Collaborative search was better than that from single observers in all three experiments, and consistently trended higher than predictions of the correlated UW model. Results imply that collaborative search can be highly efficient even when target foreknowledge is limited.

两人一组的团队在搜索任务上胜过个人，甚至超出了基于统计限制的期望值。在这里，我们旨在复制和扩展这一结果。我们使用接收器操作特征的贝叶斯分层模型来检查视觉搜索任务中的协作性能，其中自上而下的目标信息受到约束。参与者（实验1和2中每个实验N = 16组；N=实验3）中的24个团队独立或协同工作，执行了以医学图像读取任务为框架的搜索任务。刺激是通过随机扭曲原型形状生成的多边形。观察者判断一组低失真干扰物体之间是否存在极端畸变。团队成员的个人敏感度水平用于使用两种版本的统一判断加权（UW）模型来预测协作敏感度，一种用于假设随机独立的判断，另一种用于解释团队成员的判断之间的相关性。在所有三个实验中，协作搜索都比单个观察者的搜索更好，并且始终比相关UW模型的预测更高。