Collaboration improves unspeeded search in the absence of precise target information.

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.