Decision-making accuracy typically increases through collective integration of people's judgments into group decisions, a phenomenon known as the wisdom of crowds. For simple perceptual laboratory tasks, classic signal detection theory specifies the upper limit for collective integration benefits obtained by weighted averaging of people's confidences, and simple majority voting can often approximate that limit. Life-critical perceptual decisions often involve searching large image data (e.g., medical, security, and aerial imagery), but the expected benefits and merits of using different pooling algorithms are unknown for such tasks. Here, we show that expected pooling benefits are significantly greater for visual search than for single-location perceptual tasks and the prediction given by classic signal detection theory. In addition, we show that simple majority voting obtains inferior accuracy benefits for visual search relative to averaging and weighted averaging of observers' confidences. Analysis of gaze behavior across observers suggests that the greater collective integration benefits for visual search arise from an interaction between the foveated properties of the human visual system (high foveal acuity and low peripheral acuity) and observers' nonexhaustive search patterns, and can be predicted by an extended signal detection theory framework with trial to trial sampling from a varying mixture of high and low target detectabilities across observers (SDT-MIX). These findings advance our theoretical understanding of how to predict and enhance the wisdom of crowds for real world search tasks and could apply more generally to any decision-making task for which the minority of group members with high expertise varies from decision to decision.group decision rules | signal detection theory | ideal observer analyses | wisdom of crowds G roups of insects (1-4), fish (5-7), birds (8-10), mammals (11)(12)(13)(14), and primates (15-18) have been shown to aggregate their individual judgments into group decisions for various tasks (19,20). Although some groups seem to have leaders who make decisions alone on behalf of their groups (17,(21)(22)(23), it is difficult for individuals to outperform even simple aggregations of the entire group's individual judgments (4,7,9,10,19,(24)(25)(26). Perhaps that is why humans often make important decisions as a group (27-29), even if the only expedient (30, 31) but effective (24, 31-34) group decision mechanism is to use the simple majority voting rule (35).Previous human studies have shown that combining people's judgments into group decisions can lead to accuracy benefits in various domains, such as estimation (36-38), detection (34,(39)(40)(41)(42)(43)(44), identification (45-47), and prediction (46, 48-52), a phenomenon known as the wisdom of crowds (53). For artificial tasks, where perceptual decisions are limited only by noise that is internal to each observer's brain (i.e., no external noise), the maximum wisdom of crowd benefits are specified by the idealized signal dete...