Collaboration improves unspeeded search in the absence of precise target information
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Collaboration improves unspeeded search in the absence of precise target information Alison Enright 1 & Nathan Leggett 1 & Jason S McCarley 2
# The Psychonomic Society, Inc. 2020
Abstract 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. Keywords Signal detection . Visual search . Bayesian modelling
Introduction Signal detection theory (SDT; Green & Swets, 1966; Macmillan & Creelman, 2005) models decision-makers’ ability to reach discrete judgments from uncertain data. A conventional signal detection task asks observers to distinguish two states of the world, one termed signal-plus-noise and the other noise-alone, on the basis of probabilistic evidence. In the standard SDT model, the psychological evidence distributions
Public Significance Statement Visual search is an important aspect of tasks such as medical image interpretation and transportation security screening, but can be inefficient, especially when top-down target knowledge is limited. Teams of searchers outperform an algorithm that averages the isolated searchers’ judgments, indicating that collaboration improves efficiency even when top-down target knowledge is constrained. * Alison Enright [email protected] 1
College of Education, Psychology and Social Work, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia
2
School of Psychological Science, Oregon State University, Corvallis, OR, USA
corresponding to signal-plus-noise and noise-alone states are normal with different means but the same standard deviation (Macmillan & Creelman, 2005). Sensitivity denotes the ability to discriminate signals from noise, and is typically measured by d’, the distance between the means of the signal-plus-noise and noise-alone distributions, in standard deviation units (Green & Swets, 1966; Macmillan & Creelman
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