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Humans use social information to adjust their quorum thresholds adaptively in a simulated predator detection experiment
Authors:Ralf H. J. M. Kurvers  Max Wolf  Jens Krause
Affiliation:1. Department of Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Mueggelseedamm 310, 12587, Berlin, Germany
Abstract:Quorum sensing is used in many biological systems to increase decision accuracy. In quorum sensing, the probability that an individual adopts a behavior is a nonlinear function of the number of other individuals adopting this behavior. From an optimal decision-making perspective, individuals should adjust their quorum threshold to the particulars of the decision problem. Recent work predicts that a key factor here is the quality of social information. In particular, it is predicted that individuals should adjust their quorum thresholds such that it lies in between the average true and false positive rate of the other group members. We here test this prediction with a predator detection experiment. First, human groups observed a group of animals (projected on a white screen) in which a predator was present or absent, and each individual made an independent decision to escape or not. Second, individuals received social information on the decisions of their group members, after which individuals decided again. This social information, however, did not represent their own decisions but consisted of responses that either came from a high-performing group (i.e., many individuals detecting the predator) or from a low-performing group (i.e., few individuals detecting the predator). We found that individuals adaptively adjust their quorum threshold to the quality of the social information: when receiving social information from high-performing groups, individuals employed higher quorum thresholds than when receiving information from low-performing groups. Our study demonstrates that humans can quickly evaluate the quality of publicly available information and adaptively adjust their decision rules.
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