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Wisdom of the social versus non-social crowd in face identification
Authors:Géraldine Jeckeln  Carina A Hahn  Eilidh Noyes  Jacqueline G Cavazos  Alice J O'Toole
Institution:School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, Texas, USA
Abstract:Face identification is more accurate when people collaborate in social dyads than when they work alone (Dowsett & Burton, 2015, Br. J. Psychol., 106, 433). Identification accuracy is also increased when the responses of two people are averaged for each item to create a ‘non-social’ dyad (White, Burton, Kemp, & Jenkins, 2013, Appl. Cogn. Psychol., 27, 769; White et al., 2015, Proc. R. Soc. B Biol. Sci., 282, 20151292). Does social collaboration add to the benefits of response averaging for face identification? We compared individuals, social dyads, and non-social dyads on an unfamiliar face identity-matching test. We also simulated non-social collaborations for larger groups of people. Individuals and social dyads judged whether face image pairs depicted the same- or different identities, responding on a 5-point certainty scale. Non-social dyads were constructed by averaging the responses of paired individuals. Both social and non-social dyads were more accurate than individuals. There was no advantage for social over non-social dyads. For larger non-social groups, performance peaked at near perfection with a crowd size of eight participants. We tested three computational models of social collaboration and found that social dyad performance was predicted by the decision of the more accurate partner. We conclude that social interaction does not bolster accuracy for unfamiliar face identity matching in dyads beyond what can be achieved by averaging judgements.
Keywords:face identification  crowd sourcing  social collaboration  statistical fusion  crowd analysis
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