Literature DB >> 29504118

Wisdom of the social versus non-social crowd in face identification.

Géraldine Jeckeln1, Carina A Hahn1, Eilidh Noyes1, Jacqueline G Cavazos1, Alice J O'Toole1.   

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.
© 2018 The British Psychological Society.

Entities:  

Keywords:  crowd analysis; crowd sourcing; face identification; social collaboration; statistical fusion

Mesh:

Year:  2018        PMID: 29504118     DOI: 10.1111/bjop.12291

Source DB:  PubMed          Journal:  Br J Psychol        ISSN: 0007-1269


  5 in total

1.  The pairs training effect in unfamiliar face matching.

Authors:  Kay L Ritchie; Tessa R Flack; Elizabeth A Fuller; Charlotte Cartledge; Robin S S Kramer
Journal:  Perception       Date:  2022-05-17       Impact factor: 1.695

2.  Improving face identification with specialist teams.

Authors:  Tarryn Balsdon; Stephanie Summersby; Richard I Kemp; David White
Journal:  Cogn Res Princ Implic       Date:  2018-06-27

3.  Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms.

Authors:  P Jonathon Phillips; Amy N Yates; Ying Hu; Carina A Hahn; Eilidh Noyes; Kelsey Jackson; Jacqueline G Cavazos; Géraldine Jeckeln; Rajeev Ranjan; Swami Sankaranarayanan; Jun-Cheng Chen; Carlos D Castillo; Rama Chellappa; David White; Alice J O'Toole
Journal:  Proc Natl Acad Sci U S A       Date:  2018-05-29       Impact factor: 11.205

4.  Do professional facial image comparison training courses work?

Authors:  Alice Towler; Richard I Kemp; A Mike Burton; James D Dunn; Tanya Wayne; Reuben Moreton; David White
Journal:  PLoS One       Date:  2019-02-13       Impact factor: 3.240

5.  Super-recognizers: From the lab to the world and back again.

Authors:  Meike Ramon; Anna K Bobak; David White
Journal:  Br J Psychol       Date:  2019-03-20
  5 in total

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