The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Wisdom of the social versus non‐social crowd in face identification
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.
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Statistical aggregation of judgments made by multiple people has been shown to produce substantial boosts to accuracy.
“statistical aggregation of judgments made by multiple people [39, 40]”
From Do professional facial image comparison training courses work? (2019) · cites this paper as [40]
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Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms
Phillips, P. J., Yates, A. N., Hu, Y., Hahn, C. A., Noyes, E., Jackson, K., Cavazos, J. G., Jeckeln, G., Ranjan, R., Sankaranarayanan, S., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1721355115 · Cited by 461
Self-citationDo professional facial image comparison training courses work?
Towler, A., Kemp, R. I., Burton, A. M., Dunn, J. D., Wayne, T., Moreton, R., & White, D. (2019) · PLOS ONE · doi:10.1371/journal.pone.0211037 · Cited by 111
Metadata
- Authors
- Jeckeln, G., Hahn, C. A., Noyes, E., Cavazos, J. G., & O'Toole, A. J.
- Affiliations
- School of Behavioral and Brain Sciences The University of Texas at Dallas Richardson Texas USA
- ORCID
- Jeckeln, Géraldine
- Venue
- British Journal of Psychology
- Publisher
- Wiley
- Published
- 2018-03-05
- Volume
- 109
- Issue
- 4
- Pages
- 724-735
- ISSN
- 0007-1269
- 2044-8295
- Cited by
- 17
- References
- 1
- Language
- en
- Funders
- National Institute of Justice
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