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Investigating predictors of superior face recognition ability in police super-recognisers

Investigating predictors of superior face recognition ability in police super-recognisers

Davis, Josh P. ORCID: 0000-0003-0017-7159, Lander, Karen, Evans, Ray and Jansari, Ashok (2016) Investigating predictors of superior face recognition ability in police super-recognisers. Applied Cognitive Psychology, 30 (6). pp. 827-840. ISSN 0888-4080 (Print), 1099-0720 (Online) (doi:

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There are large individual differences in the ability to recognise faces. Super-recognisers are exceptionally good at face memory tasks. In London, a small specialist pool of police officers (also labelled ‘super-recognisers’ by the Metropolitan Police Service) annually makes 1000’s of suspect identifications from closed-circuit television footage. Some suspects are disguised, have not been encountered recently or are depicted in poor quality images. Across tests measuring familiar face recognition, unfamiliar face memory and unfamiliar face matching, the accuracy of members of this specialist police pool was approximately equal to a group of non-police super-recognisers. Both groups were more accurate than matched control members of the public. No reliable relationships were found between the face processing tests and object recognition. Within each group, however, there were large performance variations across tests, and this research has implications for the deployment of police worldwide in operations requiring officers with superior face processing ability.

Item Type: Article
Uncontrolled Keywords: Face recognition; Super-recognisers, Face matching
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Faculty / Department / Research Group: Faculty of Education, Health & Human Sciences
Faculty of Education, Health & Human Sciences > Department of Psychology, Social Work & Counselling
Last Modified: 28 Apr 2020 19:49
Selected for GREAT 2016: GREAT b
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: GREAT 1
Selected for REF2021: REF 2

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