Anomalistic Psychology Research Unit Invited Speaker Series 2017/18
The effective deployment of London police super-recognisers has vastly increased suspect identification rates from CCTV in recent years. Many of these super-recognisers perform exceptionally highly at empirical tests of familiar and unfamiliar face recognition, simultaneous face matching, and spotting target faces in videos of large crowds. Non-police super-recognisers are also superior to most members of the public at long-term face recognition, even when faces are heavily disguised. Most possess superior object memory ability, although effect sizes are far larger when asked to recognise faces. Nevertheless, some super-recognisers perform relatively poorly on some face processing tests, suggesting that the term super-recogniser may be a convenient label for a heterogeneous concept. It is therefore important that if evidence of identification by super-recognisers is accepted in court, legal systems are fully informed as to their cognitive limits and capabilities.
Dr Josh P Davis is a Reader in Applied Psychology at the University of Greenwich. His PhD, carried out at Goldsmiths, University of London, was on the “Forensic Identification of Unfamiliar Faces in CCTV Images” (2007). Since 2011, his research has focussed on ‘super-recognisers’ – very rare individuals with exceptionally good face recognition ability, leading to changes in the management and distribution of crime scene images by the Metropolitan Police Service (MPS). This has substantially increased suspect identification rates from CCTV, and he has since consulted other UK and international police forces. Worldwide media interest has meant that over 4 million participants have contributed to his research, while his first co-edited book (with Professor Tim Valentine at Goldsmiths) “Forensic Facial Identification: Theory and Practice of Identification from Eyewitnesses, Composites and CCTV” (Wiley Blackwell) was published in 2015 (Valentine & Davis, 2015).
Dates & times
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|28 Nov 2017||6:00pm - 7:30pm|
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