School of Human Sciences
Institute of Lifecourse Development
University of Greenwich
London SE10 9LS
super-recognisers@greenwich.ac.uk
Twitter: @GRecognisers
Research Team
Callan Dray: MSc Psychology (Conversion)
Dr Josh P Davis: Supervisor
We would like to thank the 816 participants who volunteered to take part in Callan Dray’s experiment for his MSc Psychology (Conversion) thesis. Your contribution remains essential to our research.
During July 2020, participants were invited to attempt one of three new 50-trial face matching tests developed by the University of Greenwich. Based on pilot item analyses data, supplied by super-recogniser participants (n > 200), these face matching tests were created using stimuli selected from a range of different sources, so that each test should have been of equal overall difficulty.
We then manipulated the match (same person in each pair of images)/mismatch (different people in each pair) frequency, so that each participant viewed 10%, 50% or 90% mismatched facial pairs. In doing so, we were able to explore the impact of low prevalence effects on face matching performance. Previous research has demonstrated that when target items appear less frequently, the rate of low prevalence item detection also reduces (Wolfe, Horowitz, & Kenner, 2005). If you would like to develop a deeper theoretical understanding of this topic, please see research by Papesh and Goldinger (2014).
In an attempt to identify methods which improve face matching accuracy, this experiment also delivered feature-based instructions that directed participants to focus on either internal (eyebrows, nose, mouth) or external (face shape, ears, chin/jawline) facial features. This method was adopted from use in current professional training courses (see Towler et al., 2019 for discussions of these issues).
Before conducting the tests, all participants were asked to enter their personal anonymised code. If you have your anonymised code, you may compare your results against those within the complete data set (you will find your scores below). Figure 1 shows the distribution of scores out of 50 across all trials in this research.
Do not be disheartened if your results significantly differ from previous scores, as conditions within this test were designed to be more challenging than previous experiments. Please note that some codes did not match those on our database as we could not match up all data.
We are currently writing up the results of this experiment in conjunction with a follow-up experiment conducted in September. We hope to submit for publication in a peer-reviewed journal this year, or early next year.
We will load a pre-print of this paper on the Open Science Framework (OSF) website and announce this on the www.superrecognisers.com website and on Twitter @GRecognisers.
Thanks again for your continued support,
Callan Dray and Josh Davis
Figure 1
Frequency of overall scores within all versions of the face matching tests (n = 816).
References
Papesh, M. H., & Goldinger, S. D. (2014). Infrequent identity mismatches are frequently undetected. Attention, Perception, & Psychophysics, 76(5), 1335-1349.
Towler, A., Kemp, R. I., Burton, A. M., Dunn, J. D., Wayne, T., Moreton, R., & White, D. (2019). Do professional facial image comparison training courses work? PloS one, 14(2), e0211037.
Wolfe, J. M., Horowitz, T. S., & Kenner, N. M. (2005). Rare items often missed in visual searches. Nature, 435(7041), 439-440.
Overall Scores (out of 50)
A00000253 41
A00000696 36
A00000812 40
A00000936 46
A00001015 47
A00001346 29
A00001717 31
A00001824 50
A00001856 31
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A00003422 50
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A00004576 27
A00004585 36
A00004790 50
A00004791 43
A00004818 42
A00004924 31
A00005098 30
A00005555 48
A00005652 29
A00005797 41
A00006843 50
A00007015 23
A00007026 35
A00007315 27
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A00007504 32
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A00009089 43
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A00010442 46
A00010466 42
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A00010851 44
A00011094 24
A00011097 34
A00011225 30
A00011304 50
A00011547 35
A00011568 40
A00012918 50
A00013410 50
A00013811 46
A00014205 36
A00014300 31
A00014323 40
A00014866 44
A00014883 41
A00015332 29
A00015450 27
A00015551 45
A00016036 48
A00016249 43
A00016757 28
A00017523 34
A00017613 44
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A00037008 50
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A00037919 31
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A00039655 14
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A00040286 50
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A00041300 27
A00041795 41
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A00046254 33
A00047807 38
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A00050118 34
A00050916 45
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