• Mabel Whitchurch

Results: low prevalence item identification and feature training in face matching tests

School of Human Sciences

Institute of Lifecourse Development

University of Greenwich

London SE10 9LS

www.superrecognisers.com

super-recognisers@greenwich.ac.uk

Twitter: @GRecognisers

https://orcid.org/0000-0003-0017-7159

https://www.gre.ac.uk/pg/eduhea/psych


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

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Supervisor: Dr Josh P Davis

Applied Psychology Research Group

University of Greenwich

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