The part-whole effect in super-recognisers and typical-range-ability controls

Face Feature Change Detection Ability in Developmental Prosopagnosia and Super-Recognisers

In non-clinical populations, facial features (eyes, nose, mouth) may vary in their contribution to face identity perception. Changes to whole faces are easier to detect than changes to individual features, and eye changes are typically easier to detect than mouth changes, which in turn are easier to detect than nose changes. However, how this differs for people with face recognition difficulties (developmental prosopagnosia; DP) and for individuals with superior face recognition abilities (super-recognisers; SR) is not clear; although findings from previous studies have suggested differences, the nature of this difference is not understood. The aim of this study was to examine whether differences in the ability to detect feature changes in DPs and SRs were (a) quantitative, meaning that the pattern across feature changes remained the same but there was an overall upwards or downwards shift in performance, or (b) qualitative, meaning that the pattern across feature changes was different. Using a change detection task in which individual face features (eyes, nose, mouth) changed between sequentially presented faces, we found that while prosopagnosics showed a quantitative difference in performance with a downwards shift across all conditions, super-recognisers only showed qualitative differences: they were better able to detect when the face was the same and were marginally (but not non-significantly) worse at detecting when the eyes changed. Further, the only condition which distinguished between the three groups was the ability to identify when the same face was presented, with SRs being better than controls, and controls being better than DPs. Our findings suggest that, in feature-matching tasks, differences for DPs are due to them being overall worse at the task, while SRs use a qualitatively different strategy.

Face masks and fake masks: the effect of real and superimposed masks on face matching with super-recognisers, typical observers, and algorithms

Mask wearing has been required in various settings since the outbreak of COVID-19, and research has shown that identity judgements are difficult for faces wearing masks. To date, however, the majority of experiments on face identification with masked faces tested humans and computer algorithms using images with superimposed masks rather than images of people wearing real face coverings. In three experiments we test humans (control participants and super-recognisers) and algorithms with images showing different types of face coverings. In all experiments we tested matching concealed or unconcealed faces to an unconcealed reference image, and we found a consistent decrease in face matching accuracy with masked compared to unconcealed faces. In Experiment 1, typical human observers were most accurate at face matching with unconcealed images, and poorer for three different types of superimposed mask conditions. In Experiment 2, we tested both typical observers and super-recognisers with superimposed and real face masks, and found that performance was poorer for real compared to superimposed masks. The same pattern was observed in Experiment 3 with algorithms. Our results highlight the importance of testing both humans and algorithms with real face masks, as using only superimposed masks may underestimate their detrimental effect on face identification.