The psychometrics of rating facial attractiveness using different response scales.

Perceiving facial attractiveness is an important behaviour across psychological science due to these judgments having real-world consequences. However, there is little consensus on the measurement of this behaviour, and practices differ widely. Research typically asks participants to provide ratings of attractiveness across a multitude of different response scales, with little consideration of the psychometric properties of these scales. Here, we make psychometric comparisons across nine different response scales. Specifically, we analysed the psychometric properties of a binary response, a 0-100 scale, a visual analogue scale, and a set of Likert scales (1-3, 1-5, 1-7, 1-8, 1-9, 1-10) as tools to measure attractiveness, calculating a range of commonly used statistics for each. While certain properties suggested researchers might choose to favour the 1-5, 1-7 and 1-8 scales, we generally found little evidence of an advantage for one scale over any other. Taken together, our investigation provides consideration of currently used techniques for measuring facial attractiveness and makes recommendations for researchers in this field.

Multiple images captured from a single encounter do not promote face learning.

Viewing multiple images of a newly encountered face improves recognition of that identity in new instances. Studies examining face learning have presented high-variability (HV) images that incorporate changes that occur from moment-to-moment (e.g., head orientation and expression) and over time (e.g., lighting, hairstyle, and health). We examined whether low-variability (LV) images (i.e., images that incorporate only moment-to-moment changes) also promote generalisation of learning such that novel instances are recognised. Participants viewed a single image, six LV images, or six HV images of a target identity before being asked to recognise novel images of that identity in a face matching task (training stimuli remained visible) or a memory task (training stimuli were removed). In Experiment 1 (n = 71), participants indicated which image(s) in 8-image arrays belonged to the target identity. In Experiment 2 (n = 73), participants indicated whether sequentially presented images belonged to the target identity. Relative to the single-image condition, sensitivity to identity improved and response biases were less conservative in the HV condition; we found no evidence of generalisation of learning in the LV condition regardless of testing protocol. Our findings suggest that day-to-day variability in appearance plays an essential role in acquiring expertise with a novel face.

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.