Hometown size affects the processing of naturalistic face variability

Abstract

Face exposure during development determines adults' abilities to recognize faces and the information they use to process them. Individual differences in the face categories represented in the visual environment can lead to category-specific deficits for recognizing faces that are atypical of observer's experience (e.g. the other-race effect). But what happens when observers have limited opportunities to learn about faces in general? In previous work, we found that observers from depopulated areas have poorer face recognition performance than observers from larger communities, suggesting that impoverished face experience limits face processing broadly. Here, we further investigate this phenomenon by examining how hometown size impacts the ability to assess appearance variability in natural images of faces and bodies. We asked individuals from small and large communities to complete (1) an unconstrained card-sorting task designed to test observers' ability to categorize within-person and between-person appearance variability properly, and (2) the Cambridge Face Memory Test. For both tasks, we examined the direct comparison between groups as well as the relationship between CFMT scores and sorting performance as a function of face experience. We find that small-town observers perform more poorly on the CFMT, but exhibit both better and worse performance than large-town observers on different aspects of the card-sorting task. Further, we also examine the relationship between CFMT performance and card-sorting errors. Our results suggest that individual differences in lifetime face exposure induce important variation in face processing abilities.

Comparing unfamiliar voice and face identity perception using identity sorting tasks

“…Which perceptual strategies or processes may therefore underpin face and voice sorting tasks? Previous research has reported significant correlations between different measures of face sorting tasks (e.g., overall error rates, sensitivity) and the Cambridge Face Memory Test (CFMT; Balas & Saville, 2017;Short et al, 2017). These findings in conjunction with our findings could, therefore, suggest that good performance in face sorting tasks may be in fact more closely linked to the mechanisms underpinning good performance on a face learning or recognition task as opposed to a discrimination task.…”

“…Recent studies investigating the effects of within-person variability on voice and face perception have highlighted further similarities between voice and face identity processing (For voices: Lavan, Burston, & Garrido, 2019 ; Lavan, Burston, Ladwa, et al, 2019 ; Lavan, Merriman, et al, 2019 ; Stevenage et al, 2020 , For faces: Andrews et al, 2015 ; Balas & Saville, 2017 ; Jenkins et al, 2011 ; Laurence et al, 2016 ; Redfern & Benton, 2017 ; Short et al, 2017 ; Zhou & Mondloch, 2016 ). Images of faces and recordings of voices can vary considerably from instance to instance ( Burton, 2013 ; Lavan, Burton, et al, 2019 ).…”

How many faces do people know?

“…However, aspects of the social environment may also play a role. In particular, local population density could affect the frequency of face-to-face encounters [ 29 ], and the rate at which faces are acquired across the lifespan [ 30 ]. The current estimate provides a baseline against which to assess demographic and developmental moderators.…”

Face Likeness Affects Face Learning

“…Besides this result, the lack of a difference in response criterion values is expected due to the instruction to sort the cards presented during the training phase into two piles. We have reported elsewhere that constraining the number of groups participants can make essentilaly freezes their response criterion within a signal detection framework (Balas & Saville, 2017;Balas & Pearson, 2017), so it is not surprising that imposing a '2-sort' task on participants fixes their response criterion near zero.…”