Intra- and extra-personal variability in person recognition

Balas, Benjamin; Pearson, Hannah

doi:10.1080/13506285.2016.1274809

Cited by 18 publications

(41 citation statements)

References 26 publications

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“…However, each BM in our daily life is produced by a distinct person, carrying a dynamic motion signature that informs of one's identity (e.g., Barclay, Cutting, & Kozlowski, 1978;Beardsworth & Buckner, 1981;Cutting & Kozlowski, 1977;Loula et al, 2005;Runeson & Frykholm, 1983, 1986Stevenage, Nixon, & Vince, 1999;Troje, Westhoff, & Lavrov, 2005). Although agent identity and action are processed by different neural substrates (e.g., Cai et al, 2018;Downing, Jiang, Shuman, & Kanwisher, 2001;Downing, Peelen, Wiggett, & Tew, 2006;Puce & Perrett, 2003;Urgesi, Candidi, Ionta, & Aglioti, 2007), recent studies have implied that there is an intimate relation between action and BM identity (Balas & Pearson, 2017;Pilz & Thornton, 2017;Simhi & Yovel, 2017). For instance, Pilz and Thornton (2017) found that body motion affects the processing of identity.…”

Section: Introductionmentioning

confidence: 99%

Agent identity drives adaptive encoding of biological motion into working memory

et al. 2019

Journal of Vision

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To engage in normal social interactions, we have to encode human biological motions (BMs, e.g., walking and jumping), which is one of the most salient and biologically significant types of kinetic information encountered in everyday life, into working memory (WM). Critically, each BM in real life is produced by a distinct person, carrying a dynamic motion signature (i.e., identity). Whether agent identity influences the WM processing of BMs remains unknown. Here, we addressed this question by examining whether memorizing BMs with different identities promoted the WM processing of task-irrelevant clothing colors. Two opposing hypotheses were tested: (a) WM only stores the target action (element-based hypothesis) and (b) WM stores both action and irrelevant clothing color (event-based hypothesis), interpreting each BM as an event. We required participants to memorize actions that either performed by one agent or distinct agents, while ignoring clothing colors. Then we examined whether the irrelevant color was also stored in WM by probing a distracting effect: If the color was extracted into WM, the change of irrelevant color in the probe would lead to a significant distracting effect on action performance. We found that WM encoding of BMs was adaptive: Once the memorized actions had different identities, WM adopted an event-based encoding mode regardless of memory load and probe identity (Experiment 1, different-identity group of Experiment 2, and Experiment 3). However, WM used an elementbased encoding mode when memorized-actions shared the same identity (same-identity group of Experiment 2) or were inverted (Experiment 4). Overall, these findings imply that agent identity information has a significant effect on the WM processing of BMs.

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Section: Introductionmentioning

confidence: 99%

Agent identity drives adaptive encoding of biological motion into working memory

et al. 2019

Journal of Vision

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“…Whatever properties lead a face to be rated as a poor likeness also lead to poor sorting performance for unfamiliar, even when observers know the number of unique identities in the stimulus set. In previous reports (Jenkins et al, 2011;, this cue frequently leads to high accuracy (though see Balas & Pearson, 2017 for results with a 4-category sorting task), so it is noteworthy that poor likenesses lead to such low sensitivity. 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.…”

Section: Discussionmentioning

confidence: 80%

“…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.…”

Section: Discussionmentioning

confidence: 99%

“…We analyzed the data from each participant by computing signal detection descriptors of sorting performance according to the method outlined in Balas & Pearson (2017). This approach considers the disposition of each pair of images in the full stimulus set.…”

Section: Pre-registered Analysesmentioning

confidence: 99%

“…We examined this question through the application of a card-sorting task that has been widely used in recent studies (Jenkins et al, 2011;Balas & Pearson, 2017;Laurence, Zhou & Mondloch, 2016). Participants in this task sort a large set of images into groups based on identity such that each group of images contains all and only images of a single unique individual.…”

Section: Introductionmentioning

confidence: 99%

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Face Likeness Affects Face Learning

Balas¹,

Bailly²

2019

Preprint

Self Cite

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Learning to recognize new faces depends critically on learning to cope with the substantial appearance variability that faces are subject to: The same individual can look very different under different viewing conditions. When attempting to match or discriminate between unfamiliar faces, observers tend to misinterpret instances of within-person variability as different exemplars. Exposure to high variability during face learning tends to support better performance in subsequent recognition tasks, and idiosyncratic face variability becomes a part of representations that support accurate face recognition. Though it is clear that the amount of variability is important for learning, it remains an open question whether or not different types of variability support more robust recognition abilities after training. In the present study, we addressed this question by asking observers to complete an incidental learning task in which they were either presented with good likenesses or poor likenesses of target faces. Following this training task, all participants were tested with a novel set of face images including new exemplars of the individuals presented during training. We found that the quality of face likenesses affected performance during the training task, but did not lead to different outcomes at test. Both kinds of training improved different aspects of performance relative to a control group that received no training at all, however, suggesting that both good and poor examples of an individual’s appearance are useful for establishing representations for recognition.

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Partitioning natural face image variability emphasises within-identity over between-identity representation for understanding accurate recognition

2022

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Accurately recognising faces enables social interactions. In recent years it has become clear that people's accuracy differs markedly depending on viewer's familiarity with a face and their individual skill, but the cognitive and neural bases of these accuracy differences are not understood. We examined cognitive representations underlying these accuracy differences by measuring similarity ratings to natural facial image variation. Natural variation was sampled from uncontrolled images on the internet to reflect the appearance of faces as they are encountered in daily life. Using image averaging, and inspired by the computation of Analysis of Variance, we partitioned this variation into differences between faces (betweenidentity variation) and differences between photos of the same face (within-identity variation). This allowed us to compare modulation of these two sources of variation attributable to: (i) a person's familiarity with a face and, (ii) their face recognition ability. Contrary to prevailing accounts of human face recognition and perceptual learning, we found that modulation of within-identity variation -rather than between-identity variation -was associated with high accuracy. First, familiarity modulated similarity ratings to withinidentity variation more than to between-face variation. Second, viewers that are extremely accurate in face recognition -'super-recognisers' -differed from typical perceivers mostly in their ratings of within-identity variation, compared to between-identity variation. In a final computational analysis, we found evidence that transformations of between-and withinidentity variation make separable contributions to perceptual expertise in face recognition. We conclude that inter-and intra-individual accuracy differences primarily arise from differences in the representation of within-identity image variation.

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Intra- and extra-personal variability in person recognition

Cited by 18 publications

References 26 publications

Agent identity drives adaptive encoding of biological motion into working memory

Agent identity drives adaptive encoding of biological motion into working memory

Face Likeness Affects Face Learning

Partitioning natural face image variability emphasises within-identity over between-identity representation for understanding accurate recognition

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