Stable individual differences in unfamiliar face identification: Evidence from simultaneous and sequential matching tasks

Baker, Kristen; Stabile, V J; Mondloch, Catherine J.

doi:10.1016/j.cognition.2022.105333

Cited by 8 publications

(11 citation statements)

References 99 publications

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“…The individual differences approach can provide valuable insights about the perceptual and cognitive mechanisms underlying face identification but is a largely untapped resource 25 . Individual differences in unfamiliar face identification are reliable 25 – 27 and consistent across tasks that vary in the type of judgment being made (e.g., same/different vs. detecting a target in a lineup), the amount of within-person variability in appearance, and sequential versus simultaneous stimulus presentation 26 . To date, no study has examined whether individual differences in unfamiliar face matching predict improvement in performance as additional images are presented (i.e., the ability to capitalize on variability as a face becomes familiar).…”

Section: Introductionmentioning

confidence: 72%

“…Images within match trials were taken using different cameras. Images in the AIFMT (half male) were colour photographs (652 × 260 pix) shown on a grey background 26 . Images were taken from a variety of sources and included natural variability in appearance.…”

Section: Methodsmentioning

confidence: 99%

“…Participants completed 80 trials (50% match) of the AIFMT 26 , 50 . Participants were presented with pairs of images and were asked to determine whether the image pair was of the same person or different people.…”

Section: Methodsmentioning

confidence: 99%

“…Participants completed a battery of three face identification tasks. Two tasks (The Glasgow Face Matching Test [GFMT] 11 ; The Ambient Image Face Matching Task [AIFMT] 26 , 50 ) measured face matching in wholly unfamiliar faces. We developed a novel face learning task to measure the slope of face learning as participants were exposed to an increasing number of new images (1, 3, 6, and then 9 images) of a previously unfamiliar face.…”

Section: Introductionmentioning

confidence: 99%

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Unfamiliar face matching ability predicts the slope of face learning

Baker

Mondloch

2023

Sci Rep

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We provide the first examination of individual differences in the efficiency of face learning. Investigating individual differences in face learning can illuminate potential mechanisms and provide greater understanding of why certain individuals might be more efficient face learners. Participants completed two unfamiliar face matching tasks and a learning task in which learning was assessed after viewing 1, 3, 6, and 9 images of to-be-learned identities. Individual differences in the slope of face learning (i.e., increases in sensitivity to identity) were predicted by the ability to discriminate between matched (same-identity) vs. mismatched (different-identity) pairs of wholly unfamiliar faces. A Dual Process Signal Detection model showed that three parameters increased with learning: Familiarity (an unconscious type of memory that varies in strength), recollection-old (conscious recognition of a learned identity), and recollection-new (conscious/confident rejection of novel identities). Good (vs. poor) matchers had higher Recollection-Old scores throughout learning and showed a steeper increase in Recollection-New. We conclude that good matchers are better able to capitalize on exposure to within-person variability in appearance, an effect that is attributable to their conscious memory for both learned and novel faces. These results have applied implications and will inform contemporary and traditional models of face identification.

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Section: Introductionmentioning

confidence: 72%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unfamiliar face matching ability predicts the slope of face learning

Baker

Mondloch

2023

Sci Rep

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“…There are two types of answer, one aimed at the bias differences, the other at differing abilities. Some individuals may genuinely be more cautious than others at declaring a match, for example, leading to differences in bias (Baker et al, 2023). However, there may also be distinct abilities, as indicated for example by Berger et al (2022) who report that match trials correlated with eye-processing ability, while non-match trials correlated with holistic processing.…”

Section: Discussionmentioning

confidence: 99%

Bias and sensitivity in face matching: the independence of match and mismatch trials

Hancock¹

2023

Preprint

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Crowds Improve Human Detection of AI‐Synthesised Faces

Kramer,

Cartledge

2024

Applied Cognitive Psychology

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Artificial intelligence can now synthesise face images which people cannot distinguish from real faces. Here, we investigated the wisdom of the (outer) crowd (averaging individuals' responses to the same trial) and inner crowd (averaging the same individual's responses to the same trial after completing the test twice) as routes to increased performance. In Experiment 1, participants viewed synthetic and real faces, and rated whether they thought each face was synthetic or real using a 1–7 scale. Each participant completed the task twice. Inner crowds showed little benefit over individual responses, and we found no associations between performance and personality factors. However, we found increases in performance with increasing sizes of outer crowd. In Experiment 2, participants judged each face only once, providing a binary ‘synthetic/real’ response, along with a confidence rating and an estimate of the percentage of other participants that they thought agreed with their answer. We compared three methods of aggregation for outer crowd decisions, finding that the majority vote provided the best performance for small crowds. However, the ‘surprisingly popular’ solution outperformed the majority vote and the confidence‐weighted approach for larger crowds. Taken together, we demonstrate the use of outer crowds as a robust method of improvement during synthetic face detection, comparable with previous approaches based on training interventions.

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Stable individual differences in unfamiliar face identification: Evidence from simultaneous and sequential matching tasks

Cited by 8 publications

References 99 publications

Unfamiliar face matching ability predicts the slope of face learning

Unfamiliar face matching ability predicts the slope of face learning

Bias and sensitivity in face matching: the independence of match and mismatch trials

Crowds Improve Human Detection of AI‐Synthesised Faces

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