CoChat
Sign Up
Cited by 23

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Data-driven studies in face identity processing rely on the quality of the tests and data sets

Bobak, Anna K., Jones, Alex L., Hilker, Zoe, Mestry, Natalie, Bate, Sarah, Hancock, Peter J.B.·2023·Cortex
DOI 10.1016/j.cortex.2023.05.018
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.cortex.2023.05.018

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In factor analysis, individual differences in accuracy on match and mismatch trials load onto separate factors.

    “In factor analysis, for example, individual differences in accuracy on match and mismatch trials load onto separate factors (Bobak et al., 2023)”

    From A criterion-placement theory of face matching (2026) · cites this paper as Bobak et al., 2023

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 73

  1. Bailey, 2012, Principal component analysis with noisy and/or missing data, Publications of the Astronomical Society of the Pacific, 124, 1015, 10.1086/668105 · DOI
  2. Baker, 2023, Stable individual differences in unfamiliar face identification: Evidence from simultaneous and sequential matching tasks, Cognition, 232, 10.1016/j.cognition.2022.105333 · DOI
  3. Baldassari, 2019, The importance of decision bias for predicting eyewitness lineup choices: Toward a Lineup Skills Test, Cognitive Research: Principles and Implications, 4, 2
  4. Balsdon, 2018, Improving face identification with specialist teams, Cognitive Research: Principles and Implications, 3, 25
  5. Bate, 2018, Applied screening tests for the detection of superior face recognition, Cognitive Research: Principles and Implications, 3
  6. Bate, 2021, When two fields collide: Identifying “super-recognisers” for neuropsychological and forensic face recognition research, The Quarterly Journal of Experimental Psychology: QJEP, 74, 2154, 10.1177/17470218211027695 · DOI
  7. Belanova, 2018, Cognitive and neural markers of super-recognisers’ face processing superiority and enhanced cross-age effect, Cortex; a Journal Devoted To the Study of the Nervous System and Behavior, 108, 92, 10.1016/j.cortex.2018.07.008 · DOI · Paper page
  8. Bennetts, 2022, Face specific inversion effects provide evidence for two subtypes of developmental prosopagnosia, Neuropsychologia, 174, 10.1016/j.neuropsychologia.2022.108332 · DOI
  9. Bennetts, 2017, Super-recognition in development: A case study of an adolescent with extraordinary face recognition skills, Cognitive Neuropsychology, 34, 357, 10.1080/02643294.2017.1402755 · DOI
  10. Berger, 2022, Distinct abilities associated with matching same identity faces versus discriminating different faces: Evidence from individual differences in prosopagnosics and controls, The Quarterly Journal of Experimental Psychology: QJEP, 10.1177/17470218221076817 · DOI
  11. Biotti, 2019, Is developmental prosopagnosia best characterised as an apperceptive or mnemonic condition?, Neuropsychologia, 124, 285, 10.1016/j.neuropsychologia.2018.11.014 · DOI
  12. Bland, 1997, Statistics notes: Cronbach's alpha, Bmj: British Medical Journal, 314, 572, 10.1136/bmj.314.7080.572 · DOI · Paper page
  13. Bobak, 2016, An in-depth cognitive examination of individuals with superior face recognition skills, Cortex, 82, 48, 10.1016/j.cortex.2016.05.003 · DOI
  14. Bobak, 2016, Solving the border control problem: Evidence of enhanced face matching in individuals with extraordinary face recognition skills, Plos One, 11, 10.1371/journal.pone.0148148 · DOI · Paper page
  15. Bobak, 2016, Super-recognisers in action: Evidence from face-matching and face memory tasks, Applied Cognitive Psychology, 30, 81, 10.1002/acp.3170 · DOI
  16. Bobak, 2019, A grey area: How does image hue affect unfamiliar face matching?, Cognitive Research: Principles and Implications, 4
  17. Bobak, 2016, Detecting superior face recognition skills in a large sample of young British adults, Frontiers in Psychology, 7, 10.3389/fpsyg.2016.01378 · DOI
  18. Boudry, 2023, Determinants of face recognition: The role of target prevalence and similarity, PsyArXiv
  19. Bowles, 2009, Diagnosing prosopagnosia: Effects of ageing, sex, and participant–stimulus ethnic match on the Cambridge face memory test and Cambridge face perception test, Cognitive Neuropsychology, 26, 423, 10.1080/02643290903343149 · DOI
  20. Bruce, 1999, Verification of face identities from images captured on video, Journal of Experimental Psychology. Applied, 5, 339, 10.1037/1076-898X.5.4.339 · DOI · Paper page
  21. Burton, 2016, Identity from variation: Representations of faces derived from multiple instances, Cognitive Science, 40, 202, 10.1111/cogs.12231 · DOI
  22. Burton, 2010, The Glasgow face matching test, Behavior Research Methods, 42, 286, 10.3758/BRM.42.1.286 · DOI · Paper page
  23. Carragher, 2022, Simulated automated facial recognition systems as decision-aids in forensic face matching tasks, Journal of Experimental Psychology: General, No Pagination Specified-No Pagination Specified
  24. Ćepulić, 2018, All categories are equal, but some categories are more equal than others: The psychometric structure of object and face cognition, Journal of Experimental Psychology: Learning, Memory, and Cognition, 44, 1254
  25. Cook, 2006, Current concepts in validity and reliability for psychometric instruments: Theory and application, The American Journal of Medicine, 119, 166, 10.1016/j.amjmed.2005.10.036 · DOI · Paper page
  26. Davis, 2016, Investigating predictors of superior face recognition ability in police super-recognisers, Applied Cognitive Psychology, 30, 827, 10.1002/acp.3260 · DOI
  27. DeGutis, 2022, What is the prevalence of prosopagnosia? An empirical assessment of different diagnostic cutoffs, PsyArXiv
  28. Duchaine, 2007, Family resemblance: Ten family members with prosopagnosia and within-class object agnosia, Cognitive Neuropsychology, 24, 419, 10.1080/02643290701380491 · DOI
  29. Dunn, 2020, UNSW face test: A screening tool for super-recognizers, Plos One, 15, 10.1371/journal.pone.0241747 · DOI
  30. Fysh, 2018, The kent face matching test, British Journal of Psychology, 109, 219, 10.1111/bjop.12260 · DOI · Paper page
  31. Fysh, 2022, Accurate but inefficient: Standard face identity matching tests fail to identify prosopagnosia, Neuropsychologia, 165, 10.1016/j.neuropsychologia.2021.108119 · DOI
  32. Fysh, M. C., Stacchi, L., & Ramon, M. (2020). Differences between and within individuals, and subprocesses of face cognition: Implications for theory, research and personnel selection. Royal Society Open Science, 7(9), 200233. https://doi.org/10.1098/rsos.200233. · DOI
  33. Hancock, 2023, Bias and sensitivity in face matching: The independence of match and mismatch trials, PsyArXiv
  34. Hancock, 1996, Face processing: Human perception and principal components analysis, Memory & Cognition, 24, 26, 10.3758/BF03197270 · DOI
  35. Hildebrandt, 2011, On the specificity of face cognition compared with general cognitive functioning across adult age, Psychology and Aging, 26, 701, 10.1037/a0023056 · DOI
  36. Jenkins, 2011, Variability in photos of the same face, Cognition, 121, 313, 10.1016/j.cognition.2011.08.001 · DOI
  37. Jones, 2021, Facial first impressions form two clusters representing approach-avoidance, Cognitive Psychology, 126, 10.1016/j.cogpsych.2021.101387 · DOI
  38. Kokje, 2018, Cross-race correlations in the abilities to match unfamiliar faces, Acta Psychologica, 185, 13, 10.1016/j.actpsy.2018.01.006 · DOI · Paper page
  39. Kramer, 2021, Individual differences in face and voice matching abilities: The relationship between accuracy and consistency, Applied Cognitive Psychology, 35, 192, 10.1002/acp.3754 · DOI
  40. Liljequist, 2019, Intraclass correlation – a discussion and demonstration of basic features, Plos One, 14, 10.1371/journal.pone.0219854 · DOI
  41. Lowes, 2023, Balanced integration score: A new way of classifying developmental prosopagnosia, PsyArXiv
  42. Mayer, 2022, Improving forensic perpetrator identification with Super-Recognizers, PsyArXiv
  43. McCaffery, 2018, Individual differences in face identity processing, Cognitive Research: Principles and Implications, 3, 21
  44. Megreya, 2007, Hits and false positives in face matching: A familiarity-based dissociation, Perception & Psychophysics, 69, 1175, 10.3758/BF03193954 · DOI · Paper page
  45. Nador, 2022, Face recognition in police officers: Who fits the bill?, Forensic Science International: Reports, 5
  46. Nador, 2021, Psychophysical profiles in super-recognizers, Scientific Reports, 11, 10.1038/s41598-021-92549-6 · DOI
  47. Nimon, 2012, The assumption of a reliable instrument and other pitfalls to avoid when considering the reliability of data, Frontiers in Psychology, 3, 102, 10.3389/fpsyg.2012.00102 · DOI
  48. Noyes, 2018, Face recognition ability does not predict person identification performance: Using individual data in the interpretation of group results, Cognitive Research: Principles and Implications, 3, 23
  49. O'Toole, 2007, Face recognition algorithms surpass humans matching faces over changes in illumination, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 1642, 10.1109/TPAMI.2007.1107 · DOI
  50. Phillips, 2018, Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms, Proceedings of the National Academy of Sciences, 115, 6171, 10.1073/pnas.1721355115 · DOI · Paper page
  51. Ramon, 2018, The power of how—lessons learned from neuropsychology and face processing, Cognitive Neuropsychology, 35, 83, 10.1080/02643294.2017.1414777 · DOI
  52. Ramon, 2021, Super-Recognizers – a novel diagnostic framework, 70 cases, and guidelines for future work, Neuropsychologia, 158, 10.1016/j.neuropsychologia.2021.107809 · DOI
  53. Ramon, 2018, Familiarity matters: A review on prioritized processing of personally familiar faces, Visual Cognition, 26, 179, 10.1080/13506285.2017.1405134 · DOI
  54. Richler, 2014, The vanderbilt holistic face processing test: A short and reliable measure of holistic face processing, Journal of Vision, 14, 10.1167/14.11.10 · DOI
  55. Robertson, 2016, Face recognition by metropolitan police super-recognisers, Plos One, 11, 10.1371/journal.pone.0150036 · DOI
  56. Rousseeuw, 1987, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, 20, 53, 10.1016/0377-0427(87)90125-7 · DOI · Paper page
  57. Royer, 2015, When less is more: Impact of face processing ability on recognition of visually degraded faces, Journal of Experimental Psychology: Human Perception and Performance, 41, 1179
  58. Russell, 2009, Super-recognizers: People with extraordinary face recognition ability, Psychonomic Bulletin & Review, 16, 252, 10.3758/PBR.16.2.252 · DOI · Paper page
  59. Stacchi, 2020, Normative data for two challenging tests of face matching under ecological conditions, Cognitive Research: Principles and Implications, 5, 8
  60. Stantic, 2021, The oxford face matching test: A non-biased test of the full range of individual differences in face perception, Behavior Research Methods, 10.3758/s13428-021-01609-2 · DOI
  61. Susilo, 2013, Face recognition ability matures late: Evidence from individual differences in young adults, Journal of Experimental Psychology: Human Perception and Performance, 39, 1212
  62. Sutherland, 2022, Understanding trait impressions from faces, British Journal of Psychology, 113, 1056, 10.1111/bjop.12583 · DOI
  63. Tavakol, 2011, Making sense of Cronbach's alpha, International Journal of Medical Education, 2, 53, 10.5116/ijme.4dfb.8dfd · DOI · Paper page
  64. Verhallen, 2017, General and specific factors in the processing of faces, Vision Research, 141, 217, 10.1016/j.visres.2016.12.014 · DOI
  65. Vernon, 2014, Modeling first impressions from highly variable facial images, Proceedings of the National Academy of Sciences, 111, E3353, 10.1073/pnas.1409860111 · DOI
  66. Ward, 1963, Hierarchical grouping to optimize an objective function, The Journal of the Acoustical Society of America, 58, 236
  67. White, 2021, GFMT2: A psychometric measure of face matching ability, Behavior Research Methods
  68. White, 2015, Perceptual expertise in forensic facial image comparison, Plastic and Reconstructive Surgery, 282
  69. White, 2017, Face matching impairment in developmental prosopagnosia, The Quarterly Journal of Experimental Psychology: QJEP, 70, 287, 10.1080/17470218.2016.1173076 · DOI
  70. White, 2017, Face matching impairment in developmental prosopagnosia, The Quarterly Journal of Experimental Psychology: QJEP, 70, 287, 10.1080/17470218.2016.1173076 · DOI
  71. Wilson, 2020, Science is not a signal detection problem, Proceedings of the National Academy of Sciences
  72. Wixted, 2017, The relationship between eyewitness confidence and identification accuracy: A new synthesis, Psychological Science in the Public Interest, 18, 10, 10.1177/1529100616686966 · DOI · Paper page
  73. Yarkoni, 2020, The generalizability crisis, The Behavioral and Brain Sciences, 1–37

Cited by

Sorted by citations

Metadata

Authors
Bobak, A. K., Jones, A. L., Hilker, Z., Mestry, N., Bate, S., & Hancock, P. J. B.
Venue
Cortex
Publisher
Elsevier BV
Published
2023-09
Volume
166
Pages
348-364
ISSN
0010-9452
DOI
10.1016/j.cortex.2023.05.018
Cited by
23
References
4
Language
en
Licence
Funders
  • Engineering and Physical Sciences Research Council (EP/N007743/1)
  • Leverhulme Trust (ECF-2019-416)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review