Cited by 5Face Recognition and PerceptionFace recognition and analysisEvolutionary Psychology and Human Behavior

The DOI matches its Crossref record; 99 of the 99 references it cites were found in a registry or an index; 16 of the 54 sources its claims cite were checked for support.

A criterion-placement theory of face matching

Open access

Abstract

Face matching is an important applied task that requires binary decisions to pairs of face images to determine whether these depict the same person (an identity match) or different people (a mismatch). While these choices are mutually exclusive, performance for match and mismatch trials appears to be dissociable, which poses a problem for theory development. The current study demonstrates that this dissociation arises from systematic response biases, which reflect individual differences in the placement of decision-making thresholds to distinguish matches from mismatches. When these biases are controlled or partialled out from classification accuracy, reliable associations between match and mismatch identifications are found. This is demonstrated over two experiments with a sample of over 500 participants, several face-matching tests, and a series of data simulations. These findings support a cognitive theory in which individual differences in the placement of decision-making thresholds provide the mechanism by which the identification of face matches and mismatches are linked.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

The paper argues that the widely reported dissociation between face-match and face-mismatch accuracy is caused by individual differences in decision criterion placement. When criterion is controlled or partialled out, match and mismatch performance become positively correlated, supporting a unitary account of face matching.

Key findings

  • Raw match and mismatch accuracy were unrelated or negatively related: Experiment 1 GFMT, r(247) = −0.05, p = 0.44; AIFMT, r(247) = −0.29, p < 0.001. Experiment 2 GFMT, r(297) = −0.15, p < 0.001; KFMT, r(297) = −0.42, p < 0.001.
  • After partialling out criterion, match–mismatch correlations became positive: Experiment 1 GFMT, r(247) = 0.61, p < 0.001; AIFMT, r(247) = 0.42, p < 0.001. Experiment 2 GFMT, r(297) = 0.53, p < 0.001; KFMT, r(297) = 0.28, p < 0.001.
  • Criterion was stable and transferred across tests: GFMT–AIFMT criterion correlation r = 0.59, p < 0.001; GFMT–KFMT criterion correlation r(297) = 0.55, p < 0.001.
  • Simulations reproduced the pattern: when bias varied freely, match–mismatch correlation was negative, r = −0.06, p = 0.59; when bias was controlled, it was positive, r = 0.85, p < 0.001. Further simulations showed the direction of the correlation depends on the relative variability in bias versus ability, and that item- and participant-level biases can produce similar dissociations in old/new recognition.

Methods

  • Experiment 1 analysed Session 1 data from 249 participants (female N = 166; age M = 22.56, SD = 4.29) who completed the GFMT-short and AIFMT. Accuracy was converted to signal detection measures d′ and criterion c; residual match and mismatch scores were computed after controlling for average criterion across tasks.
  • Experiment 2 analysed 299 white undergraduate students (female n = 245; age M = 20.25, SD = 4.98) who completed the GFMT-short and KFMT. The same signal detection and residual-score approach was used, with criterion averaged across tests.
  • Three simulations modelled participants’ ability and bias and item difficulty using truncated normal distributions. Simulation 1 replicated the experimental pattern; Simulation 2 varied the ratio of bias to ability variance and examined PCA outcomes; Simulation 3 modelled old/new recognition with participant- and item-level bias.

Limitations

  • The paper states that future research should examine the origin of individual differences in criterion placement, and that whether the effects extend to familiar face recognition remains an open question.
  • The paper notes that criterion shifts might also affect match–mismatch correlations, but whether this occurs when individual differences in criterion are present remains to be seen.
  • Evident from the text: the experiments are correlational and rely on partialling rather than direct manipulation of criterion, so causal claims rest partly on simulations; the simulations use deterministic or lightly noised models with assumed distributions; and the human samples are mostly young university students or Prolific volunteers, limiting generalisability.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/j.cognition.2025.106319

Cites

99 of 99 references verified0 citation errors · 0 not checkable

Statistics

24 tests recomputed · 0 with the reported and recomputed p on opposite sides of .05

Figures

Image 1
CC-BY · From the PDF
Image 2
CC-BY · From the PDF
Image 3
CC-BY · From the PDF
Image 4
CC-BY · From the PDF
Image 5
CC-BY · From the PDF
Fig. 2 Note. Fig. 2 depicts the relationships between accuracy on match and mismatch trials for the GFMT (top row) and the AIFMT (bottom). The graphs on the left depict the unadjusted percentage accuracy data. The graphs on the right show a positive association of match and mismatch performance after controlling for criterion.
Fig. 2 Note. Fig. 2 depicts the relationships between accuracy on match and mismatch trials for the GFMT (top row) and the AIFMT (bottom). The graphs on the left depict the unadjusted percentage accuracy data. The graphs on the right show a positive association of match and mismatch performance after controlling for criterion.CC-BY · From the PDF
Fig. 3 Note. Fig. 3 provides examples of match and mismatch trials for the Glasgow Face Matching Test (GFMT; top row) and the Kent Face Matching Test (KFMT; bottom row). Models from the images within the images here consented to having their images used within publications.
Fig. 3 Note. Fig. 3 provides examples of match and mismatch trials for the Glasgow Face Matching Test (GFMT; top row) and the Kent Face Matching Test (KFMT; bottom row). Models from the images within the images here consented to having their images used within publications.CC-BY · From the PDF
Fig. 4 Note. Fig. 4 depicts the relationships between accuracy on match and mismatch trials for the GFMT (top row) and the KFMT (bottom). The graphs on the left depict the unadjusted percentage accuracy data. The graphs on the right show a positive association of match and mismatch performance after controlling for criterion.
Fig. 4 Note. Fig. 4 depicts the relationships between accuracy on match and mismatch trials for the GFMT (top row) and the KFMT (bottom). The graphs on the left depict the unadjusted percentage accuracy data. The graphs on the right show a positive association of match and mismatch performance after controlling for criterion.CC-BY · From the PDF
Fig. 5 Note. Fig. 5 provides a Scatter plot of simulated participants ' match and mismatch accuracy when bias freely varies (Left) and when bias is controlled (right).
Fig. 5 Note. Fig. 5 provides a Scatter plot of simulated participants ' match and mismatch accuracy when bias freely varies (Left) and when bias is controlled (right).CC-BY · From the PDF
Image 10
CC-BY · From the PDF
Fig. 7 Note. Fig. 7 provides plots for the relationships between old (hits) and new (correct rejections; CR) responses with and without variability in criterion at the item (left) and participant (right) levels. A-B had no variability in bias at either level. C-D had variability in bias only at the participant level. E-F had variability in bias only at the item level. G-H had variability in bias at both levels.
Fig. 7 Note. Fig. 7 provides plots for the relationships between old (hits) and new (correct rejections; CR) responses with and without variability in criterion at the item (left) and participant (right) levels. A-B had no variability in bias at either level. C-D had variability in bias only at the participant level. E-F had variability in bias only at the item level. G-H had variability in bias at both levels.CC-BY · From the PDF

Verification

Claims the paper makes

43 Own interpretation · 30 Own result · 16 Not checked yet · 8 Own method · 6 No full text of the sources · 4 Partly supported · 4 No basis given · 3 Supported so far · 2 Supported · 1 Not supported · 1 Sources not found · accuracy not yet measured

Verify all 21 references — free account

Show all 77 claims

  • Probably not a substantial claim. 10
  • It may state two separate claims as one. 33
  • The quote only partly states this claim. 1
41 of 41
  • The paper relies on this without support.

    Both match and mismatch decisions rely on a visual comparison of the similarity of faces within the pair.

    “However, this match-mismatch dissociation is difficult to reconcile with a key observation-both types of decisions rely on a visual comparison of the similarity of faces within the pair.”
  • The paper relies on this without support.

    Observers must approach the identification of both stimulus types in the same manner initially, before an identification decision is reached.

    “Moreover, observers must approach the identification of both stimulus types in the same manner initially, before an identification decision is reached.”
  • The paper relies on this without support.

    Errors on match and mismatch trials are associated with different risks in applied settings.

    “This issue is also important practically as errors on match and mismatch trials are associated with different risks in applied settings.”
  • The paper relies on this without support.

    The simulation model's decision rule and parameter distributions capture the relevant processes in human face matching.

    “For the first simulation, we generated data for a deterministic model by drawing from separate truncated normal distributions (Luong, 2025) foot_2 to act as participants' ability and bias, and also for stimulus item difficulty.”
    “A match trial is scored correct if a simulated participant's combined ability and bias (i.e., ability + bias) exceeds the trial difficulty, and a mismatch trial is correct if their combined ability and bias (i.e., ability -bias) value exceeds the trial difficulty.”
4 of 4

Statistics that don’t add up

Each reported p-value recomputed from its test statistic and degrees of freedom.

1 of 1

Statistics recomputed

Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.

  • Doesn’t add upr(297) = -0.15reported p < .001 · recomputed p = .009

    “r(297) = -0.15,p < .001”

  • Consistentr(247) = 0.67reported p < .001 · recomputed p < .001

    “r(247) = 0.67, p < .001”

  • Consistentr(247) = 0.58reported p < .001 · recomputed p < .001

    “r(247) = 0.58, p < .001”

  • Consistentr(247) = 0.69reported p < .001 · recomputed p < .001

    “r(247) = 0.69, p < .001”

  • Consistentr(247) = 0.69reported p < .001 · recomputed p < .001

    “r (247) = 0.69, p < .001”

  • Consistentr(247) = -0.05reported p = .44 · recomputed p = .432

    “r(247) = -0.05,p = .44”

  • Consistentr(247) = -0.29reported p < .001 · recomputed p < .001

    “r(247) = -0.29,p < .001”

  • Consistentr(247) = 0.43reported p < .001 · recomputed p < .001

    “r (247) = 0.43, p < .001”

  • Consistentr(247) = 0.47reported p < .001 · recomputed p < .001

    “r(247) = 0.47, p < .001”

  • Consistentr(247) = 0.59reported p < .001 · recomputed p < .001

    “r(247) = 0.59, p < .001”

  • Consistentr(247) = 0.41reported p < .001 · recomputed p < .001

    “r (247) = 0.41, p < .001”

  • Consistentr(247) = 0.61reported p < .001 · recomputed p < .001

    “r(247) = 0.61, p < .001”

  • Consistentr(247) = 0.42reported p < .001 · recomputed p < .001

    “r(247) = 0.42, p < .001”

  • Consistentr(297) = 0.55reported p < .001 · recomputed p < .001

    “r(297) = 0.55, p < .001”

  • Consistentr(297) = 0.62reported p < .001 · recomputed p < .001

    “r(297) = 0.62, p < .001”

  • Consistentr(297) = 0.52reported p < .001 · recomputed p < .001

    “r(297) = 0.52, p < .001”

  • Consistentr(297) = 0.54reported p < .001 · recomputed p < .001

    “r (297) = 0.54, p < .001”

  • Consistentr(297) = -0.42reported p < .001 · recomputed p < .001

    “r(297) = -0.42,p < .001”

  • Consistentr(297) = 0.47reported p < .001 · recomputed p < .001

    “r(297) = 0.47, p < .001”

  • Consistentr(297) = 0.46reported p < .001 · recomputed p < .001

    “r(297) = 0.46, p < .001”

  • Consistentr(297) = 0.31reported p < .001 · recomputed p < .001

    “r(297) = 0.31, p < .001”

  • Consistentr(297) = 0.3reported p < .001 · recomputed p < .001

    “r(297) = 0.30, p < .001”

  • Consistentr(297) = 0.53reported p < .001 · recomputed p < .001

    “r(297) = 0.53, p < .001”

  • Consistentr(297) = 0.28reported p < .001 · recomputed p < .001

    “r(297) = 0.28, p < .001”

24 of 24

Self-check

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

Self-check results appear here once an independent check has confirmed them.

Citations

Cites

Show all 77 references

Research works printed but not linked: 1

  1. Hancock, 1996, Face processing: Human perception and principal components analysis, Memory and Cognition, 24, 26, 10.3758/bf03197270 · DOI

Cited by

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Metadata

Authors
Baker, K. A., Mondloch, C. J., Hancock, P. J. B., & Bindemann, M.
Affiliations
  • University of Kent (GB)
  • Brock University (CA)
  • University of Stirling (GB)
Corresponding author
Baker, Kristen A.
Venue
Cognition
Publisher
Elsevier BV
Published
2026-01
Volume
266
Pages
106319
ISSN
0010-0277 (ISSN-L)
OpenAlex
W4414591049
Cited by
5
References
77
Citations by year
2026: 4
FWCI
2.06
Type
journal article
Language
en
Open access
Locations
  • Cognition (publishedVersion, cc-by)
  • SSRN Electronic Journal (acceptedVersion)
  • PubMed (publishedVersion)
  • Kent Academic Repository (University of Kent) (submittedVersion)
Funders
  • Natural Sciences and Engineering Research Council of Canada (RGPIN 2022-04386)
  • Royal Society (221754)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Evolutionary Psychology and Human Behavior
Keywords
face matching, face identification, response bias, decision threshold, individual differences
MeSH terms
Facial Recognition, Adolescent, Adult, Decision Making, Face, Female, Humans, Individuality, Male, Psychological Theory, Recognition, Psychology, Young Adult

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