Evidence map›Paper›PMID 37296324›Full record

ArticleBehavior research methods2024

Face identification proficiency test designed using item response theory.

Geraldine Jeckeln, Ying Hu, Jacqueline G Cavazos, Amy N Yates, Carina A Hahn, Larry Tang, P Jonathon Phillips, Alice J O'Toole

Abstract read
In one paragraph

Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Geraldine JeckelnThe University of Texas at Dallas, Richardson, Texas, USA. geraldine.jeckeln@utdallas.edu.ORCID 0000-0001-7248-0144
Ying HuThe University of Texas at Dallas, Richardson, Texas, USA.
Jacqueline G CavazosThe University of Texas at Dallas, Richardson, Texas, USA.
Amy N YatesNational Institute of Standards and Technology, Gaithersburg, Maryland, USA.
Carina A HahnNational Institute of Standards and Technology, Gaithersburg, Maryland, USA.
Larry TangUniversity of Central Florida, Orlando, Florida, USA.
P Jonathon PhillipsNational Institute of Standards and Technology, Gaithersburg, Maryland, USA.
Alice J O'TooleThe University of Texas at Dallas, Richardson, Texas, USA.

Funding

Human Face Representation in Deep Convolutional Neural NetworksR01EY029692 · NEI · UNIVERSITY OF TEXAS DALLAS · PI O'TOOLE, ALICE J · 2019 to 2022
$1.5M
NEI NIH HHS R01 EY029692
6 · The paper itself

Abstract

Measures of face-identification proficiency are essential to ensure accurate and consistent performance by professional forensic face examiners and others who perform face-identification tasks in applied scenarios. Current proficiency tests rely on static sets of stimulus items and so cannot be administered validly to the same individual multiple times. To create a proficiency test, a large number of items of "known" difficulty must be assembled. Multiple tests of equal difficulty can be constructed then using subsets of items. We introduce the Triad Identity Matching (TIM) test and evaluate it using item response theory (IRT). Participants view face-image "triads" (N = 225) (two images of one identity, one image of a different identity) and select the different identity. In Experiment 3, university students (N = 197) showed wide-ranging accuracy on the TIM test, and IRT modeling demonstrated that the TIM items span various difficulty levels. In Experiment 3, we used IRT-based item metrics to partition the test into subsets of specific difficulties. Simulations showed that subsets of the TIM items yielded reliable estimates of subject ability. In Experiments 3a and b, we found that the student-derived IRT model reliably evaluated the ability of non-student participants and that ability generalized across different test sessions. In Experiment 3c, we show that TIM test performance correlates with other common face-recognition tests. In summary, the TIM test provides a starting point for developing a framework that is flexible and calibrated to measure proficiency across various ability levels (e.g., professionals or populations with face-processing deficits).

Indexed as

Facial RecognitionHumansStudentsFace matching testItem response theoryPerceptual face identification testTriad Identity Matching test

Identifiers

PMID37296324
PMCPMC10991046

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.