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

Anna K. Bobak, Alex Jones Orcid Logo, Zoe Hilker, Natalie Mestry, Sarah Bate, Peter J.B. Hancock

Cortex, Volume: 166, Pages: 348 - 364

Swansea University Author: Alex Jones Orcid Logo

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DOI (Published version): 10.1016/j.cortex.2023.05.018

Abstract

There is growing interest in how data-driven approaches can help understand individual differences in face identity processing (FIP). However, researchers employ various FIP tests interchangeably, and it is unclear whether these tests 1) measure the same underlying ability/ies and processes (e.g., c...

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Published in: Cortex
ISSN: 0010-9452
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63799
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spelling v2 63799 2023-07-06 Data-driven studies in face identity processing rely on the quality of the tests and data sets a24e1e2a89b0a9120fe03b481a629edd 0000-0003-3600-3644 Alex Jones Alex Jones true false 2023-07-06 HPS There is growing interest in how data-driven approaches can help understand individual differences in face identity processing (FIP). However, researchers employ various FIP tests interchangeably, and it is unclear whether these tests 1) measure the same underlying ability/ies and processes (e.g., confirmation of identity match or elimination of identity match) 2) are reliable, 3) provide consistent performance for individuals across tests online and in laboratory. Together these factors would influence the outcomes of data-driven analyses. Here, we asked 211 participants to perform eight tests frequently reported in the literature. We used Principal Component Analysis and Agglomerative Clustering to determine factors underpinning performance. Importantly, we examined the reliability of these tests, relationships between them, and quantified participant consistency across tests. Our findings show that participants’ performance can be split into two factors (called here confirmation and elimination of an identity match) and that participants cluster according to whether they are strong on one of the factors or equally on both. We found that the reliability of these tests is at best moderate, the correlations between them are weak, and that the consistency in participant performance across tests and is low. Developing reliable and valid measures of FIP and consistently scrutinising existing ones will be key for drawing meaningful conclusions from data-driven studies. Journal Article Cortex 166 348 364 Elsevier BV 0010-9452 Face identity processing (FIP), Face perception, Face memory, Individual differences, Principal component analysis, Agglomerative clustering 30 9 2023 2023-09-30 10.1016/j.cortex.2023.05.018 http://dx.doi.org/10.1016/j.cortex.2023.05.018 COLLEGE NANME Psychology COLLEGE CODE HPS Swansea University Anna K. Bobak was funded by the Leverhulme Early Career Fellowship, grant number ECF-2019-416; Peter J.B. Hancock was funded by the EPSRC, grant number EP/N007743/1. 2024-02-02T08:44:58.6773663 2023-07-06T20:23:33.9155673 Faculty of Medicine, Health and Life Sciences School of Psychology Anna K. Bobak 1 Alex Jones 0000-0003-3600-3644 2 Zoe Hilker 3 Natalie Mestry 4 Sarah Bate 5 Peter J.B. Hancock 6 63799__28286__666bcc8402ab498b927fc45ad391b132.pdf 63799.VOR.pdf 2023-08-11T10:08:56.7246485 Output 2088230 application/pdf Version of Record true © 2023 The Authors. Published by Elsevier Ltd. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title Data-driven studies in face identity processing rely on the quality of the tests and data sets
spellingShingle Data-driven studies in face identity processing rely on the quality of the tests and data sets
Alex Jones
title_short Data-driven studies in face identity processing rely on the quality of the tests and data sets
title_full Data-driven studies in face identity processing rely on the quality of the tests and data sets
title_fullStr Data-driven studies in face identity processing rely on the quality of the tests and data sets
title_full_unstemmed Data-driven studies in face identity processing rely on the quality of the tests and data sets
title_sort Data-driven studies in face identity processing rely on the quality of the tests and data sets
author_id_str_mv a24e1e2a89b0a9120fe03b481a629edd
author_id_fullname_str_mv a24e1e2a89b0a9120fe03b481a629edd_***_Alex Jones
author Alex Jones
author2 Anna K. Bobak
Alex Jones
Zoe Hilker
Natalie Mestry
Sarah Bate
Peter J.B. Hancock
format Journal article
container_title Cortex
container_volume 166
container_start_page 348
publishDate 2023
institution Swansea University
issn 0010-9452
doi_str_mv 10.1016/j.cortex.2023.05.018
publisher Elsevier BV
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str School of Psychology{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}School of Psychology
url http://dx.doi.org/10.1016/j.cortex.2023.05.018
document_store_str 1
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description There is growing interest in how data-driven approaches can help understand individual differences in face identity processing (FIP). However, researchers employ various FIP tests interchangeably, and it is unclear whether these tests 1) measure the same underlying ability/ies and processes (e.g., confirmation of identity match or elimination of identity match) 2) are reliable, 3) provide consistent performance for individuals across tests online and in laboratory. Together these factors would influence the outcomes of data-driven analyses. Here, we asked 211 participants to perform eight tests frequently reported in the literature. We used Principal Component Analysis and Agglomerative Clustering to determine factors underpinning performance. Importantly, we examined the reliability of these tests, relationships between them, and quantified participant consistency across tests. Our findings show that participants’ performance can be split into two factors (called here confirmation and elimination of an identity match) and that participants cluster according to whether they are strong on one of the factors or equally on both. We found that the reliability of these tests is at best moderate, the correlations between them are weak, and that the consistency in participant performance across tests and is low. Developing reliable and valid measures of FIP and consistently scrutinising existing ones will be key for drawing meaningful conclusions from data-driven studies.
published_date 2023-09-30T08:44:57Z
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score 11.016235

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