Investigating the other race effect: Human and computer face matching and similarity judgements.
The other race effect (ORE) in part describes how people are poorer at identifying faces of other races compared to own-race faces. While well-established with face memory, more recent studies have begun to demonstrate its presence in face matching tasks, with minimal memory requirements. However, s...
| Publicado en: | Visual Cognition Vol. 31; no. 4; pp. 314 - 326 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Apr2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173157835&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173157835 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13506285 BE5 jtl: Visual Cognition issn: 13506285 maglogo: N pubinfo: dt: Apr2023 vid: 31 iid: 4 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 173157835 170714814 173157835 173157835 10.1080/13506285.2023.2250514 173157835 ppf: 314 ppct: 12 formats: tig: atl: Investigating the other race effect: Human and computer face matching and similarity judgements. aug: au: Ritchie, Kay L. Cartledge, Charlotte Kramer, Robin S. S. affil: School of Psychology, University of Lincoln, Lincoln, UK sug: subj: Face Perception United Kingdom Race Factors Judgment Neural Networks (Computer) Human Task Performance and Analysis Algorithms Black Persons White Persons Memory Recognition (Psychology) Visual Perception Factor Analysis Adult Middle Age Male Female United Kingdom Descriptive Statistics T-Tests Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: The other race effect (ORE) in part describes how people are poorer at identifying faces of other races compared to own-race faces. While well-established with face memory, more recent studies have begun to demonstrate its presence in face matching tasks, with minimal memory requirements. However, several of these studies failed to compare both races of faces and participants in order to fully test the predictions of the ORE. Here, we utilized images of both Black and White individuals, and Black and White participants, as well as tasks measuring perceptions of face matching and similarity. In addition, human judgements were directly compared with computer algorithms. First, we found only partial support for an ORE in face matching. Second, a deep convolutional neural network (residual network with 29 layers) performed exceptionally well with both races. The DCNN's representations were strongly associated with human perceptions. Taken together, we found that the ORE was not robust or compelling in our human data, and was absent in the computer algorithms we tested. We discuss our results in the context of ORE literature, and the importance of state-of-the-art algorithms. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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