Lineup size influences voice identification accuracy.

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Title: Lineup size influences voice identification accuracy.
Authors: Harvey, Madison B. (AUTHOR), Giroux, Megan (AUTHOR), Price, Heather L. (AUTHOR)
Source: Applied Cognitive Psychology. Nov2023, Vol. 37 Issue 6, p1303-1315. 13p.
Subjects: Human voice
Abstract: We investigated the effects of lineup size, target presence, and target sex on voice identification accuracy. In Study 1, participants identified a female target from 1‐, 6‐, or 10‐voice target present or target absent lineups. Results demonstrated increasing accuracy with smaller target absent lineups. One‐voice lineups led to higher rates of innocent suspect identifications than 10‐voice lineups. In Study 2, participants identified a male or female target from 1‐, 6‐, or 10‐voice target present or target absent lineups. Results revealed increased correct identifications in 1‐ and 6‐voice and increased innocent suspect identifications in 1‐voice lineups, compared to 10‐voice lineups. Female lineups resulted in higher accuracy in target present lineups yet increased rates of innocent suspect identifications. Our findings indicate 1‐voice lineups result in both increased accuracy and increased innocent suspect identifications, a trade‐off which may not be advisable. Future research should further explore the impact of multi‐voice lineups on accuracy. [ABSTRACT FROM AUTHOR]
Copyright of Applied Cognitive Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Lineup size influences voice identification accuracy.
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  Data: <searchLink fieldCode="AR" term="%22Harvey%2C+Madison+B%2E%22">Harvey, Madison B.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Giroux%2C+Megan%22">Giroux, Megan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Price%2C+Heather+L%2E%22">Price, Heather L.</searchLink> (AUTHOR)
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  Data: We investigated the effects of lineup size, target presence, and target sex on voice identification accuracy. In Study 1, participants identified a female target from 1‐, 6‐, or 10‐voice target present or target absent lineups. Results demonstrated increasing accuracy with smaller target absent lineups. One‐voice lineups led to higher rates of innocent suspect identifications than 10‐voice lineups. In Study 2, participants identified a male or female target from 1‐, 6‐, or 10‐voice target present or target absent lineups. Results revealed increased correct identifications in 1‐ and 6‐voice and increased innocent suspect identifications in 1‐voice lineups, compared to 10‐voice lineups. Female lineups resulted in higher accuracy in target present lineups yet increased rates of innocent suspect identifications. Our findings indicate 1‐voice lineups result in both increased accuracy and increased innocent suspect identifications, a trade‐off which may not be advisable. Future research should further explore the impact of multi‐voice lineups on accuracy. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Applied Cognitive Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/acp.4124
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              Text: Nov2023
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