Murray, E., Bennetts, R., Tree, J. and Bate, S., 2022. An Update of the Benton Facial Recognition Test. Behavior Research Methods, 54, 2318-2333.
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DOI: 10.3758/s13428-021-01727-x
Abstract
The Benton Facial Recognition Test (BFRT) is a paper-and-pen task that has traditionally been used to assess face perception skills in neurological, clinical and psychiatric conditions. Despite some criticisms of its stimuli, the task enjoys a simple procedure and is rapid to administer. Further, it has recently been computerised (the BFRT-c), allowing reliable measurement of completion times, while addressing the need for online testing. Here, in response to calls for repeat-screening for the accurate detection of face recognition deficits, we present the BFRT-Revised (BFRT-r): a new version of the BFRT-c that maintains the task’s basic paradigm, but employs new, higher quality stimuli that reflect recent theoretical advances in the field. An initial validation study with typical participants indicated that the BFRT-r has good internal reliability and content validity. A second investigation indicated that while younger and older participants achieved similar accuracy scores, completion times were longer in the latter, highlighting the need for age-matched norms when assessing clinical cases. Administration of the BFRT-r and BFRT-c to 31 individuals with developmental prosopagnosia identified 16 cases with impairments in face perception. While these deficits were observed on both tests in eight of the cases, eight others only displayed deficits on one of the two tasks, primarily on the task completion time measure. These findings are discussed in relation to current diagnostic screening protocols for face perception deficits. The BFRT-r is stored in an open repository and is freely available to other researchers.
Item Type: | Article |
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ISSN: | 1554-351X |
Additional Information: | Part of an integrated thesis |
Uncontrolled Keywords: | Benton; Face matching; Face perception; Face recognition; Prosopagnosia; Response times |
Group: | Faculty of Science & Technology |
ID Code: | 35553 |
Deposited By: | Symplectic RT2 |
Deposited On: | 26 May 2021 11:15 |
Last Modified: | 25 Jan 2023 11:54 |
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