4.5 Article

Normative accuracy and response time data for the computerized Benton Facial Recognition Test (BFRT-c)

Journal

BEHAVIOR RESEARCH METHODS
Volume 50, Issue 6, Pages 2442-2460

Publisher

SPRINGER
DOI: 10.3758/s13428-018-1023-x

Keywords

Face matching; Benton test; Response times; Lighting direction; Unfamiliar faces

Funding

  1. Belgian National Research Fund (FNRS)
  2. ERC [284025]

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We report normative data from a large (N = 307) sample of young adult participants tested with a computerized version of the long form of the classical Benton Facial Recognition Test (BFRT; Benton & Van Allen, 1968). The BFRT-c requires participants to match a target face photograph to either one or three of six face photographs presented simultaneously. We found that the percent accuracy on the BFRT-c (81%-83%) was below ceiling yet well above chance level, with little interindividual variance in this typical population sample, two important aspects of a sensitive clinical test. Although the split-half reliability on response accuracy was relatively low, due to the large variability in difficulty across items, the correct response times measured in this versioncompleted in 3 min, on averageprovide a reliable and critical complementary measure of performance at individual unfamiliar-face matching. In line with previous observations from other measures, females outperformed male participants at the BFRT-c, especially for female faces. In general, performance was also lower following lighting changes than following head rotations, in line with previous studies that have emphasized participants' limited ability to match pictures of unfamiliar faces with important variations in illumination. Overall, this normative data set supports the validity of the BFRT-c as a key component of a battery of tests to identify clinical impairments in individual face recognition, such as observed in acquired prosopagnosia. However, this analysis strongly recommends that researchers consider the full test results: Beyond global indexes of performance based on accuracy rates only, they should consider the time taken to match individual faces as well as the variability in performance across items.

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