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

Abstract

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 version-completed in 3 min, on average-provide 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.

Damasio's error – Prosopagnosia with intact within‐category object recognition

“…Despite her excellent knowledge of celebrities, PS's recognition of pictures of famous faces is close to zero, as is her discrimination between personally familiar and unfamiliar faces (Busigny, Prairial, et al ., ; Busigny & Rossion, ; Rossion et al ., ). She is also severely impaired and slowed down at individual face matching tasks such as the Benton Face Recognition Test (Benton & Van Allen, ; see Busigny & Rossion, , ; Rossion & Michel, ), and impaired at explicit encoding and recognition of individual faces among distractors as in the Cambridge Face Memory Test (Duchaine & Nakayama, ; see Ramon et al ., ).…”

Immediate stimulus repetition abolishes stimulus expectation and surprise effects in fast periodic visual oddball designs

“…All participants had normal or corrected-to-normal vision, no history of psychiatric or neurological disorders or substance abuse and were righthanded as assessed by the Flinders Handedness Survey (Nicholls et al, 2013). All participants showed unimpaired facial recognition ability as measured by the electronic version of the Benton Facial Recognition Test (all global accuracy scores > 39/54 cutoff defined in Rossion & Michel, 2018). Experiment 2 was approved by the Human Research Ethics committee of the University of South Australia.…”

Comparing the sensitivity of face matching assessments to detect face perception deficits

“…C. Duchaine, 2000;Duchaine & Nakayama, 2004;Nunn et al, 2001), as they often use feature-by-feature strategy to achieve performance within the normal range. Addressing this shortcoming, Rossion and Michel (2018) developed a computerized version of the BFRT (BFRT-c) that collects response times and instructs participants to respond, "as quickly and accurately as possible". The normative accuracy of this version (M = 44.81, SD = 3.44; also see Dzhelyova et al, 2020;M = 44.8, SD = 3.77; Mage = 22.12 years), was notably .75 SDs lower than versions without speeded instructions (e.g., Albonico et al, 2017, normative M = 47.12, SD = 2.99).…”

“…In the current study we sought to compare face perception assessments in their ability to detect performance differences between DPs and controls. We compared the performance across four face matching assessments, including the more widely used BFRT-c (Rossion & Michel, 2018) and CFPT (Duchaine et al, 2007). We also included the USC Face Perception Test (USCFPT) that uses computer-generated faces and difficulty levels scaled according to the Gabor-jet model designed to capture early visual response properties (Biederman et al, 2017;Margalit et al, 2016;Yue et al, 2012).…”