Bayesian regression-based developmental norms for the Benton Facial Recognition Test in males and females

Abstract

Face identity recognition is important for social interaction and is impaired in a range of clinical disorders, including several neurodevelopmental disorders. The Benton Facial Recognition Test (BFRT; Benton & Van Allen, 1968), a widely used assessment of identity recognition, is the only standardized test of face identity perception, as opposed to face memory, that has been normed on children and adolescents. However, the existing norms for the BFRT are suboptimal, with several ages not represented and no established time limit (which can lead to inflated scores by allowing individuals with prosopagnosia to use feature matching). Here we address these issues with a large normative dataset of children and adolescents (ages 5-17, N = 398) and adults (ages 18-55; N = 120) who completed a time-limited version of the BFRT. Using Bayesian regression, we demonstrate that face identity perception increases asymptotically from childhood through adulthood, and provide continuous norms based on age and sex that can be used to calculate standard scores. We show that our time limit of 16 seconds per item yields scores comparable to the existing norms without time limits from the non-prosopagnostic samples. We also find that females (N = 156) score significantly higher than males (N = 362), supporting the existence of a female superiority effect for face identification. Overall, these results provide more robust norms for the BFRT and promote future research on face identity perception in developmental populations.

Keywords: Bayesian regression; Benton test; Development; Face perception; Face recognition; Regression-based norming.

Figure 1.

Histogram displaying the age distribution of the full study sample.

Figure 2.

Developmental pattern of BFRT raw scores and inverse Bayesian regression model for expected BRFT score based on age in males and females. Lines represent predicted BFRT score, while bands represent 68% (darker) and 95% (lighter) highest density intervals (HDIs) of the prediction distribution.

Figure 3.

Distribution of BFRT scores in the norming sample. (A) Histogram of raw scores. (B) Histogram of standard scores.

Figure 4.

Comparison of BFRT scores in the new norming sample to original BFRT norms. Error bars represent bootstrapped 95% HDIs for the mean in the new norming sample (measures of score variability were not provided for original norms). Original norms did not include scores for ages 12 or 15.