Neurocognitive mechanisms of individual differences in face cognition: A replication and extension

Abstract

Face cognition performance is related to individual differences in cognitive subprocesses, as reflected in the amplitudes and latencies of event-related brain potentials (ERPs; Herzmann, Kunina, Sommer, & Wilhelm, 2010). In order to replicate and extend these findings, 110 participants were tested on a comprehensive task battery measuring face cognition abilities and established cognitive abilities, followed by ERP recordings in a face-learning-and-recognition task. We replicated the links of the ERP components indicating the speed of structural face encoding (N170 latency) and access to structural representations in memory (early repetition effect [ERE]/N250r) with the accuracy and speed of face cognition and with established cognitive abilities. As a novel result, we differentiated between the accuracy of face perception and face memory on the behavioral and electrophysiological levels and report a relationship between basic visual processes (P100 amplitude) and face memory. Moreover, the brain-behavior relationships for the ERE/N250r held true, even though we eliminated pictorial and perceptual structural codes from the priming effects by using backward masking of the primes with novel unfamiliar faces. On a methodological level, we demonstrated the utility of the latent difference score modeling technique to parameterize ERP difference components (e.g., ERE/N250r) on a latent level and link them to face cognition abilities.

Are event-related potentials to dynamic facial expressions of emotion related to individual differences in the accuracy of processing facial expressions and identity?

“…Notably, we replicated the negative correlation of N170 latencies with face perception and memory abilities that had been observed in two previous studies, indicating that a faster process of creating structural representations of faces facilitates accurate performance in recognizing and remembering identity from faces (Herzmann et al, 2009;Kaltwasser et al, 2014). Since this relationship appears to be consistent across different tasks that focus on face identity or emotion processing, different stimulus materials (neutral or emotional, dynamic or static), and different modeling approaches (confirmatory factor analysis, LDS), the N170 latency seems to be a robust psychophysiological predictor of an individual's performance at identifying and remembering identity from faces.…”

“…The findings regarding the N170 latency and ERE were replicated and extended by Kaltwasser, Hildebrandt, Recio, Wilhelm, and Sommer (2014). They applied latent difference score modeling (LDS; McArdle, 2009) to parameterize individual differences in ERPs, defined as difference waves between primed and unprimed conditions (i.e., ERE, LRE-for details, see Kaltwasser et al, 2014). LDS helps to solve the problem of the limited variance and reliability of difference scores when two initial measures are highly correlated (e.g., Rogosa & Willett, 1983).…”

“…The authors also reported a positive correlation between two memory-related ERP components, the early and late repetition effects (ERE and LRE, respectively), and individual differences in face perception accuracy and speed. The findings regarding the N170 latency and ERE were replicated and extended by Kaltwasser, Hildebrandt, Recio, Wilhelm, and Sommer (2014). They applied latent difference score modeling (LDS; McArdle, 2009) to parameterize individual differences in ERPs, defined as difference waves between primed and unprimed conditions (i.e., ERE, LRE-for details, see Kaltwasser et al, 2014).…”

“…In the behavioral part, we assessed individual differences in face cognition abilities in general, and the specific abilities of emotion perception and recognition, using a multivariate test battery consisting of several tasks for each latent variable (Hildebrandt et al, 2015;Wilhelm, Hildebrandt, Manske, Schacht, & Sommer, 2014). In the psychophysiological part, we recorded ERPs while a subsample, randomly selected from the psychometric sample, completed three different tasks: (1) learning and recognition of facial identity (Kaltwasser et al, 2014), (2) the classification of dynamic facial expressions of emotion (Künecke et al, 2014;Recio, Schacht & Sommer, 2014), and (3) the production of facial expressions (Recio, Shmuilovich, & Sommer, 2014). In the present study, we focused on the emotion specificities of the ERPs recorded during the emotion classification task, and on their relationship to the accuracy of face identity and facial emotion processing obtained in the psychometric part.…”

“…Hence, we also modeled an LDS variable representing the difference in amplitude between two neutral conditions. Regarding brain-behavior relationships, we expected to replicate the negative correlation between the N170 latency and face identity perception (e.g., Kaltwasser et al, 2014). Moreover, we predicted that the N170 measures of amplitude and latency, reflecting structural encoding, would be related to face perception and memory abilities.…”