Individual Differences In Face Cognition Research Articles

Accurate but inefficient: Standard face identity matching tests fail to identify prosopagnosia

In recent years, the number of face identity matching tests in circulation has grown considerably and these are being increasingly utilized to study individual differences in face cognition. Although many of these tests were designed for testing typical observers, recent studies have begun to utilize general-purpose tests for studying specific, atypical populations (e.g., super-recognizers and individuals with prosopagnosia). In this study, we examined the capacity of four tests requiring binary face-matching decisions to study individual differences between healthy observers. Uniquely, we used performance of the patient PS (Rossion, 2018), a well-documented case of acquired prosopagnosia (AP), as a benchmark. Two main findings emerged: (i) PS could exhibit typical rates of accuracy in all tests; (ii) compared to age-matched controls and when considering both accuracy and speed to account for potential trade-offs, only the KFMT — but not the EFCT, PICT or GFMT — was able to detect PS's severe impairment. These findings reflect the importance of considering both accuracy and response times to measure individual differences in face matching, and the need for comparing tests in terms of their sensitivity, when used as a measure of human cognition and brain functioning.

Individual Differences in Face Cognition: A Commentary on Logie

Individual Differences in Face Cognition: A Commentary on Logie

Structural encoding processes contribute to individual differences in face and object cognition: Inferences from psychometric test performance and event-related brain potentials

The enhanced N1 component in event-related potentials (ERP) to face stimuli, termed N170, is considered to indicate the structural encoding of faces. Previously, individual differences in the latency of the N170 have been related to face and object cognition abilities. By orthogonally manipulating content domain (faces vs objects) and task demands (easy/speed vs difficult/accuracy) in both psychometric and EEG tasks, we investigated the uniqueness of the processes underlying face cognition as compared with object cognition and the extent to which the N1/N170 component can explain individual differences in face and object cognition abilities. Data were recorded from N = 198 healthy young adults. Structural equation modeling (SEM) confirmed that the accuracies of face perception (FP) and memory are specific abilities above general object cognition; in contrast, the speed of face processing was not differentiable from the speed of object cognition. Although there was considerable domain-general variance in the N170 shared with the N1, there was significant face-specific variance in the N170. The brain–behavior relationship showed that faster face-specific processes for structural encoding of faces are associated with higher accuracy in both perceiving and memorizing faces. Moreover, in difficult task conditions, qualitatively different processes are additionally needed for recognizing face and object stimuli as compared with easy tasks. The difficulty-dependent variance components in the N170 amplitude were related with both face and object memory (OM) performance. We discuss implications for understanding individual differences in face cognition.

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

Despite a wealth of knowledge about the neural mechanisms behind emotional facial expression processing, little is known about how they relate to individual differences in social cognition abilities. We studied individual differences in the event-related potentials (ERPs) elicited by dynamic facial expressions. First, we assessed the latent structure of the ERPs, reflecting structural face processing in the N170, and the allocation of processing resources and reflexive attention to emotionally salient stimuli, in the early posterior negativity (EPN) and the late positive complex (LPC). Then we estimated brain-behavior relationships between the ERP factors and behavioral indicators of facial identity and emotion-processing abilities. Structural models revealed that the participants who formed faster structural representations of neutral faces (i.e., shorter N170 latencies) performed better at face perception (r = -.51) and memory (r = -.42). The N170 amplitude was not related to individual differences in face cognition or emotion processing. The latent EPN factor correlated with emotion perception (r = .47) and memory (r = .32), and also with face perception abilities (r = .41). Interestingly, the latent factor representing the difference in EPN amplitudes between the two neutral control conditions (chewing and blinking movements) also correlated with emotion perception (r = .51), highlighting the importance of tracking facial changes in the perception of emotional facial expressions. The LPC factor for negative expressions correlated with the memory for emotional facial expressions. The links revealed between the latency and strength of activations of brain systems and individual differences in processing socio-emotional information provide new insights into the brain mechanisms involved in social communication.

Individual differences in face cognition: Within-person brain-behavior relationships and their between-person level correlates

Individual differences in face cognition: Within-person brain-behavior relationships and their between-person level correlates

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

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.

Individual differences in face cognition: Distinct component abilities and basic neural processes

Individual differences in face cognition: Distinct component abilities and basic neural processes

Individual Differences in Face Cognition: Brain–Behavior Relationships

Individual differences in perceiving, learning, and recognizing faces, summarized under the term face cognition, have been shown on the behavioral and brain level, but connections between these levels have rarely been made. We used ERPs in structural equation models to determine the contributions of neurocognitive processes to individual differences in the accuracy and speed of face cognition as established by Wilhelm, Herzmann, Kunina, Danthiir, Schacht, and Sommer [Individual differences in face cognition, in press]. For 85 participants, we measured several ERP components and, in independent tasks and sessions, assessed face cognition abilities and other cognitive abilities, including immediate and delayed memory, mental speed, general cognitive ability, and object cognition. Individual differences in face cognition were unrelated to domain-general visual processes (P100) and to processes involved with memory encoding (Dm component). The ability of face cognition accuracy was moderately related to neurocognitive indicators of structural face encoding (latency of the N170) and of activating representations of both faces and person-related knowledge (latencies and amplitudes of the early and late repetition effects, ERE/N250 and LRE/N400, respectively). The ability of face cognition speed was moderately related to the amplitudes of the ERE and LRE. Thus, a substantial part of individual differences in face cognition is explained by the speed and efficiency of activating memory representations of faces and person-related knowledge. These relationships are not moderated by individual differences in established cognitive abilities.

Individual differences in perceiving and recognizing faces—One element of social cognition.

Recognizing faces swiftly and accurately is of paramount importance to humans as a social species. Individual differences in the ability to perform these tasks may therefore reflect important aspects of social or emotional intelligence. Although functional models of face cognition based on group and single case studies postulate multiple component processes, little is known about the ability structure underlying individual differences in face cognition. In 2 large individual differences experiments (N = 151 and N = 209), a broad variety of face-cognition tasks were tested and the component abilities of face cognition-face perception, face memory, and the speed of face cognition-were identified and then replicated. Experiment 2 also showed that the 3 face-cognition abilities are clearly distinct from immediate and delayed memory, mental speed, general cognitive ability, and object cognition. These results converge with functional and neuroanatomical models of face cognition by demonstrating the difference between face perception and face memory. The results also underline the importance of distinguishing between speed and accuracy of face cognition. Together our results provide a first step toward establishing face-processing abilities as an independent ability reflecting elements of social intelligence.