No abstract

Face cognition (FC) is a specific ability that cannot be fully explained by general cognitive functions. Cortical thickness (CT) is a neural correlate of performance and learning. In this registered report, we used data from the Human Connectome Project (HCP) to investigate the relationship between CT in the core brain network of FC and performance on a psychometric task battery, including tasks with facial content. Using structural equation modelling (SEM), we tested the existence of face-specific interindividual differences at behavioural and neural levels. The measurement models include general and face-specific factors of performance and CT. There was no face-specificity in CT in functionally localized areas. In post hoc analyses, we compared the preregistered, small regions of interest (ROIs) to larger, non-individualized ROIs and identified a face-specific CT factor when large ROIs were considered. We show that this was probably due to low reliability of CT in the functional localization (intra-class correlation coefficients (ICC) between 0.72 and 0.85). Furthermore, general cognitive ability, but not face-specific performance, could be predicted by latent factors of CT with a small effect size. In conclusion, for the core brain network of FC, we provide exploratory evidence (in need of cross-validation) that areas of the cortex sharing a functional purpose did also share morphological properties as measured by CT.

Faces are a major source of information in social interaction. The ability to perceive and interpret faces thus carries paramount importance in people’s social lives. However, this crucial ability is not yet fully understood. Individual differences studies show that the speed and accuracy of face cognition (including perception and memory/recognition), the two facets targeted when measuring cognitive performance, are relatively independent traits. Unlike accuracy data, individual differences in reaction times (RTs) measured in perceptual decision tasks with or without memory load using faces and objects, do not show face-specific variance. Here, we applied the diffusion model to RT and accuracy data captured by simple perceptual decision tasks to improve understanding of the lack of face specificity. If performance speed in face cognition tasks is truly a global, nonspecific individual ability, no parameter of the diffusion model should hold face specificity. In a study on adults (N = 217), we administered two tasks of face and object perception. We used individually estimated diffusion model parameters as manifest variables to study face specificity in drift rate (ν), boundary separation (a), and nondecision time (Ter) using structural equation modeling. Furthermore, to study differential relationships between diffusion model parameters and measures of cognitive abilities, we regressed factors of face and object cognition accuracy on factors of diffusion model parameters. The results revealed face specificity only in boundary separation. This suggests face-specific adjustment in the cautiousness of information processing.

The study of socio-cognitive abilities emerged from intelligence research, and their specificity remains controversial until today. In recent years, the psychometric structure of face cognition (FC)—a basic facet of socio-cognitive abilities—was extensively studied. In this review, we summarize and discuss the divergent psychometric structures of FC in easy and difficult tasks. While accuracy in difficult tasks was consistently shown to be face-specific, the evidence for easy tasks was inconsistent. The structure of response speed in easy tasks was mostly—but not always—unitary across object categories, including faces. Here, we compare studies to identify characteristics leading to face specificity in easy tasks. The following pattern emerges: in easy tasks, face specificity is found when modeling speed in a single task; however, when modeling speed across multiple, different easy tasks, only a unitary factor structure is reported. In difficult tasks, however, face specificity occurs in both single task approaches and task batteries. This suggests different cognitive mechanisms behind face specificity in easy and difficult tasks. In easy tasks, face specificity relies on isolated cognitive sub-processes such as face identity recognition. In difficult tasks, face-specific and task-independent cognitive processes are employed. We propose a descriptive model and argue for FC to be integrated into common taxonomies of intelligence.