Improvements in task performance after practice are associated with scale-free dynamics of brain activity.

Although practicing a task generally benefits later performance on that same task, there are individual differences in practice effects. One avenue to model such differences comes from research showing that brain networks extract functional advantages from operating in the vicinity of criticality, a state in which brain network activity is more scale-free. We hypothesized that higher scale-free signal from fMRI data, measured with the Hurst exponent (H), indicates closer proximity to critical states. We tested whether individuals with higher H during repeated task performance would show greater practice effects. In Study 1, participants performed a dual-n-back task (DNB) twice during MRI (n = 56). In Study 2, we used two runs of n-back task (NBK) data from the Human Connectome Project sample (n = 599). In Study 3, participants performed a word completion task (CAST) across six runs (n = 44). In all three studies, multivariate analysis was used to test whether higher H was related to greater practice-related performance improvement. Supporting our hypothesis, we found patterns of higher H that reliably correlated with greater performance improvement across participants in all three studies. However, the predictive brain regions were distinct, suggesting that the specific spatial H↑ patterns are not task-general.

Overt attentional correlates of memorability of scene images and their relationships to scene semantics.

Computer vision-based research has shown that scene semantics (e.g., presence of meaningful objects in a scene) can predict memorability of scene images. Here, we investigated whether and to what extent overt attentional correlates, such as fixation map consistency (also called inter-observer congruency of fixation maps) and fixation counts, mediate the relationship between scene semantics and scene memorability. First, we confirmed that the higher the fixation map consistency of a scene, the higher its memorability. Moreover, both fixation map consistency and its correlation to scene memorability were the highest in the first 2 seconds of viewing, suggesting that meaningful scene features that contribute to producing more consistent fixation maps early in viewing, such as faces and humans, may also be important for scene encoding. Second, we found that the relationship between scene semantics and scene memorability was partially (but not fully) mediated by fixation map consistency and fixation counts, separately as well as together. Third, we found that fixation map consistency, fixation counts, and scene semantics significantly and additively contributed to scene memorability. Together, these results suggest that eye-tracking measurements can complement computer vision-based algorithms and improve overall scene memorability prediction.

Violence reduces attention to faces and draws attention to points of contact.

Although violence is a frequently researched topic, little is known about how different social features influence information gathering from violent interactions. Regions of an interaction that provide contextual information should receive more attention. We predicted the most informative features of a violent social interaction would be faces, points of contact, and objects being held. To test this, we tracked the eyes of 90 participants as they viewed images of social interactions that varied with respect to violence. When viewing violent interactions, participants attended significantly less to faces and significantly more to points of contact. Moreover, first-fixation analysis suggests that some of these biases are present from the beginning of scene-viewing. These findings are the first to demonstrate the visual relevance of faces and contact points in gathering information from violent social interactions. These results also question the attentional dominance of faces in active social scenes, highlighting the importance of using a variety of stimuli and contexts in social cognition research.