Affective face biases in visual and prefrontal cortex measured with visual entrainment

Abstract Facial expressions are ubiquitous and reliable social cues. Research has shown that affective faces attract attention at the cost of competing visual information, with functional neuroimaging evidence suggesting that the prefrontal cortex plays a critical role in regulating responses to emotional distractors. However, methodological constraints within neuroimaging environments often prevent the measurement of unique neural signals from multiple competing stimuli, limiting the conclusions that can be drawn regarding how affective biases in attention are generated in the brain. In the current study, we used a novel frequency tagging approach with visual entrainment during magnetoencephalography to track the unique neural signals elicited by a task-relevant Gabor patch and a concurrent, spatially overlapping face with either an angry, neutral, or happy expression. The entrainment responses were projected to the cortex using a beamformer, and a competition index was calculated per voxel to determine the bias toward either of the spatially overlapping entrained stimuli. In the prefrontal cortex, we found a stronger Gabor bias for neutral compared to angry and happy expressions, supporting prior functional neuroimaging works which point to the prefrontal cortex as critical to the regulation of emotional distractors. In the calcarine, we found a stronger face bias for angry compared to neutral and happy expressions, replicating prior findings from electroencephalography. The separate entrainment responses were also sensitive to facial expression in several regions commonly implicated in face processing, social cognition, and attention. These data highlight the utility of frequency tagging paradigms for tracking unique neural responses to concurrent and spatially overlapping stimuli, which is critical for the study of social and emotional processing.