Visual Motion Coherence Responses in Human Visual Cortex

Evidence for a role of synchrony but not common fate in the perception of biological group movements

Extensive research has shown that observers are able to efficiently extract summary information from groups of people. However, little is known about the cues that determine whether multiple people are represented as a social group or as independent individuals. Initial research on this topic has primarily focused on the role of static cues. Here, we instead investigate the role of dynamic cues. In two experiments with male and female human participants, we use EEG frequency tagging to investigate the influence of two fundamental Gestalt principles - synchrony and common fate - on the grouping of biological movements. In Experiment 1, we find that brain responses coupled to four point-light figures walking together are enhanced when they move in sync vs. out of sync, but only when they are presented upright. In contrast, we found no effect of movement direction (i.e., common fate). In Experiment 2, we rule out that synchrony takes precedence over common fate by replicating the null effect of movement direction while keeping synchrony constant. These results suggest that synchrony plays an important role in the processing of biological group movements. In contrast, the role of common fate is less clear and will require further research.

Estimating neural activity from visual areas using functionally defined EEG templates

Electroencephalography (EEG) is a common and inexpensive method to record neural activity in humans. However, it lacks spatial resolution making it difficult to determine which areas of the brain are responsible for the observed EEG response. Here we present a new easy-to-use method that relies on EEG topographical templates. Using MRI and fMRI scans of 50 participants, we simulated how the activity in each visual area appears on the scalp and averaged this signal to produce functionally defined EEG templates. Once created, these templates can be used to estimate how much each visual area contributes to the observed EEG activity. We tested this method on extensive simulations and on real data. The proposed procedure is as good as bespoke individual source localization methods, robust to a wide range of factors, and has several strengths. First, because it does not rely on individual brain scans, it is inexpensive and can be used on any EEG data set, past or present. Second, the results are readily interpretable in terms of functional brain regions and can be compared across neuroimaging techniques. Finally, this method is easy to understand, simple to use and expandable to other brain sources.