Relating brain signal variability to knowledge representation

We assessed the hypothesis that brain signal variability is a reflection of functional network reconfiguration during memory processing. In the present experiments, we use multiscale entropy to capture the variability of human electroencephalogram (EEG) while manipulating the knowledge representation associated with faces stored in memory. Across two experiments, we observed increased variability as a function of greater knowledge representation. In Experiment 1, individuals with greater familiarity for a group of famous faces displayed more brain signal variability. In Experiment 2, brain signal variability increased with learning after multiple experimental exposures to previously unfamiliar faces. The results demonstrate that variability increases with face familiarity; cognitive processes during the perception of familiar stimuli may engage a broader network of regions, which manifests as higher complexity/variability in spatial and temporal domains. In addition, effects of repetition suppression on brain signal variability were observed, and the pattern of results is consistent with a selectivity model of neural adaptation.

► Face familiarity manifests itself as less regular and more variably neural activity. ► Multiscale entropy was used to estimate EEG signal variability. ► Faces of greater familiarity elicited a more variable brain response. ► Brain signal variability increased with learning of unfamiliar faces. ► Face repetition reduced brain signal variability.