Information Content of Prefrontal Cortex Activity Quantifies the Difficulty of Narrated Stories

Abstract

The ability to realize the individuals’ impressions during the verbal communication allows social robots to significantly facilitate their social interactions in such areas as child education and elderly care. However, such impressions are highly subjective and internalized and therefore cannot be easily comprehended through behavioural observations. Although brain-machine interface suggests the utility of the brain information in human-robot interaction, previous studies did not consider its potential for estimating the internal impressions during verbal communication. In this article, we introduce a novel approach to estimation of the individuals’ perceived difficulty of stories using the quantified information content of their prefrontal cortex activity. We demonstrate the robustness of our approach by showing its comparable performance in face-to-face, humanoid, speaker, and video-chat settings. Our results contribute to the field of socially assistive robotics by taking a step toward enabling robots determine their human companions’ perceived difficulty of conversations, thereby enabling these media to sustain their communication with humans by adapting to individuals’ pace and interest in response to conversational nuances and complexity.

Higher hypnotic suggestibility is associated with the lower EEG signal variability in theta, alpha, and beta frequency bands

“…The present findings are not only of interest to the psychology and neuroscience community but also to the researchers in the field of AI and brain-computer interfaces [100,101]. For instance, the use of DE as a biomarker of hypnosis can be utilized in development of real-time EEG classifiers that detect their users' responses to hypnotic suggestions.…”