Spatiotemporal Signatures of Surprise Captured by Magnetoencephalography.

Abstract

Surprise and social influence are linked through several neuropsychological mechanisms. By garnering attention, causing arousal, and motivating engagement, surprise provides a context for effective or durable social influence. Attention to a surprising event motivates the formation of an explanation or updating of models, while high arousal experiences due to surprise promote memory formation. They both encourage engagement with the surprising event through efforts aimed at understanding the situation. By affecting the behavior of the individual or a social group via setting an attractive engagement context, surprise plays an important role in shaping personal and social change. Surprise is an outcome of the brain’s function in constantly anticipating the future of sensory inputs based on past experiences. When new sensory data is different from the brain’s predictions shaped by recent trends, distinct neural signals are generated to report this surprise. As a quantitative approach to modeling the generation of brain surprise, input stimuli containing surprising elements are employed in experiments such as oddball tasks during which brain activity is recorded. Although surprise has been well characterized in many studies, an information-theoretical model to describe and predict the surprise level of an external stimulus in the recorded MEG data has not been reported to date, and setting forth such a model is the main objective of this paper. Through mining trial-by-trial MEG data in an oddball task according to theoretical definitions of surprise, the proposed surprise decoding model employs the entire epoch of the brain response to a stimulus to measure surprise and assesses which collection of temporal/spatial components in the recorded data can provide optimal power for describing the brain’s surprise. We considered three different theoretical formulations for surprise assuming the brain acts as an ideal observer that calculates transition probabilities to estimate the generative distribution of the input. We found that middle temporal components and the right and left fronto-central regions offer the strongest power for decoding surprise. Our findings provide a practical and rigorous method for measuring the brain’s surprise, which can be employed in conjunction with behavioral data to evaluate the interactive and social effects of surprising events.