Predictive neural representations of naturalistic dynamic input

Adaptive behavior such as social interaction requires our brain to predict unfolding external dynamics. While theories assume such dynamic prediction, empirical evidence is limited to static snapshots and indirect consequences of predictions. We present a dynamic extension to representational simila...

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Bibliographic Details
Published in:Nature communications 2023-06, Vol.14 (1), p.3858-16, Article 3858
Main Authors: de Vries, Ingmar E. J., Wurm, Moritz F.
Format: Article
Language:English
Subjects:
631/378/2613
631/378/2649/1723
631/477/2811
Adaptation, Psychological
Brain
Empirical analysis
Healthy Volunteers
Humanities and Social Sciences
Humans
Magnetoencephalography
Motion Pictures
multidisciplinary
Predictions
Representations
Science
Science (multidisciplinary)
Social behavior
Social factors
Social interaction
Temporal resolution
Visual stimuli
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Summary:Adaptive behavior such as social interaction requires our brain to predict unfolding external dynamics. While theories assume such dynamic prediction, empirical evidence is limited to static snapshots and indirect consequences of predictions. We present a dynamic extension to representational similarity analysis that uses temporally variable models to capture neural representations of unfolding events. We applied this approach to source-reconstructed magnetoencephalography (MEG) data of healthy human subjects and demonstrate both lagged and predictive neural representations of observed actions. Predictive representations exhibit a hierarchical pattern, such that high-level abstract stimulus features are predicted earlier in time, while low-level visual features are predicted closer in time to the actual sensory input. By quantifying the temporal forecast window of the brain, this approach allows investigating predictive processing of our dynamic world. It can be applied to other naturalistic stimuli (e.g., film, soundscapes, music, motor planning/execution, social interaction) and any biosignal with high temporal resolution. The neural processes underlying the prediction of unfolding external dynamics are not well understood. Here, the authors combine magnetoencephalography and naturalistic dynamic stimuli and show predictive neural representations of observed actions which are hierarchical in nature.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-023-39355-y