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Predictive neural representations of naturalistic dynamic input.

Ingmar E J de Vries^1,2, Moritz F Wurm³

¹Centre for Mind/Brain Sciences (CIMeC), University of Trento, 38068, Rovereto, Italy. i.e.j.de.vries@gmail.com.

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The brain predicts unfolding events using dynamic neural representations. High-level features are predicted earlier than low-level visual details, revealing the brain

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Area of Science:

Neuroscience
Cognitive Science
Computational Neuroscience

Background:

Adaptive behavior relies on predicting dynamic external events.
Empirical evidence for neural prediction is limited to static data.
Existing methods struggle to capture real-time predictive processes.

Purpose of the Study:

To develop a dynamic extension of representational similarity analysis (RSA).
To capture neural representations of unfolding events using temporally variable models.
To investigate predictive processing in the human brain during dynamic event observation.

Main Methods:

Applied a dynamic RSA extension to magnetoencephalography (MEG) data.
Analyzed source-reconstructed MEG signals from healthy human subjects.
Quantified temporal forecast windows of neural representations.

Main Results:

Demonstrated both lagged and predictive neural representations of observed actions.
Revealed a hierarchical pattern in predictive representations: abstract features predicted earlier than visual features.
Quantified the brain's temporal forecast window for dynamic events.

Conclusions:

The dynamic RSA extension effectively captures neural predictions of unfolding events.
Neural predictions exhibit a temporal hierarchy, with abstract information predicted further in advance.
This approach offers a novel method for studying predictive processing in dynamic naturalistic contexts.