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Humans process stories with hierarchical brain activity, segmenting events at different speeds. A new model using reservoir computing mimics this narrative segmentation and processing, explaining how we construct and forget context over time.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Humans exhibit hierarchical cortical activity patterns during narrative perception, segmenting events at different temporal scales.
  • Sensory cortices process high-frequency stimulus-related segmentation, while semantic areas handle lower-frequency event transitions.
  • Neural responses align over time, with faster alignment in sensory areas and slower alignment in higher-order regions, reflecting processing time constants.

Purpose of the Study:

  • To investigate hierarchical narrative event segmentation using a neurocomputational model.
  • To demonstrate how a reservoir network with inherent time constants can simulate human cortical processing of narrative structure.
  • To explain the asymmetry in narrative construction and forgetting through the model's processing dynamics.

Main Methods:

  • Utilized a pre-trained language model for word meaning representation within a recurrent reservoir network.
  • Integrated word embeddings into the reservoir network, which possesses a gradient of functional time constants.
  • Applied a hidden Markov model (HMM) to segment activation patterns generated by the reservoir network.

Main Results:

  • The reservoir network model successfully replicated hierarchical narrative event segmentation comparable to human fMRI data.
  • Subgroups of reservoir neurons with varying time constants exhibited differential segmentation of short and long events.
  • The model demonstrated a continuum of time constants for context construction and a fixed time constant for context forgetting.

Conclusions:

  • Reservoir computing provides a viable neurocomputational framework for modeling hierarchical narrative event processing in the brain.
  • The model offers a novel explanation for the asymmetric time courses of narrative context construction and forgetting.
  • This approach extends the understanding of online discourse integration to extended narratives and cortical processing mechanisms.