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Rational arbitration between statistics and rules in human sequence processing.

Maxime Maheu1,2, Florent Meyniel3, Stanislas Dehaene3,4

  • 1Cognitive Neuroimaging Unit, CEA DRF/JOLIOT, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin Centre, Gif-sur-Yvette, France. maheu.mp@gmail.com.

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Humans process sequences using a hierarchical Bayesian inference model. This system uniquely integrates statistical learning and rule-based processing, resolving a long-standing debate in cognitive science.

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computational Modeling

Background:

  • Human ability to predict the future relies on detecting temporal regularities.
  • A key debate exists on whether humans possess separate systems for statistical learning and rule-based processing.

Purpose of the Study:

  • To investigate the cognitive mechanisms underlying human sequence processing.
  • To determine if statistical and rule-based learning are handled by distinct or integrated systems.

Main Methods:

  • Utilized high-precision finger tracking to monitor behavioral data during sequence processing.
  • Collected data on evidence accumulation, confidence, false alarms, and changes-of-mind.
  • Compared empirical data against predictions from hierarchical Bayesian inference models.

Main Results:

  • Behavioral data closely matched a hierarchical Bayesian inference model with separate hypothesis spaces for statistics and rules.
  • This integrated model successfully explained sequence processing, outperforming alternative models.
  • Evidence suggests a unified probabilistic framework underlies both statistical and rule-based sequence learning.

Conclusions:

  • Human sequence processing is underpinned by a hierarchical Bayesian inference mechanism.
  • This mechanism operates over distinct yet interconnected hypothesis spaces for statistical and rule-based information.
  • Findings resolve the debate by demonstrating an integrated system for temporal regularity detection.