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Published on: June 30, 2020
Long-horizon associative learning as a unifying framework for statistical learning across scales
Lucas Benjamin1, Ana Fló1, Fosca Al Roumi1
1Cognitive Neuroimaging Unit, CNRS Equipe de recherche labellisée 9003, INSERM U1354, Commissariat à l'Energie Atomique et aux énergies alternatives, Université Paris-Saclay, NeuroSpin Center, Gif/Yvette 91191, France.
Summary
This study introduces a unified model for statistical learning across various timescales, explaining how the brain learns temporal patterns. A single neural mechanism, based on associative traces, underlies this learning, applicable from simple transitions to complex structures.
Area of Science:
- Cognitive Neuroscience
- Computational Neuroscience
- Machine Learning
Background:
- Sensory information contains temporal patterns across multiple timescales, crucial for environmental navigation and event anticipation.
- Existing models of temporal learning are often narrowly focused and lack generalizability.
- Humans learn temporal regularities without prior knowledge of the underlying structure or relevant timescale.
Purpose of the Study:
- To present a unifying model of statistical learning that spans diverse temporal dependencies.
- To provide a biologically grounded implementation of the successor representation and free energy minimization.
- To demonstrate a single neural mechanism for learning both local and higher-order statistical regularities.
Main Methods:
- Developed a unified account of statistical learning termed 'long-horizon associative learning'.
- Implemented this model as a biologically grounded successor representation.
- Reanalyzed data from 11 previously published studies.
Main Results:
- A single neural mechanism, based on graded temporal overlap of associative traces governed by a parameter (β), captures diverse temporal dependencies.
- This mechanism effectively models both local statistical regularities and complex network structures.
- The model successfully reanalyzed data from 11 diverse studies, demonstrating its broad applicability.
Conclusions:
- The proposed framework offers a parsimonious explanation for statistical learning across various temporal scales.
- Apparent paradigm-specific effects in statistical learning literature can be viewed as expressions of a common underlying computation.
- This domain-general associative learning process may scaffold higher-level cognitive functions like categorization and memory.
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