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Human Inferences about Sequences: A Minimal Transition Probability Model.

Florent Meyniel¹, Maxime Maheu^1,2, Stanislas Dehaene^1,3

¹Cognitive Neuroimaging Unit, CEA DRF/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, France.

Plos Computational Biology

|December 29, 2016

Summary

This summary is machine-generated.

The brain infers statistical expectations of future inputs, even from random data. A Bayesian model explains how the brain learns transition probabilities, crucial for sequence knowledge.

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Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Bayesian Modeling

Background:

The brain predicts future sensory inputs based on past experiences.
Violations of these predictions generate measurable neural signals (e.g., surprise signals).
Existing models struggle to explain certain sequential effects, like repetition-alternation asymmetry.

Purpose of the Study:

To propose and test a parsimonious Bayesian model of inference.
To investigate the brain's ability to infer transition probabilities between stimuli.
To explain diverse experimental findings on expectation violations and sequence perception.

Main Methods:

Developed a Bayesian model to infer time-varying transition probabilities.
The model has a single free parameter for parsimony.
Applied the model to explain surprise signals, reaction time effects, and perception of randomness.

Main Results:

The model successfully accounts for a wide range of experimental data.
It explains the asymmetry observed between stimulus repetitions and alternations.
Demonstrates the brain's capacity to infer structure even in unpredictable sequences.

Conclusions:

The brain functions as a near-optimal inference engine for transition probabilities.
This inference mechanism is fundamental to human sequence knowledge.
The proposed model offers a unified explanation for various cognitive phenomena.