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Imprecise neural computations as a source of adaptive behaviour in volatile environments.

Charles Findling^1,2, Nicolas Chopin², Etienne Koechlin^3,4,5

¹Ecole Normale Supérieure, PSL Research University, Paris, France.

Nature Human Behaviour

|November 10, 2020

Summary

Humans adapt to changing environments using simple uncertainty inferences, not complex volatility predictions. This low-level processing, consistent with the Weber law, explains behavior better than sophisticated models.

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Area of Science:

Cognitive science
Computational neuroscience
Human behavior

Background:

Humans navigate environments with uncertainty and volatility.
Existing models of adaptive behavior require complex computations, questioning biological plausibility.
Understanding efficient adaptation in changing conditions remains a challenge.

Purpose of the Study:

To investigate whether simple, low-level inferences can explain efficient adaptive behavior in volatile environments.
To challenge the necessity of complex volatility inference in adaptive behavior models.
To propose and validate a novel model based on uncertainty and Weber law imprecision.

Main Methods:

Developed a computational model based on low-level uncertainty inferences and Weber law imprecision.
Compared model predictions against human behavioral data in volatile environments.
Evaluated against established models, including optimal adaptive models and reinforcement learning.

Main Results:

Simple, low-level uncertainty inferences can yield near-optimal adaptive behavior.
The proposed Weber-imprecision model significantly outperforms complex adaptive models.
Empirical evidence supports the Weber-imprecision model over high-level inference models.

Conclusions:

Efficient human adaptation in volatile environments may rely on simple, uncertainty-focused computations.
The Weber law's imprecision provides a parsimonious explanation for adaptive behavior.
This finding offers a biologically plausible alternative to complex inference models for adaptation.