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Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

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Representations of uncertainty in sensorimotor control.

Gergo Orbán¹, Daniel M Wolpert

¹Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK.

Current Opinion in Neurobiology

|June 22, 2011

Summary

The Bayesian framework models how the brain uses probability distributions to represent and manage uncertainty in sensorimotor control and learning. This approach is key for optimal responses to environmental stimuli.

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Area of Science:

Neuroscience
Computational Neuroscience
Cognitive Science

Background:

Sensorimotor interactions are inherently uncertain due to sensory/motor noise and environmental ambiguity.
Traditional theories often overlook the explicit representation of uncertainty.
The Bayesian framework offers a powerful approach to model inference under uncertainty.

Purpose of the Study:

To review Bayesian inference and learning models in sensorimotor control.
To highlight the sensitivity of the sensorimotor system to various forms of uncertainty.
To explore the computational-level representation of uncertainty.

Main Methods:

Review of existing literature on Bayesian models in sensorimotor control.
Analysis of studies demonstrating sensitivity to uncertainty.
Examination of research characterizing uncertainty representation.

Main Results:

Bayesian models successfully demonstrate sensorimotor system sensitivity to different types of uncertainty.
Internal models are proposed to estimate sensorimotor transformations.
Representation of uncertainty in inputs, outputs, and transformations is crucial.

Conclusions:

The Bayesian framework is essential for understanding how the central nervous system handles uncertainty.
Optimal sensorimotor control relies on representing uncertainty via probability distributions.
Further research is needed to characterize uncertainty representation at multiple computational levels.