Uncertainty or imprecision is present in all neural computations. It can arise from noise or incomplete information sensed from the environment, and from imprecision and noise in neural circuits.
An estimate of such uncertainty can improve behavioural performance.
Of the many variables we can be uncertain about, those in sensory perception and in economic outcome prediction have received the most empirical interest.
Sensory and outcome uncertainty clearly influence behaviour, and there is evidence to suggest that rule uncertainty also influences behaviour. These influences on behaviour are often close to what optimal algorithms predict.
Sensory uncertainty is represented by shallow slopes of firing rates in a neural evidence integrator. BOLD (blood oxygenation-dependent) functional MRI responses in several brain regions can be attributed to sensory uncertainty, but the neural code for sensory uncertainty is unknown.
Outcome uncertainty influences prediction error signals generated in the midbrain, and is encoded in the firing rates of orbitofrontal cortex neurons, and possibly in the rate of change in firing of dopaminergic midbrain neurons.
BOLD studies of outcome uncertainty await replication to confirm the spatial distribution of outcome uncertainty encoding in the brain.
How we estimate uncertainty is important in decision neuroscience and has wide-ranging implications in basic and clinical neuroscience, from computational models of optimality to ideas on psychopathological disorders including anxiety, depression and schizophrenia. Empirical research in neuroscience, which has been based on divergent theoretical assumptions, has focused on the fundamental question of how uncertainty is encoded in the brain and how it influences behaviour. Here, we integrate several theoretical concepts about uncertainty into a decision-making framework. We conclude that the currently available evidence indicates that distinct neural encoding (including summary statistic-type representations) of uncertainty occurs in distinct neural systems.
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We would like to thank J. Daunizeau, M. Symmonds, S. Fleming, and many others, for inspiring discussions during the work on this article. D.R.B. was supported by a personal grant from the Swiss National Science Foundation, and by a Max Planck Award to R.J.D. This work was supported by the Wellcome Trust with a programme grant to R.J.D (078865/Z/05/Z). The Wellcome Trust Centre for Neuroimaging is supported by core funding from the Wellcome Trust (091593/Z/10/Z).
The authors declare no competing financial interests.
- Summary statistic
A concise way of describing a set of observations without having to refer to each individual observation. Hence, the set of observations can be described with just a few values. For example, one for the location (for example, mean) and another for the dispersion, that is, uncertainty, (for example, variance).
- Stimulus uncertainty
Environmental uncertainty in the controlled conditions of a sensory experiment is usually due to uncertainty in the stimulus. This could be noise in the stimulus, but also other factors, such as when needing to classify a mixed stimulus into one of two categories.
- Internal noise
Fluctuations in a measured signal that arise from imprecision in the observing system. For example, from imprecision in sensor organs, in neural circuits or from suboptimal algorithms.
- Urgency gating
In temporal integrator models, a decision is made when the integrator reaches a certain fixed threshold. Urgency gating describes the idea that this threshold changes over time to enforce a decision.
- Temporal integrator models
Models that describe the accumulation of sensory evidence over time; for example, when viewing a noisy stimulus and having to decide on its identity.
- Environmental noise
Random fluctuations in a measured signal that arises from the outside world. In the context of a sensory decision-making experiment, for example, this could be from noise in the stimulus.
A subfield of statistics whereby inference of the true state of the world is represented as a degree of belief in different states, rather than as the most likely state only. This implies knowing the uncertainty associated with the estimation.
A measure for informational content that can, for example, be used to summarize a probability distribution.
- Environmental uncertainty
Uncertainty in a neural variable owing to properties in the environment
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Bach, D., Dolan, R. Knowing how much you don't know: a neural organization of uncertainty estimates. Nat Rev Neurosci 13, 572–586 (2012). https://doi.org/10.1038/nrn3289
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