Show Reference: "Uncertainty, Neuromodulation, and Attention"

Uncertainty, neuromodulation, and attention. Neuron, Vol. 46, No. 4. (19 May 2005), pp. 681-692, doi:10.1016/j.neuron.2005.04.026 by Angela J. Yu, Peter Dayan
    abstract = {
                Uncertainty in various forms plagues our interactions with the environment. In a Bayesian statistical framework, optimal inference and prediction, based on unreliable observations in changing contexts, require the representation and manipulation of different forms of uncertainty. We propose that the neuromodulators acetylcholine and norepinephrine play a major role in the brain's implementation of these uncertainty computations. Acetylcholine signals expected uncertainty, coming from known unreliability of predictive cues within a context. Norepinephrine signals unexpected uncertainty, as when unsignaled context switches produce strongly unexpected observations. These uncertainty signals interact to enable optimal inference and learning in noisy and changeable environments. This formulation is consistent with a wealth of physiological, pharmacological, and behavioral data implicating acetylcholine and norepinephrine in specific aspects of a range of cognitive processes. Moreover, the model suggests a class of attentional cueing tasks that involve both neuromodulators and shows how their interactions may be part-antagonistic, part-synergistic.
    author = {Yu, Angela J. and Dayan, Peter},
    day = {19},
    doi = {10.1016/j.neuron.2005.04.026},
    issn = {0896-6273},
    journal = {Neuron},
    keywords = {bayes, biology, learning},
    month = may,
    number = {4},
    pages = {681--692},
    pmid = {15944135},
    posted-at = {2013-01-07 11:34:12},
    priority = {2},
    title = {Uncertainty, neuromodulation, and attention.},
    url = {},
    volume = {46},
    year = {2005}

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Yu and Dayan distinguish between two kinds of uncertainty in perceptual processing: expected uncertainty and unexpected uncertainty.

Expected uncertainty is due to known unreliability in information sources.

Unexpected uncertainty is due to information sources being unreliable unexpectedly.

Yu and Dayan argue that uncertainty should suppress top-down, context-dependent factors in inference, and strengthen learning about the situation.

Yu and Dayan interpret experiments showing that the level of acetylcholine (ACh) increases with learned stochasticity of cues as supporting their theory that ACh signals expected uncertainty.

Yu and Dayan interpret experiments showing that increased levels of norapinephrine (NE) accelerates the detection of changes in cue predictivity as supporting their theory that NE signals unexpected uncertainty.

Yu and Dayan propose a model of inference and learning in which expected uncertainty is encoded by high acetylcholine (ACh) levels and unexpected uncertainty is encoded by norapinephrine (NE).