Show Reference: "Learning the value of information in an uncertain world"

Learning the value of information in an uncertain world Nature Neuroscience, Vol. 10, No. 9. (05 September 2007), pp. 1214-1221, doi:10.1038/nn1954 by Timothy E. Behrens, Mark W. Woolrich, Mark E. Walton, Matthew F. Rushworth
@article{behrens-et-al-2007,
    abstract = {Our decisions are guided by outcomes that are associated with decisions made in the past. However, the amount of influence each past outcome has on our next decision remains unclear. To ensure optimal decision-making, the weight given to decision outcomes should reflect their salience in predicting future outcomes, and this salience should be modulated by the volatility of the reward environment. We show that human subjects assess volatility in an optimal manner and adjust decision-making accordingly. This optimal estimate of volatility is reflected in the {fMRI} signal in the anterior cingulate cortex ({ACC}) when each trial outcome is observed. When a new piece of information is witnessed, activity levels reflect its salience for predicting future outcomes. Furthermore, variations in this {ACC} signal across the population predict variations in subject learning rates. Our results provide a formal account of how we weigh our different experiences in guiding our future actions.},
    address = {[1] FMRIB Centre, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK. [2] Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford OX1 3UD, UK.},
    author = {Behrens, Timothy E. and Woolrich, Mark W. and Walton, Mark E. and Rushworth, Matthew F.},
    day = {05},
    doi = {10.1038/nn1954},
    issn = {1097-6256},
    journal = {Nature Neuroscience},
    keywords = {information\_theory, learning},
    month = sep,
    number = {9},
    pages = {1214--1221},
    pmid = {17676057},
    posted-at = {2014-01-08 10:05:27},
    priority = {2},
    publisher = {Nature Publishing Group},
    title = {Learning the value of information in an uncertain world},
    url = {http://dx.doi.org/10.1038/nn1954},
    volume = {10},
    year = {2007}
}

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Bayesian models have been used to model natural cognition.

Behrens et al. modeled learning of reward probabilities using a the model of a Bayesian learner.

Behrens et al. found that humans take into account the volatility of reward probabilities in a reinforcement learning task.

The way they took the volatility into account was qualitatively modelled by a Bayesian learner.