Wunderlich, KlausSymmonds, MkaelBossaerts, PeterDolan, Raymond J.2011-12-162011-12-162011-12-16201110.1016/j.neuron.2011.07.025https://infoscience.epfl.ch/handle/20.500.14299/73505WOS:000295556500017Human subjects are proficient at tracking the mean and variance of rewards and updating these via prediction errors. Here, we addressed whether humans can also learn about higher-order relationships between distinct environmental outcomes, a defining ecological feature of contexts where multiple sources of rewards are available. By manipulating the degree to which distinct outcomes are correlated, we show that subjects implemented an explicit model-based strategy to learn the associated outcome correlations and were adept in using that information to dynamically adjust their choices in a task that required a minimization of outcome variance. Importantly, the experimentally generated outcome correlations were explicitly represented neuronally in right midinsula with a learning prediction error signal expressed in rostral anterior cingulate cortex. Thus, our data show that the human brain represents higher-order correlation structures between rewards, a core adaptive ability whose immediate benefit is optimized sampling.Decision-MakingOrbitofrontal CortexPrediction ErrorsPrefrontal CortexEconomic ValueRisk-TakingRepresentationInferenceInsulaValuestext::journal::journal article::research article