Recently, neuroscientists have become increasingly interested in studying the interactions between information sampling and choice and the mechanisms underlying these. In machine learning, introducing intrinsic rewards for exploration has been found to greatly improve artificial agents’ performance on ‘hard exploration’ problems. There is evidence that humans are intrinsically driven to sample both information that has no direct impact on reward outcome as well as information that reduces uncertainty on upcoming decisions. Recent findings from studies using a range of information sampling tasks suggest a functional dissociation between more posterior and anterior regions of prefrontal cortex (PFC). Specifically, pre-supplementary motor area (pre-SMA) and dorsal anterior cingulate cortex (dACC) are involved in decisions to sample more information to guide upcoming decisions, whereas the more anterior ventromedial prefrontal cortex (vmPFC), encodes the value of upcoming information. We argue that to effectively study information sampling in humans, the behavioral tasks we use must better reflect the large state space available to humans in real life. This, however, is challenging due to the complex model of the world humans have access to when choosing where to sample next.
Current Opinion in Behavioral Sciences
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