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Blanchard, T. C.

Publications and source records attributed to Blanchard, T. C..

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Pure Correlates of Exploration and Exploitation in the Human Brain

Balancing exploration and exploitation is a fundamental problem in reinforcement learning. Previous neuroimaging studies of the exploration-exploitation dilemma could not completely disentangle these two processes, making it difficult to unambiguously identify their neural signatures. We overcome this problem using a task in which subjects can either observe (pure exploration) or bet (pure exploitation). Insula and dorsal anterior cingulate cortex showed significantly greater activity on observe trials compared to bet trials, suggesting that these regions play a role in driving exploration. A model-based analysis of task performance suggested that subjects chose to observe until a critical evidence threshold was reached. We observed a neural signature of this evidence accumulation process in ventromedial prefrontal cortex. These findings support theories positing an important role for anterior cingulate cortex in exploration, while also providing a new perspective on the roles of insula and ventromedial prefrontal cortex.\n\nSignificance StatementSitting down at a familiar restaurant, you may choose to order an old favorite or sample a new dish. In reinforcement learning theory, this is known as the exploration-exploitation dilemma. The optimal solution is known to be intractable; therefore, humans must use heuristic strategies. Behavioral studies have revealed several candidate strategies, but identifying the neural mechanisms underlying these strategies is complicated due to the fact that exploration and exploitation are not perfectly dissociable in standard tasks. Using an \"observe or bet\" task, we identify for the first time pure neural correlates of exploration and exploitation in the human brain.

neuroscience

Robust mixture modeling reveals category-free selectivity in reward region neuronal ensembles

Classification of neurons into clusters based on their response properties is an important tool for gaining insight into neural computations. However, it remains unclear to what extent neurons fall naturally into discrete functional categories. We developed a Bayesian method that models the tuning properties of neural populations as a mixture of multiple types of task-relevant response patterns. We applied this method to data from several cortical and striatal regions in economic choice tasks. In all cases, neurons fell into only two clusters: one mixed-selectivity cluster containing all task-sensitive cells and another of no selectivity (i.e. pure noise) cells. The single cluster of task-sensitive cells argues against robust categorical tuning in these areas. The no selectivity cells were unanticipated; their identification allows for improved measurement of ensemble effects. Our findings provide a valuable tool for analysis of neural data and place strong constraints on neurocomputational models of choice and control.

neuroscience