PLoS Computational Biology · 2012 · 476 citations · 53 references
Social SciencesBehavioral SciencesPsychologyCognitive PsychologyDecision TheoryExperimental PsychologyClassical ConditioningDecision-making ModelsOperant ConditioningReinforcement LearningReward ProcessingSelective PruningDecision Tree PruningGoal-directed Action ControlGoal-directed BehaviorPruningReinforcement ProcessesReward-based Decision MakingSerotonergic Reward Modulation
When planning a series of actions, it is usually infeasible to consider all potential future sequences; instead, one must prune the decision tree. Provably optimal pruning is, however, still computationally ruinous and the specific approximations humans employ remain unknown. We designed a new sequential reinforcement-based task and showed that human subjects adopted a simple pruning strategy: during mental evaluation of a sequence of choices, they curtailed any further evaluation of a sequence as soon as they encountered a large loss. This pruning strategy was Pavlovian: it was reflexively evoked by large losses and persisted even when overwhelmingly counterproductive. It was also evident above and beyond loss aversion. We found that the tendency towards Pavlovian pruning was selectively predicted by the degree to which subjects exhibited sub-clinical mood disturbance, in accordance with theories that ascribe Pavlovian behavioural inhibition, via serotonin, a role in mood disorders. We conclude that Pavlovian behavioural inhibition shapes highly flexible, goal-directed choices in a manner that may be important for theories of decision-making in mood disorders.
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