<p>The dorsal raphe nucleus (DRN) is an important source of serotonin to the human forebrain, however there is little consensus about its behavioural function. We build on recent results from animal models to demonstrate that activity in human DRN implements changes in behavioural policy that reflect the distribution of rewards in the environment. We use a foraging-inspired behavioural task to show that human participants change their policy to pursue or reject reward opportunities as a function of the average value of opportunities in the environment. Activity in DRN—but no other neuromodulatory nucleus—signals such policy changes. Patterns of multivariate activity in dorsal anterior cingulate cortex (dACC) and anterior insular cortex (AI), meanwhile, track the relative value of reward opportunities given the average value of the environment. We therefore suggest that DRN, dACC and AI form a circuit in which dACC/AI construct representations of reward opportunities given the current context, and DRN implements changes in behavioural policy based on these representations.</p>

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Activity in human dorsal raphe nucleus signals changes in behavioural policy

  • Luke Priestley,
  • Ali Mahmoodi,
  • William D. Reith,
  • Nima Khalighinejad,
  • Matthew F. S. Rushworth

摘要

The dorsal raphe nucleus (DRN) is an important source of serotonin to the human forebrain, however there is little consensus about its behavioural function. We build on recent results from animal models to demonstrate that activity in human DRN implements changes in behavioural policy that reflect the distribution of rewards in the environment. We use a foraging-inspired behavioural task to show that human participants change their policy to pursue or reject reward opportunities as a function of the average value of opportunities in the environment. Activity in DRN—but no other neuromodulatory nucleus—signals such policy changes. Patterns of multivariate activity in dorsal anterior cingulate cortex (dACC) and anterior insular cortex (AI), meanwhile, track the relative value of reward opportunities given the average value of the environment. We therefore suggest that DRN, dACC and AI form a circuit in which dACC/AI construct representations of reward opportunities given the current context, and DRN implements changes in behavioural policy based on these representations.