TY - JOUR TI - The control of tonic pain by active relief learning AU - Zhang, Suyi AU - Mano, Hiroaki AU - Lee, Michael AU - Yoshida, Wako AU - Kawato, Mitsuo AU - Robbins, Trevor W AU - Seymour, Ben A2 - Wager, Tor VL - 7 PY - 2018 DA - 2018/02/27 SP - e31949 C1 - eLife 2018;7:e31949 DO - 10.7554/eLife.31949 UR - https://doi.org/10.7554/eLife.31949 AB - Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning to reduce the cause of the pain. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system uses an uncertainty (‘associability’) signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that reduces ongoing pain when learning about potential relief. KW - pain KW - reinforcement learning KW - relief KW - endogenous analgesia KW - cingulate cortex KW - basal ganglia JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -