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Benjamin Seymour
Researcher at University of Cambridge
Publications - 8
Citations - 300
Benjamin Seymour is an academic researcher from University of Cambridge. The author has contributed to research in topics: Chronic pain & Reinforcement learning. The author has an hindex of 6, co-authored 8 publications receiving 230 citations. Previous affiliations of Benjamin Seymour include National Institute of Information and Communications Technology.
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The habenula encodes negative motivational value associated with primary punishment in humans
Rebecca P. Lawson,Benjamin Seymour,Eleanor Loh,Antoine Lutti,Antoine Lutti,Raymond J. Dolan,Peter Dayan,Nikolaus Weiskopf,Jonathan P. Roiser +8 more
TL;DR: It is established that the habenula encodes associations with aversive outcomes in humans, specifically that it tracks the dynamically changing negative values of cues paired with painful electric shocks, consistent with a role in learning.
Journal ArticleDOI
Dissociable Learning Processes Underlie Human Pain Conditioning
TL;DR: Using computational modeling based on reinforcement learning theory, it is found that conditioning involves two distinct types of learning process, and the overall phenotype of conditioned pain behavior depends on two dissociable reinforcement learning circuits.
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Classification and characterisation of brain network changes in chronic back pain: A multicenter study.
Hiroaki Mano,Gopal Kotecha,Kenji Leibnitz,Takashi Matsubara,Christian Sprenger,Aya Nakae,Nicholas Shenker,Masahiko Shibata,Valerie Voon,Wako Yoshida,Michael Chin Han Lee,Toshio Yanagida,Mitsuo Kawato,Maria J. Rosa,Benjamin Seymour +14 more
TL;DR: Brain network architecture is investigated using resting-state fMRI data in chronic back pain patients in the UK and Japan, and a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex is found, characterised primarily by negative reorganisation.
Emotion, motivation and pain
TL;DR: In this paper, the authors propose a simple model for the brain to construct the sensory and emotional sensation of pain and challenge any standard “perception-action” model.
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Model-based and model-free pain avoidance learning:
TL;DR: A dual-system model of pain avoidance, similar to but possibly more dynamically flexible than reward-based decision-making is suggested, which involves a significantly greater tendency for subjects to switch between model-free and model-based systems in the face of changes in uncertainty.