Ben Seymour

This paper reconciles conflicting theories about dopamine signals in the tail of the striatum by proposing an entropy-regularised reinforcement learning model that integrates aversive and action prediction errors.

The paper proposes a computational framework to model injury recovery and pain transitions using a partially observable Markov decision process, explaining paradoxical behaviors and chronic pain development.

This paper introduces an entropy-regularised reinforcement-learning framework to address challenges in dopamine-mediated learning, showing it improves multi-reward learning and adapts to non-stationary environments.

The paper investigates how tonic pain modulates phasic pain-predictive cues using a virtual-reality paradigm and EEG, finding that tonic pain reconfigures internal representations of pain associations.

The paper tests the hypothesis that phasic and tonic pain serve distinct functions in adaptive behavior using a virtual reality foraging task with EEG measurements.

This study identifies increased punishment sensitivity and specific neural activity patterns in chronic pain and fatigue patients during decision-making tasks.

This study shows that the Pavlovian fear system enables flexible and efficient exploratory decision-making by balancing safety and efficiency through uncertainty-based avoidance.

This study investigates the neurochemical basis of pain relief modulation, finding that dopaminergic signaling, not opioidergic, enhances relief perception through active decision-making and unpredictability.

The paper investigates how confidence in probabilistic pain predictions modulates EEG responses to noxious stimuli using a temporal statistical learning task.

This study investigates how humans learn statistical patterns of pain over time and identifies neural correlates of Bayesian inference and uncertainty in pain prediction.

The paper builds a working memory prediction model from whole-brain functional connectivity in healthy individuals and applies it to psychiatric disorders, finding correlations between connectivity patterns and working memory performance.

This study demonstrates how endogenous brain processes influence the effectiveness of closed-loop brain-machine interfaces for pain management.

This study uses reinforcement learning modeling to show that avoidance learning generalization involves distinct perceptual and value-based processes, with value-based generalization linked to anxiety and intrusive thoughts.

This study uses machine learning and deep learning to classify chronic back pain patients based on brain network patterns, finding consistent changes in network topology and modular reorganisation.

This study identifies a central learning circuit that enables relief learning and modulates tonic pain levels through reinforcement learning and uncertainty signals.

The paper investigates ACC connectivity in rats with chronic pain and finds it correlates with reduced burrowing behavior, indicating a role in motivational suppression.