John P. O’Doherty

The paper records single neuron activity in human brain regions during reward tasks to show how value encoding shifts from domain-general to domain-segregated during decision-making.

This study investigates how the brain represents and computes values of subgoals during hierarchical decision-making using fMRI and reinforcement learning models.

The paper investigates how action affordances influence value-based learning by analyzing human behavior and fMRI data using computational models.

The paper uses fMRI decoding to show that neural activity in the posterior putamen during learning predicts individual differences in habit versus goal-directed behavior across multiple cohorts.

This study investigates how the human brain computes value for individual items and bundles, finding sub-additive discounting and context-dependent normalization in prefrontal cortex value representations.

The paper investigates how the brain computes flexible value representations for complex stimuli based on context, showing distinct neural encoding of attributes and integrated value in prefrontal cortex.

The paper examines how model-based and model-free valuation signals in the human brain relate to individual differences in behavioral control using fMRI data from a large sample.

The paper investigates how the medial prefrontal cortex (vmPFC) processes spatial margin of safety decisions, showing distinct roles for anterior and posterior regions in threat predictability and safety signaling.

The paper investigates altered fast and slow learning from losses in gambling disorder, finding impaired reversal learning and enhanced slow value learning in problem gamblers.

This study identifies brain regions involved in devaluation-sensitive and devaluation-insensitive Pavlovian learning by analyzing reward and state prediction errors using fMRI and computational modeling.

This study uses iEEG to show how reward and risk computations are temporally organized in the human brain, revealing cascading and parallel encoding patterns.

The paper investigates how neurons in the human pre-supplementary motor area (preSMA) encode decision variables during value-based choices, finding that preSMA neurons represent both separate pre-decision variables and an integrated utility signal.

This study attempts to replicate and extend findings on habit formation in humans using multi-modal MRI, but finds no clear habit formation with training duration and identifies goal-directed neural patterns instead.

This study examines how human neurons in prefrontal and medial temporal regions encode predictive associations during Pavlovian conditioning, revealing distinct roles in value prediction and error signaling.

This study uses fMRI to examine if implementation intentions improve behavioral flexibility and shift brain activity from goal-directed to habitual control, finding no such shift.

This study investigates how novelty and uncertainty influence exploration vs. exploitation in the human brain using a novel task and computational modeling.

The paper investigates how human amygdala neurons encode observational learning by tracking expected future rewards and outcome values, finding distinct neuronal subsets for self-experienced and observational value coding.

This paper develops a computational model to explain how the brain arbitrates between imitation and emulation strategies in observational learning, supported by fMRI data showing specific brain regions involved in this process.

This study shows that human aesthetic preferences for art can be predicted by integrating hierarchically structured visual features in the brain across individuals.

The paper investigates how task complexity and state-space uncertainty influence the arbitration between model-based and model-free reinforcement learning at both behavioral and neural levels.

The paper investigates how neural systems compute anticipatory value from future rewards, showing three brain regions coordinate this process.