Steven Wayne Kennerley

The paper shows that value representations in ACC and OFC develop rapidly and use distinct coding strategies, with ACC encoding generalized value and OFC showing context-dependent coding.

This study identifies neural signatures of model-based and model-free reinforcement learning in prefrontal cortex and striatum using single-neuron recordings in rhesus macaques.

The paper investigates how the brain executes programs by analyzing neural activity during mental program execution, finding that fast neural sequences represent candidate solutions.

The paper shows that ventromedial prefrontal cortex uses grid-like coding and theta frequency modulation to represent value space during economic decisions.

This study shows distributional reinforcement learning better explains prefrontal cortex responses compared to classic reinforcement learning.

The paper shows that the anterior cingulate cortex encodes the value of peripheral cues before fixation to guide subsequent fixations and choices during economic decision-making.

The paper investigates decision-making biases in health and disease using behavioural experiments, computational models, and pharmacology, revealing a neural circuit mechanism for pro-variance bias and its link to NMDA receptor hypofunction in schizophrenia.

The paper shows that humans can transfer structural knowledge across tasks by inferring abstract basis sets, improving performance in novel environments.

This study combines model-free and model-sensitive reinforcement learning in rhesus monkeys, showing both strategies influence decision-making and response vigor.

The paper demonstrates a triple dissociation of attention and decision computations across three prefrontal cortex subregions during value-guided choice.

The paper compares persistent and dynamic hypotheses of working memory coding in prefrontal cortex using a spatial WM task and finds that dynamic population-level activity in high time-constant neurons supports WM.