Laurence Tudor Hunt

This study examines how ketamine affects emotional evidence integration in changing environments, finding it shifts the balance between recent and past information based on volatility.

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.

The paper uses interpretable neural network models to predict human information gathering behavior and identifies brain regions involved in value of information processing.

The paper shows that individuals with anxiety and depression have impaired ability to adapt learning rates to reward and punishment volatility, linked to reduced neural encoding in the anterior cingulate cortex.

The paper identifies a bias in information weighting, specifically reduced recency-weighted evidence integration, as a driver of indecisiveness across obsessive-compulsive and anxiety disorders.

This study shows humans adjust their learning to environmental dynamics, distinguishing between abrupt change points and gradual random walks.

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

The paper shows that working memory shapes neural geometry in human EEG during learning by transforming task features into low-dimensional representations.

The paper compares HMM and DyNeMo for analyzing task-related oscillatory brain activity, showing DyNeMo detects more task activations than traditional methods.

The paper investigates how neural responses adapt to different environments in continuous decision-making tasks, showing changes in EEG signals reflect shifts in evidence accumulation strategies.

This paper develops an EEG measure of continuous feedback processing in humans and tests whether it aligns with the reward prediction error hypothesis.

This study shows that reward positivity (RewP) in EEG predicts timing behavior in a continuous drumming task, suggesting dopaminergic fluctuations influence interval production.

This study shows that prefrontal cortex representations change from high-dimensional and nonlinear to low-dimensional and rule-selective as animals learn a new task rule.

This study reveals consistent temporal scaling of human scalp-recorded potentials across four EEG tasks using a novel general linear modelling approach.

The paper investigates how average task value influences reward-related ACC activity, finding that reward positivity is reduced in high-value tasks independent of reward magnitude or outcome expectancy.

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 shows that medial frontal cortex activity predicts information sampling in economic decisions and explains how choice difficulty is encoded in this region.

The paper shows that non-invasive human recordings can reveal state space trajectories of choice processes, finding that choice-irrelevant features are less represented than relevant ones even after extensive training.

This study identifies two distinct processes in human parietal cortex: one for saccade decision via firing rates and another for cross-saccade prior via sensory input gain modulation.

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 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.

The paper identifies frequency-specific phase-coupling networks in resting-state magnetoencephalography data, revealing distinct brain states associated with sensory, motor, and higher-order cognitive networks.

This study compares how Borderline Personality Disorder (BPD) and control subjects weigh social versus non-social cues and learn from stable and volatile conditions using a reward learning task.