Tom Verguts

Orthogonal representational geometry in dACC underpins human hierarchical reasoning

The paper shows that orthogonal representational geometry in dACC supports hierarchical reasoning by encoding error and difficulty along separate dimensions.

Two time scales of adaptation in human learning rates

This study shows humans adapt learning rates at both fast and slow time scales, with the central orbitofrontal cortex involved in representing environment-specific learning rates.

Rhythmicity Revisited: Evidence from Robust Behavioral Oscillations

This study uses AR modeling to detect behavioral oscillations in theta band across two datasets, finding reliable oscillations but no consistent frequency shift with task difficulty.

Medial Frontal Theta Reduction Impairs Rule Switching via Prediction Error

This study shows that reducing medial frontal theta oscillations impairs rule switching in a probabilistic reversal learning task, supporting the role of prediction error signaling in cognitive flexibility.

Neural and computational evidence for a predictive learning account of the testing effect

The paper investigates the testing effect using fMRI and computational models, finding that testing enhances memory through predictive learning mechanisms involving reward-related brain areas.

Decision Confidence and Outcome Variability Optimally Regulate Separate Aspects of Hyperparameter Setting

The paper shows that decision confidence and outcome variability separately regulate hyperparameter settings in reinforcement learning tasks, optimizing exploration and value learning at different times.

Patch-leaving decisions and pupil-linked arousal systems

The paper investigates patch-leaving decisions using pupil dilation as a measure of neuromodulation, finding increased pupil dilation before leave decisions and linking it to LC-NE activity.

Humans adaptively select different computational strategies in different learning environments.

The paper shows humans adaptively choose between flat and hierarchical learning strategies based on environmental complexity.

Manipulating prior beliefs causally induces under- and overconfidence

The paper shows that manipulating prior beliefs about task performance affects confidence levels in perceptual decisions without changing actual performance.

Modulating hierarchical learning by high-definition transcranial alternating current stimulation at theta frequency

This study investigates how theta frequency transcranial stimulation affects hierarchical learning by altering low-level learning rates in a probabilistic reversal learning task.

Dynamic influences on static measures of metacognition

The paper examines how response caution affects metacognitive efficiency, showing that static measures like M-ratio are influenced by speed-accuracy tradeoffs, while a dynamic measure, v-ratio, is not.

Theta oscillations shift towards optimal frequency for cognitive control

This study shows theta oscillations adjust their peak frequency to optimize cognitive control during tasks.

Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits

The paper introduces the ACDC model, which learns and flexibly applies spatiotemporal sequences in different rhythms, demonstrating its ability to adapt tempo and rhythm without retraining.

Time-based binding as a solution to and a limitation for flexible cognition

The paper proposes that working memory limitations arise from time-based binding, a mechanism necessary for flexible cognition, and compares this theory to other resource limitation theories.

Using top-down modulation to optimally balance shared versus separated task representations

This paper evaluates modulation networks for balancing shared and separated task representations, finding that multiplicative adaptive modulation achieves the best performance.

Electrophysiological signatures of hierarchical learning

The paper investigates hierarchical learning by combining a hierarchical Bayesian model with EEG data during a probabilistic reversal learning task, showing distinct neural encoding of low- and high-level prediction errors.

Theta and alpha power across fast and slow timescales in cognitive control

The paper examines theta and alpha neural oscillations during learning, showing they operate at different timescales with distinct power changes.

Neural representations of task context and temporal order during action sequence execution

The paper reanalyzes a dataset using multivariate pattern analysis to identify brain regions involved in maintaining temporal and contextual information during action sequence execution.

Signed Reward Prediction Errors in the Ventral Striatum Drive Episodic Memory

This study shows that reward prediction errors in the ventral striatum predict episodic memory strength and that functional connectivity between brain regions increases with RPE value during learning.

Learning to synchronize: Midfrontal theta dynamics during rule switching

The paper tests a computational model of midfrontal theta oscillations in rule switching using EEG data from a probabilistic reversal learning task, finding that theta power and phase coupling correlate with negative prediction errors.

Failure to modulate reward prediction errors in declarative learning with theta (6 Hz) frequency transcranial alternating current stimulation.

This study tests if theta-frequency tACS modulates declarative learning via reward prediction errors but finds no effect.

Dynamic expressions of confidence within an evidence accumulation framework

This paper combines evidence accumulation models with confidence reporting to show that confidence dynamics depend on time and evidence strength.

Confidence predicts speed-accuracy tradeoff for subsequent decisions

The paper shows that decision confidence influences the speed-accuracy tradeoff in subsequent decisions by adjusting decision bounds based on previous confidence reports.

Learning to synchronize: How biological agents can couple neural task modules for dealing with the stability-plasticity dilemma

This paper proposes a computational framework combining synchronization and reinforcement learning to address the stability-plasticity dilemma in neural processing.

A post-decisional neural marker of confidence predicts information-seeking

The paper shows that neural markers of confidence, specifically post-decisional ERP components, predict information-seeking behavior in humans.

Dorsal Anterior Cingulate-Midbrain Ensemble As A Reinforcement Meta-Learner

This paper introduces a computational model of the dorsal anterior cingulate cortex (dACC) and midbrain circuitry that explains how dACC functions as a reinforcement meta-learner.

Preparation for mental effort recruits Dorsolateral Prefrontal Cortex: an fNIRS investigation

This study uses fNIRS to show increased DLPFC activity during anticipation of mental effort, replicating fMRI findings and demonstrating fNIRS's utility for studying effort preparation.