Nicolas W. Schuck

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.

FASTIMAGES: Validating replay detection methods in human Neuroimaging using a combined MEG and fMRI dataset

The paper introduces FASTIMAGES, a benchmark dataset combining MEG and fMRI to evaluate replay detection methods in humans, showing method-specific strengths and cross-modality challenges.

Entorhinal cortex signals dimensions of past experience that can be generalised in a novel environment

The paper shows that the entorhinal cortex signals generalisable aspects of past experience during early exposure to novel environments, improving decision-making accuracy.

Neural replay is connected to latent cause inference and supports fast generalization

The paper investigates how neural replay relates to latent cause inference and supports fast generalization in a value-based task with shared rewards.

Neural evidence that humans reuse strategies to solve new tasks

The paper shows humans reuse learned strategies for new tasks using a computational process involving successor features and generalised policy improvement, supported by neural activation patterns.

Absence of Systematic Effects of Internalizing Psychopathology on Learning Under Uncertainty

This study investigates the relationship between internalizing psychopathology and learning under uncertainty, finding no systematic effect on learning rates or task performance.

An inductive bias for slowly changing features in human reinforcement learning

This study shows humans learn better from slowly changing reward-predicting features in reinforcement learning tasks, suggesting an inductive bias toward slow features.

N2 Sleep Inspires Insight

This study investigates the relationship between sleep stages and insight, finding that N2 sleep increases insight likelihood, while spectral slopes predict insight beyond sleep stages.

Adaptive Integration of Perceptual and Reward Information in an Uncertain World

This study investigates how perceptual uncertainty and reward information influence decision-making through a human behavioral experiment and computational modeling.

Grid-like entorhinal representation of an abstract value space during prospective decision making

The paper shows that the entorhinal cortex represents an abstract value space during decision making with a grid-like pattern aligned to the most informative value axis.

Goal-seeking compresses neural codes for space in the human hippocampus and orbitofrontal cortex

The paper shows that goal-directed behavior compresses neural representations of space in the hippocampus and orbitofrontal cortex, with compressed representations predicting successful learning.

Trait anxiety is associated with hidden state inference during aversive reversal learning

The paper shows that trait anxiety is linked to greater reliance on hidden-state inference during aversive reversal learning, as evidenced by steeper expectation switches and better model fit for state inference over gradual learning.

Replay in human visual cortex is linked to the formation of successor representations and independent of consciousness

The paper shows that neural replay in visual cortex supports implicit learning of higher-order sequential relationships without conscious awareness.

Hippocampal spatio-temporal cognitive maps adaptively guide reward generalization

The paper shows how hippocampal cognitive maps adapt to guide reward generalization by shifting emphasis from temporal to spatial relationships as a task progresses.

L-DOPA enhances hippocampal directionsignals in younger and older adults

This study investigates how L-DOPA affects directional neural signals in the hippocampus and retrosplenial cortex across age groups using fMRI.

Orbitofrontal cortex and learning predictions of state transitions

This study investigates the role of the orbitofrontal cortex in learning state transitions, finding that OFC activity correlates with more optimal predictions rather than simple prediction errors.

Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis

This paper introduces a method to detect sub-second neural activation sequences using fMRI, showing it can identify image-specific patterns even when stimuli are presented with 32 ms intervals.

Representations of context and context-dependent values in vmPFC compete for guiding behavior

The paper investigates how ventromedial prefrontal cortex (vmPFC) represents context and context-dependent values during decision-making, showing that both relevant and irrelevant values are encoded and influence behavior.

Brain network dynamics during spontaneous strategy shifts and incremental task optimization

This study investigates how brain networks support strategy optimization and spontaneous strategy shifts during a perceptual decision-making task using fMRI.

Effects of Aging on the Encoding of Spatial Direction in the Human Brain

This study investigates age-related changes in neural tuning for spatial direction using fMRI, finding broader tuning in older adults in the primary visual cortex but no behavioral impact.

Sequential replay of non-spatial task states in the human hippocampus

This study shows that human hippocampus reactivates sequences of non-spatial task states during wakeful rest, linking offline reactivation to on-task orbitofrontal cortex representations.

Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias

This paper identifies bias in RSA methods using fMRI data and introduces BRSA, a Bayesian approach that reduces bias and improves precision in estimating neural representational similarity.

Connecting conceptual and spatial search via a model of generalization

The paper tests if computational principles of spatial search apply to conceptual search using a Gaussian Process model and finds similar but distinct generalization patterns.

A state representation for reinforcement learning and decision-making in the orbitofrontal cortex

The paper proposes a theory that the orbitofrontal cortex provides state representations for decision-making and reinforcement learning, integrating diverse functions through its connectivity.