Matthew R. Nassar

A parietal grid-like code rotates with cognitive maps but lags rapid behavioral transfer

This study investigates how parietal grid-like codes rotate with cognitive maps and their role in rapid behavioral transfer, finding that such rotations lag behind performance improvements.

A normative account of human temporal structure learning

This study develops a Bayesian model of temporal structure learning and shows it aligns with human behavior in predicting outcomes across different temporal environments.

Computational Signatures of Pain Chronification: Duration-Dependent Decision-Making Shifts Across Acute and Chronic Pain

This study identifies a duration-dependent decision-making shift in chronic pain patients, showing reduced reliance on global expected value and increased context-dependent reinforcement history.

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.

Adaptive learning via surprise gated attractor switching

This paper develops a basal ganglia-thalamo-cortical circuit model of adaptive learning that explains behavioral data and predicts brain region roles in complex learning.

Fluctuations in arousal reflect latent state transitions that facilitate behavioral optimization

The paper investigates how fluctuations in arousal signals reflect latent state transitions that enhance behavioral optimization through rapid mental context adjustments.

Methamphetamine-induced adaptation of learning rate dynamics depend on baseline performance.

This study examines how methamphetamine affects learning rate dynamics in probabilistic reversal learning, showing it enhances learning flexibility more in individuals with lower baseline performance.

Dissociable neural signals for reward and emotion prediction errors

The paper investigates neural signals for reward and emotion prediction errors during social learning, finding distinct neural signatures for each.

Enhancement of mediodorsal thalamus rescues aberrant belief dynamics in a mouse model with schizophrenia-associated mutation.

This study identifies the mediodorsal thalamus as critical for belief dynamics in a schizophrenia mouse model and shows that enhancing its activity rescues cognitive deficits.

Common neural choice signals can emerge artifactually amidst multiple distinct value signals

The paper shows that neural signals resembling evidence accumulation in decision-making can emerge artifactually from overlapping value signals, not from actual evidence accumulation processes.

Adaptive learning through temporal dynamics of state representation

This paper introduces a neural network model that explains adaptive learning by dynamically adjusting internal state representations based on environmental statistics and feedback.

Dynamic Representation of the Subjective Value of Information

The paper investigates how the brain represents and updates the value of information (VOI) during decision-making using fMRI and a modified beads task.

Noise correlations for faster and more robust learning.

This paper shows that positive noise correlations among similarly tuned neurons can improve the speed and robustness of readout learning in neural networks.

I knew that! Response-based Outcome Predictions and Confidence Regulate Feedback Processing and Learning

The paper shows that confidence in outcome predictions and their calibration to prediction precision improve learning efficiency through neural feedback processing.

The stability flexibility tradeoff and the dark side of detail

The paper examines how individual differences in learning behavior relate to attention to detail and stability-flexibility tradeoffs, finding that higher attention to detail correlates with greater flexibility and lower stability in information processing.

Functional brain network reconfiguration during learning in a dynamic environment

The paper shows that dynamic changes in brain functional connectivity correlate with learning adjustments in uncertain environments, particularly increased integration between fronto-parietal and other systems.

Statistical context dictates the relationship between feedback-related EEG signals and learning

The paper shows that the P300 EEG signal's relationship with learning depends on the statistical context of surprise, with larger P300s predicting more learning in changing contexts and less in outlier contexts.

Positive reward prediction errors strengthen incidental memory encoding

This study shows that reward prediction errors enhance incidental memory encoding, specifically during memoranda presentation, and this effect is not consolidation-dependent.

Dissociable forms of uncertainty-driven representational change across the human brain.

The paper investigates rapid changes in neural activity patterns during behavioral shifts, identifying distinct representations in different brain regions that reflect uncertainty and environmental changes.

Chunking as a rational strategy for lossy data compression in visual working memory

This study proposes chunking as a strategy for optimizing visual working memory by balancing storage capacity and recall precision, showing it improves performance while reducing precision and introducing inter-item dependencies.

A control theoretic model of adaptive behavior in dynamic environments

The paper tests whether a PID controller can model human adaptive behavior in dynamic environments, finding it better describes decision-making than traditional models.

Adaptive, arousal-related adjustments of perceptual biases optimize perception in a dynamic environment

The paper shows that perceptual biases adjust with stimulus predictability and arousal to optimize perception in dynamic environments.