Nicholas B. Turk‐Browne

The paper estimates intrinsic dimensionality in fMRI data across development, finding that task-relevant brain regions have higher-dimensional activity in adults, while infants show no such modulation.

This study shows the cerebellum encodes prediction errors during nonmotor statistical learning, suggesting a domain-general role in error-based learning.

This study investigates how the human hippocampus processes auditory and visual information and integrates them, finding specialized and lateralized representations.

This study shows acute stress shifts hippocampal processing from episodic encoding to statistical learning, enhancing predictive feature representation and suppressing pattern separation.

The paper tests if cartoons elicit stronger neural responses than realistic movies in infants, finding that animated versions produce more reliable visual cortex activity.

This study shows that subjective time is constructed from hierarchical event representations, with temporal distortions depending on event structure and semantic content.

The paper shows that perceptual learning enhances functional connectivity between retinotopically tuned voxels in V1 and V4, correlating with improved behavioral performance in shape detection tasks.

The paper develops a method to synthesize images for mapping neural network layers to brain regions using fMRI, finding more selective mappings than natural images.

This study shows that leveraging the intrinsic manifold geometry of brain activity can accelerate learning of a noninvasive BCI by aligning brain activity within the manifold during task performance.

The paper shows that human hippocampal neurons encode visual sequences through population-level ring topologies that preserve temporal order.

This study analyzes 766 awake infant fMRI sessions to identify factors affecting data collection and retention, offering insights to improve future research.

This paper demonstrates that neural activity patterns in the human brain can be directly modified to create new visual categories, leading to behavioral and neural biases for the sculpted categories.

This study investigates whether REM sleep facilitates hippocampal memory differentiation after prediction errors, finding greater differentiation in REM sleep conditions in specific hippocampal regions.

The paper uses real-time fMRI neurofeedback to induce coactivation of competing memories in visual cortex, leading to behavioral and neural integration in the hippocampus.

This study investigates how cortisol affects hippocampal mechanisms supporting memory encoding, finding enhanced functional connectivity and altered representation patterns that predict memory improvements for emotional information.

The paper compares functional brain networks in infants during sleep and wake states using fMRI, showing differences between states and their similarity to adult networks.

This study uses fMRI to show that movie-evoked activity in infants reveals functional organization of visual cortex, including homotopic and distinct responses across hemispheres and visual areas.

This study shows that the human hippocampus encodes real-world navigation through local field potentials in ambulatory patients.

This paper introduces an unsupervised neural network model that explains memory integration and differentiation based on competitor activity levels, showing rapid and asymmetric differentiation with anticorrelated representations.

This study identifies distinct patterns of background functional connectivity associated with perception and memory retrieval states using fMRI and full correlation matrix analysis.

The paper shows that perceptual decisions are influenced by dynamic memory retrieval and sensory evidence, with memory parameters adjusting based on trial-specific evidence.

The paper identifies neural synchronization patterns in second language processing that correlate with proficiency levels, showing high proficiency resembles native speakers and low proficiency shows more perceptual and semantic synchronization.

This study investigates statistical learning in epilepsy patients and healthy controls, finding that hippocampal volume and direct stimulation affect performance, while seizure frequency correlates with task performance.

This paper introduces T-PHATE, a nonlinear manifold learning algorithm for time-series data like fMRI, showing improvements in visualization, classification, and segmentation.

The paper compares manual and automated methods for hippocampus segmentation in infants, finding that ASHS provides more reliable results than manual or other automated approaches.

The paper shows that neural entrainment during statistical learning reveals online abstraction by detecting both exemplar and category-level regularities in intracranial recordings.

The paper examines how pandemic lockdowns affected infant face processing by comparing neural responses in pre- and post-lockdown infants using fMRI.

The paper uses intracranial EEG to study how prediction from statistical learning interferes with episodic memory in human visual cortex, showing that predicted items are more likely to be forgotten.

This paper investigates event segmentation in infants using fMRI and a hidden Markov model, finding that infants represent longer events with no timescale hierarchy, unlike adults.

This study investigates how statistical learning in spatial navigation shifts from hippocampal to medial prefrontal cortex representation over time using longitudinal iEEG data from an epilepsy patient.