Jonathan D. Cohen

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 the cerebellum's role in cognitive aging using large datasets, finding that cerebellar volume is associated with better cognitive outcomes and protection against decline.

This study compares task-switching dynamics between artificial neural networks and human EEG data using state-space modeling, revealing a shared strategy of converging to a neutral task state.

This paper uses reinforcement learning with memory replay to explain the normative value of grief in terms of reward maximization.

The paper develops an Effort Foraging Task to measure cognitive and physical effort costs in humans and finds they are positively correlated.

The paper investigates structural covariation between cerebellum, thalamus, and cerebral cortex in neurotypical and ASD children, finding altered coupling patterns in ASD.

The paper models the cost of control in neural networks executing multiple tasks and derives an optimal control policy based on information-theoretic principles.

This study evaluates the test-retest reliability of OPM-MEG for whole-brain functional connectivity, showing high group-level repeatability and individual robustness across theta, alpha, and beta frequency ranges.

This paper shows that modular agents, composed of subagents for distinct needs, outperform monolithic agents in maintaining homeostasis in changing environments.

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

This study shows that rat anterior cingulate cortex activity reflects dynamic encoding of decision variables in a patch foraging task, but these signals are not directly involved in decision-making.

This study investigates how humans adapt their inference processes to temporal structure in stimuli, showing suboptimal behavior that can be modeled by sampling-based inference.

This paper presents a rational analysis of cognitive fatigue and boredom as opportunity cost measures in controlled behavior, offering a unified framework for understanding these phenomena.

The paper shows that pupillary indices correlate with decision-making biases, with low neural gain linked to strong biases and high gain linked to reduced biases but increased indecisiveness.

This study uses closed-loop fMRI neurofeedback to reduce negative attentional bias in depression, showing decreased bias and depression severity after training.

This paper introduces fmrisim, a Python package for simulating realistic fMRI data, validated to match real noise properties and used to optimize experimental design and pre-registration.

This study compares non-human primate and deep reinforcement learning agent performance in a virtual pursuit-avoidance task, finding agents primarily learn first-order physics while the primate exhibits second-order physics.

This study investigates whether persistent homology can recover topological structure from event-related fMRI data under practical constraints.

This paper investigates how the brain integrates evidence for different perceptual attributes and responses during decision-making, finding distinct roles for ventromedial prefrontal cortex and dorsal anterior cingulate cortex.

This paper uses multi-voxel pattern analysis to measure trial-by-trial working memory use in prospective memory, showing it correlates with behavior and improves prediction when combined with behavioral measures.