Joshua W. Brown

This study compares neural and behavioral differences between objective and subjective decision-making using fMRI and behavioral tasks.

This study investigates the neural correlates of the subjective experience of free-will during value-based risky decisions using fMRI and self-reports, identifying brain regions associated with this experience.

The paper introduces a biologically plausible variant of backpropagation through time using replay in a reversible recurrent neural network, demonstrating its effectiveness in episodic learning.

This study shows that temporal interference neurostimulation can induce BOLD activation in humans with good safety, but has limited spatial precision and off-target effects.

This study introduces a novel RNN model called SPEL that mimics prefrontal cortex activity by minimizing sequence prediction error, showing it matches neural responses and behavior in monkeys.

This paper introduces a goal reduction mechanism that accelerates reinforcement learning and models human brain activity in goal-directed learning by deriving subgoals through loop-removal.

The paper extends the PRO model to explain decision-making under risk in humans and macaques, showing ACC represents outcome variance and links to mean-variance theories.

This study uses recurrent neural networks to test the two-streams hypothesis, finding that viewpoint-invariant object classification requires longer memory than orientation/size determination.

This study uses generative models and facial expression analysis to show that negative affect enhances learning of counterfactual representations and behavioral change.

The paper compares neural mechanisms for risky decisions involving nicotine vapor and monetary reward using an MR-compatible vaping device and fMRI.

This paper introduces a localist neural network model that autonomously learns environment structures and achieves arbitrary goals without re-learning reward values, demonstrating its ability to solve complex problems like the Tower of Hanoi.

The paper presents a computational neural model based on control theory that solves arbitrary problems and links brain regions to control system mechanisms.