Peter Dayan

The paper decomposes the costs of cognitive effort by analyzing wage requests for cognitive tasks and identifies specific working memory components that are costly.

This study shows distributional reinforcement learning better explains prefrontal cortex responses compared to classic reinforcement learning.

The paper introduces an infinite hidden semi-Markov model to analyze learning curves in mice, finding three major learning stages and behavior changes after rest periods.

The paper explores how dopamine signaling in the nucleus accumbens influences cognitive control through active avoidance and omission behavior.

The paper describes a modular data architecture for a neuroscience collaboration, enabling data contribution, access, and analysis across multiple labs.

This paper explores how confidence influences information search by extending models of metacognition to show diverse relationships between action, confidence, and information seeking.

The paper investigates the role of the orbitofrontal cortex in creating cognitive maps by showing that its inactivation disrupts specific cue-outcome associations but not model-based learning overall.

The paper models optimal attentional control in a detection task using a partially observable Markov decision process to analyze the trade-offs between information gain and energetic costs.

The paper identifies spatial preferences in rats during a spatial alternation task, showing how these preferences contribute to inter-animal variability in learning.

The paper shows that subjects replay sub-optimal actions under uncertainty, explaining pessimal replay through a model of decision-making and memory consolidation.

This study shows that dopamine enhances model-free credit assignment by boosting retrospective model-based inference, with effects linked to working memory and a trade-off with model-based credit assignment.

The paper shows humans use forward thinking to guide social choices, with neural activity reflecting control beliefs in specific brain regions.

The paper shows humans use socially adjusted value strategies in foraging, with fMRI revealing mid-cingulate and ventromedial prefrontal cortex encoding this value.

This study compares pruning behaviors in depressed and healthy individuals during multi-step decision-making but finds no group differences or relationship between pruning and depressive symptoms.

This paper introduces a framework for generating adversarial opponents that influence human decision-making in specific tasks, demonstrating its effectiveness through experiments on action selection and response inhibition.

This study examines how short-term fasting affects four types of impulsivity in healthy individuals, finding impaired action inhibition and reduced reflection impulsivity during fasting.

The paper introduces a neural network Poisson model to analyze neural spike train data and behavioral tasks, showing improved performance over previous methods.

This study combines model-free and model-sensitive reinforcement learning in rhesus monkeys, showing both strategies influence decision-making and response vigor.

The paper examines how online and offline neural replay during task performance and rest relates to decision-making flexibility in humans.

This study shows that the anterior cingulate cortex (ACC) represents action-state transitions and mediates model-based reinforcement learning in mice by influencing subsequent choices based on these transitions.

The paper develops a framework to represent individual differences in decision-making using low-dimensional, interpretable latent spaces derived from recurrent neural networks.

The paper investigates how neural systems compute anticipatory value from future rewards, showing three brain regions coordinate this process.

The paper introduces a recurrent neural network approach to model human decision-making and shows it outperforms traditional methods in predicting behavior in depression and bipolar disorder.

The paper introduces a method using recurrent neural networks to jointly model behavioral and neuroimaging data, revealing new insights into the temporal patterns of brain activity related to decision-making.

The paper shows that phasic pupil dilations correlate with unexpected structural violations in sensory sequences, indicating norepinephrine tracks unexpected uncertainty.

The paper uses fMRI to study human navigation behavior in dynamic environments, finding specific brain activity patterns associated with back-tracking, detours, and shortcuts.

The paper studies how rats learn to infer hidden task structures using a Hidden Markov Model, showing improved memory and reduced reliance on misleading recent observations over time.

This study investigates how noradrenaline and dopamine affect information gathering during decision-making, finding that noradrenaline blockade reduces information gathering through increased decision urgency.

This study shows that optogenetic activation of dorsal raphe serotonin neurons in mice increases learning rates for rewards after long inter-trial intervals based on recent reinforcement history.

This study examines how human theta and alpha oscillations change during navigation, revealing patterns linked to detours and shortcuts.