Peter Dayan

This study measures fast dopamine transients in human olfactory epithelium during reward learning tasks and finds they shift from outcomes to cues, matching prediction error signals.

The paper introduces a POMDP model to analyze inhibitory control in the Stop Signal Task, identifying computational attributes linked to ADHD scores in a large cohort.

This study models human sequential decision-making under uncertainty, finding that subjective risk and time urgency drive choices, with variable contributions from recency biases in different conditions.

The paper examines correlations between behavioural variability and neural coding in the hippocampus during reward-based navigation in mice.

The paper introduces CBAS, a data-driven method to identify interpretable behavioral components by analyzing action sequences across species, showing its applicability in flies, rats, and humans.

The paper quantifies ancillary information in neural populations about uncertainty using mutual information and Fisher information loss, showing non-Gaussian errors and bounds on metacognitive sensitivity.

This study examines age-related changes in neural and behavioural variability during a decision-making task in mice, finding increased neural variability and reduced variability quenching in specific brain regions.

This study identifies neural signatures of model-based and model-free reinforcement learning in prefrontal cortex and striatum using single-neuron recordings in rhesus macaques.

This study investigates dopamine's role in the human anterior cingulate cortex during Pavlovian-instrumental conflict, showing it evaluates Pavlovian responses in a reinforcement learning framework.

This study investigates metacognition in individuals with Corpus Callosum Dysgenesis across multiple cohorts and tasks, finding normal perceptual accuracy but reduced metacognitive efficiency.

This paper develops a reinforcement learning agent that combines egocentric and allocentric successor representations to improve navigation in complex environments.

The paper proposes a computational framework to model injury recovery and pain transitions using a partially observable Markov decision process, explaining paradoxical behaviors and chronic pain development.

The paper shows that the entorhinal cortex signals generalisable aspects of past experience during early exposure to novel environments, improving decision-making accuracy.

The paper investigates how neural replay relates to latent cause inference and supports fast generalization in a value-based task with shared rewards.

This study investigates how VTA dopamine neuron activity shapes stimulus and action value representations in the striatum through conditioned reinforcement in mice.

Mice infer stimulus-side priors from recent choices; prior signals are decodable pre-stimulus in 24.0% to 30.2% of 242 brain regions, including early visual and motor areas.

The paper presents a brain-wide neural activity map from 621,733 neurons across 139 mice performing a decision-making task, showing how neural activity encodes task variables across brain regions.

This study compares computational models of approach-avoidance biases in suicidal populations using hierarchical Bayesian inference, finding that simpler models with value decay better explain behavior than complex ones.

The paper applies a hybrid model to mouse decision-making data to uncover cognitive processes by combining theory-driven and data-driven approaches.

The paper shows that mice with ASD-related mutations exhibit similar computational and neural anomalies in prior updating during decision-making.

This study investigates the relationship between internalizing psychopathology and learning under uncertainty, finding no systematic effect on learning rates or task performance.

This paper extends a reinforcement learning model to explore how replay during sleep or rest can support optimal exploration in dynamic environments.

The paper investigates confidence in exploratory choices and finds no difference in confidence between immediate and total reward in a bandit task, contradicting predictions.

This study investigates how reward location variability affects hippocampal spatial representations by inducing reorganization in place cell populations under different uncertainty conditions.

This study identifies four computational mechanisms underlying effort-based decision-making in depression and finds that remitted and current depressed individuals show reduced effort acceptance bias.

This paper develops a decision-theoretic model of perceptual multistability using POMDPs to explain binocular rivalry dynamics and reward-based perceptual switching.

This study uses computational models to show distinct decision processes for 3D and motion stimuli in humans and monkeys, despite shared neural areas.

This paper models individual differences in risk-sensitive exploration using a Bayes adaptive Markov decision process model applied to animal behavior data.