P. Dayan

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Bayes-Adaptive Simulation-based Search with Value Function Approximation

Peter Dayan, David Silver, Nicolas Heess, 2014, NIPS.

TD ( X ) Converges with Probability 1

P. Dayan, T. Sejnowski, 1994 .

Reinforcing connectionism : learning the statistical way

P. Dayan, 1991 .

The "wake-sleep" algorithm for unsupervised neural networks.

Geoffrey E. Hinton, G E Hinton, P Dayan, 1995, Science.

The convergence of TD(λ) for general λ

Peter Dayan, P. Dayan, 1992, Machine Learning.

Peter Dayan, Chris Watkins, P. Dayan, 1992, Machine Learning.

Reward, Motivation, and Reinforcement Learning

P. Dayan, B. Balleine, 2002, Neuron.

The Helmholtz Machine

Geoffrey E. Hinton, Peter Dayan, Richard S. Zemel, 1995, Neural Computation.

Learning sparse multiple cause models

R. Zemel, P. Dayan, 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning

P. Dayan, J. Gläscher, N. Daw, 2010, Neuron.

Varieties of Helmholtz Machine

Geoffrey E. Hinton, Peter Dayan, P. Dayan, 1996, Neural Networks.

A normative perspective on motivation

P. Dayan, Y. Niv, D. Joel, 2006, Trends in Cognitive Sciences.

A framework for mesencephalic dopamine systems based on predictive Hebbian learning

P. Dayan, T. Sejnowski, P. Montague, 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

Temporal Difference Models and Reward-Related Learning in the Human Brain

Karl J. Friston, P. Dayan, J. O'Doherty, 2003, Neuron.

Tonic dopamine: opportunity costs and the control of response vigor

P. Dayan, N. Daw, Y. Niv, 2007, Psychopharmacology.

Dissociable Roles of Ventral and Dorsal Striatum in Instrumental Conditioning

Karl J. Friston, P. Dayan, J. O'Doherty, 2004, Science.

Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control

P. Dayan, N. Daw, Y. Niv, 2005, Nature Neuroscience.

Matters temporal

P. Dayan, 2002, Trends in Cognitive Sciences.

Using EM for Reinforcement Learning

Geoffrey E. Hinton, Peter Dayan, P. Dayan, 2000 .

Using Expectation-Maximization for Reinforcement Learning

Geoffrey E. Hinton, Peter Dayan, P. Dayan, 1997, Neural Computation.

Technical Note: Q-Learning

Peter Dayan, Christopher J.C.H. Watkins, P. Dayan, 2004, Machine Learning.

TD(λ) converges with probability 1

P. Dayan, Terrence J. Sejnowski, 2004, Machine Learning.

The Convergence of TD(λ) for General λ

P. Dayan, 1992, Machine Learning.

Model-based and model-free Pavlovian reward learning: Revaluation, revision, and revelation

P. Dayan, K. Berridge, 2014, Cognitive, affective & behavioral neuroscience.

A correlational model for the development of disparity selectivity in visual cortex that depends on prenatal and postnatal phases.

P. Dayan, T. Sejnowski, G. Berns, 1993, Proceedings of the National Academy of Sciences of the United States of America.

Model-based influences on humans’ choices and striatal prediction errors

P. Dayan, N. Daw, R. Dolan, 2011, Neuron.

Bee foraging in uncertain environments using predictive hebbian learning

Peter Dayan, Terrence J. Sejnowski, P. Read Montague, 1995, Nature.

Better Optimism By Bayes: Adaptive Planning with Rich Models pdf

Peter Dayan, David Silver, Arthur Guez, 2014, ArXiv.

Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search

Peter Dayan, David Silver, Arthur Guez, 2012, NIPS.

Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search

Peter Dayan, David Silver, Arthur Guez, 2013, J. Artif. Intell. Res..

Expectation learning in the brain using diffuse ascending projections

P Dayan, Quartz, P. R. Montague, 1992 .

Helmholtz Machines and Wake-Sleep Learning

P. Dayan, 2000 .

Twenty-Five Lessons from Computational Neuromodulation

P. Dayan, 2012, Neuron.

Feudal Reinforcement Learning

Geoffrey E. Hinton, Peter Dayan, P. Dayan, 1992, NIPS.

Analytical Mean Squared Error Curves for Temporal Difference Learning

Peter Dayan, Satinder P. Singh, Satinder Singh, 1996, Machine Learning.

Off-line replay maintains declarative memories in a model of hippocampal-neocortical interactions

P. Dayan, S. Káli, 2004, Nature Neuroscience.

Decision theory, reinforcement learning, and the brain

P. Dayan, N. Daw, 2008, Cognitive, affective & behavioral neuroscience.

A model of hippocampally dependent navigation, using the temporal difference learning rule

David J. Foster, P. Dayan, R. Morris, 2000, Hippocampus.

Temporal Difference Learning of Position Evaluation in the Game of Go

Terrence J. Sejnowski, Peter Dayan, Nicol N. Schraudolph, 1993, NIPS.

Improving Generalization for Temporal Difference Learning: The Successor Representation

Peter Dayan, P. Dayan, 1993, Neural Computation.

Fast Parametric Learning with Activation Memorization

Peter Dayan, Chris Dyer, Timothy P. Lillicrap, 2018, ICML.

Recurrent Sampling Models for the Helmholtz Machine

Peter Dayan, P. Dayan, 1999, Neural Computation.

Dopamine, uncertainty and TD learning

P. Dayan, M. Duff, Y. Niv, 2005, Behavioral and Brain Functions.

Adaptive integration of habits into depth-limited planning defines a habitual-goal–directed spectrum

P. Dayan, R. Dolan, Mehdi Keramati, 2016, Proceedings of the National Academy of Sciences.

Goals and Habits in the Brain

P. Dayan, R. Dolan, 2013, Neuron.

Learning and selective attention

S. Kakade, P. Dayan, P. Montague, 2000, Nature Neuroscience.

Acquisition in Autoshaping

Peter Dayan, Sham M. Kakade, S. Kakade, 1999, NIPS.

Opponent interactions between serotonin and dopamine

Sham M. Kakade, Nathaniel D. Daw, Peter Dayan, 2002, Neural Networks.

Acquisition and extinction in autoshaping.

S. Kakade, P. Dayan, 2002, Psychological review.

An unsupervised learning model of neural plasticity: Orientation selectivity in goggle-reared kittens

P. Dayan, A. Hsu, 2007, Vision Research.

Space and time in visual context

P. Dayan, O. Schwartz, A. Hsu, 2007, Nature Reviews Neuroscience.

tHe Cognitive neuroSCienCe of Motivation and learning

P. Dayan, M. Gluck, Y. Niv, 2008 .

Exploration bonuses and dual control

P. Dayan, T. Sejnowski, 1996 .

Fast oscillations in cortical circuits

P. Dayan, 2000 .

Neural Prediction Errors Reveal a Risk-Sensitive Reinforcement-Learning Process in the Human Brain

P. Dayan, J. O'Doherty, Y. Niv, 2012, The Journal of Neuroscience.

Reinforcement learning: The Good, The Bad and The Ugly

P. Dayan, Y. Niv, 2008, Current Opinion in Neurobiology.

Serotonin in affective control.

P. Dayan, Q. Huys, 2009, Annual review of neuroscience.

Serotonin Selectively Modulates Reward Value in Human Decision-Making

P. Dayan, N. Daw, R. Dolan, 2012, The Journal of Neuroscience.

Improving Policies without Measuring Merits

Peter Dayan, Satinder P. Singh, Satinder Singh, 1995, NIPS.

Comparison of Maximum Likelihood and GAN-based training of Real NVPs pdf

Peter Dayan, Daan Wierstra, Benigno Uria, 2017, ArXiv.

Sparse Coding Can Predict Primary Visual Cortex Receptive Field Changes Induced by Abnormal Visual Input

Peter Dayan, Jonathan J. Hunt, Geoffrey J. Goodhill, 2013, PLoS Comput. Biol..

Inference and computation with population codes.

R. Zemel, P. Dayan, A. Pouget, 2003, Annual review of neuroscience.

TD(λ) Converges with Probability 1

Terrence J. Sejnowski, Peter Dayan, P. Dayan, 1994, Machine Learning.

The Involvement of Recurrent Connections in Area CA3 in Establishing the Properties of Place Fields: a Model

P. Dayan, S. Káli, 2000, The Journal of Neuroscience.

P. Dayan, N. Daw, Y. Niv, 2006, Nature Neuroscience.

Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials

P. Dayan, J. Pfister, M. Lengyel, 2010, Nature Neuroscience.

Structure in the Space of Value Functions

Peter Dayan, David J. Foster, David J. Foster, 2002, Machine Learning.

Recognizing Handwritten Digits Using Mixtures of Linear Models

Geoffrey E. Hinton, Peter Dayan, Michael Revow, 1994, NIPS.

A Neural Substrate of Prediction and Reward

Peter Dayan, P. Read Montague, Wolfram Schultz, 1997, Science.

CONNECTIONS BETWEEN C N OMPUTATIONAL AND NEUROBIOLOGICAL PERSPECTIVES ON DECISION MAKING Decision theory, reinforcement learning, and the brain

P. Dayan, N. Daw, 2008 .

Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems

Peter Dayan, L. F. Abbott, P. Dayan, 2001 .

Static and Dynamic Values of Computation in MCTS pdf

Peter Dayan, Eren Sezener, P. Dayan, 2020, UAI.

Navigating Through Temporal Difference

Peter Dayan, P. Dayan, 1990, NIPS.

Modeling the manifolds of images of handwritten digits

Geoffrey E. Hinton, Peter Dayan, Michael Revow, 1997, IEEE Trans. Neural Networks.

The algorithmic anatomy of model-based evaluation

P. Dayan, N. Daw, 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

Uncertainty, Neuromodulation, and Attention

Angela J. Yu, P. Dayan, 2005, Neuron.

Phasic norepinephrine: A neural interrupt signal for unexpected events

Angela J. Yu, P. Dayan, 2006, Network.

Pharmacological Fingerprints of Contextual Uncertainty

P. Dayan, C. Mathys, K. Stephan, 2016, PLoS biology.

The misbehavior of value and the discipline of the will

Peter Dayan, Nathaniel D. Daw, Ben Seymour, 2006, Neural Networks.

Disorders of compulsivity: a common bias towards learning habits

P. Dayan, N. Daw, T. Robbins, 2014, Molecular Psychiatry.

Dopamine, learning, and impulsivity: a biological account of attention-deficit/hyperactivity disorder.

P. Dayan, Jonathan Williams, 2005, Journal of child and adolescent psychopharmacology.

Computational psychiatry

Karl J. Friston, P. Dayan, P. Montague, 2012, Trends in Cognitive Sciences.

A Bayesian formulation of behavioral control

P. Dayan, Q. Huys, 2009, Cognition.

Space and time in visual context

P. Dayan, O. Schwartz, A. Hsu, 2007, Nature Reviews Neuroscience.

Bayesian modelling of Jumping-to-Conclusions bias in delusional patients

P. Dayan, M. Moutoussis, R. Bentall, 2011, Cognitive neuropsychiatry.

Optimising synaptic learning rules in linear associative memories

P. Dayan, D. Willshaw, 1991, Biological Cybernetics.

Reinforcement Learning: A Computational Perspective

P. Dayan, C. Watkins, 2006 .

Factor Analysis Using Delta-Rule Wake-Sleep Learning

Peter Dayan, Radford M. Neal, P. Dayan, 1997, Neural Computation.

Hippocampal Contributions to Control: The Third Way

Peter Dayan, Máté Lengyel, P. Dayan, 2007, NIPS.

Using Aperiodic Reinforcement for Directed Self-Organization During Development

Terrence J. Sejnowski, Peter Dayan, Steven J. Nowlan, 1992, NIPS.

The Variance of Covariance Rules for Associative Matrix Memories and Reinforcement Learning

Terrence J. Sejnowski, Peter Dayan, P. Dayan, 1993, Neural Computation.

Temporal difference models describe higher-order learning in humans

Peter Dayan, Raymond J. Dolan, Karl J. Friston, 2004, Nature.

Actions , Policies , Values , and the Basal Ganglia

P. Dayan, N. Daw, Y. Niv, 2005 .

Serotonin, Inhibition, and Negative Mood

Peter Dayan, Quentin J. M. Huys, P. Dayan, 2007, PLoS Comput. Biol..

Dopamine modulation in the basal ganglia locks the gate to working memory

Boris S. Gutkin, Peter Dayan, Aaron J. Gruber, 2006, Journal of Computational Neuroscience.

Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics

Peter Dayan, Odelia Schwartz, Ruben Coen Cagli, 2012, PLoS Comput. Biol..

Perceptual organization in the tilt illusion.

P. Dayan, T. Sejnowski, O. Schwartz, 2009, Journal of vision.

Volume Learning: Signaling Covariance Through Neural Tissue

Terrence J. Sejnowski, Peter Dayan, Pr Montague, 1993 .

The Effects of Uncertainty on TD Learning

P. Dayan, M. Duff, Y. Niv, 2022 .

A simple algorithm that discovers efficient perceptual codes

Peter Dayan, Brendan J. Frey, Geoffrey E. Hinton, 1997 .

The Role of Background Statistics in Face Adaptation

Peter Dayan, Hong Xu, Ning Qian, 2009, The Journal of Neuroscience.

Adaptation across the Cortical Hierarchy: Low-Level Curve Adaptation Affects High-Level Facial-Expression Judgments

Richard M. Lipkin, P. Dayan, N. Qian, 2008, The Journal of Neuroscience.

Optimal Plasticity from Matrix Memories: What Goes Up Must Come Down

Peter Dayan, David J. Willshaw, P. Dayan, 1990, Neural Computation.

Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange

Peter Dayan, P. Read Montague, Andreas Hula, 2015, PLoS Comput. Biol..

Bayes-Optimal Chemotaxis

Peter Dayan, Kevin Burrage, Geoffrey J. Goodhill, 2011, Neural Computation.

A Bayesian model predicts the response of axons to molecular gradients

P. Dayan, K. Burrage, G. Goodhill, 2009, Proceedings of the National Academy of Sciences.

Optimizing chemotaxis by measuring unbound–bound transitions

P. Dayan, K. Burrage, G. Goodhill, 2010 .

Simple Plans or Sophisticated Habits? State, Transition and Learning Interactions in the Two-step Task

Peter Dayan, Rui M. Costa, Thomas Akam, 2015, bioRxiv.

Mapping value based planning and extensively trained choice in the human brain

P. Dayan, R. Dolan, Klaus Wunderlich, 2012, Nature Neuroscience.

Anticipation and Choice Heuristics in the Dynamic Consumption of Pain Relief

Raymond J. Dolan, Peter Dayan, Ara Darzi, 2015, PLoS Comput. Biol..

Prospective and retrospective temporal difference learning

P. Dayan, 2009, Network.

Simple Plans or Sophisticated Habits? State, Transition and Learning Interactions in the Two-Step Task

Peter Dayan, Rui Costa, Thomas E. Akam, 2015, bioRxiv.

Staying afloat on Neurath's boat - Heuristics for sequential causal learning

Peter Dayan, David A. Lagnado, Neil Bramley, 2015, CogSci.

Competition and Multiple Cause Models

Peter Dayan, Richard S. Zemel, R. Zemel, 1995, Neural Comput..

Models that learn how humans learn: The case of decision-making and its disorders

Peter Dayan, Amir Dezfouli, Bernard W Balleine, 2019, PLoS Comput. Biol..

Erratum: Computational psychiatry [Trends in Cognitive Sciences 16 (2012), 72–80]

Karl J. Friston, P. Dayan, P. Montague, 2012, Trends in Cognitive Sciences.

Expected and Unexpected Uncertainty: ACh and NE in the Neocortex

Peter Dayan, Angela J. Yu, P. Dayan, 2002, NIPS.

A Bayesian Framework for Tilt Perception and Confidence

Terrence J. Sejnowski, Peter Dayan, Odelia Schwartz, 2005, NIPS.

How fast to work: Response vigor, motivation and tonic dopamine

Peter Dayan, Nathaniel D. Daw, Yael Niv, 2005, NIPS.

Motivational effects on behavior: Towards a reinforcement learning model of rates of responding

P. Dayan, N. Daw, Y. Niv, 2005 .

Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories

Peter Dayan, Máté Lengyel, Cristina Savin, 2011, NIPS.

Matching storage and recall: hippocampal spike timing–dependent plasticity and phase response curves

P. Dayan, J. Kwag, O. Paulsen, 2005, Nature Neuroscience.

Bilinearity, Rules, and Prefrontal Cortex

Peter Dayan, P. Dayan, 2007, Frontiers Comput. Neurosci..

Models of value and choice.

Peter Dayan, P. Dayan, 2012 .

Hippocampal contributions to control: a normative perspective

P. Dayan, M. Lengyel, 2007 .

Opponency Revisited: Competition and Cooperation Between Dopamine and Serotonin

P. Dayan, Y-Lan Boureau, 2010, Neuropsychopharmacology.

Inference, Attention, and Decision in a Bayesian Neural Architecture

Peter Dayan, Angela J. Yu, P. Dayan, 2004, NIPS.

Statistical models and sensory attention

R. Zemel, P. Dayan, 1999 .

Taming the shrewdness of neural function: methodological challenges in computational psychiatry

Karl J. Friston, Peter Dayan, Karl J Friston, 2015, Current Opinion in Behavioral Sciences.

Rate- and Phase-coded Autoassociative Memory

Peter Dayan, Máté Lengyel, P. Dayan, 2004, NIPS.

Forget-me-some: General versus special purpose models in a hierarchical probabilistic task

Peter Dayan, Sven Bestmann, Louise Marshall, 2018, PloS one.

Dopamine Modulates Reward-Related Vigor

Ulrik R Beierholm, P. Dayan, R. Dolan, 2013, Neuropsychopharmacology.

Doubly Distributional Population Codes: Simultaneous Representation of Uncertainty and Multiplicity

Peter Dayan, Maneesh Sahani, P. Dayan, 2003, Neural Computation.

Disentangling the Roles of Approach, Activation and Valence in Instrumental and Pavlovian Responding

Raymond J. Dolan, Peter Dayan, Roshan Cools, 2011, PLoS Comput. Biol..

Human Pavlovian–Instrumental Transfer

P. Dayan, R. Dolan, B. Seymour, 2008, The Journal of Neuroscience.

Multiple value signals in dopaminergic midbrain and their role in avoidance contexts

Raymond J. Dolan, Peter Dayan, Francesco Rigoli, 2016, NeuroImage.

Action Dominates Valence in Anticipatory Representations in the Human Striatum and Dopaminergic Midbrain

P. Dayan, R. Dolan, Q. Huys, 2011, The Journal of Neuroscience.

Differential Encoding of Losses and Gains in the Human Striatum

P. Dayan, N. Daw, R. Dolan, 2007, The Journal of Neuroscience.

Instrumental vigour in punishment and reward

P. Dayan, 2012, The European journal of neuroscience.

Special Issue: Cognition in Neuropsychiatric Disorders

Karl J. Friston, P. Dayan, R. Dolan, 2012 .

Exploration bonuses and dual control

Peter Dayan, Terrence J. Sejnowski, P. Dayan, 1996 .

Semi-rational models of conditioning: The case of trial order

Peter Dayan, Aaron C. Courville, Nathaniel D. Daw, 2012 .

Formalizing Neurath’s Ship: Approximate Algorithms for Online Causal Learning

Thomas L. Griffiths, Peter Dayan, David A. Lagnado, 2016, Psychological review.

Exploration Bonuses and Dual Control

Terrence J. Sejnowski, Peter Dayan, P. Dayan, 1996, Machine Learning.

2009 Special Issue: Goal-directed control and its antipodes

Peter Dayan, P. Dayan, 2009 .

Pre-attentive visual selection

Peter Dayan, Zhaoping Li, P. Dayan, 2006, Neural Networks.

Combining Probabilistic Population Codes

Peter Dayan, Richard S. Zemel, R. Zemel, 1997, IJCAI.

Much Ado About Not a LoT

Peter Dayan, P. Dayan, 1992 .

Know Thy Neighbour: A Normative Theory of Synaptic Depression

Jean-Pascal Pfister, Peter Dayan, Máté Lengyel, 2009, NIPS.

Pavlovian-Instrumental Interaction in ‘Observing Behavior’

Peter Dayan, Ulrik R. Beierholm, Ulrik R Beierholm, 2010, PLoS Comput. Biol..

A common mechanism for adaptive scaling of reward and novelty

P. Dayan, R. Dolan, N. Bunzeck, 2010, Human brain mapping.

Plasticity Kernels and Temporal Statistics

Peter Dayan, Michael Häusser, P. Dayan, 2003, NIPS.

Computational Differences between Asymmetrical and Symmetrical Networks

Peter Dayan, Zhaoping Li, P. Dayan, 1998, NIPS.

The Dopaminergic Midbrain Mediates an Effect of Average Reward on Pavlovian Vigor

Raymond J. Dolan, Peter Dayan, Francesco Rigoli, 2015, Journal of Cognitive Neuroscience.

Adaptation and Unsupervised Learning

Peter Dayan, Maneesh Sahani, Gregoire Deback, 2002, NIPS.

Optimal indolence: a normative microscopic approach to work and leisure

Peter Dayan, Peter Shizgal, Ritwik K Niyogi, 2014, Journal of The Royal Society Interface.

Magnetoencephalography decoding reveals structural differences within integrative decision processes

Zeb Kurth-Nelson, Peter Dayan, Raymond J Dolan, 2018, Nature Human Behaviour.

Temporal structure in associative retrieval

P. Dayan, Z. Kurth-Nelson, R. Dolan, 2015, eLife.

Striatal structure and function predict individual biases in learning to avoid pain

P. Dayan, R. Dolan, Eran Eldar, 2016, Proceedings of the National Academy of Sciences.

Memory Alone Does Not Account for the Way Rats Learn a Simple Spatial Alternation Task

Peter Dayan, Loren M Frank, David B Kastner, 2020, The Journal of Neuroscience.

Noradrenaline modulates decision urgency during sequential information gathering

Peter Dayan, Raymond J. Dolan, Tobias U. Hauser, 2018, bioRxiv.

Increased decision thresholds trigger extended information gathering across the compulsivity spectrum

P. Dayan, R. Dolan, M. Moutoussis, 2017, Translational Psychiatry.

Conditions for Cognition

Peter Dayan, P. Dayan, 2000 .

A computational account of threat-related attentional bias

Peter Dayan, Raymond J Dolan, Jochen Michely, 2019, PLoS Comput. Biol..

Attenuation of dopamine-modulated prefrontal value signals underlies probabilistic reward learning deficits in old age

P. Dayan, L. Nyberg, M. Guitart-Masip, 2017, eLife.

The habenula encodes negative motivational value associated with primary punishment in humans

P. Dayan, R. Dolan, B. Seymour, 2014, Proceedings of the National Academy of Sciences.

Modelling avoidance in mood and anxiety disorders using reinforcement-learningReinforcement learning of avoidance in mood and anxiety

P. Dayan, J. Roiser, O. Robinson, 2017 .

Disrupted habenula function in major depression

Camilla L. Nord, David L. Thomas, P. Dayan, 2016, Molecular Psychiatry.

Adversarial manipulation of human decision-making

Richard Nock, Peter Dayan, Amir Dezfouli, 2020, bioRxiv.

Uncertainty in learning, choice, and visual fixation

Peter Dayan, Maarten Speekenbrink, Jacob L. Orquin, 2020, Proceedings of the National Academy of Sciences.

How do people learn how to plan?

Peter Dayan, Falk Lieder, Frederick Callaway, 2019, 2019 Conference on Cognitive Computational Neuroscience.

Combined model-free and model-sensitive reinforcement learning in non-human primates

Peter Dayan, Steven W Kennerley, Timothy E. J. Behrens, 2020, PLoS computational biology.

Fast Sequences of Non-spatial State Representations in Humans

Zeb Kurth-Nelson, Peter Dayan, Raymond J. Dolan, 2016, Neuron.

Decision-Theoretic Psychiatry

Peter Dayan, Raymond J. Dolan, Quentin J. M. Huys, 2015 .

A temporal difference account of avoidance learning

P. Dayan, M. Moutoussis, Jonathan Williams, 2008, Network.

Dopamine, reinforcement learning, and addiction.

P. Dayan, 2009, Pharmacopsychiatry.

Action controls dopaminergic enhancement of reward representations

P. Dayan, R. Dolan, T. Sharot, 2012, Proceedings of the National Academy of Sciences.

Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis

P. Dayan, Q. Huys, D. Pizzagalli, 2013, Biology of Mood & Anxiety Disorders.

Adversarial vulnerabilities of human decision-making

Peter Dayan, Amir Dezfouli, Richard Nock, 2020, Proceedings of the National Academy of Sciences.

Computational Psychiatry for Computers

Peter Dayan, Eric Schulz, P. Dayan, 2020, iScience.

The first steps on long marches: The costs of active observation

P. Dayan, J. Roiser, E. Viding, 2020 .

Algorithms for survival: a comparative perspective on emotions

P. Dayan, D. Bach, 2017, Nature Reviews Neuroscience.

Action versus valence in decision making

P. Dayan, R. Dolan, M. Guitart-Masip, 2014, Trends in Cognitive Sciences.

When planning to survive goes wrong: predicting the future and replaying the past in anxiety and PTSD

P. Dayan, S. Bishop, C. Gagne, 2018, Current Opinion in Behavioral Sciences.

Harm to others outweighs harm to self in moral decision making

Jenifer Z. Siegel, P. Dayan, Z. Kurth-Nelson, 2014, Proceedings of the National Academy of Sciences.

Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis.

Karl J. Friston, P. Dayan, M. Owen, 2016, The lancet. Psychiatry.

Spotting the path that leads nowhere: Modulation of human theta and alpha oscillations induced by trajectory changes during navigation

Peter Dayan, Marko Nardini, Hugo J. Spiers, 2018, bioRxiv.

The roles of online and offline replay in planning

Peter Dayan, Raymond J. Dolan, Gaëlle Lièvre, 2020, bioRxiv.

Single-Trial Inhibition of Anterior Cingulate Disrupts Model-based Reinforcement Learning in a Two-step Decision Task.

P. Dayan, X. Zhang, R. Costa, 2017 .

Anterior cingulate cortex represents action-state predictions and causally mediates model-based reinforcement learning in a two-step decision task

P. Dayan, R. Costa, T. Akam, 2020, bioRxiv.

Foraging in an Uncertain Environment Using Predictive Hebbian Learning

Terrence J. Sejnowski, Peter Dayan, P. Read Montague, 1993, NIPS.

Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics

Terrence J. Sejnowski, Peter Dayan, Odelia Schwartz, 2006, Neural Computation.

Dopamine: generalization and bonuses

Peter Dayan, Sham M. Kakade, S. Kakade, 2002, Neural Networks.

Motivated Reinforcement Learning

Peter Dayan, P. Dayan, 2001, NIPS.

Does the Wake-sleep Algorithm Produce Good Density Estimators?

Brendan J. Frey, Geoffrey E. Hinton, Peter Dayan, 1995, NIPS.

A Hierarchical Model of Binocular Rivalry

Peter Dayan, P. Dayan, 1998, Neural Computation.

When Money Is Not Enough: Awareness, Success, and Variability in Motor Learning

Peter Dayan, Jörn Diedrichsen, Harry Manley, 2014, PloS one.

Values and Actions in Aversion

Peter Dayan, Ben Seymour, P. Dayan, 2009 .

Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing

Peter Dayan, Odelia Schwartz, Ruben Coen Cagli, 2009, NIPS.

Increased decision thresholds enhance information gathering performance in juvenile Obsessive-Compulsive Disorder (OCD)

Raymond J. Dolan, Tobias U. Hauser, Michael Moutoussis, 2017, PLoS Comput. Biol..

Arbitrary Elastic Topologies and Ocular Dominance

Peter Dayan, P. Dayan, 1993, Neural Computation.

Statistical Models of Conditioning

Peter Dayan, Theresa Long, P. Dayan, 1997, NIPS.

A Hierarchical Model of Visual Rivalry

Peter Dayan, P. Dayan, 1996, NIPS.

Psychiatry: Insights into depression through normative decision-making models

Peter Dayan, Joshua T. Vogelstein, Quentin J. M. Huys, 2008, NIPS.

Change-Based Inference in Attractor Nets: Linear Analysis

Peter Dayan, Reza Moazzezi, P. Dayan, 2010, Neural Computation.

Change-Based Inference in Attractor Nets: Linear Analysis

P. Dayan, R. Moazzezi, 2010, Neural Computation.

Change-based inference for invariant discrimination

P. Dayan, R. Moazzezi, 2008, Network.

Fast Population Coding

Peter Dayan, Richard S. Zemel, Quentin J. M. Huys, 2007, Neural Computation.

Spatial Representations in Related Environments in a Recurrent Model of Area CA3 of the Rat

Peter Dayan, S Kali, P. Dayan, 1999 .

Bayesian Model of Behaviour in Economic Games

Peter Dayan, P. Read Montague, Debajyoti Ray, 2008, NIPS.

Encoding and Decoding Spikes for Dynamic Stimuli

Peter Dayan, Richard S. Zemel, Quentin J. M. Huys, 2008, Neural Computation.

The role of value systems in decision making.

Peter Dayan, P. Dayan, 2008 .

The influence of contextual reward statistics on risk preference

Raymond J. Dolan, Peter Dayan, Francesco Rigoli, 2016, NeuroImage.

Hippocampally-Dependent Consolidation in a Hierarchical Model of Neocortex

Szabolcs Káli, Peter Dayan, P. Dayan, 2000, NIPS.

Using neural networks to learn intractable generative models

Peter Dayan, Ge Hinton, Rm Neal, 1994 .

Forgetful inference in a sophisticated world model

Peter Dayan, Peter Shizgal, Kent Conover, 2018, bioRxiv.

Dissociating neural learning signals in human sign- and goal-trackers

Peter Dayan, Miriam Sebold, Henrik Walter, 2019, Nature Human Behaviour.

Memory, modelling and Marr: a commentary on Marr (1971) ‘Simple memory: a theory of archicortex’

P. Dayan, D. Willshaw, R. Morris, 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.

Computational Phenotyping of Two-Person Interactions Reveals Differential Neural Response to Depth-of-Thought

Peter Dayan, Ting Xiang, P. Read Montague, 2012, PLoS Comput. Biol..

A mathematical model explains saturating axon guidance responses to molecular gradients

P. Dayan, G. Goodhill, Z. Pujic, 2016, eLife.

Explaining Away in Weight Space

Peter Dayan, Sham M. Kakade, S. Kakade, 2000, NIPS.

Dynamics of attentional selection under conflict: toward a rational Bayesian account.

Angela J. Yu, Jonathan D. Cohen, P. Dayan, 2009, Journal of experimental psychology. Human perception and performance.

Neuronal Correlates of Decision Making

P. Dayan, W. Singer, R. Ratcliff, 2008 .

Dopamine Bonuses

Peter Dayan, Sham M. Kakade, S. Kakade, 2000, NIPS.

Tamping Ramping: Algorithmic, Implementational, and Computational Explanations of Phasic Dopamine Signals in the Accumbens

Peter Dayan, Kevin Lloyd, P. Dayan, 2015, PLoS Comput. Biol..

Differential Priors for Elastic Nets

Miguel Á. Carreira-Perpiñán, Peter Dayan, Geoffrey J. Goodhill, 2005, IDEAL.

Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models

Peter Dayan, Fabio Tozeto Ramos, Bernard W. Balleine, 2018, bioRxiv.

Bonsai Trees in Your Head: How the Pavlovian System Sculpts Goal-Directed Choices by Pruning Decision Trees

Peter Dayan, Quentin J. M. Huys, Jonathan P. Roiser, 2012, PLoS Comput. Biol..

The Effect of Correlated Variability on the Accuracy of a Population Code

Peter Dayan, L. F. Abbott, P. Dayan, 1999, Neural Computation.

Exploration from Generalization Mediated by Multiple Controllers

Peter Dayan, P. Dayan, 2013, Intrinsically Motivated Learning in Natural and Artificial Systems.

ACh, Uncertainty, and Cortical Inference

Peter Dayan, Angela J. Yu, P. Dayan, 2001, NIPS.

Control of neurite growth and guidance by an inhibitory cell-body signal

Peter Dayan, Geoffrey J. Goodhill, Zac Pujic, 2018, PLoS Comput. Biol..

The limits of chemosensation vary across dimensions

Brendan A. Bicknell, P. Dayan, G. Goodhill, 2015, Nature Communications.

Levels of analysis in neural modelling

Peter Dayan, P. Dayan, 2006 .

Neurobiological modeling : squeezing top down to meet bottom up

P. Dayan, P. Montague, 1998 .

Neural Network Poisson Models for Behavioural and Neural Spike Train Data

Richard Nock, Peter Dayan, Ehsan Arabzadeh, 2020, bioRxiv.

Acetylcholine contributes through muscarinic receptors to attentional modulation in V1

P. Dayan, A. Thiele, M. Roberts, 2008, Nature.

Early childhood investment impacts social decision-making four decades later

P. Dayan, C. Ramey, S. LaConte, 2018, Nature Communications.

Author ' s personal copy A Bayesian formulation of behavioral control

P. Dayan, Q. Huys, 1997 .

Safety out of control: dopamine and defence

P. Dayan, K. Lloyd, 2016, Behavioral and Brain Functions.

Special Issue : Cognition in Neuropsychiatric Disorders Computational psychiatry

Karl J. Friston, P. Dayan, P. Montague, 2015 .

Cortical substrates for exploratory decisions in humans

P. Dayan, N. Daw, J. O'Doherty, 2006, Nature.

Depression: a decision-theoretic analysis.

P. Dayan, N. Daw, Q. Huys, 2015, Annual review of neuroscience.

2 Uncertainty , associability and the Kalman filter

Angela J. Yu, P. Dayan, 2002 .

Explicit knowledge of task structure is a primary determinant of human model-based action

P. Dayan, T. Akam, H. Simpson, 2020, Nature Human Behaviour.

Uncertainty and Learning

Peter Dayan, P. Dayan, Angela Jyu, 2003 .

Spatial preferences account for inter-animal variability during the continual learning of a dynamic cognitive task

Demetris K. Roumis, Daniel F. Liu, P. Dayan, 2022, Cell reports.

Altered learning under uncertainty in unmedicated mood and anxiety disorders

Rebecca L. Bond, P. Dayan, J. Roiser, 2019, Nature Human Behaviour.

Computational modelling

P. Dayan, 1994, Current Opinion in Neurobiology.

Optimal decisions for contrast discrimination.

Adam N. Sanborn, P. Dayan, 2011, Journal of vision.

Metabolic network analysis of the causes and evolution of enzyme dispensability in yeast

Karl J. Friston, Richard S. J. Frackowiak, P. Dayan, 2004 .

Selective Bayes: Attentional load and crowding

P. Dayan, J. Solomon, 2010, Vision Research.

Dopamine restores reward prediction errors in old age

P. Dayan, R. Dolan, Q. Huys, 2013, Nature Neuroscience.

Multi-level visual adaptation: Dissociating curvature and facial-expression aftereffects produced by the same adapting stimuli

P. Dayan, N. Qian, Hong Xu, 2012, Vision Research.

Acetylcholine, Norepinephrine, and Spatial Attention

P. Dayan, Aj Yu, 2003 .

How Humans Integrate the Prospects of Pain and Reward during Choice

P. Dayan, R. Dolan, S. Kiebel, 2009, The Journal of Neuroscience.

Differential, but not opponent, effects of l-DOPA and citalopram on action learning with reward and punishment

P. Dayan, R. Dolan, M. Frank, 2013, Psychopharmacology.

Effort and Valuation in the Brain: The Effects of Anticipation and Execution

P. Dayan, R. Dolan, M. Guitart-Masip, 2013, The Journal of Neuroscience.

Dopamine Increases a Value-Independent Gambling Propensity

P. Dayan, R. Dolan, Francesco Rigoli, 2016, Neuropsychopharmacology.

The value of what’s to come: neural mechanisms coupling prediction error and reward anticipation

P. Dayan, Z. Kurth-Nelson, R. Dolan, 2019, bioRxiv.

Hippocampal-midbrain circuit enhances the pleasure of anticipation in the prefrontal cortex

P. Dayan, Z. Kurth-Nelson, R. Dolan, 2019 .

Dopamine and performance in a reinforcement learning task: evidence from Parkinson's disease.

P. Dayan, R. Dolan, B. Seymour, 2012 .

RAPPORT DE STAGE D'OPTION SCIENTIFIQUE Rewards, Punishment and the Brain

P. Dayan, Y.-Lan Boureau, 2005 .

Foraging for foundations in decision neuroscience: insights from ethology

Pete C. Trimmer, P. Dayan, D. Blumstein, 2018, Nature Reviews Neuroscience.

The Neural Basis of Aversive Pavlovian Guidance during Planning

P. Dayan, Q. Huys, J. Roiser, 2017, The Journal of Neuroscience.

The roles of online and offline replay in planning

P. Dayan, R. Dolan, Eran Eldar, 2020, bioRxiv.

Title: The habenula encodes motivation associated with primary punishment in humans

P. Dayan, R. Dolan, B. Seymour, 2019 .

Learning Contextual Reward Expectations for Value Adaptation

P. Dayan, R. Dolan, Francesco Rigoli, 2018, Journal of Cognitive Neuroscience.

the statistical significance of all results in the main text is based on a priori region of interests ( see Table S 5 ) . x y z Z voxels p ( FWE ) Region Hemi Planning

P. Dayan, R. Dolan, Klaus Wunderlich, 2015 .

A computational and neural model of momentary subjective well-being

P. Dayan, R. Dolan, R. Rutledge, 2014, Proceedings of the National Academy of Sciences.

An effect of serotonergic stimulation on learning rates for rewards apparent after long intertrial intervals

Madalena S. Fonseca, P. Dayan, Z. Mainen, 2018, Nature Communications.

The Long and the Short of Serotonergic Stimulation: Optogenetic activation of dorsal raphe serotonergic neurons changes the learning rate for rewards

Madalena S. Fonseca, P. Dayan, Z. Mainen, 2017, bioRxiv.

The specificity of Pavlovian regulation is associated with recovery from depression

P. Dayan, R. Dolan, Q. Huys, 2016, Psychological Medicine.

Neurobiology and computational structure of decision-making in addiction

P. Dayan, P. Fletcher, A. Heinz, 2014 .

Computations Underlying Social Hierarchy Learning: Distinct Neural Mechanisms for Updating and Representing Self-Relevant Information

D. Hassabis, P. Dayan, D. Kumaran, 2016, Neuron.

Dopamine, uncertainty and TD learning

P. Dayan, M. Duff, Y. Niv, 2005, Behavioral and Brain Functions.

Curved Gaussian models with application to modeling foreign exchange rates

P. Dayan, J. Lin, 2000 .

Pavlovian-instrumental interactions in active avoidance: The bark of neutral trials

P. Dayan, K. Lloyd, 2019, Brain Research.

Prior preferences beneficially influence social and non-social learning

P. Dayan, D. Kumaran, B. De Martino, 2017, Nature Communications.

Charting the landscape of priority problems in psychiatry, part 2: pathogenesis and aetiology.

Karl J. Friston, P. Dayan, M. Owen, 2016, The lancet. Psychiatry.

Optimal indolence : How long to work and how long to play

K., P. Dayan, P. Shizgal, 2013 .

The social contingency of momentary subjective well-being

P. Dayan, R. Dolan, Archy O. de Berker, 2016, Nature Communications.

Space, Time, and Fear: Survival Computations along Defensive Circuits

Drew B. Headley, P. Dayan, D. Mobbs, 2020, Trends in Cognitive Sciences.

Realizing the Clinical Potential of Computational Psychiatry: Report From the Banbury Center Meeting, February 2019

Albert R. Powers, Christopher H. Chatham, P. Dayan, 2019, Biological Psychiatry.

Necessary, Yet Dissociable Contributions of the Insular and Ventromedial Prefrontal Cortices to Norm Adaptation: Computational and Lesion Evidence in Humans

Peter Dayan, Xiaosi Gu, Robert T Knight, 2015, The Journal of Neuroscience.

The Protective Action Encoding of Serotonin Transients in the Human Brain

Peter Dayan, Terry Lohrenz, Thomas L Ellis, 2018, Neuropsychopharmacology.

FactorAnalysisUsingDelta-RuleWake-SleepLearning

Peter Dayan, Radford M. Neal, P. Dayan, 1996 .

Moral transgressions corrupt neural representations of value

Jenifer Z. Siegel, P. Dayan, Z. Kurth-Nelson, 2017, Nature Neuroscience.

Decodability of Reward Learning Signals Predicts Mood Fluctuations

Peter Dayan, Eran Eldar, Raymond J. Dolan, 2018, Current Biology.

The value of what’s to come: Neural mechanisms coupling prediction error and the utility of anticipation

P. Dayan, Z. Kurth-Nelson, J. O'Doherty, 2020, Science Advances.

The modulation of savouring by prediction error and its effects on choice

Zeb Kurth-Nelson, Peter Dayan, Kiyohito Iigaya, 2016, eLife.

Persecutory delusions and the conditioned avoidance paradigm: Towards an integration of the psychology and biology of paranoia

Michael Moutoussis, Peter Dayan, P. Dayan, 2007, Cognitive neuropsychiatry.

Beta-Blocker Propranolol Modulates Decision Urgency During Sequential Information Gathering

P. Dayan, R. Dolan, M. Moutoussis, 2018, The Journal of Neuroscience.

Abstract rule representations in a bilinear model

P. Dayan, K. Krueger, 2009 .

Parsing the Role of the Hippocampus in Approach–Avoidance Conflict

P. Dayan, Z. Kurth-Nelson, R. Dolan, 2016, Cerebral cortex.

How receptor diffusion influences gradient sensing

P Dayan, G J Goodhill, P. Dayan, 2015, Journal of The Royal Society Interface.

The influence of receptor positioning on chemotactic information.

P Dayan, G J Goodhill, P. Dayan, 2014, Journal of theoretical biology.

CHAPTER 35 – Attention in Conditioning

P. Dayan, 2005 .

Acquisition and extinction in autoshaping

S. Kakade, P. Dayan, 2002 .

Forming global estimates of self-performance from local confidence

P. Dayan, S. Fleming, M. Rouault, 2019, Nature Communications.

Optimal Recall from Bounded Metaplastic Synapses: Predicting Functional Adaptations in Hippocampal Area CA3

Peter Dayan, Máté Lengyel, Cristina Savin, 2014, PLoS Comput. Biol..

Impaired adaptation of learning to contingency volatility in internalizing psychopathology

P. Dayan, S. Bishop, C. Gagne, 2020, eLife.

Reward and punisher experience alter rodent decision-making in a judgement bias task

P. Dayan, M. Mendl, E. Paul, 2020, Scientific Reports.

Neural Models for Part-whole Hierarchies 1 Hierarchical Models

P. Dayan, M. Riesenhuber, 1996 .

Uncertainty-based competition between prefrontal and striatal systems for behavioural control

Yael Niv, Peter Dayan, Nathaniel D. Daw, 2005 .

Introduction for 2002 Special Issue: Computational Models of Neuromodulation

Kenji Doya, Michael E. Hasselmo, Peter Dayan, 2002, Neural Networks.

Volume Learning: Signaling Covariance Through Neural Tissue

P. Dayan, T. Sejnowski, P. Montague, 1993 .

Dynamic population codes for sensorimotor processing

Peter Dayan, Richard S. Zemel, Rama Natarajan, 2006 .

Humans Use Forward Thinking to Exert Social Control

P. Dayan, X. Gu, A. Hula, 2019, bioRxiv.

Flexible shaping: How learning in small steps helps

P. Dayan, K. Krueger, 2009, Cognition.

Static and Dynamic Values of Computation in MCTS

P. Dayan, Eren Sezener, 2020, UAI.

39th Annual European Brain and Behaviour Society Abstracts

Maria V. Sanchez-Vives, R. C. Honey, J. L. Moore, 2007, Neural Plasticity.

The tilt illusion, population decoding, and natural scene statistics

Peter Dayan, Odelia Schwartz, Tc Sejnowski, 2007 .

A comparison of ‘pruning’ during multi-step planning in depressed and healthy individuals

P. Dayan, Q. Huys, J. Roiser, 2020, bioRxiv.

The company they keep: Background similarity influences transfer of aftereffects from second- to first-order stimuli

P. Dayan, N. Qian, 2013, Vision Research.

Pupil-linked phasic arousal evoked by violation but not emergence of regularity within rapid sound sequences

P. Dayan, F. Dick, S. Furukawa, 2019, Nature Communications.

Second Symposium on Biology of Decision-Making

P. Dayan, R. Dolan, M. Guitart-Masip, 2012 .

Neurofeedback through the lens of reinforcement learning

P. Dayan, T. Hendler, C. Paret, 2022, Trends in Neurosciences.

Neural Reinforcement Learning 2: Choice

Peter Dayan, P. Dayan, 2018 .

Neuronal biophysics: optimised for information processing?

Peter Dayan, Máté Lengyel, Balazs Ujfalussy, 2011 .

Author response: Temporal structure in associative retrieval

Zeb Kurth-Nelson, Peter Dayan, Raymond J. Dolan, 2014 .

Learning in Natural Neural Systems

Peter Dayan, P. Dayan, 2019 .

Orientation selectivity in goggle-reared kittens: An overcomplete unsupervised learning model

Peter Dayan, A Hsu, P. Dayan, 2007 .

Representational pursuit: Population codes for dynamic environments

Peter Dayan, Richard S. Zemel, Qjm Huys, 2005 .

The Anterior Cingulate Cortex Predicts Future States to Mediate Model-Based Action Selection

Peter Dayan, Michael Pereira, Thomas Akam, 2020, Neuron.

Learning and performance in Parkinson's disease

Peter Dayan, Raymond J. Dolan, Ben Seymour, 2011 .

Neural signatures of detours , shortcuts and backtracking during navigation Abbreviated Title : Neural responses to detours , shortcuts and backtracking

P. Dayan, M. Nardini, H. Spiers, 2018 .

Multiplicative Modulation of Bump Attractors

P. Dayan, M. Sahani, 2000 .

Author Correction: Assessing animal affect: an automated and self-initiated judgement bias task based on natural investigative behaviour

P. Dayan, M. Mendl, E. Paul, 2018, Scientific Reports.

Computational psychiatry (vol 16, pg 72, 2012)

Karl J. Friston, Peter Dayan, R. J. Dolan, 2012 .

Temporal difference learning model accounts for responses in human ventral striatum

Karl J. Friston, Peter Dayan, Hugo D. Critchley, 2002 .

Hippocampal Contributions to Control : The Third Way – Supporting Material –

P. Dayan, M. Lengyel, 2008 .

Decoding future state representations during planning

Zeb Kurth-Nelson, Peter Dayan, Demis Hassabis, 2013 .

Methods in Mind.Cognitive Neuroscience.Edited byCarl Senior,, Tamara Russell, and, Michael S Gazzaniga.Cambridge (Massachusetts): MIT Press. $55.00. xi + 382 p + 18 pl; ill.; index. ISBN: 0‐262‐19541‐0. 2006.

P. Dayan, 2007 .

Rewards and uncertainty jointly drive the attention dynamics in reinforcement learning

Peter Dayan, Maarten Speekenbrink, Hrvoje Stojic, 2019 .

Betwixt fast and slow: Integrating model-free and model-based decision-making

Peter Dayan, Mehdi Keramati, Raymond J. Dolan, 2017 .

Dissecting the links between reward and loss, decision-making, and self-reported affect using a computational approach

P. Dayan, M. Mendl, E. Paul, 2020, PLoS Comput. Biol..

The pursuit of happiness: A reinforcement learning perspective on habituation and comparisons

P. Dayan, T. Griffiths, Rachit Dubey, 2022, PLoS Comput. Biol..

Biased belief priors versus biased belief updating: Differential correlates of depression and anxiety

P. Dayan, Christian Gagné, S. Bishop, 2022, PLoS Comput. Biol..

Multistability, perceptual value, and internal foraging

P. Dayan, Shervin Safavi, 2022, Neuron.

Individual animals use distinct strategies in a changing spatial memory task

Loren M. Frank, D Kastner, A Gillespie, 2018 .

Peril, Prudence and Planning as Risk, Avoidance and Worry

P. Dayan, C. Gagne, 2021, Journal of Mathematical Psychology.

Freezing revisited: coordinated autonomic and central optimization of threat coping

P. Dayan, Karin Roelofs, 2022, Nature Reviews Neuroscience.

Interactions between attributions and beliefs at trial-by-trial level: Evidence from a novel computer game task

P. Dayan, E. Zamfir, 2022, PLoS Comput. Biol..

Memory alone does not account for the speed of learning of a simple spatial alternation task in rats

P. Dayan, L. Frank, D. Kastner, 2020 .

'Liking' as a First Draft of the Affective Future

P. Dayan, 2021 .

Bayesian Models of Dynamic Attentional Selection

Angela J. Yu, Jonathan D. Cohen, P. Dayan, 2007 .

Population Coding in a fast-changing world

Peter Dayan, Qjm Huys, Richard S. Zemel, 2005 .

Author response: The modulation of savouring by prediction error and its effects on choice

Zeb Kurth-Nelson, Peter Dayan, Giles W. Story, 2016 .

Motion-gradient defined facial expressions and the nature of face representation

Peter Dayan, Hong Xu, Jianhua Wu, 2010 .

Representation learning facilitates different levels of generalization

Christian F. Doeller, Nicolas W. Schuck, P. Dayan, 2022, 2022 Conference on Cognitive Computational Neuroscience.

Matching storage and recall: constructing optimal rate and phase-coded autoassociative memories

Peter Dayan, Jeehyun Kwag, Máté Lengyel, 2005 .

Neural Reinforcement Learning 1: Prediction

Peter Dayan, P. Dayan, 2018 .

malignant evaluation :: reinforcement learning, neuromodulation and depression

Peter Dayan, Q Huys, P. Dayan, 2006 .

A Bayesian model of behaviour in sequential economic games with applications to computational psychiatry

Peter Dayan, Debajyoti Ray, PR Montague, 2009 .

Slothful serial: perilous parallel processing

Peter Dayan, P. Dayan, 2019 .

Computation on the Transient

Peter Dayan, R Moazzezi, P. Dayan, 2007 .

EFFICIENT DECISION-MAKING IS DEPENDENT ON THE EFFICIENT PRUNING OF DECISION TREES

Peter Dayan, Q Huys, Paul Faulkner, 2011 .

Trials-with-fewer-errors: Feature-based learning and exploration

Peter Dayan, Maarten Speekenbrink, Hrvoje Stojic, 2017 .

Expected and unexpected uncertainties control allocation of attention in a novel attentional learning task.

Peter Dayan, Raymond J. Dolan, Jon Driver, 2004 .

Dopamine Modulation in a Basal Ganglio-Cortical Network of Working Memory

Peter Dayan, Aaron J. Gruber, Sara A. Solla, 2003, NIPS 2003.

The roles of online and offline replay in planning

Peter Dayan, Raymond J. Dolan, Eran Eldar, 2020, bioRxiv.

Methods in Mind. Cognitive Neuroscience.

P. Dayan, 2007 .

Differential, but not opponent, effects of l-DOPA and citalopram on action learning with reward and punishment

P. Dayan, R. Dolan, M. Frank, 2013, Psychopharmacology.

Formalising social representation to explain psychiatric symptoms

P. Dayan, V. Bell, J. Barnby, 2023, Trends in Cognitive Sciences.

Graphical Models: Foundations of Neural Computation.Computational Neuroscience. Edited byMichael I Jordanand, Terrence J Sejnowski.A Bradford Book. Cambridge (Massachusetts): MIT Press. $32.95 (paper). xxiv + 421 p; ill.; index. ISBN: 0–262–60042–0. 2001.

P. Dayan, 2002 .

Comment Uncertainty or Prediction Error in Dopamine Ramps

P. Dayan, M. Duff, Y. Niv, 2022 .

Information processing with population codes

R. Zemel, P. Dayan, A. Pouget, 2000, Nature Reviews Neuroscience.

Safety out of control: dopamine and defence

P. Dayan, K. Lloyd, 2016, Behavioral and Brain Functions.

Model-based and model-free Pavlovian reward learning: Revaluation, revision, and revelation

P. Dayan, K. Berridge, 2014, Cognitive, Affective, & Behavioral Neuroscience.

Mallarmé's "Divine Transposition": Real and Apparent Sources of Literary Value

P. Dayan, 1986 .

Correcting Experience Replay for Multi-Agent Communication pdf

Peter Dayan, Sanjeevan Ahilan, S. Ahilan, 2020, ICLR.

Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning pdf

Peter Dayan, Sanjeevan Ahilan, S. Ahilan, 2019, ICLR 2019.

Interrupting behaviour: Minimizing decision costs via temporal commitment and low-level interrupts

Peter Dayan, Kevin Lloyd, P. Dayan, 2018, PLoS Comput. Biol..

Music Writing Literature, from Sand via Debussy to Derrida

P. Dayan, 2006 .

On Nature, Music, and Meaning in Debussy's Writing

P. Dayan, 2005 .

Edinburgh Research Explorer Nature, music, and meaning in Debussy's writings

P. Dayan, 2005 .

Interart Contraband: What Passed between García, Fiszt and Sand in “Le Contrebandier”

P. Dayan, 2008 .

Erik Satie's Poetry

P. Dayan, 2008, Modern Language Review.

Derrida Writing Architectural or Musical Form

P. Dayan, 2003 .

'Waking the face that no one is': A study in the musical context of symbolist poetics.

P. Dayan, 2005 .

Art as Music, Music as Poetry, Poetry as Art, from Whistler to Stravinsky and Beyond

P. Dayan, 2011 .

Selected letters of Stéphane Mallarmé

P. Dayan, R. Lloyd, S. Mallarmé, 1988 .

Truth in Art, and Erik Satie's Judgement

P. Dayan, 2009, Nineteenth-Century Music Review.

Association of Neural and Emotional Impacts of Reward Prediction Errors With Major Depression

Jenifer Z. Siegel, Michael Moutoussis, Peter Dayan, 2017, JAMA psychiatry.

Probabilistic Interpretation of Population Codes

Alexandre Pouget, Peter Dayan, Richard S. Zemel, 1996, Neural Computation.

Recognition in hierarchical models

Peter Dayan, P. Dayan, 1997 .

Retrospective model-based inference guides model-free credit assignment

Peter Dayan, Mehdi Keramati, Rani Moran, 2019, Nature Communications.

A Probabilistic Palimpsest Model of Visual Short-term Memory

Peter Dayan, Loic Matthey, Paul M. Bays, 2015, PLoS Comput. Biol..

WHO IS THE NARRATOR IN INDIANA

P. Dayan, 1998 .

Optimal helplessness: a normative framework for depression

Peter Dayan, Q Huys, P. Dayan, 2006 .

A computational process-tracing method for measuring people’s planning strategies and how they change over time

Paul M. Krueger, P. Dayan, T. Griffiths, 2022, Behavior Research Methods.

Cognitive Bias in Ambiguity Judgements: Using Computational Models to Dissect the Effects of Mild Mood Manipulation in Humans

P. Dayan, Wittawat Jitkrittum, M. Mendl, 2016, PloS one.

Images, Frames, and Connectionist Hierarchies

Peter Dayan, P. Dayan, 2006, Neural Computation.

Neural Models for Part-Whole Hierarchies

Peter Dayan, Maximilian Riesenhuber, P. Dayan, 1996, NIPS.

Modeling Avoidance in Mood and Anxiety Disorders Using Reinforcement Learning

P. Dayan, J. Roiser, O. Robinson, 2017, Biological Psychiatry.

Forming global estimates of self-performance from local confidence

P. Dayan, S. Fleming, M. Rouault, 2019, Nature Communications.

The Effects of Motivation on Extensively Trained Behavior

P. Dayan, Y. Niv, D. Joel, 2006 .

Tracking human skill learning with a hierarchical Bayesian sequence model

P. Dayan, K. Janacsek, D. Németh, 2022, bioRxiv.

Altered learning under uncertainty in unmedicated mood and anxiety disorders

P. Dayan, J. Roiser, W. Ahn, 2019, Nature Human Behaviour.

Nonpolitical Images Evoke Neural Predictors of Political Ideology

Peter Dayan, Gideon Yaffe, Xiaosi Gu, 2014, Current Biology.

Putting the computation back into computational modeling.

P Dayan, P. Dayan, J Williams, 2006, Pharmacopsychiatry.

Increased persuadability and credulity in people with corpus callosum dysgenesis

Ryan J. Dean, P. Dayan, L. Richards, 2021, Cortex.

“Liking” as an early and editable draft of long-run affective value

P. Dayan, 2022, PLoS biology.

Simple Substrates for Complex Cognition

Peter Dayan, P. Dayan, 2008, Front. Neurosci..

Formalising social representation to explain psychiatric symptoms

P. Dayan, V. Bell, J. Barnby, 2023, Trends in Cognitive Sciences.

The role of the lateral orbitofrontal cortex in creating cognitive maps

Matthew P. H. Gardner, P. Dayan, G. Schoenbaum, 2022, Nature Neuroscience.

Locus coeruleus integrity in old age is selectively related to memories linked with salient negative events

Peter Dayan, Nikolaus Weiskopf, Emrah Düzel, 2018, Proceedings of the National Academy of Sciences.

Pattern formation and cortical maps

Peter Dayan, P. Dayan, 2003, Journal of Physiology-Paris.

Deep brain stimulation of the subthalamic nucleus modulates sensitivity to decision outcome value in Parkinson’s disease

P. Dayan, R. Dolan, B. Seymour, 2016, Scientific Reports.

Touché: the feeling of choice

Peter Dayan, Peter E Latham, P. Dayan, 2005, Nature Neuroscience.

Short-Term Fasting Selectively Influences Impulsivity in Healthy Individuals

P. Dayan, J. Roiser, P. Burgess, 2018, bioRxiv.

Replay, Repair and Consolidation

Szabolcs Káli, Peter Dayan, P. Dayan, 2002, NIPS.

Predictive Hebbian learning

Terrence J. Sejnowski, Peter Dayan, P. Read Montague, 1995, COLT '95.

Retrospective model-based inference guides model-free credit assignment

P. Dayan, R. Dolan, Mehdi Keramati, 2019, Nature Communications.

Go and no-go learning in reward and punishment: Interactions between affect and effect

Raymond J. Dolan, Peter Dayan, Quentin J. M. Huys, 2012, NeuroImage.

Retraction: A mathematical model explains saturating axon guidance responses to molecular gradients

P. Dayan, G. Goodhill, Z. Pujic, 2018, eLife.

Nonlinear ideal observation and recurrent preprocessing in perceptual learning

Michael H. Herzog, Li Zhaoping, Peter Dayan, 2003 .

Nonlinear ideal observation and recurrent preprocessing in perceptual learning.

P. Dayan, L. Zhaoping, M. Herzog, 2003, Network.

Dopamine Modulation in a Basal Ganglio-cortical Network Implements Saliency-based Gating of Working Memory

Boris S. Gutkin, Peter Dayan, Aaron J. Gruber, 2003, NIPS.

Correlational-Based Development of Disparity Sensitivity

Terrence J. Sejnowski, Peter Dayan, Gregory S. Berns, 1993 .

Humans use forward thinking to exploit social controllability

P. Dayan, X. Gu, A. Hula, 2021, eLife.

Optimism and pessimism in optimised replay

P. Dayan, Eran Eldar, C. Gagne, 2021, bioRxiv.

Prefrontal Dynamics Associated with Efficient Detours and Shortcuts: A Combined Functional Magnetic Resonance Imaging and Magnetoencenphalography Study

William D. Penny, Hugo J. Spiers, Dharshan Kumaran, 2019, Journal of Cognitive Neuroscience.

Dissociable Effects of Serotonin and Dopamine on the Valuation of Harm in Moral Decision Making

Jenifer Z. Siegel, Giles W. Story, P. Dayan, 2015, Current Biology.

Vigilance, arousal, and acetylcholine: Optimal control of attention in a simple detection task

P. Dayan, K. Lloyd, Sahiti Chebolu, 2022, bioRxiv.

A Local Temporal Difference Code for Distributional Reinforcement Learning

Pablo Tano, Alexandre Pouget, Peter Dayan, 2020, NeurIPS.

Bayesian retrieval in associative memories with storage errors

Peter Dayan, Friedrich T. Sommer, P. Dayan, 1998, IEEE Trans. Neural Networks.

Early childhood investment impacts social decision-making four decades later

P. Dayan, C. Ramey, S. LaConte, 2018, Nature Communications.

A model of risk and mental state shifts during social interaction

Peter Dayan, P. Read Montague, Iris Vilares, 2017, PLoS Comput. Biol..

Dopaminergic Modulation of Decision Making and Subjective Well-Being

P. Dayan, R. Dolan, R. Rutledge, 2015, The Journal of Neuroscience.

Exploring Replay

P. Dayan, Georgy K. Antonov, 2023, bioRxiv.

Dynamic preferences account for inter-animal variability during the continual learning of a cognitive task

Peter Dayan, Loren M. Frank, David B. Kastner, 2019, bioRxiv.

Learning to use past evidence in a sophisticated world model

Peter Dayan, Peter Shizgal, Ritwik K Niyogi, 2019, PLoS Comput. Biol..

How to set the switches on this thing

P. Dayan, 2012, Current Opinion in Neurobiology.

Competition and Multiple Cause Models

R. Zemel, P. Dayan, 1995, Neural Computation.

Control over patch encounters changes foraging behavior

Peter Dayan, Nicolas W. Schuck, Sam Hall-McMaster, 2021, iScience.

Control over patch encounters changes foraging behaviour

Nicolas W. Schuck, P. Dayan, S. Hall-McMaster, 2021, bioRxiv.

Backtracking during navigation is correlated with enhanced anterior cingulate activity and suppression of alpha oscillations and the ‘default-mode’ network

Peter Dayan, Marko Nardini, Hugo J. Spiers, 2019, Proceedings of the Royal Society B.

Modelling avoidance in mood and anxiety disorders using reinforcement-learningReinforcement learning of avoidance in mood and anxiety

P. Dayan, J. Roiser, O. Robinson, 2017 .

Modelling avoidance in pathologically anxious humans using reinforcement-learning

P. Dayan, J. Roiser, O. Robinson, 2016, bioRxiv.

Probabilistic Meta-Representations Of Neural Networks pdf

Peter Dayan, Zoubin Ghahramani, Theofanis Karaletsos, 2018, ArXiv.

Combined model-free and model-sensitive reinforcement learning in non-human primates

Peter Dayan, Bruno Miranda, Timothy E Behrens, 2019, bioRxiv.

Assignment of Multiplicative Mixtures in Natural Images

Terrence J. Sejnowski, Peter Dayan, Odelia Schwartz, 2004, NIPS.

Sensory Conflict Disrupts Activity of the Drosophila Circadian Network

Ross E. F. Harper, P. Dayan, R. Stanewsky, 2016, Cell Reports.

Human back-tracking behaviour is associated with suppression of default-mode region activity and enhanced dorsal anterior cingulate activity

P. Dayan, D. Kumaran, M. Nardini, 2018, bioRxiv.

Neural signatures of detours, shortcuts and back-tracking during navigation

William D. Penny, Marko Nardini, Hugo J. Spiers, 2018 .

How People Use Social Information to Find out What to Want in the Paradigmatic Case of Inter-temporal Preferences

Raymond J. Dolan, Michael Moutoussis, Peter Dayan, 2016, PLoS Comput. Biol..

Internality and the internalisation of failure: Evidence from a novel task

Peter Dayan, Jonathan P. Roiser, Federico Mancinelli, 2021, PLoS Comput. Biol..

Subjective Beliefs In, Out, and About Control: A Quantitative Analysis

P. Dayan, J. Roiser, Federico Mancinelli, 2020, bioRxiv.

Interplay of approximate planning strategies

Peter Dayan, Erich Seifritz, Samuel J Gershman, 2015, Proceedings of the National Academy of Sciences.

Using Primary Reinforcement to Enhance Translatability of a Human Affect and Decision-Making Judgment Bias Task

Peter Dayan, Iain D Gilchrist, Michael Mendl, 2021, Journal of Cognitive Neuroscience.

Author Correction: Assessing animal affect: an automated and self-initiated judgement bias task based on natural investigative behaviour

P. Dayan, M. Mendl, E. Paul, 2018, Scientific Reports.

Assessing animal affect: an automated and self-initiated judgement bias task based on natural investigative behaviour

P. Dayan, M. Mendl, E. Paul, 2018, Scientific Reports.

Author response: Humans use forward thinking to exploit social controllability

P. Dayan, X. Gu, A. Hula, 2021 .

Neural encoding of perceived patch value during competitive and hazardous virtual foraging

P. Dayan, D. Mobbs, B. Silston, 2021, Nature Communications.

P. Dayan, L. Abbott, 2002 .

Models of Collaboration in Nineteenth-Century French Literature: Several Authors, One Pen (review)

P. Dayan, 2012 .

The Routledge Companion to Music and Modern Literature

P. Dayan, Axel Englund, Katharina Clausius, 2022 .

ZURICH DADA'S FORGOTTEN MUSIC MASTER: HANS HEUSSER

P. Dayan, 2015, Modern Language Review.

Are you sure about that? On the origins of confidence in concept learning

Peter Dayan, Hrvoje Stojic, Eran Eldar, 2018 .

Rationalizable Irrationalities of Choice

Peter Dayan, P. Dayan, 2014, Top. Cogn. Sci..

ReviewReward , Motivation , and Reinforcement Learning brain

P. Dayan, B. Balleine, 2002 .

Pupil-linked phasic arousal evoked by violation but not emergence of regularity within rapid sound sequences

P. Dayan, F. Dick, S. Furukawa, 2019, Nature Communications.

Risk Taking for Potential Reward Decreases across the Lifespan

Rick A Adams, P. Dayan, R. Dolan, 2016, Current Biology.

Serotonin's many meanings elude simple theories

P. Dayan, Q. Huys, 2015, eLife.

Effect of Inflation on Minimum Wages in Kenya

P. Dayan, 2015 .

Disentangled behavioural representations

Richard Nock, Cheng Soon Ong, Omar Ghattas, 2019, NeurIPS.

Metacognitive Computations for Information Search: Confidence in Control

P. Dayan, S. Fleming, Lion Schulz, 2021, bioRxiv.

Hierarchical fragmentation and memoization in planning

Peter Dayan, Samuel J. Gershman, Qjm Huys, 2013 .

Probabilistic Computation in Spiking Populations

Peter Dayan, Richard S. Zemel, Quentin J. M. Huys, 2004, NIPS.

Distributional Population Codes and Multiple Motion Models

Peter Dayan, Richard S. Zemel, R. Zemel, 1998, NIPS.

Change, stability, and instability in the Pavlovian guidance of behaviour from adolescence to young adulthood

Michael Moutoussis, Peter Dayan, Peter Fonagy, 2018, PLoS Comput. Biol..

Efficiency and prioritization of inference-based credit assignment

P. Dayan, R. Dolan, R. Moran, 2021, Current Biology.

Human subjects exploit a cognitive map for credit assignment

P. Dayan, R. Dolan, R. Moran, 2021, Proceedings of the National Academy of Sciences.

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis

Peter Dayan, David S. Holder, Marzia De Lucia, 2008, Medical & Biological Engineering & Computing.

Prior preferences beneficially influence social and non-social learning

P. Dayan, D. Kumaran, B. De Martino, 2017, Nature Communications.

A Bayesian View of Sensory Conflicts in Decision-Making March 3 , 2006 1 Introduction

Peter Dayan, Angela J. Yu, P. Dayan, 2006 .

Load and Attentional Bayes

Peter Dayan, P. Dayan, 2008, NIPS.

Pavlovian influences on learning differ between rats and mice in a counter-balanced Go/NoGo judgement bias task

P. Dayan, M. Mendl, E. Paul, 2017, Behavioural Brain Research.

The limits of narrative : essays on Baudelaire, Flaubert, Rimbaud, and Mallarmé

P. Dayan, N. Wing, 1988 .

Disentangled behavioral representations

Richard Nock, Peter Dayan, Cheng Soon Ong, 2019, bioRxiv.

The role of the orbitofrontal cortex in creating cognitive maps

Matthew P. H. Gardner, P. Dayan, G. Schoenbaum, 2022, bioRxiv.

Vigor in the Face of Fluctuating Rates of Reward: An Experimental Examination

Peter Dayan, Raymond J. Dolan, Ulrik R. Beierholm, 2011, Journal of Cognitive Neuroscience.

Acetylcholine in cortical inference

Peter Dayan, Angela J. Yu, P. Dayan, 2002, Neural Networks.

Peripheral Serotonin 1B Receptor Transcription Predicts the Effect of Acute Tryptophan Depletion on Risky Decision-Making

P. Dayan, R. Dolan, J. Roiser, 2016, The international journal of neuropsychopharmacology.

A (dis-)information Theory of Revealed and Unrevealed Preferences: Emerging Deception and Skepticism via Theory of Mind

P. Dayan, J. Rosenschein, Nitay Alon, 2023, Open Mind.

A Brain-Wide Map of Neural Activity during Complex Behaviour

Julia M. Huntenburg, Tatiana A. Engel, Eric E. J. DeWitt, 2023, bioRxiv.

When unsupervised training benefits category learning

Bradley C. Love, Peter Dayan, Franziska Bröker, 2021, Cognition.

Brain-wide representations of prior information in mouse decision-making

Julia M. Huntenburg, Jonathan W. Pillow, Eric E. J. DeWitt, 2023, bioRxiv.

Dopamine enhances model-free credit assignment through boosting of retrospective model-based inference

Peter Dayan, Rani Moran, Lorenz Deserno, 2021, Biological Psychiatry.

Uncertainty, phase and oscillatory hippocampal recall

Peter Dayan, Máté Lengyel, P. Dayan, 2006, NIPS.

The Effect of Motivation on Movement: A Study of Bradykinesia in Parkinson’s Disease

Raymond J. Dolan, Ben Seymour, Mkael Symmonds, 2011, PloS one.

Models that learn how humans learn: the case of depression and bipolar disorders

Bernard W. Balleine, Fabio Ramos, Amir Dezfouli, 2018, bioRxiv.

Reply to ‘Post-encounter freezing during approach–avoidance conflict: the role of the hippocampus’

Felix H. Klaassen, P. Dayan, Karin Roelofs, 2023, Nature Reviews Neuroscience.

A Step-by-Step Guide to Dopamine

P. Dayan, M. Walton, 2012, Biological Psychiatry.

Metacognitive Information Theory

P. Dayan, 2023, Open Mind.

Correction: Disentangling the Roles of Approach, Activation and Valence in Instrumental and Pavlovian Responding

P. Dayan, R. Dolan, Q. Huys, 2014, PLoS Computational Biology.

Neuron NeuroView An International Laboratory for Systems and Computational Neuroscience

Kenneth D. Harris, Surya Ganguli, Peter E. Latham, 2018 .

Two steps to risk sensitivity pdf

Peter Dayan, Chris Gagne, P. Dayan, 2021, NeurIPS.

Catastrophe, Compounding & Consistency in Choice pdf

Peter Dayan, Chris Gagne, P. Dayan, 2021, ArXiv.

Reframing dopamine: Controlling the controller at the limbic-motor interface

P. Dayan, K. Lloyd, 2023, bioRxiv.

Erratum: Harm to others outweighs harm to self in moral decision making (Proc Natl Acad Sci USA (2014) 111 (17320-17325) DOI: 10.1073/pnas.1408988111)

Jenifer Z. Siegel, P. Dayan, Z. Kurth-Nelson, 2015 .

A computational model of aesthetic value.

P Dayan, Aenne A. Brielmann, P. Dayan, 2021, Psychological review.

When will's wont wants wanting

P. Dayan, 2021, Behavioral and Brain Sciences.

1 2 Dopamine enhances model-free credit assignment through boosting of retrospective 3 model-based inference 4 5 6

P. Dayan, L. Deserno, R. Dolan, 2022 .

Light Dominates Peripheral Circadian Oscillations in Drosophila melanogaster During Sensory Conflict

Ross E. F. Harper, P. Dayan, R. Stanewsky, 2017, Journal of biological rhythms.

Uncertainty , phase and oscillatory hippocampal recall supplementary material

P. Dayan, M. Lengyel, 2022 .

Learning to evaluate Go positions via temporal difference methods

Terrence J. Sejnowski, Peter Dayan, Nicol N. Schraudolph, 2001 .

Models and Methods for Reinforcement Learning

Peter Dayan, Hiroyuki Nakahara, P. Dayan, 2018 .

2009 Special Issue: Goal-directed control and its antipodes

P. Dayan, 2009 .

Dynamic decision making: neuronal, computational, and cognitive underpinnings

Peter Dayan, Nigel Harvey, Maarten Speekenbrink, 2012, CogSci.

Model-based influences on humans’ choices and striatal prediction errors

P. Dayan, N. Daw, R. Dolan, 2011, Neuron.

Between prudence and paranoia: Theory of Mind gone right, and wrong

P. Dayan, J. Barnby, Nitay Alon, 2023 .

Emergent deception and skepticism via theory of mind

P. Dayan, J. Rosenschein, Nitay Alon, 2023 .

Reinforcement Learning with Simple Sequence Priors

P. Dayan, Eric Schulz, N. Éltető, 2023, arXiv.org.

Computational Properties of Peri-Dendritic Calcium Fluctuations

P R Montague, P. Dayan, P. Montague, 1998, The Journal of Neuroscience.

An International Laboratory for Systems and Computational Neuroscience

Jonathan W. Pillow, Ilana B. Witten, Larry F. Dora E. Matteo Anne K. Yang Peter Sophie Ila Su Abbott Angelaki Carandini Churchland Dan Da, 2017, Neuron.

Hippocampal Model of Rat Spatial Abilities Using Temporal Difference Learning

Peter Dayan, David J. Foster, Richard G. M. Morris, 1997, NIPS.

Magnetoencephalography decoding reveals structural differences within integrative decision processes

Gyung Jin Bae, P. Dayan, Z. Kurth-Nelson, 2018, Nature Human Behaviour.

The three R's of trust

Peter Dayan, Terry Lohrenz, P Read Montague, 2015, Current Opinion in Behavioral Sciences.

Some Work and Some Play: Microscopic and Macroscopic Approaches to Labor and Leisure

Peter Dayan, Peter Shizgal, Ritwik K. Niyogi, 2014, PLoS Comput. Biol..

Probabilistic Interpretation of Population Codes

R. Zemel, P. Dayan, A. Pouget, 1996, NIPS.

Brain-wide representations of prior information in mouse decision-making

Julia M. Huntenburg, Tatiana A. Engel, Jonathan W. Pillow, 2023, bioRxiv.