A model for learning based on the joint estimation of stochasticity and volatility by Payam Piray, Nathaniel D. Daw

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Computational processes of simultaneous learning of stochasticity and volatility in humans by Payam Piray, Nathaniel D. Daw

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Linear reinforcement learning in planning, grid fields, and cognitive control by Payam Piray, Nathaniel D. Daw

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Biased belief updating and suboptimal choice in foraging decisions by Neil Garrett, Nathaniel D. Daw

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A simple model for learning in volatile environments. by Payam Piray, Nathaniel D Daw

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Humans rationally balance detailed and temporally abstract world models by Ari E. Kahn, Nathaniel D. Daw

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Cortical and hippocampal correlates of deliberation during model-based decisions for rewards in humans. by Aaron M Bornstein, Nathaniel D Daw

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Publisher Correction: Humans rationally balance detailed and temporally abstract world models by Ari E. Kahn, Nathaniel D. Daw

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The expanding role of dopamine by Bradley B Doll, Nathaniel D Daw

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Grid cells, place cells, and geodesic generalization for spatial reinforcement learning. by Nicholas J Gustafson, Nathaniel D Daw

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Proactive and reactive construction of memory-based preferences by Jonathan Nicholas, Nathaniel D. Daw, Daphna Shohamy

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Anxiety, Avoidance, and Sequential Evaluation by Samuel Zorowitz, Ida Momennejad, Nathaniel D. Daw

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Uncertainty alters the balance between incremental learning and episodic memory by Jonathan Nicholas, Nathaniel D Daw, Daphna Shohamy

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Model based control can give rise to devaluation insensitive choice by Neil Garrett, Sean Allan, Nathaniel D. Daw

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Neurocomputational mechanisms of confidence in self and others by Dan Bang, Rani Moran, Nathaniel D. Daw, Stephen M. Fleming

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Extraversion differentiates between model-based and model-free strategies in a reinforcement learning task by Anya eSkatova, Anya eSkatova, Anya eSkatova, Anya eSkatova, Patricia Angie Chan, Patricia Angie Chan, Nathaniel D Daw, Nathaniel D Daw

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Suboptimal Criterion Learning in Static and Dynamic Environments. by Elyse H Norton, Stephen M Fleming, Nathaniel D Daw, Michael S Landy

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Reminders of past choices bias decisions for reward in humans by Aaron M. Bornstein, Mel W. Khaw, Daphna Shohamy, Nathaniel D. Daw

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Offline replay supports planning in human reinforcement learning by Ida Momennejad, A Ross Otto, Nathaniel D Daw, Kenneth A Norman

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Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies. by Payam Piray, Amir Dezfouli, Tom Heskes, Michael J Frank, Nathaniel D Daw

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