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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Neurocomputational mechanisms of confidence in self and others od 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 od 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. od 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 od Aaron M. Bornstein, Mel W. Khaw, Daphna Shohamy, Nathaniel D. Daw

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Offline replay supports planning in human reinforcement learning od 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. od Payam Piray, Amir Dezfouli, Tom Heskes, Michael J Frank, Nathaniel D Daw

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