Evolution of Reinforcement Learning in Uncertain Environments: Emergence of Risk-Aversion and Matching. - Yael Niv; Daphna Joel; Isaac Meilijson; Eytan Ruppin (ECAL, 2001)

Evolution of Reinforcement Learning in Uncertain Environments: A Simple Explanation for Complex Foraging Behaviors. - Yael Niv; Daphna Joel; Isaac Meilijson; Eytan Ruppin (Adapt. Behav., 2002)

Evolution of reinforcement learning in foraging bees: a simple explanation for risk averse behavior. - Yael Niv; Daphna Joel; Isaac Meilijson; Eytan Ruppin (Neurocomputing, 2002)

Actor-critic models of the basal ganglia: new anatomical and computational perspectives. - Daphna Joel; Yael Niv; Eytan Ruppin (Neural Networks, 2002)

How fast to work: Response vigor, motivation and tonic dopamine. - Yael Niv; Nathaniel D. Daw; Peter Dayan (NIPS, 2005)

The misbehavior of value and the discipline of the will. - Peter Dayan; Yael Niv; Ben Seymour; Nathaniel D. Daw (Neural Networks, 2006)

Learning to Use Working Memory in Partially Observable Environments through Dopaminergic Reinforcement. - Michael T. Todd; Yael Niv; Jonathan D. Cohen 003 (NIPS, 2008)

Operant conditioning. - John R. Staddon; Yael Niv (Scholarpedia, 2008)

Tutorial summary: The neuroscience of reinforcement learning. - Yael Niv (ICML, 2009)

Computational, Neuroscientific, and Lifespan Perspectives on the Exploration-Exploitation Dilemma. - A. Ross Otto; W. Bradley Knox; Bradley C. Love; Samuel Gershman; Yael Niv; Darrell A. Worthy; W. Todd Maddox; Jared M. Hotaling; Jerome R. Busemeyer; Richard M. Shiffrin (CogSci, 2011)

Neural Computations Supporting Cognition: Rumelhart Prize Symposium in Honor of Peter Dayan. - Kenji Doya; John P. O'Doherty; Alexandre Pouget; Peter Bossaerts; Nathaniel D. Daw; Yael Niv (CogSci, 2012)

Divide and Conquer: Hierarchical Reinforcement Learning and Task Decomposition in Humans. - Carlos Diuk; Anna C. Schapiro; Natalia Córdova; José Ribas-Fernandes; Yael Niv; Matthew M. Botvinick (Computational and Robotic Models of the Hierarchical Organization of Behavior, 2013)

Neural and Psychological Maturation of Decision-making in Adolescence and Young Adulthood. - Anastasia Christakou; Samuel Gershman; Yael Niv; Andrew Simmons 001; Mick Brammer; Katya Rubia (J. Cogn. Neurosci., 2013)

Statistical Computations Underlying the Dynamics of Memory Updating. - Samuel Gershman; Angela Radulescu; Kenneth A. Norman; Yael Niv (PLoS Comput. Biol., 2014)

Optimal Behavioral Hierarchy. - Alec Solway; Carlos Diuk; Natalia Córdova; Debbie Yee; Andrew G. Barto; Yael Niv; Matthew M. Botvinick (PLoS Comput. Biol., 2014)

Is Model Fitting Necessary for Model-Based fMRI? - Robert C. Wilson; Yael Niv (PLoS Comput. Biol., 2015)

Novelty and Inductive Generalization in Human Reinforcement Learning. - Samuel J. Gershman; Yael Niv (Top. Cogn. Sci., 2015)

A Bayesian method for reducing bias in neural representational similarity analysis. - Mingbo Cai; Nicolas W. Schuck; Jonathan W. Pillow; Yael Niv (NIPS, 2016)

Translating a Reinforcement Learning Task into a Computational Psychiatry Assay: Challenges and Strategies. - Peter Hitchcock; Yael Niv; Angela Radulescu; Chris R. Sims (CogSci, 2017)

Efficiency of learning vs. processing: Towards a normative theory of multitasking. - Yotam Sagiv; Sebastian Musslick; Yael Niv; Jonathan D. Cohen 003 (CogSci, 2018)

Gender and collaboration patterns in a temporal scientific authorship network. - Gecia Bravo Hermsdorff; Valkyrie Felso; Emily Ray; Lee M. Gunderson; Mary E. Helander; Joana Maria; Yael Niv (Appl. Netw. Sci., 2019)

Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias. - Mingbo Cai; Nicolas W. Schuck; Jonathan W. Pillow; Yael Niv (PLoS Comput. Biol., 2019)

Learning what is relevant for rewards via value-based serial hypothesis testing. - Mingyu Song; Yael Niv; Mingbo Cai (CogSci, 2020)

Using Recurrent Neural Networks to Understand Human Reward Learning. - Mingyu Song; Yael Niv; Mingbo Cai (CogSci, 2021)

Minimal cross-trial generalization in learning the representation of an odor-guided choice task. - Mingyu Song; Yuji K. Takahash; Amanda C. Burton; Matthew R. Roesch; Geoffrey Schoenbaum; Yael Niv; Angela Langdon (PLoS Comput. Biol., 2022)

Humans combine value learning and hypothesis testing strategically in multi-dimensional probabilistic reward learning. - Mingyu Song; Persis A. Baah; Ming Bo Cai; Yael Niv (PLoS Comput. Biol., 2022)

Modelling Rumination as a State-Inference Process. - Rachel L. Bedder; Sashank Pisupati; Yael Niv (CogSci, 2023)

Human inductive biases for aversive continual learning - a hierarchical Bayesian nonparametric model. - Sashank Pisupati; Isabel M. Berwian; Jamie C. Chiu; Yongjing Ren; Yael Niv (CoLLAs, 2023)

Affect-congruent attention modulates generalized reward expectations. - Daniel Bennett; Angela Radulescu; Sam Zorowitz; Valkyrie Felso; Yael Niv (PLoS Comput. Biol., 2023)

States as goal-directed concepts: an epistemic approach to state-representation learning. - Nadav Amir; Yael Niv; Angela Langdon (RLJ, 2024)

The Ubiquity of Time in Latent-cause Inference. - Dan-Mircea Mirea; Yeon Soon Shin; Sarah Dubrow; Yael Niv (J. Cogn. Neurosci., 2024)

Emotions Shape Effort-Reward Choices: Positive Valence Decreases Effort, Except Under High Arousal. - Jamie C. Chiu; Nicholas Budny; Aetizaz Sameer; Persis A. Baah; Dan-Mircea Mirea; Isabel M. Berwian; Yael Niv (ETRA, 2025)