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Yael Niv
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- affiliation: Princeton University, Princeton Neuroscience Institute and Department of Psychology, USA
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2020 – today
- 2024
- [j16]Nadav Amir, Yael Niv, Angela Langdon:
States as goal-directed concepts: an epistemic approach to state-representation learning. RLJ 5: 2096-2106 (2024) - 2023
- [j15]Daniel Bennett
, Angela Radulescu, Sam Zorowitz, Valkyrie Felso, Yael Niv
Affect-congruent attention modulates generalized reward expectations. PLoS Comput. Biol. 19(12) (2023) - [c13]Rachel L. Bedder, Sashank Pisupati, Yael Niv:
Modelling Rumination as a State-Inference Process. CogSci 2023 - [c12]Sashank Pisupati, Isabel M. Berwian, Jamie Chiu, Yongjing Ren, Yael Niv:
Human inductive biases for aversive continual learning - a hierarchical Bayesian nonparametric model. CoLLAs 2023: 337-346 - [i2]Nadav Amir, Yael Niv, Angela Langdon:
States as goal-directed concepts: an epistemic approach to state-representation learning. CoRR abs/2312.02367 (2023) - 2022
- [j14]Mingyu Song
Minimal cross-trial generalization in learning the representation of an odor-guided choice task. PLoS Comput. Biol. 18(3) (2022) - [j13]Mingyu Song
Humans combine value learning and hypothesis testing strategically in multi-dimensional probabilistic reward learning. PLoS Comput. Biol. 18(11): 1010699 (2022) - 2021
- [c11]Mingyu Song, Yael Niv, Mingbo Cai:
Using Recurrent Neural Networks to Understand Human Reward Learning. CogSci 2021 - 2020
- [c10]Mingyu Song, Yael Niv, Mingbo Cai:
Learning what is relevant for rewards via value-based serial hypothesis testing. CogSci 2020 - [i1]Gecia Bravo Hermsdorff, Valkyrie Felso, Emily Ray, Lee M. Gunderson, Mary E. Helander, Joana Maria, Yael Niv:
Gender and collaboration patterns in a temporal scientific authorship network. CoRR abs/2005.13512 (2020)
2010 – 2019
- 2019
- [j12]Gecia Bravo Hermsdorff
Gender and collaboration patterns in a temporal scientific authorship network. Appl. Netw. Sci. 4(1): 112 (2019) - [j11]Mingbo Cai
Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias. PLoS Comput. Biol. 15(5) (2019) - 2018
- [c9]Yotam Sagiv, Sebastian Musslick, Yael Niv, Jonathan D. Cohen:
Efficiency of learning vs. processing: Towards a normative theory of multitasking. CogSci 2018 - 2017
- [c8]Peter Hitchcock, Yael Niv, Angela Radulescu, Chris R. Sims:
Translating a Reinforcement Learning Task into a Computational Psychiatry Assay: Challenges and Strategies. CogSci 2017 - 2016
- [c7]Mingbo Cai, Nicolas W. Schuck, Jonathan W. Pillow, Yael Niv:
A Bayesian method for reducing bias in neural representational similarity analysis. NIPS 2016: 4952-4960 - 2015
- [j10]Robert C. Wilson, Yael Niv:
Is Model Fitting Necessary for Model-Based fMRI? PLoS Comput. Biol. 11(6) (2015) - [j9]Samuel J. Gershman, Yael Niv:
Novelty and Inductive Generalization in Human Reinforcement Learning. Top. Cogn. Sci. 7(3): 391-415 (2015) - 2014
- [j8]Alec Solway, Carlos Diuk, Natalia Córdova, Debbie Yee, Andrew G. Barto, Yael Niv, Matthew M. Botvinick:
Optimal Behavioral Hierarchy. PLoS Comput. Biol. 10(8) (2014) - [j7]Samuel Gershman, Angela Radulescu
Statistical Computations Underlying the Dynamics of Memory Updating. PLoS Comput. Biol. 10(11) (2014) - 2013
- [j6]Anastasia Christakou, Samuel Gershman, Yael Niv, Andrew Simmons, Mick Brammer, Katya Rubia
Neural and Psychological Maturation of Decision-making in Adolescence and Young Adulthood. J. Cogn. Neurosci. 25(11): 1807-1823 (2013) - [p1]Carlos Diuk, Anna C. Schapiro
Divide and Conquer: Hierarchical Reinforcement Learning and Task Decomposition in Humans. Computational and Robotic Models of the Hierarchical Organization of Behavior 2013: 271-291 - 2012
- [c6]Kenji Doya, John P. O'Doherty, Alexandre Pouget, Peter Bossaerts, Nathaniel D. Daw, Yael Niv:
Neural Computations Supporting Cognition: Rumelhart Prize Symposium in Honor of Peter Dayan. CogSci 2012 - 2011
- [c5]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:
Computational, Neuroscientific, and Lifespan Perspectives on the Exploration-Exploitation Dilemma. CogSci 2011
2000 – 2009
- 2009
- [c4]Yael Niv:
Tutorial summary: The neuroscience of reinforcement learning. ICML 2009: 16 - 2008
- [j5]John R. Staddon, Yael Niv:
Operant conditioning. Scholarpedia 3(9): 2318 (2008) - [c3]Michael T. Todd, Yael Niv, Jonathan D. Cohen:
Learning to Use Working Memory in Partially Observable Environments through Dopaminergic Reinforcement. NIPS 2008: 1689-1696 - 2006
- [j4]Peter Dayan
The misbehavior of value and the discipline of the will. Neural Networks 19(8): 1153-1160 (2006) - 2005
- [c2]Yael Niv, Nathaniel D. Daw, Peter Dayan:
How fast to work: Response vigor, motivation and tonic dopamine. NIPS 2005: 1019-1026 - 2002
- [j3]Yael Niv, Daphna Joel
Evolution of Reinforcement Learning in Uncertain Environments: A Simple Explanation for Complex Foraging Behaviors. Adapt. Behav. 10(1): 5-24 (2002) - [j2]Yael Niv, Daphna Joel
Evolution of reinforcement learning in foraging bees: a simple explanation for risk averse behavior. Neurocomputing 44-46: 951-956 (2002) - [j1]Daphna Joel
Actor-critic models of the basal ganglia: new anatomical and computational perspectives. Neural Networks 15(4-6): 535-547 (2002) - 2001
- [c1]Yael Niv, Daphna Joel
Evolution of Reinforcement Learning in Uncertain Environments: Emergence of Risk-Aversion and Matching. ECAL 2001: 252-261
Coauthor Index
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