Maria K. Eckstein

发表

Computational evidence for hierarchically structured reinforcement learning in humans

Maria K. Eckstein, Anne G. E. Collins, 2020, Proceedings of the National Academy of Sciences.

Modeling the development of decision making in volatile environments using strategies, reinforcement learning, and Bayesian inference

Ronald E. Dahl, Maria K. Eckstein, Anne G. E. Collins, 2019 .

Reinforcement Learning and Bayesian Inference Provide Complementary Models for the Unique Advantage of Adolescents in Stochastic Reversal

Ronald E. Dahl, Maria K. Eckstein, Sarah L. Master, 2020, bioRxiv.

Learning under uncertainty changes during adolescence

Maria K. Eckstein, Sarah L. Master, Linda Wilbrecht, 2020, CogSci.

What do Reinforcement Learning Models Measure? Interpreting Model Parameters in Cognition and Neuroscience

Maria K. Eckstein, Linda Wilbrecht, Anne G.E. Collins, 2021 .

Beyond eye gaze: What else can eyetracking reveal about cognition and cognitive development?

Maria K. Eckstein, Belén Guerra-Carrillo, S. Bunge, 2016, Developmental Cognitive Neuroscience.

Distentangling the systems contributing to changes in learning during adolescence

Maria K. Eckstein, Sarah L. Master, Linda Wilbrecht, 2019, Developmental Cognitive Neuroscience.

How the inference of hierarchical rules unfolds over time

Maria K. Eckstein, S. Bunge, Ariel Starr, 2019, Cognition.

Distentangling the systems contributing to changes in learning during adolescence

Maria K. Eckstein, Sarah L. Master, Linda Wilbrecht, 2020, Developmental Cognitive Neuroscience.

Modeling changes in probabilistic reinforcement learning during adolescence

Maria K. Eckstein, Sarah L. Master, Anne G E Collins, 2020, bioRxiv.

Reinforcement learning and Bayesian inference provide complementary models for the unique advantage of adolescents in stochastic reversal

Maria K. Eckstein, Sarah L. Master, L. Wilbrecht, 2022, Developmental Cognitive Neuroscience.

The interpretation of computational model parameters depends on the context

Maria K. Eckstein, Sarah L. Master, L. Wilbrecht, 2021, bioRxiv.

Learning agents that acquire representations of social groups

Joel Z. Leibo, Edgar A. Duéñez-Guzmán, Maria K. Eckstein, 2022, Behavioral and Brain Sciences.

OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning

Joel Z. Leibo, Alexander Sasha Vezhnevets, Yuhuai Tony Wu, 2020, ICML.

What do reinforcement learning models measure? Interpreting model parameters in cognition and neuroscience

Maria K. Eckstein, Anne G E Collins, L. Wilbrecht, 2021, Current Opinion in Behavioral Sciences.

Reinforcement learning and Bayesian inference provide complementary models for the unique advantage of adolescents in stochastic reversal

Maria K. Eckstein, Sarah L. Master, L. Wilbrecht, 2020, Developmental Cognitive Neuroscience.

The Unique Advantage of 1 Adolescents in Probabilistic Reversal: 2 Reinforcement Learning and 3 Bayesian Inference Provide 4 Adequate and Complementary 5 Models 6

Maria K. Eckstein, Sarah L. Master, L. Wilbrecht, 2021 .

Computational evidence for hierarchically structured reinforcement learning in humans

Anne Collins, Maria Eckstein, Maria K. Eckstein, 2019, Proceedings of the National Academy of Sciences.

Predictive and Interpretable: Combining Artificial Neural Networks and Classic Cognitive Models to Understand Human Learning and Decision Making

Kevin J. Miller, Maria K. Eckstein, N. Daw, 2023, bioRxiv.

How the Mind Creates Structure: Hierarchical Learning of Action Sequences.

Maria K. Eckstein, A. Collins, 2021, CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference.

Cognitive Model Discovery via Disentangled RNNs

Kevin J. Miller, Maria K. Eckstein, M. Botvinick, 2023, bioRxiv.

Reinforcement learning and Bayesian inference provide complementary models for the unique advantage of adolescents in stochastic reversal

Maria K. Eckstein, Sarah L. Master, L. Wilbrecht, 2022, Developmental Cognitive Neuroscience.

The Unique Advantage of 1 Adolescents in Probabilistic Reversal: 2 Reinforcement Learning and 3 Bayesian Inference Provide 4 Adequate and Complementary 5 Models 6

Maria K. Eckstein, Sarah L. Master, L. Wilbrecht, 2021 .