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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

The statistical structures of reinforcement learning with asymmetric value updates
Kentaro Katahira
Journal of Mathematical Psychology (2018) Vol. 87, pp. 31-45
Open Access | Times Cited: 75

Showing 1-25 of 75 citing articles:

The computational roots of positivity and confirmation biases in reinforcement learning
Stefano Palminteri, Maël Lebreton
Trends in Cognitive Sciences (2022) Vol. 26, Iss. 7, pp. 607-621
Open Access | Times Cited: 78
The interpretation of computational model parameters depends on the context
Maria K. Eckstein, Sarah L. Master, Liyu Xia, et al.
eLife (2022) Vol. 11
Open Access | Times Cited: 73
What do reinforcement learning models measure? Interpreting model parameters in cognition and neuroscience
Maria K. Eckstein, Linda Wilbrecht, Anne Collins
Current Opinion in Behavioral Sciences (2021) Vol. 41, pp. 128-137
Open Access | Times Cited: 86
Information about action outcomes differentially affects learning from self-determined versus imposed choices
Valérian Chambon, Héloïse Théro, Marie Vidal, et al.
Nature Human Behaviour (2020) Vol. 4, Iss. 10, pp. 1067-1079
Open Access | Times Cited: 83
Sex differences in learning from exploration
Cathy S. Chen, Evan Knep, Autumn Han, et al.
eLife (2021) Vol. 10
Open Access | Times Cited: 66
Maturation of striatal dopamine supports the development of habitual behavior through adolescence
Daniel Petrie, Ashley C. Parr, Valerie J. Sydnor, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2025)
Open Access | Times Cited: 1
The actions of others act as a pseudo-reward to drive imitation in the context of social reinforcement learning
Anis Najar, Emmanuelle Bonnet, Bahador Bahrami, et al.
PLoS Biology (2020) Vol. 18, Iss. 12, pp. e3001028-e3001028
Open Access | Times Cited: 52
Two sides of the same coin: Beneficial and detrimental consequences of range adaptation in human reinforcement learning
Sophie Bavard, Aldo Rustichini, Stefano Palminteri
Science Advances (2021) Vol. 7, Iss. 14
Closed Access | Times Cited: 47
Dissociation between asymmetric value updating and perseverance in human reinforcement learning
Michiyo Sugawara, Kentaro Katahira
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 45
Shared and specialized coding across posterior cortical areas for dynamic navigation decisions
Shih-Yi Tseng, Selmaan N. Chettih, Charlotte Arlt, et al.
Neuron (2022) Vol. 110, Iss. 15, pp. 2484-2502.e16
Open Access | Times Cited: 36
Tonic exploration governs both flexibility and lapses
R. Becket Ebitz, Brianna J. Sleezer, Hank P. Jedema, et al.
PLoS Computational Biology (2019) Vol. 15, Iss. 11, pp. e1007475-e1007475
Open Access | Times Cited: 51
A Normative Account of Confirmation Bias During Reinforcement Learning
Germain Lefebvre, Christopher Summerfield, Rafał Bogacz
Neural Computation (2021) Vol. 34, Iss. 2, pp. 307-337
Open Access | Times Cited: 37
Inattentive responding can induce spurious associations between task behavior and symptom measures
Samuel Zorowitz, Johanne Solis, Yael Niv, et al.
(2021)
Open Access | Times Cited: 30
Modeling changes in probabilistic reinforcement learning during adolescence
Liyu Xia, Sarah L. Master, Maria K. Eckstein, et al.
PLoS Computational Biology (2021) Vol. 17, Iss. 7, pp. e1008524-e1008524
Open Access | Times Cited: 30
Flexibility in valenced reinforcement learning computations across development
Kate Nussenbaum, Juan A. Velez, Bradli T. Washington, et al.
Child Development (2022) Vol. 93, Iss. 5, pp. 1601-1615
Open Access | Times Cited: 20
Individuals with problem gambling and obsessive-compulsive disorder learn through distinct reinforcement mechanisms
Shinsuke Suzuki, Xiaoliu Zhang, Amir Dezfouli, et al.
PLoS Biology (2023) Vol. 21, Iss. 3, pp. e3002031-e3002031
Open Access | Times Cited: 11
The asymmetric learning rates of murine exploratory behavior in sparse reward environments
Hiroyuki Ohta, Khalid Satori, Yu Takarada, et al.
Neural Networks (2021) Vol. 143, pp. 218-229
Closed Access | Times Cited: 23
A reinforcement learning model with choice traces for a progressive ratio schedule
Keiko Ihara, Yu Shikano, Sae Kato, et al.
Frontiers in Behavioral Neuroscience (2024) Vol. 17
Open Access | Times Cited: 3
Applying reinforcement learning to the psychopathology of obsessive-compulsive and gambling disorders: practices and pitfalls in computational model fitting
Shinsuke Suzuki, Kentaro Katahira
(2024)
Open Access | Times Cited: 3
Does the reliability of computational models truly improve with hierarchical modeling? Some recommendations and considerations for the assessment of model parameter reliability
Kentaro Katahira, Takeyuki Oba, Asako Toyama
Psychonomic Bulletin & Review (2024)
Open Access | Times Cited: 3
Biases in estimating the balance between model-free and model-based learning systems due to model misspecification
Asako Toyama, Kentaro Katahira, Hideki Ohira
Journal of Mathematical Psychology (2019) Vol. 91, pp. 88-102
Open Access | Times Cited: 24
Pupil Correlates of Decision Variables in Mice Playing a Competitive Mixed-Strategy Game
Hongli Wang, Heather K. Ortega, Huriye Atilgan, et al.
eNeuro (2022) Vol. 9, Iss. 2, pp. ENEURO.0457-21.2022
Open Access | Times Cited: 13
Reinforcement Learning With Parsimonious Computation and a Forgetting Process
Asako Toyama, Kentaro Katahira, Hideki Ohira
Frontiers in Human Neuroscience (2019) Vol. 13
Open Access | Times Cited: 21
Revisiting the importance of model fitting for model-based fMRI: It does matter in computational psychiatry
Kentaro Katahira, Asako Toyama
PLoS Computational Biology (2021) Vol. 17, Iss. 2, pp. e1008738-e1008738
Open Access | Times Cited: 18
The value of confidence: Confidence prediction errors drive value-based learning in the absence of external feedback
Lena Esther Ptasczynski, Isa Steinecker, Philipp Sterzer, et al.
PLoS Computational Biology (2022) Vol. 18, Iss. 10, pp. e1010580-e1010580
Open Access | Times Cited: 12
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