論文―Papers
2025
Mizoguchi, H., Katahira, K., Inutsuka, A., Kaneko, R., Ono, D., Hada, K., Murata, M., Yasuike, S., Isobe, M., Kusaba, M., Dong, Y., Iida, H., Fukumoto, K., Yanagawa, Y., Yamanaka, A., & Yamada, K. (2025). Activation of orexin neurons changes reward-based decision-making strategies. PNAS Nexus, 4(11), pgaf322. [Link]
Katahira, K. (2025). Excessive Flexibility? Recurrent Neural Networks Can Accommodate Individual Differences in Reinforcement Learning Through In-Context Adaptation. Computational Brain & Behavior. https://doi.org/10.1007/s42113-025-00254-8 [Link]
Sugiyama, Y., Morisaki, S., Toyama, A., & Katahira, K. (2025). Relationship between Model-based Decision-making and the Comprehension Performance of Source Code with Confusing Patterns. IEEE Transactions on Software Engineering 51, no. 6, pp. 1783-1800, doi: 10.1109/TSE.2025.3566537. [Link]
Suganuma, H., Naito, A., Katahira, K., Kameda, T. (2025). When to stop social learning from a predecessor in an information-foraging task. Evolutionary Human Sciences 7, e2. https://doi.org/10.1017/ehs.2024.29
Sumiya, M., Katahira, K., Akechi, H., & Senju, A. (2025). The preference for surprise in reinforcement learning underlies the differences in developmental changes in risk preference between autistic and neurotypical youth. Molecular Autism 16, 3. https://doi.org/10.1186/s13229-025-00637-5
Zhu, J., Katahira, K., Hirakawa, M., Nakao, T. (2025). Externally Provided Rewards Increase Internal Preference, but Not as Much as Preferred Ones Without Extrinsic Rewards. Computational Brain & Behavior, 8, 71–91. https://doi.org/10.1007/s42113-024-00198-5 [Link]
2024
Zhu, J., Katahira, K., Hirakawa, M. & Nakao, T. (2024). The more random people’s preference judgments are, the more they explore in gambling tasks. BMC Psychology 12, 766. https://doi.org/10.1186/s40359-024-02252-0
Katahira, K., Takano, K., Oba, T., & Kimura, K. (2024). Evaluating the performance of personality-based profiling in predicting physical activity. BMC Psychology 12, 733. https://doi.org/10.1186/s40359-024-02268-6
Takano, K., Oba, T., Katahira, K., & Kimura, K. (2024). Deconstructing Fitbit to Specify the Effective Features in Promoting Physical Activity Among Inactive Adults: Pilot Randomized Controlled Trial. JMIR mHealth and uHealth, 12, e51216. doi:10.2196/51216
Konishi, N., Oba, T., Takano, K., Katahira, K., & Kimura, K. (2024). Functions of Smartphone Apps and Wearable Devices Promoting Physical Activity: Six-Month Longitudinal Study on Japanese-Speaking Adults. JMIR mHealth and uHealth, 12(1), e59708. doi:10.2196/59708
Oba, T ., Takano, K., Katahira, K., & Kimura., K. (2024). Exploring individual, social and environmental factors related to physical activity: a network analysis. BMJ Open Sport & Exercise Medicine 10 (3), e001983. https://doi.org/10.1136/bmjsem-2024-001983
Zhu, J., Katahira, K., Hirakawa, M., & Nakao, T. (2024). Internally Formed Preferences for Options only Influence Initial Decisions in Gambling Tasks, while the Gambling Outcomes do not Alter these Preferences. Journal of Gambling Studies. https://doi.org/10.1007/s10899-024-10326-2
Katahira, K., Oba, T. & Toyama, A. (2024). Does the reliability of computational models truly improve with hierarchical modeling? Some recommendations and considerations for the assessment of model parameter reliability. Psychonomic Bulletin & Review, 31, 2465–2486. https://doi.org/10.3758/s13423-024-02490-8
Oba, T., Takano, K., Katahira, K., & Kimura, K. (2024). Revisiting the Transtheoretical Model for Physical Activity: A Large-Scale Cross-Sectional Study on Japanese-Speaking Adults. Annals of Behavioral Medicine, kaad069. [Link]
Oba, T., Katahira, K., Kimura, K., & Takano, K. (2024). A network analysis on psychopathy and theoretically relevant personality traits. Personality and Individual Differences, 217, 112437. [Link]
2023
Oba, T., Takano, K., Katahira, K., & Kimura, K. (2023). Use Patterns of Smartphone Apps and Wearable Devices Supporting Physical Activity and Exercise: Large-Scale Cross-Sectional Survey. JMIR mHealth and uHealth, 11, e49148. [Link]
Tomyta, K., Ohira, H., & Katahira, K. (2023). Asymmetric Error Correction in the Synchronization Tapping Task. Timing & Time Perception, 1(aop), 1-10. [Link]
Toyama, A., Katahira, K., & Kunisato, Y. (2023). Examinations of Biases by Model Misspecification and Parameter Reliability of Reinforcement Learning Models. Computational Brain & Behavior, 6, 651–670. https://doi.org/10.1007/s42113-023-00175-4. [Link]
Fujita, K., Okada, K., & Katahira, K. (2023). Stimulus selection in a Q-learning model using Fisher information and Monte Carlo simulation. Computational Brain & Behavior, 6 (2), 262 - 279. [Link]
Katahira, K. (2023). Evaluating the predictive performance of subtyping: A criterion for cluster mean‐based prediction. Statistics in Medicine. 42, 1045-1065, https://doi.org/10.1002/sim.9656 [Link]
Katahira, K. & Kimura, K. (2023). Influences of Reinforcement and Choice Histories on Choice Behavior in Actor-Critic Learning. Computational Brain & Behavior. 6 (2), 172 - 194. https://doi.org/10.1007/s42113-022-00145-2
2022
Tomyta, K., Katahira, K., & Ohira, H. (2022). Effects of interoceptive accuracy on timing control in the synchronization tapping task. Frontiers in Neuroscience, 16. [Link]
Kimura, K., Kanayama, N., & Katahira, K. (2022). Cardiac Cycle Affects Risky Decision-making. Biological Psychology, 108471. [Link]
Matsumoto, N., Katahira, K., & Kawaguchi, J. (2022). Cognitive reactivity amplifies the activation and development of negative self-schema: A revised mnemic neglect paradigm and computational modelling. Cognitive Therapy and Research. [Link; プレスリリース: 信州大学, 追手門学院大学, 名古屋大学]
Sugawara, M., & Katahira, K. (2022). Choice perseverance underlies pursuing a hard-to-get target in an avatar choice task, Frontiers in Psychology, 13:924578. doi: 10.3389/fpsyg.2022.924578 [Link]
Shimada, D., & Katahira, K. (2022). Sequential Dependencies of Responses in a Questionnaire Survey and Their Effects on the Reliability and Validity of Measurement. Behavior Research Methods. https://doi.org/10.3758/s13428-022-01943-z [Link]
Kimura, K., Kanayama, N., Toyama, A., & Katahira, K. (2022). Cardiac cycle affects the asymmetric value updating in instrumental reward learning, Frontiers in Neuroscience, 16:889440. doi: 10.3389/fnins.2022.889440. [Link]
Naito, A., Katahira, K., & Kameda, T. (2022). Insights about the common generative rule underlying an information foraging task can be facilitated via collective search. Scientific Reports, 12, 8047 https://doi.org/10.1038/s41598-022-12126-3 [Link]
2021
Oba, T., Katahira, K., & Ohira, H. (2021). A learning mechanism shaping risk preferences and a preliminary test of its relationship with psychopathic traits. Scientific Reports, 11, 20853. [Link]
Suzuki, S., Yamashita, Y., & Katahira, K. (2021). Psychiatric symptoms influence reward-seeking and loss-avoidance decision-making through common and distinct computational processes. Psychiatry and Clinical Neurosciences 75(9), p. 277-303. [Link]
島田大祐, 片平健太郎. (2021). 質問紙調査における無回答の発生過程およびその個人差について. 人間環境学研究. Vol. 19, No. 1, p. 15-24. [Link]
Sugawara, M. & Katahira, K. (2021). Dissociation between asymmetric value updating and perseverance in human reinforcement learning. Scientific Reports, 11, 3574. [Link]
Katahira, K. & Toyama, A. (2021). Revisiting the importance of model fitting for model-based fMRI: It does matter in computational psychiatry. PLOS Computational Biology 17(2): e1008738. [Link]
Zhu, J., Hashimoto, J., Katahira, K., Hirakawa, M., Nakao, T. (2021). Computational modeling of choice-induced preference change: A Reinforcement-Learning-based approach. PLoS ONE 16(1): e0244434. [Link]
2020
Sumiya, M. & Katahira, K. (2020). Commentary: Altered learning under uncertainty in unmedicated mood and anxiety disorders. Frontiers in Human Neuroscience-Cognitive Neuroscience, 4:561770. [Link]
Sumiya, M. & Katahira, K. (2020). Surprise acts as a reducer of outcome value in human reinforcement learning. Frontiers in Neuroscience, 14:852 [Link]
大谷佳名, 片平健太郎. (2020). 自身の選択に対する固執性が他者の行動の予測に与える効果―行動実験と計算論モデリングによる検証―. 人間環境学研究. Vol. 18, No. 2, p. 91-97. [Link]
Katahira, K., Kunisato, Y., Okimura, T., & Yamashita, Y. (2020). Retrospective surprise: a computational component for active inference. Journal of Mathematical Psychology, 96, 102347.[Link]
Katahira, K., Kunisato, Y., Yamashita, Y., & Suzuki, S. (2020). Commentary: A robust data-driven approach identifies four personality types across four large data sets. Frontiers in Big Data 3:8. [Link]
2019
Oba, T., Katahira, K., & Ohira, H. (2019). The Effect of Reduced Learning Ability on Avoidance in Psychopathy: A Computational Approach. Frontiers in Psychology, 10:2432. [Link]
菅原通代, 片平健太郎. (2019). 強化学習における認知バイアスと固執性―選択行動を決めているのは過去の “選択の結果” か “選択そのもの” か?―. 基礎心理学研究, Vol. 38, No. 1, 48–55. [Link]
Toyama, A., Katahira, K., & Ohira, H. (2019). Biases in estimating the balance between model-free and model-based learning systems due to model misspecification. Journal of Mathematical Psychology, 91, 88-102. [Link]
Toyama, A., Katahira, K., & Ohira, H. (2019). Reinforcement learning with parsimonious computation and a forgetting process. Frontiers in Human Neuroscience, 13, 153 [Link]
2018
Katahira, K. (2018). The statistical structures of reinforcement learning with asymmetric value updates. Journal of Mathematical Psychology, 87, 31-45. [Link]
2017
Toyama, A., Katahira, K., & Ohira, H. (2017). A simple computational algorithm of model-based choice preference. Cognitive, Affective, & Behavioral Neuroscience, 17(4), 764-783. [Link]
Katahira, K., Yuki, S., & Okanoya, K. (2017). Model-based estimation of subjective values using choice tasks with probabilistic feedback. Journal of Mathematical Psychology, 79, 29-43. [Link]
Katahira, K., & Yamashita, Y. (2017). A theoretical framework for evaluating psychiatric research strategies. Computational Psychiatry, 1, 184-207. [Link]
2016
Katahira, K. (2016). How hierarchical models improve point estimates of model parameters at the individual level. Journal of Mathematical Psychology, 73, 37-58. [Link]
Nakao, T., Kanayama, N., Katahira, K., Odani, M., Ito, Y., Hirata, Y., Nasuno, R., Ozaki, H., Hiramoto, R., Miyatani, M., & Northoff, G. (2016). Post-response βγ power predicts the degree of choice-based learning in internally guided decision-making. Scientific Reports, 6, 32477. [Link]
2015
Katahira, K. (2015). The relation between reinforcement learning parameters and the influence of reinforcement history on choice behavior. Journal of Mathematical Psychology, 66, 59-69. [Link]
Mizoguchi, H., Katahira, K., Inutsuka, A., Fukumoto, K., Nakamura, A., Wang, T., Nagai, T., Sato, J., Sawada, M., Ohira, H., Yamanaka, A. & Yamanaka, A. (2015). Insular neural system controls decision-making in healthy and methamphetamine-treated rats. Proceedings of the National Academy of Sciences, 112(29), E3930-E3939. [Link]
Bai, Y., Katahira, K., & Ohira, H. (2015). Valence-separated representation of reward prediction error in feedback-related negativity and positivity. Neuroreport, 26(3), 157-162. [Link]
学会発表―Conferences
大谷佳名, 片平健太郎 (2020). 他者の行動の予測を促進する行動特性の研究－強化学習を用いた行動モデリング－, 第175回ヒューマンインタフェース学会研究会「コミュニケーション支援および一般（SIG-CE-21）」オンライン（電子会議システム）2020年 5月14日 [Link]
Oba, T., Katahira, K., & Ohira, H. (2019). The learning mechanism of shaping risk preference and relations with psychopathic traits, The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM2019), 112, Poster 86 [Link]
Oshima, S. & Katahira, K. (2019). Pseudo-Learning Rate Modulation by the Forgetting of Action Value when Environmental Volatility Changes, The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM2019), 337-341, Paper # 285. [Link]
Sugawara, M. & Katahira, K. (2019). Validation of cognitive bias represented by reinforcement learning with asymmetric value updates, The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM2019), 431-435 Paper # 285. [Link]
Toyama, A., Katahira, K., & Ohira, H. (2019). Forgetting Process in Model-Free and Model-Based Reinforcement Learning, The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM2019), 455-459, Paper # 125. [Link]
Book Chapter
Shinsuke Suzuki, & Kentaro Katahira. (2025). Applying Reinforcement Learning to the Psychopathology of Obsessive–Compulsive and Gambling Disorders: Practices and Pitfalls in Computational Model Fitting. Pages 253-271. Decision Making Fundamentals and Applications.
その他―Misc
片平 健太郎: 心理学における計算論モデリングと統計モデリングの接点，関西学院大学 第9回KG-RCSP合同ゼミ 2020年8月4日 [Link]
片平 健太郎: 行動の計算論モデリングと計算論的精神医学，名古屋大学教育発達科学研究科 第12回認知科学セミナー 2019年12月19日 [Link]
片平 健太郎 (指定討論) 統計モデリングの役割，注意点，発展，日本社会心理学会第60回大会WS09ワークショップ「社会心理学における統計モデリングの可能性」2019年11月10日
片平 健太郎: 行動データの計算論モデリングの基礎と精神疾患研究への適用， 慶應義塾大学計算論的精神医学研究室 第4回研究会 2019年11月8日 [Link]
片平 健太郎: 「機械学習」による個人差のモデリング，日本心理学会第83回大会 チュートリアル・ワークショップ004 「機械学習と心理学の接点」 2019年9月11日 [補足資料]