論文―Papers
2025
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 /comments]
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]
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]