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Computational Neuroscience Laboratories

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Articles

Blaming luck, claiming skill: Self-attribution bias in error assignment

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By Naoyuki Okamoto, Computational Neuroscience Laboratories
| PLOS
Loading metrics Open Access Peer-reviewed Research Article ? This is an uncorrected proof. Citation: Okamoto N, Taylor M, Kubo T, Ishii S, De Martino B, Cortese A (2025) Blaming luck, claiming skill: Self-attribution bias in error assignment. PLoS Comput Biol 21(12): e1013787. https://doi.org/10.1371/journal.pcbi.1013787 Editor: Zhaolei Zhang, University of Toronto, CANADA Received: April 20, 2025; Accepted: November 25, 2025; Published: December 16, 2025 Copyright: © 2025 Okamoto et al.
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Bayesian Estimation of Potential Performance Improvement Elicited by Robot-Guided Training

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By Computational Neuroscience Laboratories, Osaka City, Mitsuhiro Hayashibe, Atsushi Takagi
| Frontiers
Introduction Collaboration between robots and humans can expand human capabilities and has been investigated on the applicability in fields ranging from rehabilitation to collaborative manufacturing. Many different approaches have been developed to train human movements with robots by providing motor instructions and feedback.
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Unconscious reinforcement learning of hidden brain states supported by confidence

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By Aurelio Cortese, Hakwan Lau, Mitsuo Kawato, Computational Neuroscience Laboratories
| Nature Verified
Abstract Can humans be trained to make strategic use of latent representations in their own brains? We investigate how human subjects can derive reward-maximizing choices from intrinsic high-dimensional information represented stochastically in neural activity. Reward contingencies are defined in real-time by fMRI multivoxel patterns; optimal action policies thereby depend on multidimensional brain activity taking place below the threshold of consciousness, by design.
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