{"title":"Human Brain Dynamics and Coordination Reflect the Task Difficulty of Optical Image Relational Reasoning.","authors":"Wen-Chi Chou, Hsiao-Ching She, Tzyy-Ping Jung","doi":"10.1142/S0129065723500181","DOIUrl":null,"url":null,"abstract":"<p><p>Despite advances in neuroscience, the mechanisms by which human brain resolve optical image formation through relational reasoning remain unclear, particularly its relationships with task difficulty. Therefore, this study explores the underlying brain dynamics involved in optical image formation tasks at various difficulty levels, including those with a single convex lens and a single mirror. Compared to single convex lens relational reasoning with high task difficulty, the single mirror relational reasoning exhibited significantly higher response accuracy and shorter latency. As compared to single mirror tasks, single convex tasks exhibited greater frontal midline theta augmentation and right parietal alpha suppression during phase I and earlier phase II, and augmentation of frontal midline theta, right parietal-occipital alpha, and left mu alpha suppression during late phase II. Moreover, the frontal midline theta power in late phase II predicts the likelihood of solving single convex tasks the best, while the parietal alpha power in phase I is most predictive. In addition, frontal midline theta power exhibited stronger synchronization with right parietal alpha, right occipital alpha, and mu alpha power when solving single convex tasks than single mirror tasks. In summary, having stronger brain dynamics and coordination is vital for achieving optical image formation with greater difficulty.</p>","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":null,"pages":null},"PeriodicalIF":6.6000,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Neural Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1142/S0129065723500181","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Despite advances in neuroscience, the mechanisms by which human brain resolve optical image formation through relational reasoning remain unclear, particularly its relationships with task difficulty. Therefore, this study explores the underlying brain dynamics involved in optical image formation tasks at various difficulty levels, including those with a single convex lens and a single mirror. Compared to single convex lens relational reasoning with high task difficulty, the single mirror relational reasoning exhibited significantly higher response accuracy and shorter latency. As compared to single mirror tasks, single convex tasks exhibited greater frontal midline theta augmentation and right parietal alpha suppression during phase I and earlier phase II, and augmentation of frontal midline theta, right parietal-occipital alpha, and left mu alpha suppression during late phase II. Moreover, the frontal midline theta power in late phase II predicts the likelihood of solving single convex tasks the best, while the parietal alpha power in phase I is most predictive. In addition, frontal midline theta power exhibited stronger synchronization with right parietal alpha, right occipital alpha, and mu alpha power when solving single convex tasks than single mirror tasks. In summary, having stronger brain dynamics and coordination is vital for achieving optical image formation with greater difficulty.
期刊介绍:
The International Journal of Neural Systems is a monthly, rigorously peer-reviewed transdisciplinary journal focusing on information processing in both natural and artificial neural systems. Special interests include machine learning, computational neuroscience and neurology. The journal prioritizes innovative, high-impact articles spanning multiple fields, including neurosciences and computer science and engineering. It adopts an open-minded approach to this multidisciplinary field, serving as a platform for novel ideas and enhanced understanding of collective and cooperative phenomena in computationally capable systems.