人脑动力学和协调性反映了光学图像关联推理的任务难度。

IF 6.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Wen-Chi Chou, Hsiao-Ching She, Tzyy-Ping Jung
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引用次数: 0

摘要

尽管神经科学取得了进步,但人类大脑通过关系推理解决光学图像形成的机制仍不清楚,特别是它与任务难度的关系。因此,本研究探讨了不同难度的光学成像任务中涉及的潜在大脑动力学,包括单凸透镜和单镜子。与任务难度较高的单凸透镜关系推理相比,单镜像关系推理的反应准确率显著提高,延迟时间显著缩短。与单镜任务相比,单凸任务在第一阶段和第二阶段早期表现出更大的额中线θ增强和右侧顶叶α抑制,在第二阶段后期表现出额中线θ增强、右侧顶叶-枕叶α和左侧α抑制。此外,第二阶段后期的额叶中线θ波功率对解决单个凸任务的可能性预测效果最好,而第一阶段的顶叶α波功率预测效果最好。此外,在解决单个凸面任务时,额中线θ波功率与右顶α、右枕α和mu α功率的同步性较强。综上所述,拥有更强的脑动力学和协调性对于实现更困难的光学成像至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Human Brain Dynamics and Coordination Reflect the Task Difficulty of Optical Image Relational Reasoning.

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.

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来源期刊
International Journal of Neural Systems
International Journal of Neural Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
28.80%
发文量
116
审稿时长
24 months
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.
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