【作 者】：
韩中美， 张 歆
【关 键 词 】：
人机协同； 复合脑； 群体认知失调； 生理—心理—行为耦合
【栏 目】：
理论探讨
【中文摘要】：
教育元宇宙、生成式人工智能等新一代人工智能是群体认知联结与跃升的驱动力，其（外脑）协同人类智慧（内脑）解析群体生理—心理—行为作用过程，为缓解群体认知失调提供人机复合脑启发的新思路。群体认知失调因个体与群体观点分歧、新旧经验悖离等触发，其协调是人类学习与心智发展的基础。然而，当前研究多聚焦个体认知，鲜有涉及群体认知失调的生理、心理等多维要素，尤其在人机复合脑下，其耦合机制仍不清晰。因此，文章厘清群体认知失调内涵及其在学习者生理、心理和行为层面的全过程耦合特征，明确人机协同复合脑对群体认知失调的调节作用，阐明复合脑在生理（感知脑）、心理（认知脑）和行为（执行脑）层面的启发机制，并解析“生理—心理”“心理—行为”和“生理—行为”三个耦合调节过程，提出人在旁路（辅助）、人在回路（共生）和人在领路（主导）三种耦合新模式，凸显人机复合脑助力学习者适应失调的潜力。
【英文摘要】：
The new generation of artificial intelligence such as the educational metaverse and generative artificial intelligence serve as the driving force for the connection and leap of group cognition. As "external brains", they collaborate with human intelligence ("internal brain") to analyze the physiological-psychological-behavioral process of the group, and provide a new human-machine composite brain-inspired approach to alleviating group cognitive dissonance. Group cognitive dissonance is triggered by factors such as the difference between individual and group perspectives, or contradictions between old and new experiences, and its coordination is the foundation of human learning and mental development. However, current research mostly focuses on individual cognition, with limited exploration of the physiological and psychological factors involved in group cognitive dissonance, especially under the human-machine composite brain, where the coupling mechanism remains unclear. Therefore, this paper clarifies the connotation of group cognitive dissonance and its coupling characteristics in the whole process of learners' physiological, psychological and behavioral levels, and defines the regulatory role of the human-machine collaborative composite brain in group cognitive dissonance. Based on this, the inspired mechanism of the composite brain is elucidated at the levels of physiological (perceptual brain), psychological (cognitive brain) and behavioral (executive brain), and the "physiological-psychological", "psychological-behavioral" and "physiological-behavioral" coupling regulation processes are further analyzed. Finally, three new coupling models, namely human-in-the-bypass (auxiliary), human-in-the-loop (symbiotic) and human-in-the-lead (dominant) are put forward to highlight the potential of the human-machine composite brain to help learners adapt to cognitive dissonance.