作为神经可塑性训练环境的人类与人工智能代理交互

中文摘要 AI 辅助

与人工智能代理的交互已成为日常数字生活中最频繁的活动之一。无论是与助手交谈、使用编码副驾驶还是生成图像，交互都遵循一个常见的迭代循环。我们发现这个循环是一系列高频接触事件，使日常代理交互成为一个未被认识的神经可塑性训练环境。当结果令人失望时，会反复引发不耐烦、完美主义、沮丧和自我批评等反应模式。我们提出可以利用相同的训练环境产生相反的效果，将条件反应模式视为物理神经元路径，开发了一个框架，通过三层观察和两种应用模式来实现。通过生成图像提示说明了该框架，展示了观察与否在行为上几乎相同，但神经学上相反的情况。

英文摘要

Interaction with AI agents has become one of the most frequent activities of everyday digital life. Whether conversing with an assistant, working with a coding copilot, or generating images, the interaction follows a common iterative loop: a request is issued, a result returned, appraised, and the request revised. We observe that this loop is a high-frequency stream of contact events -- moments at which a result meets a person and a conditioned response may fire before deliberate appraisal -- making everyday agent interaction an unrecognised neuroplastic training environment. When a result disappoints, reactive patterns of impatience, perfectionism, frustration, and self-criticism are repeatedly evoked, and under activity-dependent synaptic plasticity each uninterrupted cycle deepens the underlying pathway through long-term potentiation. Ordinary agent use may thus quietly strengthen the very patterns it provokes. We propose that the same training environment can be engaged to the opposite effect. Treating conditioned reactive patterns as physical neurone paths -- activated through a pre-cognitive feeling tone that opens a brief regulatory gap -- we develop a framework in which, at that gap, in place of the reactive re-prompt, a person performs behind-the-scenes observation: watching the neural process operate so the cascade does not complete and long-term depression weakens the path rather than potentiation strengthening it. We characterise this practice through three layers of observation and two modes of application: a user-guided mode requiring no change to existing tools, and an agent-assisted mode in which an ordinary agent is lightly configured to support observation at the gap. We illustrate the framework through generative image prompting, showing how a single frustrating session is behaviourally nearly identical whether or not it is observed, yet neurologically opposite.

发表机构

• Old Dominion University（奥多明尼昂大学）
• AI Motion Labs（人工智能运动实验室）
• Nanyang Technological University（南洋理工大学）
• University of Colombo（科伦坡大学）
• Accenture Technology Labs（埃森哲技术实验室）
• GSI Scandinavia AB（GSI斯堪的纳维亚公司）
• Lithuanian University of Health Sciences（立陶宛健康科学大学）
• Department of Psychiatry and Neurobehavioral Sciences, University of Virginia School of Medicine（弗吉尼亚大学医学院精神病学和神经行为科学系）
• Blanchfield Army Community Hospital（布兰奇菲尔德陆军社区医院）
• McDonald Army Health Center（麦克唐纳陆军健康中心）

机构由 AI 辅助整理，请以论文原文为准。