Human-AI Agent Interaction as a Neuroplastic Training Environment

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Eranga Bandara, Ross Gore, Asanga Gunaratna, Ravi Mukkamala, Nihal Siriwardanagea, Gihan Siriwardanagea, Sachini Rajapakse, Isurunima Kularathna, Pramoda Karunarathna, Chalani Rajapakse, Sachin Shetty, Christopher K. Rhea, Ng Wee Keong, Kasun De Zoysa, Amin Hass, Shaifali Kaushik, Wathsala Herath, Preston Samuel, Anita H. Clayton, Atmaram Yarlagadd
• Venue: arXiv: cs.AI
• Published: 2026-07-14
• Collected: 2026-07-15
• URL: http://arxiv.org/abs/2607.12823v1
• PDF: https://arxiv.org/pdf/2607.12823v1
• DOI: UNKNOWN

Plain-Language Brief

The text discusses how interacting with AI agents can be seen as a form of neuroplastic training due to the frequent iterative loop in human-AI interaction. It suggests that these interactions may reinforce negative patterns such as impatience and frustration. The authors propose a framework for using this training environment to weaken these negative patterns by performing behind-the-scenes observation during the brief regulatory gap in the AI interaction.

What the Document Says

• Interaction with AI agents is frequent in everyday digital life.
• AI interaction follows an iterative loop: request, result, appraisal, revision.
• This loop can be seen as a high-frequency stream of contact events.
• Negative patterns such as impatience, perfectionism, frustration, and self-criticism may be strengthened through AI interaction due to activity-dependent synaptic plasticity.
• The authors propose a framework for using AI interaction to weaken negative patterns by performing observation during the brief regulatory gap.
• The framework is characterized through three layers of observation and two modes of application: user-guided and agent-assisted.

Key Concepts Mentioned

• AI agents
• human-AI interaction
• neuroplastic training
• negative patterns
• synaptic plasticity
• observation
• framework

Claims Made by the Document

• AI interaction can be seen as a neuroplastic training environment.
• Negative patterns may be strengthened through AI interaction.

Evidence Used

• The loop in human-AI interaction is a high-frequency stream of contact events which can lead to repeated evocation of negative patterns.
• Under activity-dependent synaptic plasticity, each uninterrupted cycle deepens the underlying pathway.

Methods / Approach

• Development of a framework for behind-the-scenes observation during the brief regulatory gap in AI interaction

Limitations Stated by the Authors

The text does not provide specific data or empirical evidence to support the claims.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The agent is limited by the information provided in the source and may not fully understand the practical application or effectiveness of the proposed framework.

Open Questions

• What are the specific details of the proposed framework for using AI interaction to weaken negative patterns?
• How effective is this framework in practice?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text does not provide detailed information about the proposed framework or its practical application.

Raw Abstract / Excerpt

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.