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 interactions with AI agents can serve as a neuroplastic training environment due to the repetitive loop of request, response, and appraisal. It proposes a framework for using these interactions to counteract negative patterns such as impatience or frustration by introducing a brief regulatory gap during which the individual observes their neural processes.

What the Document Says

• AI agent interaction is frequent in everyday digital life.
• Interaction loop involves request, result, appraisal, and revision.
• This loop can be considered a high-frequency stream of contact events.
• Disappointing results may evoke patterns of impatience, perfectionism, frustration, and self-criticism.
• Each uninterrupted cycle deepens these patterns through long-term potentiation under activity-dependent synaptic plasticity.
• The same training environment can be engaged to weaken these patterns.
• A framework is proposed for observing the neural process during a regulatory gap, preventing the cascade and promoting long-term depression instead of potentiation.
• The framework consists of three layers of observation and two modes of application: user-guided and agent-assisted.

Key Concepts Mentioned

• AI agents
• neuroplastic training environment
• conditioned reactive patterns
• synaptic plasticity
• observation
• generative image prompting

Claims Made by the Document

• AI agent interaction can serve as a neuroplastic training environment.
• Negative patterns such as impatience and frustration can be strengthened by ordinary AI use.
• The proposed framework can counteract these negative patterns.

Evidence Used

• Observation of the loop in everyday digital life
• Explanation of how disappointing results evoke negative patterns and deepen them through long-term potentiation

Methods / Approach

• Development of a framework for observation during a regulatory gap

Limitations Stated by the Authors

The paper does not provide empirical evidence supporting the claims.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The model does not specify the exact mechanisms by which observation weakens negative patterns.

Open Questions

• What is the empirical evidence supporting the claims made in the paper?
• How exactly does observation weaken negative patterns?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper proposes a theoretical framework but does not provide empirical evidence to support its claims.

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.