Cognitive Echo in Human–LLM Interaction

This preprint proposes an interpretive framework for understanding how interaction structure shapes alignment in large language models. Rather than focusing on who provides feedback, the paper argues that dialog-level properties themselves play a critical role in post-training behavior. Two idealized interaction modes are distinguished: instrumental (short, task-oriented exchanges) and exploratory (iterative, open-ended dialogue). The central hypothesis is that exploratory interactions generate a denser alignment-relevant signal by repeatedly forcing local adaptation of the model’s response policy. This asymmetry is captured through the notions of interaction signal overrepresentation and the Signal Leverage Effect. Building on this, the paper introduces the concept of Cognitive Echo to describe how repeated interaction with aligned systems may selectively amplify users’ cognitive styles over time. This phenomenon is treated as an emergent consequence of existing alignment dynamics rather than a new learning mechanism. Although theoretical, the framework yields testable predictions about long-term human–LLM interaction patterns and suggests implications for alignment evaluation, inclusive interface design, and user-side cognitive effects. The paper aims to provide conceptual groundwork for future empirical work at the intersection of alignment and human–computer interaction.