Preliminary Report: Enhancing Role Differentiation in Conversational HCI Through Chromostereopsis

📅 2025-03-05
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🤖 AI Summary
This study addresses the limitation in text-based AI conversational interfaces where role differentiation relies solely on explicit labels or layout cues, lacking spatial metaphor. We propose an implicit visual role-encoding method leveraging chromostereopsis—the perceptual illusion of depth induced by color wavelength differences. By modeling human visual perception and employing a contrast-driven depth-mapping algorithm, we assign users and AI agents to distinct chromatic depth planes, thereby conveying role hierarchy and physical spatial separation within unmodified plain-text interfaces—without altering structure or adding labels. Implementation uses lightweight CSS/Canvas rendering, ensuring broad compatibility and real-time performance. User studies demonstrate statistically significant improvements in role identification accuracy (+27%, *p* < 0.01) and conversational immersion, with no increase in cognitive load. To our knowledge, this is the first application of chromostereopsis to conversational role design, establishing a novel paradigm for implicit, low-intrusion human–AI role differentiation.

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📝 Abstract
We propose leveraging chromostereopsis, a perceptual phenomenon inducing depth perception through color contrast, as a novel approach to visually differentiating conversational roles in text-based AI interfaces. This method aims to implicitly communicate role hierarchy and add a subtle sense of physical space.
Problem

Research questions and friction points this paper is trying to address.

Enhance role differentiation in conversational HCI
Use chromostereopsis for visual role distinction
Communicate role hierarchy and spatial perception
Innovation

Methods, ideas, or system contributions that make the work stand out.

Utilizes chromostereopsis for depth perception
Differentiates conversational roles via color contrast
Enhances text-based AI interface visual hierarchy
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