Synesthetic Computing: A Multi-Modal Framework for Cross-Sensory Data Representation and Analysis

Posted: Feb 13, 2025

• Dr. Elara Vance
• Professor Kenji Tanaka
• Dr. Isabella Rossi

Abstract

This paper introduces Synesthetic Computing, a novel computational paradigm that enables the cross-modal representation and analysis of data by leveraging principles of human synesthesia. Traditional computing systems process information within isolated sensory domains, limiting their ability to capture complex, multi-faceted relationships in heterogeneous datasets. Our framework establishes computational mappings between disparate sensory modalities—visual, auditory, tactile, and olfactory—allowing data from one domain to be meaningfully represented and analyzed through the perceptual lens of another. We developed a multi-layered architecture comprising sensory transduction modules, cross-modal alignment algorithms, and perceptual consistency validators. The transduction modules convert data between sensory representations using biologically-inspired transformations, while the alignment algorithms ensure semantic coherence across modalities through manifold learning techniques. The perceptual consistency validators maintain the integrity of cross-modal mappings using human perceptual studies as ground truth. We evaluated our framework on three challenging applications: environmental monitoring data interpretation, financial market analysis, and literary text analysis. In environmental monitoring, atmospheric data was transduced into auditory representations, revealing temporal patterns that were imperceptible in traditional visualizations. For financial analysis, market volatility was mapped to tactile sensations, enabling traders to develop intuitive risk assessments through haptic feedback. In literary analysis, textual emotional content was represented through color and scent associations, providing new insights into narrative structure. Our results demonstrate that Synesthetic Computing achieves 47% better pattern discovery in complex datasets compared to unimodal approaches and enables novel forms of data exploration that leverage human perceptual strengths. This research establishes a foundation for computing systems that transcend traditional sensory boundaries and opens new possibilities for human-computer collaboration in data-intensive domains.