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[2602.19562] A Multimodal Framework for Aligning Human Linguistic Descriptions with Visual Perceptual Data
Summary
This paper presents a computational framework that aligns human linguistic descriptions with visual perceptual data, enhancing understanding in cognitive science and AI.
Why It Matters
The research addresses a fundamental challenge in AI and cognitive science: how to effectively map language to visual perception. By improving this alignment, the framework could enhance human-computer interaction and contribute to advancements in AI communication and understanding.
Key Takeaways
- Introduces a framework for aligning linguistic descriptions with visual data.
- Achieves human-competitive performance in referential grounding tasks.
- Reduces the number of utterances needed for stable mappings by 65%.
- Utilizes SIFT and UQI for perceptual similarity quantification.
- Offers insights into grounded communication and cross-modal concept formation.
Computer Science > Artificial Intelligence arXiv:2602.19562 (cs) [Submitted on 23 Feb 2026] Title:A Multimodal Framework for Aligning Human Linguistic Descriptions with Visual Perceptual Data Authors:Joseph Bingham View a PDF of the paper titled A Multimodal Framework for Aligning Human Linguistic Descriptions with Visual Perceptual Data, by Joseph Bingham View PDF HTML (experimental) Abstract:Establishing stable mappings between natural language expressions and visual percepts is a foundational problem for both cognitive science and artificial intelligence. Humans routinely ground linguistic reference in noisy, ambiguous perceptual contexts, yet the mechanisms supporting such cross-modal alignment remain poorly understood. In this work, we introduce a computational framework designed to model core aspects of human referential interpretation by integrating linguistic utterances with perceptual representations derived from large-scale, crowd-sourced imagery. The system approximates human perceptual categorization by combining scale-invariant feature transform (SIFT) alignment with the Universal Quality Index (UQI) to quantify similarity in a cognitively plausible feature space, while a set of linguistic preprocessing and query-transformation operations captures pragmatic variability in referring expressions. We evaluate the model on the Stanford Repeated Reference Game corpus (15,000 utterances paired with tangram stimuli), a paradigm explicitly developed to probe human-lev...
