Harmonizing the Senses: Designing a Cross-Modal Interactive Art System to Enhance Older Adults' Affective Experiences

Authors
SiHan An, Yifan Wu, Zihan Zhang, Yuanlinxi Li, Mengqi Jiang, Jiaxin Zhang, Qingchuan Li
Year
2026
Publication
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26)
DOI
10.1145/3772318.3790993

Summary

This CHI 2026 paper investigates how cross-modal correspondence and congruency across visual, auditory, and kinetic modalities shape older adults' affective experiences in digital art systems, and how an AI-infused drawing mode can scaffold those experiences. The authors frame the work against demographic context (the over-65 population is projected to exceed 1.5 billion by 2050) and against age-related declines in perception, cognition, and emotional regulation that can limit engagement with poorly calibrated multisensory stimulation (MSS). The study uses two linked experiments and a follow-on evaluation. Experiment I (N=35, ages 65-79) examines timbre-color associations with ten traditional Chinese instruments and the 37-color Berkeley BCP-37 palette, using PrEmo cards to measure emotional ratings. Participants were stratified by Montreal Cognitive Assessment-Basic (MoCA-BC) scores into low- and high-cognitive-ability groups. Experiment II (N=32) builds a TouchDesigner-based cross-modal ink-painting system on a Wacom tablet, mapping stroke speed to timbre rhythm and stroke vertical position to timbre pitch across five synchrony conditions (no correspondence, correspondence without congruency, speed-rhythm, position-pitch, combined). Facial expression data were captured with Noldus FaceReader 9. A third phase adds an AI-infused mode using Stream Diffusion, a VAE, and ControlNet to transform strokes into real-time Chinese ink-style artworks, evaluated against the non-AI condition. The work targets both empirical contributions on affective congruency and design guidelines for emotionally supportive MSS systems for older adults.

Key Findings

Experiment I confirmed an emotion-mediated link between timbre and color: brighter, sharper timbres predicted saturated, light, yellow-red color choices (PMCA_S/U beta=0.891, p<.01; R^2=.793), and joyful/hopeful/desirous timbres aligned with the same visual attributes. Experiment II found no significant main effect of synchrony condition on self-reported affect, but a significant t-test result showed lower-cognitive-ability participants reported higher pleasure than higher-ability peers under the correspondence-without-congruency (CNC) condition (M=0.20 vs 0.08, p=.006) — many described high-synchrony modes as 'overwhelming' or 'distracting'. The AI-infused evaluation (paired t-tests, N=32) produced the strongest results: AI mode significantly increased pleasure (mean diff=0.494, p<.001), surprise (0.713, p=.003), and reduced sadness (-0.213, p=.025). Facial-expression ANOVA showed AI significantly raised valence (eta_p^2=.311) and arousal (.234) and lowered sadness and anger. Crucially, AI compensated for cognitive ability: low-cognitive-ability participants, who showed a sharp decline in pleasure and valence and a rise in sadness without AI, matched or exceeded high-ability peers with AI support.

Relevance to Practice

For accessibility practitioners working with older adults and people with cognitive decline, the paper offers concrete evidence that high-synchrony multisensory output — often promoted as richer or more immersive — can overwhelm users with reduced cognitive flexibility and sensory tolerance. Lower-complexity, predictable pairings should be the default, with optional AI scaffolding that lowers operational demands while preserving user agency (the authors call this 'graduated autonomy'). The finding that AI-infused creative tools closed the gap between cognitive-ability groups is relevant for care-home and rehabilitation settings where mixed-ability groups share the same equipment. Limitations to flag: the sample skewed toward digitally familiar, active older adults from one community center in Mandarin-speaking China, so findings may not transfer to users with severe sensory impairment, lower digital literacy, or rural contexts. The study was short-term and lab-based — novelty effects may inflate AI's apparent benefit — so longitudinal deployment in real settings remains an open question.