Multisensory Integration and Sensory Substitution Across Vision, Audition, and Haptics: Answering the What, Which, and When in Study Protocols
July 24, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Andrew Jeyathasan, Swati Banerjee
arXiv ID
2507.18401
Category
cs.HC: Human-Computer Interaction
Cross-listed
q-bio.NC
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
We experience the world through multiple senses that work together to create a cohesive perception, whether in daily life or immersive technologies. Understanding this multisensory integration (MSI) requires examining the interactions between sensory modalities, each with unique temporal dynamics and characteristics. While most research focuses on unimodal or bimodal cues, the integration of three or more modalities remains underexplored. MSI studies must account for factors like cross-modal correspondence, congruence, cognitive load, and stimulus timing, which become increasingly complex as modalities multiply. This article examines these key factors and how they can be applied to 8 design effective MSI study protocols.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted