Attention, Action, and Memory: How Multi-modal Interfaces and Cognitive Load Alter Information Retention
September 07, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Omar Elgohary, Zhu-Tien
arXiv ID
2509.05898
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Each year, multi-modal interaction continues to grow within both industry and academia. However, researchers have yet to fully explore the impact of multi-modal systems on learning and memory retention. This research investigates how combining gaze-based controls with gesture navigation affects information retention when compared to standard track-pad usage. A total of twelve participants read four textual articles through two different user interfaces which included a track-pad and a multi-modal interface that tracked eye movements and hand gestures for scrolling, zooming, and revealing content. Participants underwent two assessment sessions that measured their information retention immediately and after a twenty-four hour period along with the NASA-TLX workload evaluation and the System Usability Scale assessment. The initial analysis indicates that multi-modal interaction produces similar targeted information retention to traditional track-pad usage, but this neutral effect comes with higher cognitive workload demands and seems to deteriorate with long-term retention. The research results provide new knowledge about how multi-modal systems affect cognitive engagement while providing design recommendations for future educational and assistive technologies that require effective memory performance.
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