Working Memory Capacity for Mid-Air Gestures in Human–Computer Interaction.
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| Title: | Working Memory Capacity for Mid-Air Gestures in Human–Computer Interaction. |
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| Authors: | Zhou, Xiaozhou (AUTHOR), Lu, Zhengyang (AUTHOR), Bai, Ruidong (AUTHOR), Wang, Yixue (AUTHOR), Zhang, Ziwei (AUTHOR), Qiu, Xu-Yi (AUTHOR) |
| Source: | International Journal of Human-Computer Interaction. Jul2025, Vol. 41 Issue 14, p8565-8580. 16p. |
| Subjects: | Human-computer interaction, Gesture, Interactive computer systems, Empirical research, Short-term memory, Memory span, Cognitive load |
| Abstract: | Mid-air gesture interactions have emerged as one of the predominant modalities used in human-computer interaction (HCI). However, extant research on gestures predominantly concentrates on the design of gestures for singular functionalities, which requires a comprehensive investigation into gesture interaction design from an integrative HCI system perspective. Working memory capacity plays a pivotal role in constraining the number of gestures accommodated within an HCI system. This study adopts the change detection paradigm to conduct an empirical investigation into the working memory capacities associated with mid-air gestures, aiming to elucidate the working memory capacity for such gestures. The experimental design included two independent variables: five distinct categories of mid-air gestures (physical, symbolic, metaphorical, abstract, and mixed) and memory set sizes ranging from two to nine items. The findings revealed that working memory capacities for different types of gestures fluctuate between three and five items, with symbolic gestures presenting the most negligible cognitive load for recall and abstract gestures proving to be the most challenging. The outcomes of this research have significant implications for both the design of individual gestures and the overarching design of gesture interaction systems, serving as a valuable reference for future endeavors in HCI design optimization. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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