Full metadata
Title
Spatial-temporal characteristics of multisensory integration
Description
We experience spatial separation and temporal asynchrony between visual and
haptic information in many virtual-reality, augmented-reality, or teleoperation systems.
Three studies were conducted to examine the spatial and temporal characteristic of
multisensory integration. Participants interacted with virtual springs using both visual and
haptic senses, and their perception of stiffness and ability to differentiate stiffness were
measured. The results revealed that a constant visual delay increased the perceived stiffness,
while a variable visual delay made participants depend more on the haptic sensations in
stiffness perception. We also found that participants judged stiffness stiffer when they
interact with virtual springs at faster speeds, and interaction speed was positively correlated
with stiffness overestimation. In addition, it has been found that participants could learn an
association between visual and haptic inputs despite the fact that they were spatially
separated, resulting in the improvement of typing performance. These results show the
limitations of Maximum-Likelihood Estimation model, suggesting that a Bayesian
inference model should be used.
haptic information in many virtual-reality, augmented-reality, or teleoperation systems.
Three studies were conducted to examine the spatial and temporal characteristic of
multisensory integration. Participants interacted with virtual springs using both visual and
haptic senses, and their perception of stiffness and ability to differentiate stiffness were
measured. The results revealed that a constant visual delay increased the perceived stiffness,
while a variable visual delay made participants depend more on the haptic sensations in
stiffness perception. We also found that participants judged stiffness stiffer when they
interact with virtual springs at faster speeds, and interaction speed was positively correlated
with stiffness overestimation. In addition, it has been found that participants could learn an
association between visual and haptic inputs despite the fact that they were spatially
separated, resulting in the improvement of typing performance. These results show the
limitations of Maximum-Likelihood Estimation model, suggesting that a Bayesian
inference model should be used.
Date Created
2017
Contributors
- Sim, Sung Hun (Author)
- Wu, Bing (Thesis advisor)
- Cooke, Nancy J. (Committee member)
- Gray, Robert (Committee member)
- Branaghan, Russell (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
vi, 105 pages : illustrations
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.45954
Statement of Responsibility
by Sung Hun Sim
Description Source
Viewed on January 31, 2018
Level of coding
full
Note
thesis
Partial requirement for: Ph. D., Arizona State University, 2017
bibliography
Includes bibliographical references (pages 92-102)
Field of study: Human systems engineering
System Created
- 2017-12-01 07:01:07
System Modified
- 2021-08-26 09:47:01
- 3 years 3 months ago
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