Becoming An Animal? Exploring Proteus Effect Based on Human-avatar Hand Gesture Consistency

Human cognition and behavior can be unconsciously affected by personal avatars in the virtual world, a phenomenon known as the Proteus Effect. When using first-person non-human avatars, the characteristics of virtual hands may also induce relevant cognitive and even behavioral patterns in real human hands. Therefore, evaluating human-avatar gesture consistency may be a potentially effective method for objectively assessing the Proteus Effect when using non-human avatars. To explore this question, we first created human and non-human avatars, including three animals. Then, we constructed a dataset of hand gestures and trained a model to dynamically recognize real-hand gestures that were consistent with corresponding avatar hands. Next, we designed a virtual reality experimental task involving grasping objects with intuitive gestures and performed a 2 (avatar type: human/non-human) $* 2$ (virtual hand: presence/absence of spontaneous movement) within-subject experiment to examine the effects of avatar characteristics on self-illusion and human-avatar gesture consistency. The results showed that participants performed a significantly larger percentage of gestures that were consistent with their currently used avatars. Additionally, participants did experience self-illusion when using non-human avatars, although the levels were significantly lower than those when using human avatars. Therefore, self-illusion may serve as a perceptual antecedent of the Proteus Effect, even with non-human avatars, inadvertently altering the behavioral gestures of their real hands. In conclusion, detecting human-avatar gesture consistency can help evaluate the Proteus Effect.

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Publication Details

Published
2024-10-21
DOI
https://doi.org/10.1109/ismar62088.2024.00093
Citations
2
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.74

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article

Becoming An Animal? Exploring Proteus Effect Based on Human-avatar Hand Gesture Consistency

Ying Lin, Yulong Bian, Tangjun Qu, Junjie Wang et al.
2 citations
Hand Gesture Recognition Systems
0.74
article

Becoming An Animal? Exploring Proteus Effect Based on Human-avatar Hand Gesture Consistency

Ying Lin, Yulong Bian, Tangjun Qu, Junjie Wang, Juan Liu, Chao Zhou, Kaiyuan Jiang, Baiqiao Zhang
article en
2 citations

Abstract

Human cognition and behavior can be unconsciously affected by personal avatars in the virtual world, a phenomenon known as the Proteus Effect. When using first-person non-human avatars, the characteristics of virtual hands may also induce relevant cognitive and even behavioral patterns in real human hands. Therefore, evaluating human-avatar gesture consistency may be a potentially effective method for objectively assessing the Proteus Effect when using non-human avatars. To explore this question, we first created human and non-human avatars, including three animals. Then, we constructed a dataset of hand gestures and trained a model to dynamically recognize real-hand gestures that were consistent with corresponding avatar hands. Next, we designed a virtual reality experimental task involving grasping objects with intuitive gestures and performed a 2 (avatar type: human/non-human) $* 2$ (virtual hand: presence/absence of spontaneous movement) within-subject experiment to examine the effects of avatar characteristics on self-illusion and human-avatar gesture consistency. The results showed that participants performed a significantly larger percentage of gestures that were consistent with their currently used avatars. Additionally, participants did experience self-illusion when using non-human avatars, although the levels were significantly lower than those when using human avatars. Therefore, self-illusion may serve as a perceptual antecedent of the Proteus Effect, even with non-human avatars, inadvertently altering the behavioral gestures of their real hands. In conclusion, detecting human-avatar gesture consistency can help evaluate the Proteus Effect.

Shandong University (CN), Institute of Software (CN)
Shandong University, Ministry of Education
Openalex Percentile: Top 26%
Hand Gesture Recognition Systems
0.74
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