WeChat Mini Program
Old Version Features

Confusing Virtual Reality with Reality – an Experimental Study

Michael Wiesing, Gemma Comadran,Mel Slater

iScience(2025)

Cited 0|Views0
Abstract
Virtual reality's (VR) ability to create convincing illusions of presence makes it a powerful media for therapeutic and prosocial applications. Correspondingly, it also presents ethical challenges as the line between the real and virtual blurs, the focus of our study. Forty-nine participants interacted with a virtual experimenter that invited participants to sit on a chair, with 20% doing so without checking for a real chair. The virtual experimenter had also placed a tablet in a drawer, and a week later, in the corresponding real room, 45% of participants expected a tablet to be in the same place. Additionally, implicit attitudes were influenced based on subtle differences in wording of interview questions asked by the virtual experimenter. A Bayesian analysis supported these findings. Hence, participants may take virtual objects as real, risking their safety, or may inappropriately apply observations in VR to real-life problem solving, resulting in vulnerability to deception.
More
Translated text
Key words
Psychology
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined