TY - JOUR
T1 - Accuracy and precision of visual and auditory stimulus presentation in virtual reality in Python 2 and 3 environments for human behavior research
AU - Tachibana, Ryo
AU - Matsumiya, Kazumichi
N1 - Funding Information:
This research was supported by The Telecommunications Advancement Foundation (J190003004) to RT and the Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology (JPMJPR16DB) to KM. We would like to thank WorldViz and Oculus for their technical support. We also would like to thank Kosuke Yamamoto and Riku Asaoka for their advice.
Publisher Copyright:
© 2021, The Author(s).
PY - 2022/4
Y1 - 2022/4
N2 - Virtual reality (VR) is a new methodology for behavioral studies. In such studies, the millisecond accuracy and precision of stimulus presentation are critical for data replicability. Recently, Python, which is a widely used programming language for scientific research, has contributed to reliable accuracy and precision in experimental control. However, little is known about whether modern VR environments have millisecond accuracy and precision for stimulus presentation, since most standard methods in laboratory studies are not optimized for VR environments. The purpose of this study was to systematically evaluate the accuracy and precision of visual and auditory stimuli generated in modern VR head-mounted displays (HMDs) from HTC and Oculus using Python 2 and 3. We used the newest Python tools for VR and Black Box Toolkit to measure the actual time lag and jitter. The results showed that there was an 18-ms time lag for visual stimulus in both HMDs. For the auditory stimulus, the time lag varied between 40 and 60 ms, depending on the HMD. The jitters of those time lags were 1 ms for visual stimulus and 4 ms for auditory stimulus, which are sufficiently low for general experiments. These time lags were robustly equal, even when auditory and visual stimuli were presented simultaneously. Interestingly, all results were perfectly consistent in both Python 2 and 3 environments. Thus, the present study will help establish a more reliable stimulus control for psychological and neuroscientific research controlled by Python environments.
AB - Virtual reality (VR) is a new methodology for behavioral studies. In such studies, the millisecond accuracy and precision of stimulus presentation are critical for data replicability. Recently, Python, which is a widely used programming language for scientific research, has contributed to reliable accuracy and precision in experimental control. However, little is known about whether modern VR environments have millisecond accuracy and precision for stimulus presentation, since most standard methods in laboratory studies are not optimized for VR environments. The purpose of this study was to systematically evaluate the accuracy and precision of visual and auditory stimuli generated in modern VR head-mounted displays (HMDs) from HTC and Oculus using Python 2 and 3. We used the newest Python tools for VR and Black Box Toolkit to measure the actual time lag and jitter. The results showed that there was an 18-ms time lag for visual stimulus in both HMDs. For the auditory stimulus, the time lag varied between 40 and 60 ms, depending on the HMD. The jitters of those time lags were 1 ms for visual stimulus and 4 ms for auditory stimulus, which are sufficiently low for general experiments. These time lags were robustly equal, even when auditory and visual stimuli were presented simultaneously. Interestingly, all results were perfectly consistent in both Python 2 and 3 environments. Thus, the present study will help establish a more reliable stimulus control for psychological and neuroscientific research controlled by Python environments.
KW - Accuracy & precision
KW - Head-mounted display
KW - Python 2 & 3
KW - Stimulus presentation
KW - Virtual reality
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U2 - 10.3758/s13428-021-01663-w
DO - 10.3758/s13428-021-01663-w
M3 - Article
C2 - 34346042
AN - SCOPUS:85111832368
SN - 1554-351X
VL - 54
SP - 729
EP - 751
JO - Behavior Research Methods
JF - Behavior Research Methods
IS - 2
ER -