% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@PHDTHESIS{Wiesing:1005102,
author = {Wiesing, Michael},
title = {{O}bject-based attentional expectancies in virtual reality},
school = {University of Cologne},
type = {Dissertation},
reportid = {FZJ-2023-01301},
pages = {100 p.},
year = {2022},
note = {Dissertation, University of Cologne, 2022},
abstract = {Modern virtual reality (VR) technology has the promise to
enable neuroscientists and psychologists to conduct
ecologically valid experiments, while maintaining precise
experimental control. However, in recent studies, game
engines like Unreal Engine or Unity, are used for stimulus
creation and data collection. Yet game engines do not
provide the underlying architecture to measure the time of
stimulus events and behavioral input with the accuracy or
precision required by many experiments. Furthermore, it is
currently not well understood, if VR and the underlying
technology engages the same cognitive processes as a
comparable real-world situation. Similarly, not much is
known, if experimental findings obtained in a standard
monitor-based experiment, are comparable to those obtained
in VR by using a head-mounted display (HMD) or if the
different stimulus devices also engage different cognitive
processes.The aim of my thesis was to investigate if modern
HMDs affect the early processing of basic visual features
differently than a standard computer monitor.In the first
project (chapter 1), I developed a new behavioral paradigm,
to investigate how prediction errors of basic object
features are processed. In a series of four experiments, the
results consistently indicated that simultaneous prediction
errors for unexpected colors and orientations are processed
independently on an early level of processing, before object
binding comes into play.My second project (chapter 2)
examined the accuracy and precision of stimulus timing and
reaction time measurements, when using Unreal Engine 4 (UE4)
in combination with a modern HMD system. My results
demonstrate that stimulus durations can be defined and
controlled with high precision and accuracy. However,
reaction time measurements turned out to be highly imprecise
and inaccurate, when using UE4’s standard application
programming interface (API). Instead, I proposed a new
software-based approach to circumvent these limitations.
Timings benchmarks confirmed that the method can measure
reaction times with a precision and accuracy in the
millisecond range.In the third project (chapter 3), I
directly compared the task performance in the paradigm
developed in chapter 1 between the original experimental
setup and a virtual reality simulation of this experiment.
To establish two identical experimental setups, I recreated
the entire physical environment in which the experiments
took place within VR and blended the virtual replica over
the physical lab. As a result, the virtual environment (VE)
corresponded not only visually with the physical laboratory
but also provided accurate sensory properties of other
modalities, such as haptic or acoustic feedback. The results
showed a comparable task performance in both the non-VR and
the VR experiments, suggesting that modern HMDs do not
affect early processing of basic visual features differently
than a typical computer monitor.},
cin = {INM-3},
cid = {I:(DE-Juel1)INM-3-20090406},
pnm = {5251 - Multilevel Brain Organization and Variability
(POF4-525)},
pid = {G:(DE-HGF)POF4-5251},
typ = {PUB:(DE-HGF)11},
url = {https://juser.fz-juelich.de/record/1005102},
}