OpenMaze: An Open-Source Toolbox for Creating Virtual Navigation Experiments

Alsbury-Nealy, Kyle; Wang, Hongyu; Howarth, Cody; Гордиенко, А. И.; Duncan, Katherine

doi:10.31234/osf.io/bsj47

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Many of these have been developed specifically for investigating spatial navigation in humans (e.g. PyEPL[24], MazeSuite[25], PandaEPL[26], EVE[27], VREX[28], UXF[29], NavWell [30], bmITUX[31], Landmarks[32] and OpenMaze [33]). Almost without exception, these toolboxes use Unity to create their VR environments.…”

Section: Discussionmentioning

confidence: 99%

DomeVR: A setup for experimental control of an immersive dome virtual environment created with Unreal Engine 4

Shapcott

Weigand

Glukhova

et al. 2022

Preprint

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Immersive virtual reality (VR) environments are a powerful tool to explore cognitive processes ranging from memory and navigation to visual processing and decision making - and to do so in a naturalistic yet controlled setting. As such, they have been employed across different species, and by a diverse range of research groups. Unfortunately, designing and implementing behavioural tasks in such environments often proves complicated. To tackle this challenge, we created DomeVR, an immersive VR environment built using Unreal Engine 4 (UE4). UE4 is a powerful game engine with photo-realistic graphics containing a visual scripting language designed for use by non-programmers. As a result, virtual environments are easily created using drag-and-drop elements. DomeVR aims to make these features accessible to neuroscience experiments. This includes a logging and synchronization system to solve timing uncertainties inherent in UE4; an interactive GUI for scientists to observe subjects during experiments and adjust task parameters on the fly, and a dome projection system for full task immersion in non-human subjects. These key features are modular and can easily be added individually into other UE4 projects. Finally, we present proof-of-principle data highlighting the functionality of DomeVR in three different species: human, macaque and mouse.

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Section: Discussionmentioning

confidence: 99%

DomeVR: A setup for experimental control of an immersive dome virtual environment created with Unreal Engine 4

Shapcott

Weigand

Glukhova

et al. 2022

Preprint

View full text Add to dashboard Cite

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Experiments as Code and its application to VR studies in human-building interaction

Aguilar,

Gath-Morad,

Grübel

et al. 2024

Sci Rep

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Experiments as Code (ExaC) is a concept for reproducible, auditable, debuggable, reusable, & scalable experiments. Experiments are a crucial tool to understand Human-Building Interactions (HBI) and build a coherent theory around it. However, a common concern for experiments is their auditability and reproducibility. Experiments are usually designed, provisioned, managed, and analyzed by diverse teams of specialists (e.g., researchers, technicians, engineers) and may require many resources (e.g., cloud infrastructure, specialized equipment). Although researchers strive to document experiments accurately, this process is often lacking. Consequently, it is difficult to reproduce these experiments. Moreover, when it is necessary to create a similar experiment, the “wheel is very often reinvented”. It appears easier to start from scratch than trying to reuse existing work. Thus valuable embedded best practices and previous experiences are lost. In behavioral studies, such as in HBI, this has contributed to the reproducibility crisis. To tackle these challenges, we propose the ExaC paradigm, which not only documents the whole experiment, but additionally provides the automation code to provision, deploy, manage, and analyze the experiment. To this end, we define the ExaC concept, provide a taxonomy for the components of a practical implementation, and provide a proof of concept with an HBI desktop VR experiment that demonstrates the benefits of its “as code” representation, that is, reproducibility, auditability, debuggability, reusability, & scalability.

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Flexible Use of Spatial Frames of Reference for Object–Location Memory in Older Adults

et al. 2021

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In memory, representations of spatial features are stored in different reference frames; features relative to our position are stored egocentrically and features relative to each other are stored allocentrically. Accessing these representations engages many cognitive and neural resources, and so is susceptible to age-related breakdown. Yet, recent findings on the heterogeneity of cognitive function and spatial ability in healthy older adults suggest that aging may not uniformly impact the flexible use of spatial representations. These factors have yet to be explored in a precisely controlled task that explicitly manipulates spatial frames of reference across learning and retrieval. We used a lab-based virtual reality task to investigate the relationship between object–location memory across frames of reference, cognitive status, and self-reported spatial ability. Memory error was measured using Euclidean distance from studied object locations to participants’ responses at testing. Older adults recalled object locations less accurately when they switched between frames of reference from learning to testing, compared with when they remained in the same frame of reference. They also showed an allocentric learning advantage, producing less error when switching from an allocentric to an egocentric frame of reference, compared with the reverse direction of switching. Higher MoCA scores and better self-assessed spatial ability predicted less memory error, especially when learning occurred egocentrically. We suggest that egocentric learning deficits are driven by difficulty in binding multiple viewpoints into a coherent representation. Finally, we highlight the heterogeneity of spatial memory performance in healthy older adults as a potential cognitive marker for neurodegeneration, beyond normal aging.

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OpenMaze: An Open-Source Toolbox for Creating Virtual Navigation Experiments

Cited by 3 publications

References 17 publications

DomeVR: A setup for experimental control of an immersive dome virtual environment created with Unreal Engine 4

DomeVR: A setup for experimental control of an immersive dome virtual environment created with Unreal Engine 4

Experiments as Code and its application to VR studies in human-building interaction

Flexible Use of Spatial Frames of Reference for Object–Location Memory in Older Adults

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