Landmarks: A solution for spatial navigation and memory experiments in virtual reality

Starrett, Michael J.; McAvan, Andrew S.; Huffman, Derek J.; Stokes, Jared; Kyle, Colin T.; Smuda, Dana N.; Kolarik, Branden S.; Laczko, Jason; Ekstrom, Arne D.

doi:10.3758/s13428-020-01481-6

Cited by 45 publications

(27 citation statements)

References 19 publications

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“…The task was created in Unity 2018.4.11f1 using the Landmarks 2.0 framework [ 44 ]. Participants wore an HTC Vive Pro with a wireless Adapter and held pair of HTC Vive controllers ( Fig 1A ).…”

Section: Methodsmentioning

confidence: 99%

Combination and competition between path integration and landmark navigation in the estimation of heading direction

2022

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Successful navigation requires the ability to compute one’s location and heading from incoming multisensory information. Previous work has shown that this multisensory input comes in two forms: body-based idiothetic cues, from one’s own rotations and translations, and visual allothetic cues, from the environment (usually visual landmarks). However, exactly how these two streams of information are integrated is unclear, with some models suggesting the body-based idiothetic and visual allothetic cues are combined, while others suggest they compete. In this paper we investigated the integration of body-based idiothetic and visual allothetic cues in the computation of heading using virtual reality. In our experiment, participants performed a series of body turns of up to 360 degrees in the dark with only a brief flash (300ms) of visual feedback en route. Because the environment was virtual, we had full control over the visual feedback and were able to vary the offset between this feedback and the true heading angle. By measuring the effect of the feedback offset on the angle participants turned, we were able to determine the extent to which they incorporated visual feedback as a function of the offset error. By further modeling this behavior we were able to quantify the computations people used. While there were considerable individual differences in performance on our task, with some participants mostly ignoring the visual feedback and others relying on it almost entirely, our modeling results suggest that almost all participants used the same strategy in which idiothetic and allothetic cues are combined when the mismatch between them is small, but compete when the mismatch is large. These findings suggest that participants update their estimate of heading using a hybrid strategy that mixes the combination and competition of cues.

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Section: Methodsmentioning

confidence: 99%

Combination and competition between path integration and landmark navigation in the estimation of heading direction

2022

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“…The virtual environment (Figures 1A-D) and experimental tasks (Figure 1E) were built in Unity 3D (Unity Technologies ApS, San Francisco, CA) using the Landmarks virtual reality navigation package (Starrett et al, 2020). The navigable virtual environment was approximately 5 × 5 m in size, with the full space spanning 750 × 750 m. Four distally rendered mountains (unevenly spaced) were visible from within the 5 × 5 m space.…”

Section: Methodsmentioning

confidence: 99%

Older Adults Show Reduced Spatial Precision but Preserved Strategy-Use During Spatial Navigation Involving Body-Based Cues

McAvan

Oyao

et al. 2021

Front. Aging Neurosci.

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Older adults typically perform worse on spatial navigation tasks, although whether this is due to degradation of memory or an impairment in using specific strategies has yet to be determined. An issue with some past studies is that older adults are tested on desktop-based virtual reality: a technology many report lacking familiarity with. Even when controlling for familiarity, these paradigms reduce the information-rich, three-dimensional experience of navigating to a simple two-dimensional task that utilizes a mouse and keyboard (or joystick) as means for ambulation. Here, we utilize a wireless head-mounted display and free ambulation to create a fully immersive virtual Morris water maze in which we compare the navigation of older and younger adults. Older and younger adults learned the locations of hidden targets from same and different start points. Across different conditions tested, older adults remembered target locations less precisely compared to younger adults. Importantly, however, they performed comparably from the same viewpoint as a switched viewpoint, suggesting that they could generalize their memory for the location of a hidden target given a new point of view. When we implicitly moved one of the distal cues to determine whether older adults used an allocentric (multiple landmarks) or beaconing (single landmark) strategy to remember the hidden target, both older and younger adults showed comparable degrees of reliance on allocentric and beacon cues. These findings support the hypothesis that while older adults have less precise spatial memories, they maintain the ability to utilize various strategies when navigating.

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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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Landmarks: A solution for spatial navigation and memory experiments in virtual reality

Cited by 45 publications

References 19 publications

Combination and competition between path integration and landmark navigation in the estimation of heading direction

Combination and competition between path integration and landmark navigation in the estimation of heading direction

Older Adults Show Reduced Spatial Precision but Preserved Strategy-Use During Spatial Navigation Involving Body-Based Cues

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

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