The Virtual Navigation Toolbox: Providing tools for virtual navigation experiments

Müller, Martin M.; Scherer, Jonas; Unterbrink, Patrick; Bertrand, Olivier J. N.; Egelhaaf, Martin; Boeddeker, Norbert

doi:10.1371/journal.pone.0293536

Cited by 4 publications

(4 citation statements)

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“…The experiment was carried out over six sessions (one training + five main sessions), split evenly over two experimental days, which were not more than 1 calendar day apart. The experiment was provided in the form of a stand-alone Windows application, created using the virtual navigation toolbox for the Unity3D game engine and was executed remotely on participants' desktop home computers (Müller et al, 2023 ). A supervisor was present to instruct and assist participants according to a standardised protocol during the initial training session via video call.…”

Section: Methodsmentioning

confidence: 99%

“…For the design of this study, we analysed data from a previous pilot study with a virtual triangle completion task and different numbers of trees (Müller et al, 2023 ) and did power analyses for repetitions per condition and for number of participants using the one-sample t -test function in G*Power 3.1 (Faul et al, 2007 ). We planned to collect data from N = 20 participants with n = 24 repetitions per condition each.…”

Section: Methodsmentioning

confidence: 99%

“…We also thank the two reviewers for their careful reading of our manuscript and their many insightful comments and suggestions. Additionally, we acknowledge that an earlier version of this manuscript was previously shared as a preprint on bioRxiv (Scherer and Müller et al, 2023) and we appreciate the valuable feedback received during the preprint stage. Moreover, we recognise the prior publication of this work as a PhD thesis titled "Finding Back Home: From Vector Models to Virtual Forests" by Müller (2023).…”

Section: Acknowledgmentsmentioning

confidence: 99%

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Not seeing the forest for the trees: combination of path integration and landmark cues in human virtual navigation

Scherer,

Müller,

Unterbrink

et al. 2024

Front. Behav. Neurosci.

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IntroductionIn order to successfully move from place to place, our brain often combines sensory inputs from various sources by dynamically weighting spatial cues according to their reliability and relevance for a given task. Two of the most important cues in navigation are the spatial arrangement of landmarks in the environment, and the continuous path integration of travelled distances and changes in direction. Several studies have shown that Bayesian integration of cues provides a good explanation for navigation in environments dominated by small numbers of easily identifiable landmarks. However, it remains largely unclear how cues are combined in more complex environments.MethodsTo investigate how humans process and combine landmarks and path integration in complex environments, we conducted a series of triangle completion experiments in virtual reality, in which we varied the number of landmarks from an open steppe to a dense forest, thus going beyond the spatially simple environments that have been studied in the past. We analysed spatial behaviour at both the population and individual level with linear regression models and developed a computational model, based on maximum likelihood estimation (MLE), to infer the underlying combination of cues.ResultsOverall homing performance was optimal in an environment containing three landmarks arranged around the goal location. With more than three landmarks, individual differences between participants in the use of cues are striking. For some, the addition of landmarks does not worsen their performance, whereas for others it seems to impair their use of landmark information.DiscussionIt appears that navigation success in complex environments depends on the ability to identify the correct clearing around the goal location, suggesting that some participants may not be able to see the forest for the trees.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

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Not seeing the forest for the trees: combination of path integration and landmark cues in human virtual navigation

Scherer,

Müller,

Unterbrink

et al. 2024

Front. Behav. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…For the design of this study, we analysed data from a previous pilot study with a virtual triangle completion task and different numbers of trees (Müller et al, 2023) and did power analyses for repetitions per condition and for number of participants using the one-sample t-test function in G*Power 3.1 (Faul et al, 2007). We planned to collect data from N=20 participants with n=24 repetitions per condition each.…”

Section: Methodsmentioning

confidence: 99%

Not seeing the forest for the trees: Combination of path integration and landmark cues in human virtual navigation

Scherer,

Müller,

Unterbrink

et al. 2023

Preprint

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Our brain combines sensory inputs from various sources by dynamically weighting cues according to their reliability and relevance for a given task. In our daily lives, one such task is to navigate familiar but also unknown environments, as we move from place to place. Two pertinent cues in navigation are the spatial arrangement of landmarks, and the continuous path integration of travelled distances and changes in direction. Although several modelling studies have shown that Bayesian integration of cues provides a good explanation for navigation in environments with up to three objects at the population level, it remains unclear how navigation of individuals is affected by more complex environments. To investigate how humans process and combine cues of varying reliability about landmarks and path integration in complex environments, we conducted virtual reality experiments. We varied the number of landmarks to study navigation in environments ranging from an open steppe to a dense forest, thus going beyond environments with three landmarks that have been studied in the past. We analysed spatial behaviour at both the population and individual level with linear regression models and developed a computational model, based on maximum likelihood estimation (MLE), to infer the underlying dynamic combination of cues. With more than three landmarks, individual differences between participants in the use of cues are striking. For some, the addition of landmarks does not worsen their performance, whereas for others it seems to impair their use of landmark information. It appears that navigation success in complex environments depends on the ability to identify the correct clearing around the goal location, suggesting that some participants may not be able to see the forest for the trees while others do.Author summaryImagine you wake up in the morning in the middle of a forest. It is chilly, and you have no phone or GPS connection. Leaving your backpack behind, you meander through the terrain in quest for firewood and eventually want to return to your belongings. How do you navigate? We investigated this exact question using a first-person computer game, behavioural data and computational modelling. Naturally, your strategy depends on the features that the landscape offers: deserts are different from forests. In principle, all people can use surrounding objects (landmarks) and keep track of the path they walked (path integration). We found that while some people are good navigators in all different scenarios, from open steppes to dense vegetation, others can’t see the forest for the trees.

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Combining Unity with machine vision to create low latency, flexible, and simple virtual realities

Ogawa,

Aoukar,

Leibbrandt

et al. 2024

Preprint

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In recent years, virtual reality arenas have become increasingly popular for quantifying visual behaviors. By using the actions of a constrained animal to control the visual scenery, the animal is provided the perception of moving through a simulated environment. As the animal is constrained in space, this allows detailed behavioral quantification. Additionally, as the world is generally computer-generated this allows for mechanistic quantification of visual triggers of behavior.We created a novel virtual arena combining machine vision with the gaming engine Unity. For tethered flight, we enhanced an existing multi-modal virtual reality arena, MultiMoVR (Kaushik et al., 2020) but tracked hoverfly wing movements using DeepLabCut-live (DLC-live, Kane et al., 2020). For trackball experiments, we recorded the motion of a ball that a tethered crab was walking on using FicTrac (Moore et al., 2014). In both cases, real-time tracking was interfaced with Unity to control the movement of the tethered animals’ avatars in the virtual world. We developed a user-friendly Unity Editor interface, CAVE, to simplify experimental design and data storage without the need for coding.We show that both the DLC-live-Unity and the FicTrac-Unity configurations close the feedback loop effectively with small delays, less than 50 ms. Our FicTrac-Unity integration highlighted the importance of closed-loop feedback by reducing behavioral artifacts exhibited by the crabs in open-loop scenarios. We show thatEristalis tenaxhoverflies, using the DLC-live-Unity integration, navigate towards flowers. The effectiveness of our CAVE interface is shown by implementing experimental sequencing control based on avatar proximity to virtual structures.Our results show that combining Unity with machine vision tools such as DLC-live and FicTrac provides an easy and flexible virtual reality (VR) environment that can be readily adjusted to new experiments and species. This can be implemented programmatically in Unity, or by using our new tool CAVE, which allows users to design and implement new experiments without programming in code. We provide resources for replicating experiments and our interface CAVE via GitHub, together with user manuals and instruction videos, for sharing with the wider scientific community.

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The Virtual Navigation Toolbox: Providing tools for virtual navigation experiments

Cited by 4 publications

References 46 publications

Not seeing the forest for the trees: combination of path integration and landmark cues in human virtual navigation

Not seeing the forest for the trees: combination of path integration and landmark cues in human virtual navigation

Not seeing the forest for the trees: Combination of path integration and landmark cues in human virtual navigation

Combining Unity with machine vision to create low latency, flexible, and simple virtual realities

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