Finding landmarks - An investigation of viewing behavior during spatial navigation in VR using a graph-theoretical analysis approach

Walter, Jasmin L.; Essmann, Lucas; König, Sabine U.; König, Peter

doi:10.1371/journal.pcbi.1009485

Cited by 13 publications

(9 citation statements)

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“…Because we do not know what features of the visual surroundings are remembered by participants and also cannot differentiate between different variants of cue use in our experiment, we consider the trees in our experimental design as only “objects” and not “landmarks”, taking into account [45] who highlights that the term “landmark” is often used in an ambiguous manner. In order to avoid an ambiguous definition of a landmark, recent research established objective identification of salient buildings in a VR city, using a graph-theoretical model approach based on a participant’s view fixations and saccades in corresponding eye tracking data [14]. However, how exactly a certain navigator uses such categorised landmarks in any specific environment still remains unclear.…”

Section: Discussionmentioning

confidence: 99%

“…The use of landmarks most often refers to the usage of external environmental October 24, 2023 2/45 features to identify and locate places of interest in a navigator's surroundings [7,9,14].…”

Section: Introductionmentioning

confidence: 99%

“…Both are used by non-human and human navigators alike (Etienne and Jeffery, 2004). The use of landmarks most often refers to the usage of external environmental features to identify and locate places of interest in a navigator's surroundings (Zhao and Warren, 2015b;Jetzschke et al, 2017;Walter et al, 2022). Landmarks can be used for guidance towards a goal ("turn left at the gas station") as well as for self-localisation ("I see the Eiffel Tower, so I must be in Paris").…”

Section: Introductionmentioning

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. 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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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. 2023

Preprint

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“…Afterward, invalid periods smaller than 250 ms were interpolated (median amount of interpolated data across subjects = 2.823%; IQR = 1.881% - 3.914%). This rather long period was chosen in accordance with Walter et al (2022). There, the interpolation period was justified with the analysis that subjects were unlikely to fixate on one object, move their fixation to another object, and then back again within the period of 250 ms (Walter et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

Combining EEG and Eye-Tracking in Virtual Reality - Obtaining Fixation-Onset ERPs and ERSPs

Nolte,

Vidal De Palol,

Keshava

et al. 2024

Preprint

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Extensive research conducted in controlled laboratory settings has prompted an inquiry into how results can be generalized to real-world situations influenced by the subjects' actions. Virtual reality lends itself ideally to investigating complex situations but requires accurate classification of eye movements, especially when combining it with time-sensitive data such as EEG. We recorded eye-tracking data in virtual reality and classified it into gazes and saccades using a velocity-based classification algorithm, and we cut the continuous data into smaller segments to deal with varying noise levels, as introduced in the REMoDNav algorithm. Furthermore, we corrected for participants' translational movement in virtual reality. Various measures, including visual inspection, event durations, and the velocity and dispersion distributions before and after gaze onset, indicate that we can accurately classify the continuous, free-exploration data. Combining the classified eye-tracking with the EEG data, we generated fixation-onset ERPs and ERSPs, providing further evidence for the quality of the eye movement classification and timing of the onset of events. Finally, investigating the correlation between single trials and the average ERP and ERSP identified that fixation-onset ERSPs are less time-sensitive, require fewer repetitions of the same behavior, and are potentially better suited to study EEG signatures in naturalistic settings. We modified, designed, and tested an algorithm that allows the combination of EEG and eye-tracking data recorded in virtual reality.

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“…Wang et al, 2018), which may suggest evidence for an accumulation process for selecting and assigning attributes to spatial memories. For example, a building with unique colors that suggest value as a landmark may be weighted more by the navigator than other more drab buildings on that street so that it may act as an anchor for memory and orientation (Janzen & Van Turennout, 2004;Walter et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Frontal-midline theta and posterior alpha oscillations index early processing of spatial representations during active navigation

Liang

McAvan

et al. 2023

Preprint

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Previous research has demonstrated that humans combine multiple sources of spatial information, such as allothetic and idiothetic cues, while navigating through an environment. However, it is unclear whether this involves comparing multiple representations from multiple sources during encoding (parallel hypothesis) or primarily accumulating idiothetic information until the end of the navigation to integrate with allothetic information (serial hypothesis). We tested these two hypotheses in an active navigation task using mobile scalp EEG recordings. Participants walked through an immersive virtual hallway with or without conflicts between allothetic and idiothetic cues and pointed toward the starting position of the hallway. By analyzing the scalp oscillatory activities during the navigation phase, we found that path segments including memory anchors -- such as path intersections -- were more strongly associated with the pointing error, regardless of when they appeared during encoding. This indicates that the integration of spatial information of a walked path likely begins in the early stages of navigation rather in late stages alone, supporting the parallel hypothesis. Furthermore, theta oscillations in frontal-midline regions during active navigation were related to memory of the path rather than only movement through the path, supporting a mnemonic role of theta oscillations.

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Finding landmarks - An investigation of viewing behavior during spatial navigation in VR using a graph-theoretical analysis approach

Cited by 13 publications

References 58 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

Combining EEG and Eye-Tracking in Virtual Reality - Obtaining Fixation-Onset ERPs and ERSPs

Frontal-midline theta and posterior alpha oscillations index early processing of spatial representations during active navigation

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