Pointing Errors in Non-metric Virtual Environments

Muryy, Alexander; Glennerster, Andrew

doi:10.1007/978-3-319-96385-3_4

Cited by 10 publications

(25 citation statements)

References 40 publications

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“…Participants' success in this task contrasted markedly with the drastic failures in pointing to previouslyviewed targets that we have described before [1] despite the fact that both measures were obtained contemporaneously from the same participants in the same experimental setup. Our main finding is that participants' choices at junctions in the complex, non-physically-realisable, 'wormhole' conditions were predicted by a rewarded-choice model better than a shortest-distance model.…”

Section: Discussionmentioning

confidence: 61%

“…Finally, it is worth comparing the navigation data in the current paper to the pointing data in our previous paper collected in the same environment [1], because, unlike the navigation task, pointing is a direct way of testing whether participants can form a Euclidean representation of the scene. Muryy and…”

Section: Discussionmentioning

confidence: 99%

“…Glennerster [1] applied different models to the pointing data and concluded that a Euclidean representation could not account for the pointing responses of participants in the three-wormhole condition as successfully as a non-Euclidean one. The non-Euclidean model in that case allowed both the perceived location and orientation of the observer to vary as they moved around the maze (yellow bars in Figure 13).…”

Section: Discussionmentioning

confidence: 99%

“…It is not clear how these local representations are used by participants when they are 3 confronted by a spatial task (such as pointing) that forces them to integrate information across different local reference frames except that, as Meilinger and colleagues say [7], pointing appears effortful and performance depends on many factors such as the order in which the route was learned. Experimental evidence suggests that performance in this case relies on a representation (or a process of accessing information from a representation) that is not only distorted but also inconsistent with the idea of a single global map [1,[19][20][21].…”

Section: Introductionmentioning

confidence: 92%

“…In this paper, we build on our previous study of human pointing errors in a virtual maze [1] which, like the current study, examined the consequences of exploring a physically impossible maze. The maze had long corridors with many turns in a way that could not be realized in the real world ('wormholes'), similar to the manipulations many other researchers have used to explore spatial behaviours in non-Euclidean environments ( [9,19,37,38]).…”

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Route selection in non-Euclidean virtual environments

Muryy

Glennerster

2020

Preprint

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The way people choose routes through unfamiliar environments provides clues about the underlying representation they use. One way to test the nature of observers' representation is to manipulate the structure of the scene as they move through it and measure which aspects of performance are significantly affected and which are not. We recorded the routes that participants took in virtual mazes to reach previously-viewed targets. The mazes were either physically realizable or impossible (the latter contained 'wormholes' that altered the layout of the scene without any visible change at that moment). We found that participants could usually find the shortest route between remembered objects even in physically impossible environments, despite the gross failures in pointing that an earlier study showed are evident in the physically impossible environment (Muryy A, Glennerster A. Pointing errors in non-metric virtual environments. Lecture Notes in Computer Science, 11034. pp. 43-57). In the physically impossible conditions, the choice made at a junction was influenced to a greater extent by whether that choice had, in the past, led to the discovery of a target (compared to a shortest-distance prediction). In the physically realizable mazes, on the other hand, junction choices were determined more by the shortest distance to the target. This pattern of results is compatible with the idea of a graph-like representation of space that can include information about previous success or failure for traversing each edge and also information about the distance between nodes. Our results suggest that complexity of the maze may dictate which of these is more important in influencing navigational choices.

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Section: Discussionmentioning

confidence: 61%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 92%

mentioning

confidence: 99%

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Route selection in non-Euclidean virtual environments

Muryy

Glennerster

2020

Preprint

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A map of spatial navigation for neuroscience

Parra-Barrero

Vijayabaskaran

Seabrook

et al. 2023

Neuroscience & Biobehavioral Reviews

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The format of the cognitive map depends on the structure of the environment.

Peer,

Nadar,

Epstein

2024

Journal of Experimental Psychology: General

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Humans and animals form cognitive maps that allow them to navigate through large-scale environments.Here we address a central unresolved question about these maps: whether they exhibit similar characteristics across all environments, or-alternatively-whether different environments yield different types of maps. To investigate this question, we examined spatial learning in three virtual environments: an open courtyard with patios connected by paths (open maze), a set of rooms connected by corridors (closed maze), and a set of isolated rooms connected only by teleporters (teleport maze). All three environments shared the same underlying topological graph structure. Postlearning tests showed that participants formed representations of the three environments that varied in accuracy, format, and individual variability. The open maze was most accurately remembered, followed by the closed maze, and then the teleport maze. In the open maze, most participants developed representations that reflected the Euclidean structure of the space, whereas in the teleport maze, most participants constructed representations that aligned more closely with a mental model of an interconnected graph. In the closed maze, substantial individual variability emerged, with some participants forming Euclidean representations and others forming graph-like representations. These results indicate that an environment's features shape the quality and nature of the spatial representations formed within it, determining whether spatial knowledge takes a Euclidean or graph-like format. Consequently, experimental findings obtained in any single environment may not generalize to others with different features. Public Significance StatementThis study shows that people form different types of mental maps of different types of spaces. If the space being learned is open, people are likely to form a mental map that has information about the straight-line distances and directions between different locations. In contrast, if the space has limited visibility between its parts (e.g., a maze), people are likely to form a mental map that has information about the routes that connect different places ("cognitive graph"), and less about their exact locations. These findings illuminate how people create mental representations of different physical spaces and may have implications about how they learn abstract spaces.

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Pointing Errors in Non-metric Virtual Environments

Cited by 10 publications

References 40 publications

Route selection in non-Euclidean virtual environments

Route selection in non-Euclidean virtual environments

A map of spatial navigation for neuroscience

The format of the cognitive map depends on the structure of the environment.

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