Visual space perception at different levels of depth description

Šikl, Radovan; Šimeček, Michal

doi:10.3758/s13414-015-0917-2

Cited by 2 publications

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“…During the estimation of distance, the operation of object boundary differentiation may have not been universally clear (or understood) by participants. To eliminate the potential intervening variables, we therefore suggest re-creating the experiment with more simplistic objects (e.g., a collection of unified gray boxes-a virtual implementation of research from Šikl and Šimeček [66]). The lens of the VR headset may have also introduced some distortion in the perception of distance.…”

Section: Impact Of Findingsmentioning

confidence: 99%

Spatial Analysis of Navigation in Virtual Geographic Environments

et al. 2019

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Human performance and navigation activity in virtual environments can be measured and assessed with the aim to draw specific conclusions about human cognition. This paper presents an original virtual geographic environment (VGE) designed and used for this purpose. The presented research is rooted in an interdisciplinary approach combining knowledge and principles from the fields of psychology, cartography, and information technologies. The VGE was embedded with user logging functionality to provide a basis from which conclusions about human cognitive processes in a VGE could be drawn. The scope of this solution is introduced, described, and discussed under a behavioral measurement framework. An exploratory research design was adopted to demonstrate the environment’s utility in proof-of-concept user testing. Twenty participants were observed in interactive, semi-interactive and non-interactive tasks, their performance and individual differences were analyzed. The behavioral measurements were supplemented by Object-Spatial Imagery and a Verbal Questionnaire to determine the participants’ cognitive styles. In this sample, significant differences in exploration strategies between men and women were detected. Differences between experienced and non-experienced users were also found in their ability to identify spatial relations in virtual scenes. Finally, areas for future research areas and development were pinpointed.

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Section: Impact Of Findingsmentioning

confidence: 99%

Spatial Analysis of Navigation in Virtual Geographic Environments

et al. 2019

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“…In addition to testing ordinal depth perception, we evaluated human performance in a ratio depth task, where participants judged the interval of depth separating pairs of surfaces. Prior work has shown that ordinal depth judgements are more precise than interval/ratio judgements (Norman & Todd, 1998;Šikl & Šimeček, 2015), yet interval/ratio estimation is still important for understanding real-world scene structure, and has received scant attention in the literature. As such, we sought to describe the accuracy of both ordinal and interval/ratio depth judgements in real-world scenes.…”

Section: Research Questionmentioning

confidence: 99%

Category and Depth Discrimination in Real-World Scenes

Anderson¹

2022

Preprint

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Visual understanding of real-world scenes is near-instantaneous. Humans can extracta wealth of information, including spatial structure, semantic category, and theidentity of embedded objects, from images viewed for fewer than 100 msecs. Visualprocessing has capacity limits, and, as a result, the computational processes thatunderlie this behaviour must be highly efficient. Computational theories of realworldscene perception model early image processing in various ways. In Chapter 1,I review these theories, and in Chapter 2, I review the role of depth cues in rapidvisual processing. This discussion reveals three problems: (i) Tests of the agreementbetween model predictions and human responses may be biased by the arbitrarychoice of category system, (ii) Current models posit that scene semantics isestimated from spatial structure properties, but empirical support for this position isinconsistent, and (iii) The time-course of depth estimation in real-world scenes ispoorly understood. To address these problems, three empirical papers are presentedin Chapters 3, 4, and 5. In Chapter 3, I propose and validate a novel clusteringalgorithm that can be applied to image databases to derive category systems forvisual experiments. In Chapters 3 and 4, I examine the relationship between spatialstructure and semantic information, and find little support for the position that spatialstructure properties inform semantic discrimination. In Chapters 4 and 5, Icharacterize the time-course of depth processing for images presented for <267msecs, and conclude that binocular disparity and elevation cues contribute to realworldperception shortly after image onset (<50 msecs). These findings are discussedtogether in Chapter 6. This thesis contributes to the evaluation of modern models ofreal-world scene perception, and helps to characterize how visual understandingunfolds over time.

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Visual space perception at different levels of depth description

Cited by 2 publications

References 50 publications

Spatial Analysis of Navigation in Virtual Geographic Environments

Spatial Analysis of Navigation in Virtual Geographic Environments

Category and Depth Discrimination in Real-World Scenes

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