MétaCan
Menu
Back to cohort
Record W4406276723 · doi:10.1109/ismar62088.2024.00043

Investigating the Effects of Physical Landmarks on Spatial Memory for Information Visualisation in Augmented Reality

2024· article· en· W4406276723 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAugmented realityComputer scienceVisualizationInformation visualizationHuman–computer interactionData visualizationComputer graphics (images)Artificial intelligence

Abstract

fetched live from OpenAlex

Augmented Reality (AR) is touted to be beneficial in supporting situated information display, allowing virtual information panels to be overlaid on real-world scenes. People must then use their spatial memory to navigate among these virtual panels effectively. While spatial memory has been studied in physical environments (wall displays) and virtual reality environments, there has been little research on how physical surroundings might affect memorisation of virtual content in a mixed environment like AR. Therefore, we provide the first AR study of spatial memory, comparing two different room settings with two different situated layouts of virtual targets on an abstract spatial memory task. We find that participants recall spatial patterns with greater accuracy and higher subjective ratings in a room with furniture compared to an empty room. Our findings lead to important design implications for mixed-reality user interfaces, particularly in information-rich applications like situated analytics and small-multiples information visualisation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.301
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations7
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicAugmented Reality ApplicationsFrench-language works237,207