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Record W2025153817 · doi:10.1145/2628257.2628264

Evaluating affective features of 3D motionscapes

2014· article· en· W2025153817 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicColor perception and design
Canadian institutionsSimon Fraser University
FundersNetworks of Centres of Excellence of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsAffect (linguistics)Motion (physics)Computer scienceHuman–computer interactionCraftPresentation (obstetrics)Cognitive psychologyArtificial intelligencePsychologyVisual artsCommunicationArt

Abstract

fetched live from OpenAlex

Abstract motion textures are widely applied in visual design and immersive environments such as games to imbue the environment or presentation with affect. While visual designers and artists carefully manipulate visual elements such as colour, form and motion to evoke affect, understanding what aspects of motion contribute to this still remains a matter of designer craft rather than validated principle. We report an empirical study of how simple features of motion in 3D textures, or motionscapes, contribute to the elicitation of affect. 12 university students were recruited to evaluate a series of 3D motionscapes. Results showed basic motion properties including speed, direction, path curvature and shape had significant influence on affective impressions such as valence, comfort, urgency and intensity, suggesting further directions for applications and explorations in this design space.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.984

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)0.0170.000

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 head0.065
GPT teacher head0.412
Teacher spread0.347 · 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

Citations18
Published2014
Admission routes2
Has abstractyes

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