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Record W2065069480 · doi:10.1145/2030441.2030461

Simple motion textures for ambient affect

2011· article· en· W2065069480 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
FundersNatural Sciences and Engineering Research Council of CanadaMicrosoft
KeywordsAffect (linguistics)Motion (physics)Computer scienceAtmosphere (unit)Valence (chemistry)Human–computer interactionComputer visionCognitive psychologyPsychologyCommunicationGeographyPhysics

Abstract

fetched live from OpenAlex

The communication of emotion and the creation of affect are core to creating immersive and engaging experiences, such as those in performance, games and simulation. They often rely on atmospheric cues that influence how an environment feels. The design of such ambient visual cues for affect is an elusive topic that has been studied by painters, theatre directors, scenic designers, lighting designers, filmmakers, producers, and artists for years. Research shows that simple motions have the capacity to be both perceptually efficient and powerfully evocative, and motion textures -- patterns of ambient motion throughout the scene -- are frequently used to imbue the atmosphere with affect. To date there is little empirical evidence of what properties of motion texture are most influential in this affect. In this paper we report the results of a study of simple, abstract motion textures that show path curvature, speed and texture layout can influence affective impressions such as valence, comfort, urgency and intensity.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score1.000

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)0.0320.001

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 head0.111
GPT teacher head0.364
Teacher spread0.253 · 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

Citations20
Published2011
Admission routes2
Has abstractyes

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