MétaCan
Menu
Back to cohort
Record W1988510761 · doi:10.1145/1358628.1358728

Emotional response as a measure of human performance

2008· article· en· W1988510761 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
FieldPsychology
TopicColor perception and design
Canadian institutionsUniversity of Toronto
FundersInstitut national de recherche en informatique et en automatique (INRIA)
KeywordsComputer scienceStoryboardValence (chemistry)Human–computer interactionCognitionAffective computingKey (lock)UsabilityArousalMeasure (data warehouse)MultimediaPsychology

Abstract

fetched live from OpenAlex

Emotional reactions are a key part of the user experience, and are particularly of interest to the design of systems that consider user emotions. This dissertation studies methods of measuring emotional responses through a novel two-dimensional tool, based on the model of valence and arousal. A study on reactions to storyboard and video prototypes motivated the need for continuous, quantitative, affective self-report. A pilot study with a slider revealed significant differences between the experiences of several video conferencing techniques. Next steps include prototyping two-dimensional affective self-report capture devices, and an experiment to compare relative ease of use and cognitive complexity of different methods of emotional measurement.

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: Empirical
Teacher disagreement score0.827
Threshold uncertainty score0.999

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)0.0470.002

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 head0.096
GPT teacher head0.352
Teacher spread0.256 · 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

Citations17
Published2008
Admission routes1
Has abstractyes

Explore more

Same topicColor perception and designFrench-language works237,207