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Record W4281654248 · doi:10.21203/rs.3.rs-1638368/v1

Continuous measures of audience immersion: validating behavioural and physiological measures against self-report

2022· preprint· en· W4281654248 on OpenAlexaff
Hugo Hammond, Michael Armstrong, Graham A. Thomas, Iain D. Gilchrist

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsBC Research (Canada)
FundersEngineering and Physical Sciences Research Council
KeywordsImmersion (mathematics)Skin conductanceNarrativePsychologyHeart rateTask (project management)CLIPSSocial psychologyComputer scienceArtMedicineLiteratureMathematicsBlood pressureArtificial intelligenceInternal medicineEngineering

Abstract

fetched live from OpenAlex

Abstract When an audience member becomes immersed, attention is shifted towards the media and story, and this is accompanied by the allocation of mental resources to represent events and characters. Here we investigate whether it is possible to measure immersion using continuous behavioural or physiological measures. Using television and film clips, we validated dual-task reaction times, heart rate, and skin conductance against self-reported narrative engagement. We find that reaction times in the dual-task were strongly positively correlated with immersion: slower reaction times were indicative of greater immersion, particularly emotional engagement. Synchrony in heart rate between participants was associated with attentional and emotional engagement with the story, although we found no such relationship with skin conductance. These results establish both dual-task reaction times and heart rate as candidate measures for the real-time, continuous, assessment of audience immersion.

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.

How this classification was reachedexpand

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)0.004
metaresearch head score (Gemma)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.360
GPT teacher head0.416
Teacher spread0.057 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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