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Record W2346073365 · doi:10.1145/2851581.2892356

Are We in Flow Neurophysiological Correlates of Flow States in a Collaborative Game

2016· article· en· W2346073365 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
TopicFlow Experience in Various Fields
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsNeurophysiologyElectroencephalographyFlow (mathematics)Computer scienceVideo gameState (computer science)PsychologyCognitive psychologyMultimediaNeuroscienceMathematics

Abstract

fetched live from OpenAlex

Playing video games with a partner can be fun, but are the players in flow? The study of flow, a state of intense immersion in an activity, is an important element of game research. Recently, partners in multiplayer games have been shown to impact a player's flow state. However, as flow can be difficult to assess during a game, brain activity, measured with electroencephalography, has recently been employed as a tool to evaluate flow state continuously and without bias. Thus, this paper investigates the relationship between two partners' flow states and brain activity. We carried out a preliminary empirical study in which participants played doubles in a tennis game, while EEG data and psychometric measures were acquired. Our results show an interaction between a player's neurophysiological activity and a partner's flow state. In the long run, this work opens the door for games designed to optimize positive emotional contagion.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.307
Teacher spread0.284 · 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

Citations24
Published2016
Admission routes1
Has abstractyes

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