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Record W2889934031

Neural Correlates of Flow Experience.

2018· article· en· W2889934031 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

VenueJournal of the Association for Information Systems · 2018
Typearticle
Languageen
FieldPsychology
TopicFlow Experience in Various Fields
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceFlow (mathematics)Mathematics
DOInot available

Abstract

fetched live from OpenAlex

An optimal experience, termed “flow,” has been experienced by users who are deeply involved in human-computer interaction. The most common and traditional approaches to assess user experience are self-reported measures (e.g., using questionnaires and interviews) that are typically retrospective in nature and could be subjected to biases (e.g., social desirability and recall biases). With advancements in technology, electroencephalogram (EEG) offers an alternative approach for assessing user experience. To identify EEG correlates of flow experience in an online gaming context, we conducted a laboratory experiment to capture EEG data for the flow and resting states of video game players and carried out data analysis to compare the EEG power of frequency bands between the flow and resting states. The results suggest that the flow state is manifested mainly in the left frontal region of the brain.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.301
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