Understanding the Sport Viewership Experience using Functional Near-Infrared Spectroscopy
Bibliographic record
Abstract
Abstract Subjective evaluation of a sport event in real time is normally assessed using self-report measures, but neural indices of evaluative processing may provide new insights. The extent of evaluative processing of a sporting event at the neural level may depend on the degree of emotional investment by the viewer, as well as the key moment of the game play being observed. Those with high ego involvement might show more activation within evaluative processing nodes, and this pattern may be most pronounced during critical moments of game play. In the current study, we examined neural activations within the medial and lateral prefrontal cortex during game play as a function of ego-involvement, using video clips featuring key moments in a European league ice hockey game. A total of 343 participants were pre-screened to identify 20 high and low ego-involved individuals. These subgroups then viewed a game segment containing 12 key play moments, while undergoing neuroimaging using fNIRS. Findings indicated more engagement of the dmPFC throughout all key moments for high ego-involved participants, but particularly during critical game moments. Overall, findings suggest that neural indices of evaluative processing might contribute meaningfully to understanding when emotionally invested individuals are most engaged in an action sequence during a sporting event.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".