Distinct fNIRS Inter-Brain Coupling Patterns for Cooperation versus Competition in a Tennis Game
Bibliographic record
Abstract
Cooperation and competition represent two fundamental modes of social interaction, yet their underlying neural mechanisms remain incompletely understood. Functional near-infrared spectroscopy hyperscanning, enabling simultaneous measurement of hemodynamic activity across individuals, offers unique insights into the neural substrates underlying naturalistic interactions. Using this technique, we investigated cross-channel inter-brain coupling (IBC) between interacting individuals during cooperative and competitive play in a motion-sensing tennis game. Compared to resting-state and solo gameplay with observation, both conditions elicit significantly enhanced not only IBC between the dyads' sensorimotor regions, but also cross-regional coupling between one participant's sensorimotor cortex and the other's dorsolateral prefrontal cortex (DLPFC) as well as temporoparietal junction, suggesting the contribution of high-order cognition networks to the observed IBC. Notably, competitive interactions produce stronger cross-regional IBC between DLPFC and sensorimotor regions than cooperative ones, implying an intensified demand for cognitive control during competition. Conversely, cooperation enhances neural coupling between team-mates within their prefrontal cortices, which could reflect shared goal representations. Behavioural cooperation performance is negatively correlated with the DLPFC-sensorimotor IBC. These spatially distinct patterns of condition-dependent neural coupling advance our understanding of the neural underpinnings of naturalistic social interactions.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".