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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".