Online HD-tRNS Over the Right Temporoparietal Junction Modulates Social Inference But Not Motor Coordination
Bibliographic record
Abstract
Social interactions are fundamental to human cognition, with the right temporoparietal junction (rTPJ) playing a key role in integrating motor coordination and social inference. While transcranial random noise stimulation (tRNS) is a promising technique for modulating cortical excitability in real time, its effect on dynamic social processes remains largely unexplored. This study applied high-definition tRNS (HD-tRNS) over the rTPJ during an interactive task to modulate motor coordination and social inference. Eighty neurotypical adults (49 female) were equally distributed across two experiments: Experiment 1, a block design with randomized active and sham stimulation blocks; or Experiment 2, a trial-by-trial design with intermixed stimulation protocols. Participants performed a coordination task with a covert virtual partner programmed to behave cooperatively or competitively. Kinematic data and self-reported attributions of humanness and cooperativeness were analyzed. The results showed that HD-tRNS over the rTPJ did not affect motor coordination or overall task performance in either experiment. However, in Experiment 1, active stimulation progressively reduced attributed humanness and cooperativeness toward the competitive virtual partner, suggesting enhanced detection of antagonistic intent. This gradual modulation of social inference was absent in Experiment 2, where frequent protocol switching likely disrupted the buildup of stimulation effects. Together, these findings highlight the rTPJ's causal role in self-other distinction, underscore the importance of stimulation protocol design in shaping social cognition, and support the exploration of targeted neuromodulation in clinical and developmental populations with atypical social cognition.
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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 | 0.000 |
| 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".