Inhibitory Theta Burst Stimulation Highlights the Role of Left aIPS and Right TPJ during Complementary and Imitative Human–Avatar Interactions in Cooperative and Competitive Scenarios
Bibliographic record
Abstract
Competitive and cooperative interactions are based on anticipation or synchronization with the partner's actions. Both forms of interaction may either require performing imitative or complementary movements with respect to those performed by our partner. We explored how parietal regions involved in the control of imitative behavior (temporo-parietal junction, TPJ), goal coding and visuo-motor integration (anterior intraparietal sulcus, aIPS) contribute to the execution of imitative and complementary movements during cooperative and competitive interactions. To this aim, we delivered off-line non-invasive inhibitory brain stimulation to healthy individuals' left aIPS and right TPJ before they were asked to reach and grasp an object together with a virtual partner by either performing imitative or complementary interactions. In different blocks, participants were asked to compete or cooperate with the virtual partner that varied its behavior according to cooperative or competitive contexts. Left aIPS and right TPJ inhibition impaired individuals' performance (i.e., synchrony in cooperative task and anticipation in competition) during complementary and imitative interactions, respectively, in both cooperative and competitive contexts, indicating that aIPS and TPJ inhibition affects own-other action integration and action imitation (that are different in complementary vs imitative interactions) more than action synchronization or anticipation (that are different in cooperative vs competitive contexts).
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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) | 0.002 | 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".