The neural basis of sharing information through goal-directed conversation: A hyperscanning functional magnetic resonance imaging study
Bibliographic record
Abstract
The human brain maintains internal models of physical and social environments, representing an individual's "subjectivity". Through conversation, two or more individuals share their models and modify them based on the exchange, a process that represents and is referred to as "intersubjectivity." To investigate the neural substrates of this dynamic process, hyperscanning functional magnetic resonance imaging was conducted to test the hypothesis that Inter-Brain Synchronization (IBS) in the default mode network (DMN) is involved in representing intersubjectivity. Twenty-four Japanese-speaking participant pairs played maze games over a two-day period. Each participant pair received a different maze, i.e., a maze with a different pathway to its goal. Although pairs shared a maze, each participant in a pair had only partial knowledge of the maze layout and what they knew about the layout differed. Taking turns, participants moved their pieces to their goals. Since each had only partial information about the pathway, effective communication between partners was important. Behavioral data showed participants' conversation about potential maze piece moves significantly increased as the game proceeded, implying that the exchange for such information was critical. Correspondingly, the DMN increased task-related activation, including the dorsomedial prefrontal cortex (dmPFC) and the bilateral temporoparietal junction (TPJ), extending through the superior temporal sulcus to the temporal pole and the right middle frontal gyrus. Within these areas, the dmPFC and the right TPJ showed task- and partner-specific IBS throughout all games. Thus, the DMN is likely required for representing intersubjectivity, based on internal models shared through real-time conversations.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".