Bringing together multimodal and multilevel approaches to study the emergence of social bonds between children and improve social AI
Why this work is in the frame
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Bibliographic record
Abstract
This protocol paper outlines an innovative multimodal and multilevel approach to studying the emergence and evolution of how children build social bonds with their peers, and its potential application to improving social artificial intelligence (AI). We detail a unique hyperscanning experimental framework utilizing functional near-infrared spectroscopy (fNIRS) to observe inter-brain synchrony in child dyads during collaborative tasks and social interactions. Our proposed longitudinal study spans middle childhood, aiming to capture the dynamic development of social connections and cognitive engagement in naturalistic settings. To do so we bring together four kinds of data: the multimodal conversational behaviors that dyads of children engage in, evidence of their state of interpersonal rapport, collaborative performance on educational tasks, and inter-brain synchrony. Preliminary pilot data provide foundational support for our approach, indicating promising directions for identifying neural patterns associated with productive social interactions. The planned research will explore the neural correlates of social bond formation, informing the creation of a virtual peer learning partner in the field of Social Neuroergonomics. This protocol promises significant contributions to understanding the neural basis of social connectivity in children, while also offering a blueprint for designing empathetic and effective social AI tools, particularly for educational contexts.
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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 | 0.000 |
| 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) | 0.000 | 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 it