Investigation of the neural correlates of mentalizing through the Dynamic Inference Task, a new naturalistic task of social cognition
Bibliographic record
Abstract
Understanding others' intentions requires both the identification of social cues (e.g., emotional facial expressions, gaze direction) and the attribution of a mental state to another. The neural substrates of these processes have often been studied separately, and results are heterogeneous, in part attributable to the variety of paradigms used. The aim of the present study was to explore the neural regions underlying these sociocognitive processes, using a novel naturalistic task in which participants engage with human protagonists featured in videos. A total of 51 right-handed volunteers underwent functional magnetic resonance imaging while performing the Dynamic Inference Task (DIT), manipulating the degree of inference (high vs. low), the presence of emotion (emotional vs. nonemotional), and gaze direction (direct vs. averted). High nonemotional inference elicited neural activation in temporal regions encompassing the right posterior superior temporal sulcus. The presence (vs. absence) of emotion in the high-inference condition elicited a bilateral pattern of activation in internal temporal areas around the amygdala and orbitofrontal structures, as well as activation in the right dorsomedial part of the superior frontal gyrus and the left precuneus. On account of its dynamic, naturalistic approach, the DIT seems a suitable task for exploring social interactions and the way we interact with others, both in nonclinical and clinical populations.
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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 | 0.001 |
| 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".