Assessing the consistency and sensitivity of the neural correlates of narrative stimuli using functional near-infrared spectroscopy
Bibliographic record
Abstract
Abstract Investigating how the brain responds to rich and complex narratives, such as engaging movies, has helped researchers study higher-order cognition in “real-world” scenarios. These neural correlates are particularly useful in populations where behavioral evidence of cognition alone is inadequate, such as children and certain patient populations. While this research has been primarily conducted in fMRI and EEG, whether functional near-infrared spectroscopy (fNIRS) can reliably detect these neural correlates at an individual level, which is required for effective use in these populations, has yet to be established. This study replicated widespread inter-subject correlations (ISCs) in the frontal, parietal, and temporal cortices in fNIRS in healthy participants when they watched part of the TV episode Bang! You're Dead and listened to an audio clip from the movie Taken. Conversely, these ISCs were primarily restricted to temporal cortices when participants viewed scrambled versions of those clips. To assess whether these results were reliable at the single-participant level, two follow-up analyses were conducted. First, the consistency analysis compared each participant’s ISCs against group results that excluded that individual. This approach found that 24 out of 26 participants in Bang! You’re Dead and 20/26 participants in Taken were statistically similar to the group. Second, the sensitivity analysis measured whether machine-learning algorithms could decode between intact conditions and their scrambled counterparts. This approach yielded balanced accuracy scores of 81% in Bang! You’re Dead and 79% in Taken. Overall, the neural correlates of narrative stimuli, as assessed by fNIRS, are reproducible across participants, supporting its broad application to clinical and developmental 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".