Neurofunctional Differences Between the Processing of Short and Long Auditory Time Intervals
Bibliographic record
Abstract
Abstract Previous psychophysical studies have suggested that time intervals above and below 1.2 second are processed differently in the human brain. However, the neural underpinnings of this dissociation are still unclear. In the present study, we investigate whether distinct or common brain networks and dynamics support the passive perception of short (below 1.2s) and long (above 1.2s) empty time intervals. Twenty participants underwent an EEG recording during an auditory oddball paradigm with .8- and 1.6-s standard time intervals and deviants. We computed the auditory event-related potentials for each condition at the sensor and source levels. Then we performed cluster-based permutation statistics around N1 and P2 time periods, testing deviants against standards. At the sensor level, fronto-central components were elicited by deviance detection during N1 for long intervals, and during P2 for short intervals. Source reconstructions revealed that for short intervals, deviance detection was associated with activity in the left auditory cortex, bilateral supplementary motor areas and bilateral cingulate cortices. For long intervals, deviance detection was associated with activity in the left inferior parietal sulcus (IPS), bilateral cingulate cortices, and the right motor cortex. These results suggest that distinct brain dynamics and networks support the perception of short and long time intervals. Main Text
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".