The event-related brain potentials to uncorrelated fragment of noise
Bibliographic record
Abstract
Two correlated waveforms of broadband noise presented to the left and right ear simultaneously or with a short delay are often perceptually fused into one sound image. However, when an uncorrelated noise fragment (UCNF) is inserted in a long duration sound, listeners report hearing a transient burst of noise. The detection of the UCNF is dependent on the duration of the UCNF and the binaural delays. Here, we recorded event-related brain potentials (ERPs) to 100-ms UCFN and varied the binaural delay (0, 3, 10, and 20 ms) from trial to trial. The likelihood of detecting the UCNF decreased with increasing binaural delay. At 0 delay, the UCNF elicited negative and positive waves peaking at about 100 and 200 ms after UCNF onset (N1-P2 complex), which was present even when the stimuli were ignored. The conscious detection of the UCNF elicited an additional positive wave between 250 and 450 ms at parietal and occipital sites (P3b). The P3b latency was longer and its amplitude larger for binaural delay of 3 than 0 ms. Our results show that the detection of UCF involved both automatic and attention-dependent processes especially when a binaural delay is introduced between two source of correlated noises.
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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) | 0.001 | 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".