Human electrophysiological examination of the buildup of the precedence effect (Clifton effect)
Bibliographic record
Abstract
The relationship between behavioral measures of buildup of precedence effect [or Clifton effect (CE)] and electrophysiological responses using an event-related potential (ERP) paradigm was examined in 14 young adults with normal hearing. The CE was elicited using a binaural paired-click (left and right speaker delay) in sound field. This study aimed to determine whether ERP measures were related to the perception of buildup. Subjects participated in two sessions: (i) Session ♯1 psychoacoustic measures of the degree of buildup as function of click delay (left and right side). (ii) Session ♯2 recorded cortical ERPs using click delays (varying from 6 to 8 ms) that would maximize the degree of buildup in each subject. Stimuli consisted of repeated trains containing 5 paired-clicks (left speaker leading) with a noise burst separating consecutive trains. Results: Significant N1 amplitude hemispheric asymmetries were observed related to click position. N1 responses decreased (43%) as a function click position in the train at electrode sites contralateral to the leading stimulus and, conversely, increased (30%) at electrode sites ipsilateral to the lead. These results suggest that differential refractoriness of the N1 response may lead to the lateralized perception of buildup to the precedence effect. [Work supported by NSERC-Canada.]
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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) | 0.002 | 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".