Conjoining Three Auditory Features: An Event-Related Brain Potential Study
Bibliographic record
Abstract
The mechanisms of auditory feature processing and conjunction were examined with event-related brain potential (ERP) recording in a task in which participants responded to target tones defined by the combination of location, frequency, and duration features amid distractor tones varying randomly along all feature dimensions. Attention effects were isolated as negative difference (Nd) waves by subtracting ERPs to tones with no target features from ERPs to tones with one, two, or three target features. Nd waves were seen to all tones sharing a single feature with the target, including tones sharing only target duration. Nd waves associated with the analysis of frequency and location features began at latencies of 60 msec, whereas Nd-Duration waves began at 120 msec. Nd waves to tones with single target features continued until 400+ msec, suggesting that once begun, the analysis of tone features continued exhaustively to conclusion. Nd-Frequency and Nd-Human Location waves had distinct scalp distributions, consistent with generation in different auditory cortical areas. Three stages of feature processing were identified: (1) Parallel feature processing (60-140 msec): Nd waves combined linearly, such that Nd-wave amplitudes following tones with two or three target features were equal to the sum of the Nd waves elicited by tones with only one target feature. (2) Conjunction-specific (CS) processing (140-220 msec): Nd amplitudes were enhanced following tones with any pair of attended features. (3) Target-specific (TS) processing (220-300 msec): Nd amplitudes were specifically enhanced to target tones with all three features. These results are consistent with a facilitatory interactive feature analysis (FIFA) model in which feature conjunction is associated with the amplified processing of individual stimulus features. Activation of N-methyl-D-aspartate (NMDA) receptors is proposed to underlie the FIFA process.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".