Effect of stimulus type and pitch salience on pitch-sequence processing
Bibliographic record
Abstract
Using a same-different discrimination task, it has been shown that discrimination performance for sequences of complex tones varying just detectably in pitch is less dependent on sequence length (1, 2, or 4 elements) when the tones contain resolved harmonics than when they do not [Cousineau, Demany, and Pessnitzer (2009). J. Acoust. Soc. Am. 126, 3179-3187]. This effect had been attributed to the activation of automatic frequency-shift detectors (FSDs) by the shifts in resolved harmonics. The present study provides evidence against this hypothesis by showing that the sequence-processing advantage found for complex tones with resolved harmonics is not found for pure tones or other sounds supposed to activate FSDs (narrow bands of noise and wide-band noises eliciting pitch sensations due to interaural phase shifts). The present results also indicate that for pitch sequences, processing performance is largely unrelated to pitch salience per se: for a fixed level of discriminability between sequence elements, sequences of elements with salient pitches are not necessarily better processed than sequences of elements with less salient pitches. An ideal-observer model for the same-different binary-sequence discrimination task is also developed in the present study. The model allows the computation of d' for this task using numerical methods.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".