On the generalization of tones: A detailed exploration of non-speech auditory perception stimuli
Bibliographic record
Abstract
The dynamic changes in natural sounds' temporal structures convey important event-relevant information. However, prominent researchers have previously expressed concern that non-speech auditory perception research disproportionately uses simplistic stimuli lacking the temporal variation found in natural sounds. A growing body of work now demonstrates that some conclusions and models derived from experiments using simplistic tones fail to generalize, raising important questions about the types of stimuli used to assess the auditory system. To explore the issue empirically, we conducted a novel, large-scale survey of non-speech auditory perception research from four prominent journals. A detailed analysis of 1017 experiments from 443 articles reveals that 89% of stimuli employ amplitude envelopes lacking the dynamic variations characteristic of non-speech sounds heard outside the laboratory. Given differences in task outcomes and even the underlying perceptual strategies evoked by dynamic vs. invariant amplitude envelopes, this raises important questions of broad relevance to psychologists and neuroscientists alike. This lack of exploration of a property increasingly recognized as playing a crucial role in perception suggests future research using stimuli with time-varying amplitude envelopes holds significant potential for furthering our understanding of the auditory system's basic processing capabilities.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".