Screening For Dichotic Acoustic Context And Headphones In Online Crowdsourced Hearing Studies
Bibliographic record
Abstract
Experimental evidence suggests that crowdsourced online experiments, where suitable, may produce better data than in-lab studies. The absence of a reliable screening method for headphones and dichotic auditory context (perfect separation of the stereo channels) is one of the main reasons why online crowdsourcing is rarely possible for auditory studies. As the evidence demonstrates, the responses to the questions “Are you wearing headphones” and “Are the headphones stereo channels separated” are providing unreliable and even conflicting results. Here we show that the interference beating phenomenon can be used as a screening method for dichotic context with satisfactory accuracy. We collected data through an in-lab experiment to test the method’s performance against the reference (the truth), avoiding the uncontrolled biases of the online experiments, achieving Cohen’s Kappa of 0.79 (95% CI, [0.52, 1.06], p<0.001), yielding “Substantial agreement” when calculated over the whole sample, and Cohen’s Kappa of 1 (95% CI, [1, 1], p=0.001), yielding “Almost perfect agreement” when calculated only over the true dichotic cases. Also, we collected data by using the only other method found in the literature that attempts screening for headphones usage, to compare both methods over the same participants and auditory contexts. The usage of the new method is tested in a crowdsourced setting, involving over 2000 online participants. The in-lab and online results suggest that the method introduced in this study is suitable, and therefore, an enabler of auditory online crowdsourced studies.
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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 | 0.051 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".