Evaluating the Validity of Gazepoint GP3 HD in Assessing Listening Effort: A Pupillometry Study
Bibliographic record
Abstract
PURPOSE: Individuals with hearing loss typically experience greater listening effort, which is the additional recruitment of cognitive/mental resources such as attention and memory to understand speech and can be aversive and tiring. Reducing effort is an important goal of the hearing health care industry. Pupillometry is an objective and increasingly popular measure of listening effort, but gold standard measures of pupil size are expensive and unwieldy. The purpose of this study was to compare a low-cost and portable pupillometry device (Gazepoint GP3 HD) to a more traditional gold standard pupillometry tool (EyeLink 1000) for indexing listening effort via pupil size. METHOD: Twenty normal-hearing young adults (age range: 18-23 years) were recruited in this study. Participants' pupil size was measured using the Gazepoint and EyeLink pupillometry devices while listening to Hearing in Noise Test sentences in stationary speech-shaped noise at signal-to-noise ratios (SNRs) ranging from -8 to +8 dB. RESULTS: Participants' word report accuracy increased from approximately 12% to 100% when the SNRs increased from -8 to +8 dB. Peak pupil diameter decreased for both devices and was smaller with the Gazepoint device. Data quality was comparable for the two devices. CONCLUSION: Gazepoint appeared to be an effective pupillometry device that records pupil dilation across a wide range of SNRs, without interfering with the auditory task.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".