Changing Hearing Performance and Sound Preference With Words and Expectations: Meaning Responses in Audiology
Bibliographic record
Abstract
OBJECTIVES: In this article, we explore two manipulations of "meaning response," intended to either "impart" meaning to participants through the manipulation of a few words in the test instructions or to "invite" meaning by making the participant feel involved in the setting of their preferred sound. DESIGN: In experiment 1, 59 adults with normal hearing were randomly assigned to one of the two groups. Group 1 was told "this hearing in noise test (HINT) you are about to do is really hard," while the second group was told "this HINT test is really easy." In experiment 2, 59 normal-hearing adults were randomly assigned to one of two groups. Every participant was played a highly distorted sound file and given 5 mystery sliders on a computer to move as often and as much as they wished until the sound was "best" to them. They were then told we applied their settings to a new file and they needed to rate their sound settings on this new file against either (1) another participant in the study, or (2) an expert audiologist. In fact, we played them the same sound file twice. RESULTS: In experiment 1, those who were told the test was hard performed significantly better than the easy group. In experiment 2, a significant preference was found in the group when comparing "my setting" to "another participant." No significant difference was found in the group comparing "my setting" to the "expert." CONCLUSIONS: Imparting or inviting meaning into the context of audiological outcome measurement can alter outcomes even in the absence of any additional technology or treatment. These findings lend support to a growing body of research about the many nonauditory factors including motivation, effort, and task demands that can impact performance in our clinics and laboratories.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".