Consistency of Individual Measurements Between Different Sensory Modalities: Vision vs. Audition and the Haptic Senses
Bibliographic record
Abstract
Purpose. In recent work (VSS 2013, 2015), we have made comparisons among the human senses, focusing primarily on common patterns within general threshold responding. Results show that vision (notably contrast sensitivity) shares some commonalities with audition and surprisingly, and also with some aspects within the sense of pain. Another question, particularly for clinicians, is which of these modalities yields the most consistent results across assessments. To address this question, we conducted an intensive assessment of adults tested repeatedly with primarily psychophysical measures of vision, hearing, touch and pain. Method. 15 young adults were tested repeatedly (M = 8 repetitions) with the most widely used clinical measures of spatial vision [log MAR optotype acuity, contrast sensitivity (FACT, Vector Vision, Rabin tests), and refractive error (autorefraction)], audition (complete audiology exam), touch (Von Frey fibres on the ventral and dorsal aspects of the hand), and pain (fingertip pressure algometry). Results. To make measures comparable, data across all tests and modalities were standardized to individual logarithmic scales. Coefficient of reliability analyses showed that except for autorefraction, all measures of vision showed the most consistency, followed by measures of touch, audition, and pain. Conclusions. Our data indicate that, except for optical measurements (likely due to varying accommodation), repeated assessments of vision (often weeks apart) are highly stable, and are superior in this regard to the other sensory modalities. Although this may be due in part to the precision and scaling of the existing psychophysical techniques for measuring vision, it also may be due to the fact that threshold decision making within visual tasks requires the highest degree of cortical integration among all sensory modalities. Meeting abstract presented at VSS 2018
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".