The effect of blindfolding on sound localization
Bibliographic record
Abstract
While vision has been shown to play an important role in calibrating the spatial representations of other senses (Knudsen and Knudsen, 1990; Withington et al., 1994), numerous recent reports have suggested that individuals deprived of vision are actually able to develop heightened auditory spatial abilities (Lessard et al., 1998; Voss et al., 2004). However, most such cases have compared the blind to blindfolded sighted individuals, a procedure that might introduce a strong performance bias in that blind individuals, who have had their whole lives to adapt to this condition, whereas sighted individuals might be put at a severe disadvantage when suddenly being asked to localize sounds without visual input. To address this unknown, we compared the sound localization ability of eight sighted individuals with and without blindfold using a 3D sound presentation device in a hemianechoic chamber. We used a 2 × 2 × 2 factorial design, where we compared two vision conditions (blindfold vs. non-blindfold), two sound planes (horizontal vs. vertical) and two pointing methods (finger vs. head). A 2 × 2 × 2 repeated measures ANOVA revealed a significant effect of vision (no-blindfold > blindfold; p vertical; p < 0 . 001 ). Moreover, a vision × plane × pointing triple interaction was also significant ( p = 0 . 004 ), and was primarily driven by a significantly poorer performance of head pointing in the horizontal plane when blindfolded, compared to the non-blindfolded condition. This result argues strongly against the use of head pointing methodologies with blindfolded individuals, particularly in the horizontal plane, as it likely introduces a robust bias when comparing them to blind individuals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".