Effects of Gaze Position on Touch Localization
Bibliographic record
Abstract
Previous research has shown that the direction of gaze relative to the body affects the perceived location of touch, and has argued that these effects indicate that a gaze-centered reference frame is used for touch localization. In this dissertation I examine a discrepancy in the existing literature: why do different studies report opposite directions of effects when eye and head positions are manipulated separately? I resolve this discrepancy by showing that it is not due to whether eye or head position is manipulated (chapter 2) but is in fact due to the nature of the task (chapter 3). I also find that the effect occurs on the back of the body (chapter 4), a body part that is not normally in view and thus would be less likely to use gaze as a reference point. I test theories for why these effects occur (chapter 5), and find that results are compatible with the perceived location of a touch being attracted towards the location of gaze, at least for perceptual measures. When location was reported by pointing, an action-based measure, I find no effect of gaze direction on touch localization, suggesting that a gaze-independent reference frame is used for action. These behavioral results are complementary to recent neurophysiological and neuroimaging findings indicating that spatial locations are coded in a range of different reference frames, and indicate that gaze-related reference frames are behaviorally relevant in tactile localization.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".