Perceived Size Change Induced by Nonvisual Signals in Darkness: The Relative Contribution of Vergence and Proprioception
Bibliographic record
Abstract
Most of the time, the human visual system computes perceived size by scaling the size of an object on the retina with its perceived distance. There are instances, however, in which size-distance scaling is not based on visual inputs but on extraretinal cues. In the Taylor illusion, the perceived afterimage that is projected on an observer's hand will change in size depending on how far the limb is positioned from the eyes-even in complete darkness. In the dark, distance cues might derive from hand position signals either by an efference copy of the motor command to the moving hand or by proprioceptive input. Alternatively, there have been reports that vergence signals from the eyes might also be important. We performed a series of behavioral and eye-tracking experiments to tease apart how these different sources of distance information contribute to the Taylor illusion. We demonstrate that, with no visual information, perceived size changes mainly as a function of the vergence angle of the eyes, underscoring its importance in size-distance scaling. Interestingly, the strength of this relationship decreased when a mismatch between vergence and proprioception was introduced, indicating that proprioceptive feedback from the arm also affected size perception. By using afterimages, we provide strong evidence that the human visual system can benefit from sensory signals that originate from the hand when visual information about distance is unavailable.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".