Do the eyes really have it? Ocular and visuomanual judgments of spatial extent
Bibliographic record
Abstract
Models of line bisection implicitly consider distance to be the metric by which spatial extent is processed. For example, if a 20 cm line is presented visually, the brain infers or computes its length from the visual angle subtended. An alternate hypothesis would suggest that length (D) is determined from the product of velocity (V) over time (T). We refer to this as the DVT model, which reflects an ‘indirect’ computation of spatial extent because it does not rely on a direct measurement of distance (D). To investigate the DVT model in a healthy population, we conducted a series of experiments which measured pointing and ocular judgments of spatial extent using the line bisection task. We manipulated line length, position, and the direction of ocular scanning prior to bisection. Scanning led to different biases in bisection than did free viewing suggesting that the mechanism involved in scanning introduced additional perceptual biases of spatial extent. Pointing behavior showed a robust influence from scan direction (i.e., left-to-right scanning created a bias leftward to that of right-to-left scanning), whereas the speed of scanning was inversely related to ocular fixation biases (i.e., slower speeds induced exaggerated biases). We were unable to show a strong effect of timing on bisection behavior perhaps because of the probe(s) used. Rather, to our surprise, we found that ocular behavior, presumably operating in a gaze-centered reference frame, and pointing behavior, operating in a hand-centered reference frame, produced distinct patterns of bisection. In general, pointing behavior generated systematic errors that were impervious to manipulations such as length, line position, or speed of scanning, whereas ocular behavior was far more variable and more susceptible to these manipulations. This suggests that judgments of spatial extent can be made independently for the hand and eye.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".