The neural basis of the high degree of stereoanomaly present in the normal population
Bibliographic record
Abstract
The human visual system calculates depth from binocular disparity. This study explored the variability in stereoacuity (minimum discriminable disparity) and in the relative sensitivity to crossed (near) and uncrossed (far) disparity in adults. The task measured thresholds for identifying the location of a depth-defined shape in a field of dots. The surface appeared to be either in front of (crossed) or behind the screen (uncrossed disparity). Performance for each direction was measured separately. We measured thresholds for 53 adults (28 males) with normal vision. Thresholds ranged from 24 to 275 arc second. This range did not display a bimodal distribution (contrary to previous reports). We then used an equivalent noise approach to determine if elevation in thresholds can be attributed to larger internal input noise or reduced processing efficiency. We measured thresholds with different levels of disparity noise (affecting the disparity of each dot) in 18 subjects. Performance was unaffected at low levels of added noise, however beyond a critical value, thresholds increased with the standard deviation of the noise. This transition point indicated when the effect of the stimulus disparity noise was equivalent to the internal noise of the visual system. Thresholds calculated at high external noise levels indicate the efficiency of the system when processing the noisy input. We found differences in processing efficiency largely explained individual differences in performance. Enhanced efficiency for one direction also explained significant within-subject differences in sensitivity between crossed and uncrossed disparities. For subjects lacking a bias in either disparity direction, there was a tendency for increased equivalent internal noise to be balanced out by increased efficiency for the same direction. Our results show it is variations in the quality of processing and not in the quality of the input into disparity-processing mechanisms that explain individual differences in stereoacuity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".