The Contribution of Left and Right Visual Fields to Perceived Orientation
Bibliographic record
Abstract
Vision plays an important role in our ability to orient. A major factor in self orientation is the perceived direction of up. Previous studies involving perceptual upright (PU: the orientation at which objects are best recognized) have demonstrated a consistent leftward bias in a character recognition task. We hypothesized that this leftward bias may be due to unequal visual orientation cue ratings across the visual field. We assessed the perceptual upright using OCHART (Oriented CHAracter Recognition Task) which measures the orientation at which a letter probe is “perceptually upright” (Dyde et al., 2006 Exp Brain Res. 173: 612). The probe was presented in the centre of a background masked down to a circle subtending 35° and divided into two with a vertical black line separating the two halves. Images on each half were photographs of an outdoor scene upright, tilted clockwise or counter-clockwise by 112.5°, or a grey field of equal average luminance. The effects of the two sides were additive in determining the PU with no obvious dominance of one side or the other. However, when the oriented scene was presented only on one side (with the other side grey) the strongest effect on the probe was found when visual cues were tilted in the same direction as the visual field on which they were presented. Our data suggest that orientation cues presented on the left and right sides of space are weighted approximately equally by the brain. The brain integrates the conflicting visual cues on either side of the visual field when determining perceptual upright.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".