Seeing Differently in Near and Far: For Detection but Not Identification of Peripheral Targets
Bibliographic record
Abstract
Do human observers process the same retinal information differently when it comes from near versus far space? Based on neurophysiological and neuropsychological evidence researchers have proposed that visual information for near space (peripersonal, within arm's reach) and far space (extrapersonal, beyond arm's reach) is mediated predominantly by dorsal and ventral visual pathways respectively. Here we provide behavioural evidence showing that neurologically normal human observers perceive visual information in near and far space differently when the visual stimuli in both viewing conditions subtended an equal visual angle and had equal luminance. Specifically, in tasks requiring participants to detect a briefly presented target appearing at one of many possible peripheral locations on a screen, under far viewing-distance conditions, visual accuracy declined more steeply as the eccentricity of the peripheral target increased compared to near viewing-distance conditions. This near-far difference in the slopes of the accuracy-eccentricity curve was not, however, observed for visual identification tasks using the same stimulus configuration. This remarkable near/far influence on perceptual behavior observed here suggests that the brain can actively modulate the information processing in different neural streams based on the target distance information, and consequently facilitate the ecological use of the retinal information.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".