Object affordance modulates the near space advantage in 2D imagery
Bibliographic record
Abstract
Depth perception is a critical aspect of the human visual system which supports localization and interactions with objects in space. Over the years, psychophysical and neuropsychological research has showed that sensory processing is modulated by the location of stimuli relative to the body. In particular, objects closer to the observer benefit from enhanced processing, appropriately termed the “close advantage” effect. Previously, evidence of the close advantage was limited to multisensory studies, with little focus on a purely visual component. However, recent investigations show that depth information modulates visuo-perceptual resolution, even when other visual attributes (such as retinal size) are held constant. While it is clear that this phenomenon is robust and generalizable across stimuli and tasks, the underlying mechanism is poorly understood. One hypothesis is that objects that are perceived as closer to the observer are more behaviorally relevant (e.g., one can grasp a hammer only if it is within arm’s reach), and therefore benefit from enhanced processing. To evaluate this proposal, we assessed the close advantage effect for shapes that either offer a potential for action (affordable – e.g., elongated) or do not potentiate action (non-affordable – e.g., non-elongated). The stimuli were presented on either the ‘close’ or ‘far’ regions of a Ponzo Illusion arrangement. A method of constant stimuli was used to measure precision and reaction times were recorded. Our results replicate previous findings by demonstrating an enhanced discrimination for both affordable and non-affordable shapes in the “close” space. Critically, and consistent with our hypothesis, affordable shapes elicited a stronger “close advantage” than non-affordable shapes. Together, these results provide novel evidence for the role of affordance in mediating the close advantage effect.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".