No evidence for a ‘close advantage’ effect in virtual reality
Bibliographic record
Abstract
There is evidence that the human visual system prioritises information which appears to be near to an observer. For instance, orientation discrimination thresholds are more precise when targets appear closer than further away. This improvement in precision has been elicited using a variety of tasks, superimposed onto a 2D Ponzo illusion. If the effect is due to the increased likelihood of interaction with objects in near space, it should also be evident in stereoscopic, 3D stimuli. To assess this, a virtual reality headset was used to display stimuli with multiple sources of depth information. In the first experiment, participants (n=25) were asked to discriminate the relative orientation of line pairs positioned at two distances in an Oculus Quest 2. The retinal size of the stimuli was fixed to remove any modulation of performance due to resolution/visibility. Reaction time and discrimination thresholds were recorded using a forced choice paradigm. The results of these performance measures were indistinguishable for the near and far surfaces. To determine whether this was due to the complexity of the scene, a second experiment (n=20) was conducted with the same task but in a sparse virtual environment. Here, a 2D Ponzo illusion similar to that in the original publication was used, presented at a fixed distance from observers. Despite the similarity of the stimuli used here and in the 2D study, neither discrimination thresholds nor reaction times were lower for the supposedly near surface. In sum, there was no evidence of a close advantage in these virtual environments. This is puzzling given that the original effect has been replicated for a variety of stimuli and tasks. One explanation for the discrepancy, and the focus of ongoing studies, is that the presence of multiple, conflicting sources of depth information interferes with the phenomenon by disrupting the attentional focus.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".