Temporal acuity of vision decreases with eccentricity in virtual reality and is associated with schizotypy
Bibliographic record
Abstract
Temporal acuity reflects our ability to consciously detect a perceptual change within a short period of time, such as an asynchrony separating two visual events. In this virtual reality study, fifty participants performed a simultaneity judgment task to estimate temporal acuity across the visual field and filled the schizotypal personality questionnaire. Topographic maps were computed to visualize asynchrony discrimination skills across the visual space in two different (natural and artificial) static virtual environments. We investigate visual temporal acuity in periphery, and how estimates of temporal acuity in a psychophysical-like setting translates into a naturalistic-like scenario. First, the temporal acuity of vision decreases as the eccentricity of the targets increases, but it remains constant across meridians. Second, this deterioration of temporal coding in peripheral vision concerns non-medicated individuals self-reporting perceptual and cognitive schizotypal traits. Third, temporal acuity estimated in a traditional psychophysical visual context does not generalize to an ecologically-valid landscape scenery, such that asynchrony discrimination skills are reduced under natural vision conditions. The results suggest that distinct temporal mechanisms drive visual temporal acuity in central and peripheral vision. Furthermore, perceptual and cognitive disturbances in the neurotypical population may be linked to abnormal temporal processing in peripheral vision. Overall, these findings may pave the way toward novel investigations into the variety of time experiences across neurotypical and neurodivergent populations.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".