Impact of myopia on visual attention and the potential link with cultural differences in visual perception
Bibliographic record
Abstract
Easterners and Westerners have been shown to differ in many visual perceptual tasks, and evidence supports a broader allocation of attention among Easterners than Westerners. For instance, Easterners have a larger global advantage than Westerners in a Navon Task (McKone et al., 2010); they fixate less the eyes and mouth, and more the centre of the face during its processing (Blais et al., 2008); they also tend to process faces in lower spatial frequencies (Tardif et al., in press). Although it has been proposed that perceptual differences emerge from the cultural values (individualistic vs. collectivistic) assumed by each culture (Nisbett et al., 2001), a recent study didn't succeed at finding links between those cultural values and the eye fixation pattern during face processing (Ramon et al., VSS2016). In this study we explored another lower-level hypothesis that could explain the perceptual differences observed between Easterners and Westerners: the impact of myopia on visual attention. Recent evidence suggests that myopes are less affected by crowding in peripheral vision (Caroll et al., VSS2016). Since myopia prevalence is higher among Chinese compared to Caucasians individuals (Lam et al., 2012), this could potentially explain the visual perception differences observed between Easterners and Westerners. The ability to detect global versus local target letters was measured with myopes (N=12) and emmetropes (N=17) using Navon's paradigm. No global/local bias differences were found between the groups [t(28)=1.08, p=0.29]. These results do not support the hypothesis that the difference in the prevalence of myopia between both groups underlies the higher global advantage observed in Easterners. More studies will allow us to verify if myopia can explain the cultural differences observed in fixation patterns and spatial frequency utilization during face perception. Meeting abstract presented at VSS 2017
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".