Believing Is Seeing
Bibliographic record
Abstract
These experiments show that vision can be improved by manipulating mind-sets. In Study 1, participants were primed with the mind-set that pilots have excellent vision. Vision improved for participants who experientially became pilots (by flying a realistic flight simulator) compared with control participants (who performed the same task in an ostensibly broken flight simulator). Participants in an eye-exercise condition (primed with the mind-set that improvement occurs with practice) and a motivation condition (primed with the mind-set "try and you will succeed") demonstrated visual improvement relative to the control group. In Study 2, participants were primed with the mind-set that athletes have better vision than nonathletes. Controlling for arousal, doing jumping jacks resulted in greater visual acuity than skipping (perceived to be a less athletic activity than jumping jacks). Study 3 took advantage of the mind-set primed by the traditional eye chart: Because letters get progressively smaller on successive lines, people expect that they will be able to read the first few lines only. When participants viewed a reversed chart and a shifted chart, they were able to see letters they could not see before. Thus, mind-set manipulation can counteract physiological limits imposed on vision.
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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 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".