Bibliographic record
Abstract
It has been shown (Rensink & Baldridge, 2010) that the perception of Pearson correlation in scatterplots can be described by two simple laws: the just noticeable difference (jnd) follows a Weber-like law, and the subjective estimate a Fechner-like law. This suggests that correlation is (or is associated with) a perceptually simple property, even though it is conveyed by a relatively complex medium. To investigate further, the timecourse of this process was examined. Scatterplots of extent 5.0 deg were used, each containing 100 randomly-distributed points. Means were set to 0.5 of this range, and standard deviations to 0.2. An initial scatterplot was presented for 100, 400, or 1600 ms, followed by a mask of 200 ms. A second scatterplot was then presented, and remained on until the observer responded. The observer was asked to select which scatterplot was more highly correlated. Jnds were measured for base correlations of 0.3, 0.6, and 0.9. 20 observers were tested. Results show that jnd remained Weber-like for all timescales examined. Performance for the 400 ms and 1600 ms conditions were virtually identical. Performance for the 100 ms condition was much the same as for the other two, with only a slight decrement found, indicating that the process was largely complete by that time. This pattern held for both gaussian and uniform distributions, with nearly identical performance found for both. These results suggest that the rapid estimation of statistical properties (either directly or indirectly) can occur not only for first-order quantities (Ariely, 2001) but for second-order quantities as well.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".