Interference in the Perception of Two-Population Scatterplots
Bibliographic record
Abstract
The visual system represents correlation in much the same way that it represents simple visual quantities such as density or brightness (Rensink 2014). For example, just noticeable differences (jnds) show linear behavior closely approximating Weber’s law. But until recently, testing was done using only single populations of data. However, displaying two or more populations in a single graph is a common practice. Participants completed a two-condition correlation discrimination task, which was counterbalanced to control for order effects. The first condition closely resembled the original task from Rensink & Baldridge (2010); observers viewed two side-by-side scatterplots, each containing a single data population. They were instructed to select the plot with the higher correlation. The second condition had two data populations, each of a different color. As in the first condition, observers chose the higher correlation of the first (target) population, but were now required to ignore the second (distractor) population. Results from the first condition replicated earlier findings (Rensink, 2014). However, results from the second condition showed that when a distractor population was present, strong violations of Weber’s law appeared. Jnds were now larger overall, and deviated from the linear pattern most when the correlations of target and distractor populations were the same. This suggests that the perception of correlation in scatterplots with two populations differs from the process underlying the perception a single population. These findings also stand in contrast to assertions that color feature selection, even with distinct colors, aids tasks such as visual search and target discrimination. Meeting abstract presented at VSS 2015
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".