Further Insight into the Perception of Quantitative Information: Judgments of Gist in Treatment Decisions
Bibliographic record
Abstract
PURPOSE: To compare relative accuracy and relative response times (RTs) as well as impact of foreground and background colors in a treatment decision context of judging larger/smaller when the following elements are added to the graphics studied previously: 1) a number (the displayed percentage), 2) a referent scale, and 3) a number and a referent scale. METHOD: An experiment compared pie charts, vertical bars, horizontal bars, digits, systematic ovals, and random ovals. On each trial, participants saw 2 percentages (in 1 format) and were asked to choose the larger chance of survival or the smaller chance of side effects. Outcomes were errors and RT. Formats were either black and white or blue and yellow; background color was either white or blue. Participants were 216 volunteers from the community older than 50 years. RESULTS: Formats with a number produced the same relative errors and relative RT as the formats with a number and scale. Formats with only a scale, however, shifted relative performance: Errors increased with more difficult formats (pie charts and random ovals by 3%-4% v. approximately 1% with other formats), but RT decreased with easier formats (vertical bars, horizontal bars, and systematic ovals decreased 100-200 ms v. an increase of 0-300 ms with other formats). Vertical bars with scales were the fastest and most accurately processed. Neither foreground nor background color had any impact on either outcome. CONCLUSIONS: For supporting older people's judgments of relative extent, risk information is best presented using vertical bars with a scale; the format systematic ovals with a scale are among the next most easily processed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.043 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".