Where are you? The effect of uncertainty and its visual representation on location judgments in GPS-like displays.
Bibliographic record
Abstract
Two experiments revealed how nonexperts interpret visualizations of positional uncertainty on GPS-like displays and how the visual representation of uncertainty affects their judgments. Participants were shown maps with representations of their current location; locational uncertainty was visualized as either a circle (confidence interval) or a faded glyph (indicating the probability density function directly). When shown a single circle or faded glyph, participants assumed they were located at the center of the uncertain region. In a task that required combining 2 uncertain estimates of their location, the most common strategy-integration-was to take both estimates into account, with more weight given to the more certain estimate. Participants' strategies were not affected by how uncertainty was visualized, but visualization affected the consistency of responses, both within individuals and in relation to models of individual's preferred strategies. The results indicate that nonexperts have an intuitive understanding of uncertainty. Rather than arguing for a particular method of visualizing uncertainty, the data suggest that the best visualization method is task dependent. (PsycINFO Database Record
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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 | 0.003 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".