Bibliographic record
Abstract
In studies of change detection, observers’ ability to detect changes to an item in a display declines as set size increases. While the bulk of research has investigated the role of encoding and storage limits in change detection performance, less attention has been given to the role of comparison limits; that is, limitations on the rate or number of comparisons that can be made between test items and memory representations. Our study tested observers’ change detection ability for 3, 6, or 9 coloured circles presented for 1000 ms, followed by a 500ms mask and subsequent 500ms blank screen. At test, 3, 6, or 9 coloured circles again appeared in the same locations as the sample display, and observers were required to report whether a change occurred to one of the circles (50% of trials). On some trials one or two cues were presented at test, indicating which coloured circles may have changed, thus reducing the number of comparisons needed between the test display and memory representations. Our results showed that detection ability for the one cue condition (d’ = 1.8, k = 2.7) was significantly better than for the no cue condition (d’ = 1.6, k = 2.3), t(22) = 2.9, p = .008 (d’); t(22) = 3.07, p = .005 (k). We also found that observers adopted a conservative change detection bias at set size 9 when not provided a cue (c = 0.25), which was reduced when a cue was provided (c = 0.1), t(22) = 2.19, p = .04. We conclude that comparison limits do contribute to the decline in change detection performance, and that when searching for changes at large set sizes, observers favour reporting no change due to increasing comparison uncertainty. Meeting abstract presented at VSS 2013
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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 | 0.018 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".