Abolition of Search Asymmetry
Bibliographic record
Abstract
Although visual search has been studied for years, some aspects remain poorly understood. For example, Westerners show a search asymmetry for line length: search for long lines among short is faster than for short among long. In contrast, Asians given the same task show no asymmetry (Ueda et al., 2017). And asymmetry for long-term Asian immigrants in a Western country depends on the language in which task instructions are given (Cramer et al., 2016). To examine how this asymmetry depends on preceding task, 16 Westerners were given a pre-task before visual search. They were shown a sequence of 14 images of real-world scenes, and asked to count the number of animals in the series. In the subsequent search for line length (5 blocks of 30 trials per block for each target type), average target-present slope was 42.5 ms/item for long targets and 53.9 ms/item for short (t-test: p = 0.024); average ratio of short- to long-target slopes was 1.39 (z-test: p = 0.002). Search was therefore asymmetric, consistent with that of Westerners tested on similar stimuli (e.g., Cramer et al., 2016). Another 16 Westerners were then shown exactly the same sequence of scenes, but with a different pre-task: rate (on a scale of 1-7) how much they liked each one. Average target-present slope was now 47.7 ms/item for long targets and 44.0 ms/item for short (t-test: p = 0.45); average slope ratio was 0.99 (z-test: p = 0.45). Search asymmetry was therefore abolished, with behavior similar to that of Asians tested on the same stimuli (Ueda et al., 2017). These results suggest that attention in visual search has at least two modes, with selection of mode affected by the preceding task. Different deployment of these modes may also explain some of the differences found in observers from different cultures. Meeting abstract presented at VSS 2018
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".