Bibliographic record
Abstract
We examined the characteristics of endogenous exploratory behaviors in a generalized search task in which guidance signals (e.g., landmarks, semantics, visual saliency, layout) were limited or precluded. Individuals looked for the highest valued cell in an array and were scored on the quality of the best value they could find. Exploration was guided only by the cells that had been previously examined, and the value of this guidance was manipulated by adjusting spatial autocorrelation to produce relatively smooth and rough landscapes-that is, arrays in which nearby cells had unrelated values (low correlation = rough) or similar values (high correlation = smooth). For search in increasingly rough as compared with smooth arrays, we found reduced performance despite increased sampling and increased time spent searching after revelation of a searcher's best cell. Spatially, sampling strategies tended toward more excursive, branching, and space-filling patterns as correlation decreased. Using a novel generalized-recurrence analysis, we report that these patterns reflect an increase in systematic search paths, characterized by regularized sweeps with localized infilling. These tendencies were likewise enhanced for high-performance as compared with low-performance participants. The results suggest a trade-off between guidance (in smooth arrays) and systematicity (in rough arrays), and they provide insight into the particular strategic approaches adopted by searchers when exogenous guiding information is minimized.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".