From lab to life: Cognitive strategy fails to influence real-world search
Bibliographic record
Abstract
We perform numerous visual searches every day, from looking for our car keys to finding a book on a shelf. When searching meaningless stimuli (i.e. circles interrupted by gaps) on a computer display, passively allowing the target to pop into view leads to more efficient search than actively directing attention to locate the target (Smilek et al., 2006). Here we ask whether this finding extends to search in a real-world environment. Participants were instructed to use either a passive or an active strategy while searching in a cluttered office for five common objects (e.g. keys, coffee mug). The time to find the target items was measured and head and body movements were filmed during search. Search time varied systematically across participants, with some objects and locations resulting in generally easy search and others in more difficult search. Participants also differed systematically from one another, with some finding all the objects more quickly than other participants. However, response latencies failed to show a difference between passive and active cognitive strategies, in contrast to the benefit of a passive search strategy in the computer-based search task. There remain many questions concerning why the effect of cognitive strategy did not transfer from lab to life. For example, perhaps strategies are most effective when all items are present within a very small field of view, as they are in computer-based search tasks, and less effective when large head and eye movements must be made to bring a target into view. These and other possibilities will be investigated in additional studies. We will also be reporting on our analyses of the video recordings in an effort to identify behavioral features of participants who were more versus less efficient in real-world visual search.
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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 | 0.033 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".