Not All Locations Are Created Equal: Exploring How Adults Hide and Search for Objects
Bibliographic record
Abstract
Little is known about the strategies people use to effectively hide objects from others, or to search for objects others have hidden. The present research extends a recent investigation of people's hiding and searching strategies in a simple room with 9 cache location. In the present studies, people hid and searched for three objects under more than 70 floor tiles in complex real and virtual rooms. Experiment 1 replicated several finding of Talbot et al within the more complex real and virtual environments. Specifically, people traveled further from origin and selected more dispersed locations when hiding than when searching. Experiments 2 and 3 showed that: 1) people were attracted to an area of darkness when searching and avoided locations close to a window when hiding, 2) when search attempts were limited to three choices, people searched farther from origin and dispersed their locations more when hiding than when searching, and 3) informing people that they would need to recover their hidden objects altered their hiding behavior and increased recovery accuracy. Across all experiments, consistencies in location preferences emerged, with more preference for the middle of the room during hiding and more preference for corners of the room during searching. Even though the same people participated in both the hiding and searching tasks, it appears that people use different strategies to select hiding places than to search for objects hidden by others.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".