Hide and Seek: The Ultimate Mind Game
Bibliographic record
Abstract
While a large body of research has focussed on how people visually search for objects, few studies have investigated how people hide objects when given the choice of multiple possible hiding places, an aspect particularly pertinent for security services. We therefore presented an array of items on a touch-sensitive screen and participants indicated under which item they would hide an unspecified target for a 'colleague'. Displays were four by four grids of colored bars which were either homogeneous or included a unique color or orientation item. On each trial, the colleague was identified as either a 'friend' or a 'foe', so that the target was hidden either where it was easy or hard to find. This allowed us to consider two issues. First, whether participants hiding items would be sensitive to the pop-out targets which, as shown by decades of visual search experiments, are most readily selected by people looking for items in the visual field. Second, whether the concepts from embodied cognition might influence the pattern of item choice in the absence of clear visual cues as to where to hide the target. The data suggest both were relevant. When hiding for a friend, more targets were placed behind the unique item or behind items either horizontally or vertically adjacent to this singleton. On homogeneous displays, a selection bias was evident towards items closest to the participant. Targets hidden for a foe, however, were placed away from the singleton, and, in the absence of this unique item, were positioned further from the participant. The corresponding 'find' version, in which participants look for hidden targets, is currently underway. Comparing performance will offer insights in the conceptual differences between hiding and finding, as well as providing an objective and flexible paradigm to test perspective taking and Theory of Mind. Meeting abstract presented at VSS 2012
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".