To Cue or Not to Cue: Toddlers’ Use of Beacons and Associative Cues in Object‐displacement Tasks
Bibliographic record
Abstract
Two experiments examined 24- and 30-month-olds' use of different forms of landmark information in an object-displacement task involving a car rolling down a ramp whose trajectory was occluded by a screen containing doors. A pompom attached to the car, visible through a transparent window running across the screen, served as a cue for the car's location and functioned either as a beacon cue, directly guiding search to a given location, or an associative cue, indirectly marking target location. Interestingly, one way in which the cue information was modified from a beacon to an associative cue was in terms of the structure of the search apparatus, and not necessarily the cue information itself. Consistent with previous literature, 24-month-olds' search was significantly influenced by the shift from beacon to associative cue information, whereas 30-month-olds, although affected by the shift from one to the other, were less affected by this variation. These findings suggest that the cue drives attention to specific locations in space, with search behavior being more accurate when the cue directly marks the hiding location (i.e., beacon) than when the cue indirectly marks it (i.e., associative cue).
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".