Evidence for intact memory‐guided attention in school‐aged children
Bibliographic record
Abstract
Visual scenes contain many statistical regularities such as the likely identity and location of objects that are present; with experience, such regularities can be encoded and can ultimately facilitate the deployment of spatial attention to important locations. Memory-guided attention has been extensively examined in adults with the 'contextual cueing' paradigm and has been linked to specific neural substrates - a medial temporal lobe (MTL)-frontoparietal network. However, it currently remains unknown when this ability comes 'online' during development. Thus, we examined the performance of school-aged children on an age-appropriate version of the contextual cueing paradigm. Children searched for a target fish among distractor fish in new displays and in 'old' displays on a touchscreen computer. Old displays repeated across blocks of trials and thus provided an opportunity for prior experience with the invariant configuration of the stimuli to guide attentional deployment. We found that over time children searched old displays significantly faster than new displays, thus revealing intact memory-guided attention and presumed function of an MTL-frontoparietal network in 5- to 9-year-olds. More generally, our findings suggest that children are remarkably sensitive to the inherent structure of their visual environment and this enables attentional deployment to become more efficient with experience.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".