Proactive control in early and middle childhood: An ERP study
Bibliographic record
Abstract
Children experience important cognitive control improvements in the transition to school. This study examined 4-5-year-olds' (n=17) and 7-8-year-olds' (n=22) ability to proactively deploy cognitive control. Children performed a cued task-switching paradigm presenting them with a cue indicating which attribute, color or shape, they should use to sort the upcoming stimulus. Following both cue and stimulus, we analyzed two event-related potentials: the P2 and P3, positive peaks reflecting sensory and attentional components of cognitive control, respectively. Following the cue, we also analyzed a positive slow-wave, indexing working memory engagement. We predicted that on switch trials, which required switching tasks, proactive control would result in larger cue-P3 amplitudes, reflecting recognition of the need to switch, and larger slow-wave amplitudes, reflecting maintenance of the new task-sets over the post-cue delay. This pattern was observed in both age groups. At the stimulus, in switch trials, both age groups had shorter stimulus-P2 latencies, consistent with processing facilitation. These results suggest that both 4-5- and 7-8-year-olds engaged cognitive control proactively. Older children, however, demonstrated better performance and larger cue-P2 amplitudes, suggesting more effective proactive control engagement in middle childhood.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".