Source Monitoring and Executive Function in 2.5- to 3-Year-Olds
Bibliographic record
Abstract
Hala, Brown, McKay, and San Juan (2013) found that children as young as 2.5 years of age demonstrated high levels of accuracy when asked to recall whether they or the experimenter had carried out a particular action. In the research reported here, we examined the relation of early-emerging source monitoring to executive function abilities. Participants were children aged 2.5- to 3-years old. For the source-monitoring procedure, we used the Hala et al. (2013) task in which children and the experimenter took turns placing a total of 20 items on a model farm (encoding phase). For the source memory test, children were asked who had placed each item (retrieval phase). Executive function measures included assessments of working memory, delay-inhibitory control, and conflict-inhibitory control. The main finding was that inhibitory control measures were significantly related to performance on the source-monitoring task. This relation held for the conflict-inhibitory control measures even when controlling for age and vocabulary. The findings of this research suggest that even at the early age of 2.5 years, development of executive control is linked to the emergence of source-monitoring ability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".