Self‐Reflection and the Cognitive Control of Behavior: Implications for Learning
Bibliographic record
Abstract
ABSTRACT— In this article, we suggest that self‐reflection and self‐control—studied under the rubric of “executive function” (EF)—have the potential to transform the way in which learning occurs, allowing for the relatively rapid emergence of new behaviors. We describe 2 lines of research that indicate that reflecting on a task and its affordances helps children to respond flexibly in a more top‐down fashion despite interference from prior learning or perceptually salient aspects of the task. Research on A‐not‐B tasks with infants and young children revealed that postswitch flexibility is an inverted U‐shaped function of number of preswitch trials. Overlearning may provide additional opportunities for reflection, in part by freeing up cognitive resources as behavior becomes automatized. Findings from the Flexible Item Selection Task with preschoolers and adults revealed that, although labeling the relevant dimension facilitates performance, performance declines when participants are prohibited from labeling. Labeling one’s perspective on a situation not only helps make that perspective an explicit object of consideration, but it may also help children access more abstract conceptual descriptions of a stimulus. Research on EF has broad implications for the way in which human learning differs from learning in other species and the way in which human learning may change over the course of development.
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 | 0.002 | 0.006 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".