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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".