The Neural Correlates of Cognitive Behavioural Self-Regulation in Early Development
Bibliographic record
Abstract
To examine individual differences in cognitive behavioural self-regulation early in development, neural activity during a probabilistic learning task were correlated to measures of impulsivity in 8 normally developing children (10-12 years of age). Children completed a probabilistic learning task in an fMRI scanner and a delay discounting task, used to measure impulsivity, outside the scanner. Choices on the delay discounting task were modeled using a quasi-hyperbolic function, representing participant’s subjective interpretation of a reward as a function of the delay period in which they must wait to obtain the reward. Subsequently, ventral striatum BOLD activity was correlated to individual performance during the probabilistic learning task, as well as to the quasi-hyperbolic model of the participant’s delay discounting curve. It was hypothesized that the magnitude of ventral striatal activity would positively correlate to parameters of individual delay discounting functions. More impulsive individuals, denoted by steeper delay discounting functions, were expected to have more BOLD activity in the ventral striatum during the probabilistic learning task than those with a more modest delay discounting function, or who are less impulsive. Contrary to these predictions, ventral striatal activity was found to be negatively correlated to the quasi-hyperbolic function, modeling a developmental switch of cognitive processing in regard to self-regulation. The present results with regard to ventral striatal activity, have the potential to serve as a biomarker of individual differences in cognitive behavioural self-regulation in normally developing children.
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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 | 0.002 |
| 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 | 0.000 | 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 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".