Neural network topology in children’s deceptive behaviors: The role of cognitive control and reward processing
Bibliographic record
Abstract
The neural mechanisms related to children's deceptive behaviors remain relatively unexplored. This study aims to address this gap by using measures of functional brain network topology, focusing on the cognitive control and reward processing networks that are closely related to children's deceptive behaviors. The study included 113 6-year-old children from the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) project, a birth cohort study. Children participated in the Dart Game designed to assess their tendencies to cheat and lie. During the game, children were required to throw the ball at a long-distance dartboard without supervision, which provided opportunities to cheat by breaking the rules. After the game, children were questioned about whether they had followed the rule, which provided them with opportunities to lie. Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from all children at the same age during a different visit. We compared three network topology measures (cognitive control network recruitment, reward processing network recruitment and reward-control network integration) between non-cheaters and cheaters, as well as between non-liars and liars. The results showed that a higher degree of cognitive control network recruitment was associated with a greater likelihood of lying. Moreover, a higher degree of reward-cognitive control network integration was associated with a lower likelihood of cheating and lying. The degree of reward processing network recruitment was not associated with deceptive behaviors. These findings help to elucidate how neural mechanisms of cognitive control and reward processing contribute to deceptive behaviors in young 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".