Utilizing the Activation-Decision-Construction-Action Theory to predict children's hypothetical decisions to deceive
Bibliographic record
Abstract
The Decision component of the Activation-Decision-Construction-Action-Theory (ADCAT) utilizes a cost-benefit formula to explain the cognitive, motivational and social processes involved in deception. Three prior studies suggest that ADCAT can be used to predict adults' future deceptive behavior; however, no study has assessed the potential relevance of ADCAT with children. The present study is the first to date to examine whether this cost-benefit formula can predict children's hypothetical decisions to tell three types of lies, and whether there are specific developmental factors that need to be considered. The results indicate that the cost-benefit formula was only effective for predicting children's hypothetical lies for self-gain at no cost to another (Self-No Cost lies) and lies for others when there was a personal cost (Other-Cost to Self). More specifically, expected value of telling the truth was related to lower willingness to tell hypothetical Self-No Cost and Other-Cost to Self lies. On the other hand, the expected value of lying was not related to children's hypothetical decisions to tell Self-No Cost, Self-Cost to Other or Other-Cost to Self lies. Children's inhibitory control and theory of mind were significant covariates for some of the ADCAT predictor variables and children's hypothetical truth and lying behaviors. Altogether, these findings indicate that the effectiveness of the ADCAT cost-benefit formula for predicting children's lying behavior is affected by developmental factors and the type of lie being analyzed.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".