Exploring the decision component of the Activation‐Decision‐Construction‐Action Theory for different reasons to deceive
Bibliographic record
Abstract
Objectives To explore how reasons to lie impact upon the decision component of Activation‐Decision‐Construction‐Action Theory. Design Specifically, the study looked at how beneficiary of the lie (self vs. another) and additional cost of lying (no cost vs. cost to self/other) might influence decisions to lie. Methods Ninety‐one undergraduate students read four hypothetical scenarios representing the four reasons to lie. They stated whether they would decide to tell the truth/lie for each scenario and also estimated the probability and valence of being believed, or not, if they did decide to tell the truth/lie. These estimations were inputted into the ADCAT formulae. Results Higher expected values of truth‐telling only reduced likelihood to decide to lie when the lie benefitted another. Beneficiary of the lie and additional cost together did not moderate any of the relationships between the ADCAT variables and hypothetical decisions to lie. However, the additional cost (e.g., to self or another) was a significant predictor of anticipated lying behaviour. The more likely there was a cost to self or other, the less likely the participants were to decide to lie. Conclusions Weighing up the expected cost and benefits of truth‐telling and lying was associated with hypothetical decisions to lie or not. However, other variables, such as additional cost to self or another, should be considered in the ADCAT model to extend our understanding of this decision‐making process. Future research is required to investigate whether these relationships can be manipulated to promote honesty and deter deceit.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".