Resistance to cognitive biases: Longitudinal trajectories and associations with cognitive abilities and academic achievement across development
Bibliographic record
Abstract
Abstract Cognitive failures on several reasoning and judgment tasks can be explained by miserly information processing tendencies. These tasks have been examined in child and youth samples, and we extend this work by examining the developmental trajectory of performance on these cognitive bias tasks and their association with other markers of cognitive sophistication. A longitudinal design was used to examine the development of resistance to cognitive biases in a sample of 204 typically developing children and youth. These youth were 8–14 years of age at first assessment and were assessed at three measurement occasions separated by 3 years. Resistance to cognitive biases as represented by performance on five reasoning and judgment tasks, including ratio bias, belief‐bias syllogisms, attribute framing problems, base‐rate sensitivity, and temporal discounting. The developmental trajectory of resistance to cognitive biases was examined. We also estimated associations between trajectories of resistance to cognitive biases and measures of cognitive abilities, actively open‐minded thinking, and superstitious thinking to examine how individual differences in other measures of cognitive sophistication were associated with the development of resistance to cognitive biases. Cognitive ability measures included intelligence (verbal and nonverbal) and executive function tasks (interference control and set‐shifting). Growth modeling results showed that resistance to cognitive biases increased linearly from 8 to 15 years of age, followed by a flat mean trajectory up to age 20. Cognitive ability, actively open‐minded thinking, and superstitious thinking predicted individual differences in resistance to cognitive biases, but not changes in resistance to cognitive biases. Performance on resistance to cognitive biases tasks was positively correlated with self‐ and parent‐reported academic achievement.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".