Development of Judgment, Decision Making, and Rationality
Bibliographic record
Abstract
Abstract There is a long tradition of studying children’s reasoning and thinking in cognitive development and education. The initial studies in the cognitive development of reasoning were motivated by Piagetian models, and developmental age was thought to bring the gradual onset of logical thinking. The introduction of heuristics and biases tasks in adults and dual process models have provided new perspectives for understanding the development of reasoning, judgment, and decision-making skills. These heuristics and biases tasks provided a way to operationalize the systematic errors that people make in their judgments. Dual process models have advanced our understanding of the basic processes implicated in both optimal and non-optimal responders on several types of paradigms, including heuristics and biases tasks and classic reasoning paradigms. Importantly, these skills and competencies are generally separable from the types of higher cognition assessed on measures of intelligence and executive function task performance. Given the history of the study of reasoning in cognitive development, there is a need to integrate our understanding across these somewhat separate literatures. This is especially true given the opposite predictions that seem to be suggested in these different research traditions. Specifically, there is a focus on increasing logical development in the classic cognitive developmental literature and alternatively, there has been a focus on systematic errors in judgment and decision-making in the study of reasoning in adults. This article provides an integration of the two aforementioned perspectives that are rooted in different empirical and historical traditions. These considerations are addressed by drawing upon their research traditions and by summarizing more recent developmental work that has investigated these paradigms.
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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 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".