Modélisation de tâches créatives de résolution de problèmes à partir d'une approche computationnelle et neuroéducative
Bibliographic record
Abstract
Creativity is a complex process that has been studied in different fields with a high level of diversity in relation to the types of tasks, contexts, and assessment methodologies. In this study, we focus on ill-defined individual creative problem-solving (CPS) tasks with the objective of creating a computational model based on the regulatory process of CPS, inspired by the neuroscientific knowledge of the related cognitive processes, and artificial cognitive architectures. The model operationalization considers the emergent character of the path engaged by the learner when solving an ill-defined task and the geometrization of this path within a problem space describing the task. Stimulus-based and goal-directed creative behavior are then distinguished on the computational processes underlying creativity. Through a computational and neuroeducational approach, the study introduces a model of creative problem-solving tasks and provides an operational geometric definition of problem-solving tasks, emphasizing the challenges associated with ill-defined problems. We finish discussing creativity as a semantic grounding process with a focus on data representation, as well as symbolic data manipulation using inference and metric space algorithms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".