Evaluating Cognitive Efficiency by Measuring Information Contained in Designers’ Cognitive Processes
Bibliographic record
Abstract
Cognitive efficiency describes how individuals optimize limited mental resources to achieve improvements in learning and problem-solving. Research on expert performance and expertise has shown that expert designers structure the organization of cognitive actions more efficiently than novices. However, cognitive efficiency in engineering design processes has not been well studied because of technical limitations at the neurological level and lack of quantitative methods for analyzing information contained in designers’ cognitive processes at the performance level. The purpose of this study is to introduce Kolmogorov complexity to measure information contained in the changes of sketches generated by designers. The Kolmogorov complexity of each design move is calculated by the number of cognitive actions and transitions between different levels of information processing. In this study, sketches and verbal protocols generated by 15 participants were analyzed. Cognitive efficiency was determined by the quality of design outcomes and the expenditure of mental effort. The results indicate that Kolmogorov complexity is negatively related to cognitive efficiency; the higher the Kolmogorov complexity, the lower the cognitive efficiency.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".