A Novel Approach to Quantifying Designer’s Mental Stress in the Conceptual Design Process
Bibliographic record
Abstract
The objective of this paper is to quantify designer’s mental stress during the conceptual design process. Quantifying the designer’s mental stress would assist the effort of understanding the designer’s creative and innovative process. In this paper, Recursive Object Modelling (ROM) is used as a formal tool to represent the designer’s mental state in each step of the conceptual design process. During the conceptual design process, designers usually describe the design states using natural language, combined with sketches. The description based on natural language will be transformed into ROM diagram through the lexical, syntactic, and structure analysis. A cognitive experiment, which is to design a new litter-disposal system in a passenger compartment located in the trains of NS (Dutch Railways), is built to study designer’s thinking process. ROM is used to analyze and quantify the designer’s mental stress based on the protocol data collected in the experiment. The validation through the cognitive experiment shows that ROM is an efficient design evaluation methodology, which reflects the nature and the characteristics of the design process. The designer’s mental stress presents dynamic, nonlinear, and spiral trend.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".