Informing Design Defixation Using Interventions for Psychiatric Disorders
Bibliographic record
Abstract
Abstract Design fixation has been extensively studied in the context of engineering design, leading to several interventions to reduce its negative effects. The concept of mental fixation has roots in diverse psychological contexts from Freudian psychoanalysis to Gestaltism and eating disorders. Although the underlying concepts are similar, the phenomenon has different names, including mental set, rumination, functional fixedness, obsession, etc. Mental fixation in its various forms is always a barrier to problem solving, whether the problem is a psychological disorder or an engineering-design task. The present paper explores the applicability to design fixation of cognitive therapy, a form of psychotherapy that relies on questioning to identify and modify inaccurate perceptions. Originally developed to treat depression, it is now used to treat a variety of psychiatric disorders. Specific interventions used in cognitive therapy are described in detail towards developing new means of overcoming design fixation. These interventions include cognitive restructuring and exposure response prevention. Also explored are links to other research results from psychology and cognitive science, including focused distraction, and the effects of music and physical exercise. In addition to developing new interventions, existing design-fixation interventions can also be supplemented using insights from these research results.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".