A Dynamic Approach to Compulsive Fantasy: Constraints and Creativity in “Maladaptive Daydreaming”
Bibliographic record
Abstract
Compulsive fantasy, often called "maladaptive daydreaming," involves frequent engagement with immersive fantasies that can sometimes interfere with everyday life and cause distress. This paper expands on Christoff and colleagues' Dynamic Framework of Thought (DFT) to offer a process-based analysis of compulsive fantasy as it relates to other mental phenomena such as daydreaming and creative thought. Drawing on the existing literature and posts on online forums by self-identified maladaptive daydreamers, we also propose an account of how compulsive fantasy episodes may unfold in terms of the oscillating dynamics of various constraints on thought, and how these dynamics may be related to a perceived struggle with agency. Automatic constraints, including affective salience and mental habits, may bring about a fantasy episode. During a fantasy episode, automatic constraints may be relatively high throughout, whereas deliberate constraints may be intermittently engaged to influence the fantasy. Our analysis supports the use of "compulsive fantasy" as a more accurate designation than "maladaptive daydreaming" for this phenomenon: compulsive fantasies are not daydreams, because they are more constrained in their mental dynamics. We show that fantasy and daydreaming are not inherently harmful but can become so when they are accompanied by relatively strong and sustained automatic constraints on thought.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".