Perturbance: Unifying Research on Emotion, Intrusive Mentation and Other Psychological Phenomena with AI
Bibliographic record
Abstract
Intrusive mentation, rumination, obsession, and worry, referred to by Watkins as "repetitive thought" (RT), are of great interest to psychology. This is partly because every typical adult is subject to "RT". In particular, a critical feature of "RT" is also of transdiagnostic significance—for example obsessive compulsive disorder, insomnia and addictions involve unconstructive "RT". We argue that "RT" cannot be understood in isolation of models of whole minds. Researchers must adopt the designer stance in the tradition of Artificial Intelligence augmented by systematic conceptual analysis. This means developing, exploring and implementing cognitive-affective architectures. Empirical research on "RT" needs to be driven by such theories, and theorizing about "RT" needs to consider such data. We draw attention to H-CogAff theory of mind (motive processing, emotion, etc.) and a class of emotions it posits called perturbance (or tertiary emotions), as a foundation for the research programme we advocate. Briefly, a perturbance is a mental state in which motivators tend to disrupt executive processes. We argue that grief, limerence (the attraction phase of romantic love) and a host of other psychological phenomena involving "RT" should be conceptualized in terms of perturbance and related design-based constructs. We call for new taxonomies of "RT" in terms of information processing architectures such as H-CogAff. We claim general theories of emotion also need to recognize perturbance and other architecture-based aspects of emotion. Meanwhile "cognitive" architectures need to consider requirements of autonomous agency, leading to cognitive affective architectures.
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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 | 0.003 | 0.006 |
| 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 | 0.009 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".