Bibliographic record
Abstract
Understanding intrusive mentation, rumination, obsession, and worry, known also as "repetitive thought" (RT), is important for understanding cognitive and affective processes in general. RT is of transdiagnostic significance—for example obsessive-compulsive disorder, insomnia and addictions involve counterproductive RT. It is also a key but under-acknowledged feature of emotional episodes. We argue that RT cannot be understood in isolation but must rather be considered within models of whole minds and for this purpose we suggest an integrative design-oriented (IDO) approach. This approach involves the design stance of theoretical Artificial Intelligence (the central discipline of cognitive science), augmented by systematic conceptual analysis, aimed at explaining how autonomous agency is possible. This requires developing, exploring and implementing cognitive-affective-conative information-processing architectures. Empirical research on RT and emotions needs to be driven by such theories, and theorizing about RT needs to consider such data. Mental perturbance is an IDO concept that, we argue, can help characterize, explain, and theoretically ground the concept of RT. Briefly, perturbance is a mental state in which motivators tend to disrupt, or otherwise influence, executive processes even if reflective processes were to try to prevent or minimize the motivators’ influence. We draw attention to an IDO architecture of mind, H-CogAff, to illustrate the IDO approach to perturbance. We claim, further, that the intrusive mentation of some affective states— including grief and limerence (the attraction phase of romantic love) — should be conceptualized in terms of perturbance and the IDO architectures that support perturbance. We call for new taxonomies of RT and emotion in terms of IDO architectures such as H-CogAff. We point to areas of research in psychology that would benefit from the concept of perturbance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".