Die implizite Selbstregulation am Beispiel des Essverhaltens: Konsequenzen für die Psychotherapie
Bibliographic record
Abstract
Explicit processes of self-regulation require insight and control during implementation and are therefore often experienced as being strenuous, while implicit processes of steering behavior are automatic, rapid and effortless. However, self-regulation is not always either explicit or implicit; all variants ranging from those that are entirely automatic to those being entirely under control are present. As individuals are not aware of their underlying implicit modes of self-regulation, it is necessary to create an approach that is proximal to affective processing, by-passing the cognitive, verbal level. Promising approaches of this kind are such including embodied experiences or such shifting the body to a state, in which the apperception of implicit mechanisms is facilitated. Given that therapeutic work of self-regulation is in many cases carried out on an explicit level of processing, the need for novel, neurobiologically founded strategies intervening on the implicit (pre-verbal) level are called for. Correspondent paradigms, e. g. the approach-avoidance task (AAT) for the assessment of implicit processes are presented here with regard to food-intake regulation. This work is a narrative (qualitative) review aiming at illustrating the field of implicit bias research as well as the development of new implicit bias training paradigms to be used as add-on in future psychotherapeutic treatments. Therefore, a selection of relevant studies based on subjective criteria was made. Thus, this work is not a systematic review and does not claim to be an exhaustive description of studies of this kind.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".