Neurobiological and Cognitive Assessments of Affective Processing on Behavioural Control Across Disorders of Impulse Control
Bibliographic record
Abstract
Many psychiatric disorders are characterized by difficulties in working towards long-term goals. Effort-based decision-making (EBDM) provides a useful framework for understanding this phenomenon, particularly for parsing motivation into various components, and exploring the underpinnings of cost-benefit computations. Importantly, large changes in arousal, like those introduced by strong emotions and stress, can significantly influence high-order cognitive processes. However, the mechanistic properties underlying associations between emotions and various components of EBDM remain unclear, particularly at psychological, neurological and endocrinological levels. The following experiments were designed to examine the effect of positive and negative emotions on various components of EBDM across psychiatric conditions characterized by motivational and impulse-related deficits. In the first experiment, comparing emotional versus behavioural inhibitory systems in binge eating disorder, inverse relationships between disgust sensitivity, inhibitory control and binge-eating behaviours were found, suggesting unique maintenance functions of cognitive-affective links with emotion regulation on eating attitudes. In the second experiment examining neural correlates of effort- and reward-processing in a cannabis using population, findings indicate fronto-striatal but also posterior cortical processing alterations during prospective signaling of effort and reward signals and during effort-reward information integration. In the final experiment assessing the effects of childhood trauma on acute stress responses and gambling urges in a population of problem gamblers, increased reports of childhood trauma were noted relative to a healthy control group. Childhood trauma subsequently predicted subjective and physiological stress responses, and emotional and physical neglect in childhood was further linked to increased gambling urges. Taken as a whole, these studies suggest that emotions plays a crucial role in moderating various components of EBDM, underscoring the significant impact of emotional states on higher-order cognitive functioning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".