EXPLORING COMPETITION BETWEEN COGNITIVE AND EMOTIONAL RESPONSE CUES IN SCHIZOPHRENIA
Bibliographic record
Abstract
The primary goal of this thesis was to characterize the parameters under which faulty emotion-cognition interactions emerge in Schizophrenia (SCZ). Its theoretical basis rests on neurobiological models specifying two related, yet independent, brain systems that govern how cognitive versus emotional processing impacts behaviour, and related research indicating differential impairment of the cognitive system in SCZ. These models predict that the disruptive impact of emotional information may be greatest when it is actionable and signals a competing response. However, most previous research on patients with SCZ has focused on the influence of extraneous emotional interference on primary cognitive processing. Thus, the central hypothesis guiding these experiments was that patients with SCZ will have the most difficulty prioritizing goal-directed, cognitive response cues in the face of countermanding emotional cues which impel an alternative response. Several different experimental tasks were used to interrogate this hypothesis, at both the behavioural and neural level. Overall, the results confirm that SCZ patients have difficulty prioritizing cognitive determinants of behaviour when emotion-laden information serves as an actionable and opposing response cue. However, the data are not conclusive; effect sizes were generally modest and results were not entirely consistent across studies. Therefore, while these experiments support dual-system neurobiological models of SCZ-related brain pathology, and provide interesting tentative suggestions for novel clinical approaches to treatment and remediation, further research is needed to fully understand dysregulated emotion-cognition antagonism in this clinical population.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".