Decision Making Under Chronic Stress and Anxiety: State and Trait Anxiety Impact Contextual Updating but not Feedback Learning
Bibliographic record
Abstract
Stress may alter executive functioning by causing structural and functional changes to the brain. Sub-optimal decisions made under high levels of stress and anxiety may act as a mediator for stress-related health effects. We examined the effect of three personality traits–chronic stress, state anxiety, and trait anxiety–on updating working memory and feedback learning across 330 participants, using electroencephalography (EEG). We hypothesized a decrease in P300 (updating working memory) and reward positivity (feedback learning) amplitudes with increasing chronic stress and anxiety scores. The three personality traits were not correlated with reward positivity amplitudes. Additionally, chronic stress had no effect on P300 amplitudes. However, state and trait anxiety were negatively correlated with P300 amplitudes. Anxiety appears to impact working memory processes, and this effect was amplified with decreasing anxiety score quantiles to reflect the tails of the distribution. Our results are evidence of the beginnings of a correlation between anxiety and the neural correlates of decision-making, offering insight into anxiety-related adverse health outcomes.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".