The Effects of Acute Stress on the Neural Correlates of Decision-Making
Bibliographic record
Abstract
Abstract Stress has been defined in many ways and is typically induced as a response to a threat to homeostasis. Stress affects decision-making, and the effects of stress on subcomponents of decision-making can be indirectly measured through EEG. The purpose of this study was to investigate the effects of acute stress on the neural correlates of decision-making. We hypothesized that acute stress would decrease the reward and attentional sensitivity, seen through reduced P300 and reward positivity component activity. The results were that the mean percent change from baseline for heart rate was higher for the stress condition during the TSST. The stress group also had decreased positive affect scores and increased negative affect scores for the STAI questionnaire and decreased positive affect scores for the PANAS questionnaire. Additionally, while not significant, there was a trend towards reduced P300 component activity in the stress condition, potentially indicative of reduced attentional sensitivity. Further research is needed to explore the implications for reward sensitivity, utilizing multiple tasks, and including cortisol measurement. Stress is common to everyday life and has been implicated chronically in numerous health conditions. Understanding how stress affects executive function, particularly decision-making, is therefore crucial in both the short- and long-term. Keywords: stress; decision-making; ERPs; P300 component; reward positivity component *This research was supported by a Jamie Cassels Undergraduate Research Award, University of Victoria.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".