Blunted neural response to errors prospectively predicts increased symptoms of depression during the COVID-19 pandemic.
Bibliographic record
Abstract
Symptoms of depression have increased during the COVID-19 pandemic, possibly due to increases in both chronic and episodic stress exposure. Yet these increases are being driven by a subset of people, leading to questions of what factors make some people more vulnerable. Individual differences in neural response to errors may confer vulnerability to stress-related psychopathology. However, it is unclear whether neural response to errors prospectively predicts depressive symptoms within the context of chronic and episodic stress exposure. Prior to the pandemic, neural response to errors, measured by the error-related negativity (ERN), and depression symptoms were collected from 105 young adults. Beginning in March 2020 and ending in August 2020, we collected symptoms of depression and exposure to pandemic-related episodic stressors at eight time points. Using multilevel models, we tested whether the ERN predicted depression symptoms across the first 6 months of the pandemic, a period of chronic stress. We also examined whether pandemic-related episodic stressors moderated the association between the ERN and depression symptoms. A blunted ERN predicted increased depression symptoms across the early part of the pandemic, even after adjusting for baseline depression symptoms. Moreover, episodic stress interacted with the ERN to predict concurrent symptoms of depression: For individuals exposed to greater episodic stress, a blunted ERN was associated with increased depressive symptoms at each timepoint during the pandemic. These findings indicate that blunted neural response to errors may enhance risk for depression symptoms under conditions of real-world chronic and episodic stress. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".