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Record W3039994923 · doi:10.1177/2167702620917463

Risk and Resilience in an Acute Stress Paradigm: Evidence From Salivary Cortisol and Time-Frequency Analysis of the Reward Positivity

2020· article· en· W3039994923 on OpenAlexaffabout
Paige Ethridge, Nida Ali, Sarah E. Racine, Jens C. Pruessner, Anna Weinberg

Bibliographic record

VenueClinical Psychological Science · 2020
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyPsychopathologyPsychological resilienceStress (linguistics)Developmental psychologyClinical psychologyFight-or-flight responseNeuroscienceAudiologyMedicinePsychotherapist

Abstract

fetched live from OpenAlex

Both abnormal stress and reward responsivity are consistently linked to multiple forms of psychopathology; however, the nature of the associations between stress and reward sensitivity remains poorly understood. In the present study, we examined associations between the hypothalamic-pituitary-adrenal-axis stress response and event-related potentials sensitive to the receipt of reward-related feedback in a pre–post experimental paradigm. Neural responses were recorded while male participants completed a simple monetary-reward guessing task before and after the Montreal Imaging Stress Task. Results demonstrated that acute psychosocial stress significantly reduced the magnitude of neural responses to feedback in the reward-sensitive delta-frequency band but not the loss-sensitive theta-frequency band. In addition, a larger delta-frequency response to rewards at baseline predicted reduced overall cortisol response in the stress condition. These findings suggest, therefore, that neural reward circuitry may be associated with both risk for and resilience to stress-related psychopathology.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.122
GPT teacher head0.435
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2020
Admission routes2
Has abstractyes

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