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Record W2726189854 · doi:10.1080/02699931.2017.1346500

Stressing the feedback: attention and cardiac vagal tone during a cognitive stress task

2017· article· en· W2726189854 on OpenAlexafffund
Muhammad Abid Azam, Paul Ritvo, Samantha Fashler, Joel Katz

Bibliographic record

VenueCognition & Emotion · 2017
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsYork University
FundersCanadian Institutes of Health Research
KeywordsPsychologyCognitive psychologyTask (project management)CognitionTone (literature)Vagal toneStress (linguistics)NeuroscienceHeart rateMedicineHeart rate variabilityLinguisticsBlood pressureInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: The present study examined relationships among gaze behaviour and cardiac vagal tone using a novel stress-inducing task. METHODS: Participants' (N = 40) eye movements and heart rate variability (HRV) were measured during an unsolvable computer-based task randomly presenting feedback of "Right" and "Wrong" answers distinctly onscreen after each trial. Subgroups were created on the basis of more frequent eye movements to the right ("Correct"-Attenders; n = 23) or wrong ("Incorrect"-Attenders; n = 17) areas onscreen. RESULTS: Correct-Attenders maintained HRV from baseline to the stress task. In contrast, Incorrect-Attenders spent significantly more time viewing "Wrong" feedback, exhibited a reduction in HRV during the stress condition (p < .01), and were more likely to negatively self-evaluate performance. CONCLUSIONS: Results demonstrate that pervasive attention to negative feedback ("Wrong") elicits perseverative stress and negative self-evaluations among university students. This study highlights the potential for studying attentional biases and emotional distress through combined measures of gaze behaviour and cardiac vagal tone.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.849
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.291
Teacher spread0.272 · 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 teacher head, 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

Citations1
Published2017
Admission routes2
Has abstractyes

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