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Record W3084226510

The Impact of Emotional Information on Task Performance in Unimodal vs. Cross-modal Paradigms

2020· article· en· W3084226510 on OpenAlexfundno aff
Emma K. Stewart

Bibliographic record

VenueScholarship@Western (Western University) · 2020
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
FundersCanada First Research Excellence Fund
KeywordsTask (project management)ModalPsychologyCognitive psychologyComputer scienceSocial psychologyEngineering
DOInot available

Abstract

fetched live from OpenAlex

Emotional stimuli can disrupt or enhance task performance, and this may depend on the sensory modality involved. In unimodal paradigms (e.g. visual task-irrelevant stimuli during a visual task) emotional stimuli frequently produce distraction effects; it is unclear how emotion affects task performance in cross-modal paradigms (e.g. auditory stimuli during a visual task). This project explored task performance as a function of sensory modality and emotional valence. In Study 1, participants (N=50) completed a visual task in the presence of task-irrelevant negative and neutral images and sounds. Response times and accuracy were disrupted in the presence of visual but not auditory emotional stimuli, particularly when the target and task-irrelevant stimulus appeared simultaneously. In Study 2, participants (N=38) completed an equivalent auditory task. Response times and accuracy were enhanced in the presence of auditory emotional stimuli at the first timepoint but disrupted at later timepoints. There was no effect for visual stimuli.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.061
GPT teacher head0.335
Teacher spread0.274 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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