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Record W3160852668 · doi:10.1162/jocn_a_01743

Drawn to Distraction: Anxiety Impairs Neural Suppression of Known Distractor Features in Visual Search

2021· article· en· W3160852668 on OpenAlexafffund
Christine Salahub, Stephen M. Emrich

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsBrock University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyDistractionAnxietyVisual searchCognitive psychologyAttentional controlMechanism (biology)Control (management)Selective attentionNeuroscienceCognitionArtificial intelligence

Abstract

fetched live from OpenAlex

When searching for a target, it is possible to suppress the features of a known distractor. This suppression may prevent distractor processing altogether or only after the distractor initially captures attention (i.e., search and destroy). However, suppression may be impaired in individuals with attentional control deficits, such as in high anxiety. In this study (n = 48), we used ERPs to examine the time course of attentional enhancement and suppression when participants were given pretrial information about target or distractor features. Consistent with our hypothesis, we found that individuals with higher levels of anxiety had lower neural measures of suppressing the template-matching distractor, instead showing enhanced processing. These findings indicate that individuals with anxiety are more likely to use a search-and-destroy mechanism of negative templates-highlighting the importance of attentional control abilities in distractor-guided search.

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 score0.002
Threshold uncertainty score0.008

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.089
GPT teacher head0.427
Teacher spread0.338 · 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

Citations13
Published2021
Admission routes2
Has abstractyes

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