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Record W2040948448 · doi:10.1037/0894-4105.21.5.646

Congruency, attentional set, and laterality effects with emotional words.

2007· article· en· W2040948448 on OpenAlexaff
Cheryl Techentin, Daniel Voyer

Bibliographic record

VenueNeuropsychology · 2007
Typearticle
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPsychologyLateralityStimulus (psychology)Cognitive psychologySet (abstract data type)AudiologyDevelopmental psychology

Abstract

fetched live from OpenAlex

The present study investigated the influence of attention and word-emotion congruency on auditory asymmetries with stimuli that include verbal and emotional components. Words were presented dichotically to 80 participants and were pronounced in either congruent or incongruent emotional tones. Participants were asked to identify the presence of a target word or emotion under 1 of 2 conditions. The blocked condition required detection of a word or emotional target in separate blocks. In the randomized condition, the target was changed across trials by means of a postcue. A right-ear advantage (REA) and a left-ear advantage (LEA) were found for word and emotion targets, respectively. However, the finding of a Condition x Stimulus Type x Ear x Congruency interaction indicated that in the randomized condition, a REA was obtained for words when the stimuli were congruent and a LEA was observed for emotions when the stimuli were incongruent. The findings suggest that randomizing the target reduced the influence of the attentional set established by blocking the target. It is likely that this promoted the detection of hemispheric interference in the randomized condition.

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)0.001
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 score0.870
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.303
Teacher spread0.281 · 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

Citations16
Published2007
Admission routes1
Has abstractyes

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