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Record W2270077297 · doi:10.1080/1357650x.2015.1079214

Laterality effects in cross-modal affective priming

2015· article· en· W2270077297 on OpenAlexafffund
Jennifer Harding, Daniel Voyer

Bibliographic record

VenueLaterality Asymmetries of Body Brain and Cognition · 2015
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyPriming (agriculture)Affect (linguistics)Cognitive psychologyContext (archaeology)Response primingDichotic listeningLateralityActive listeningSocial psychologyLexical decision taskCognitionCommunicationDevelopmental psychologyNeuroscience

Abstract

fetched live from OpenAlex

The present study pursued M. P. Bryden's legacy by investigating how contextual factors can affect laterality effects. Specifically, a cross-modal affective priming paradigm was used in two experiments to determine whether priming with facial expressions would affect responses to emotional sounds. Experiment 1 established that cross-modal priming could be obtained when presenting the emotional sounds binaurally by showing more accurate responses when prime and target were congruent than when they were incongruent, although this extended to response time only for the happy emotion. This priming effect justified Experiment 2, in which the priming paradigm was integrated into a dichotic listening task. The central finding of Experiment 2 was a congruency by ear interaction on number of correct reports, showing that presentation of a facial emotion congruent with a left target produced a large left ear advantage that was reduced when a right ear congruent prime or an incongruent pairing was used. Implications of these findings for emotion processing in the context of Bryden's legacy are discussed.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.045
GPT teacher head0.366
Teacher spread0.321 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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