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Record W2770653986 · doi:10.1080/1357650x.2017.1404095

The effect of unimodal affective priming on dichotic emotion recognition

2017· article· en· W2770653986 on OpenAlexafffund
Daniel Voyer, Daniel Myles

Bibliographic record

VenueLaterality Asymmetries of Body Brain and Cognition · 2017
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyDichotic listeningLateralityBinaural recordingPriming (agriculture)Lateralization of brain functionCognitive psychologyAudiologyCognitionDevelopmental psychologyNeuroscience

Abstract

fetched live from OpenAlex

The present report concerns two experiments extending to unimodal priming the cross-modal priming effects observed with auditory emotions by Harding and Voyer [(2016). Laterality effects in cross-modal affective priming. Laterality: Asymmetries of Body, Brain and Cognition, 21, 585-605]. Experiment 1 used binaural targets to establish the presence of the priming effect and Experiment 2 used dichotically presented targets to examine auditory asymmetries. In Experiment 1, 82 university students completed a task in which binaural targets consisting of one of 4 English words inflected in one of 4 emotional tones were preceded by binaural primes consisting of one of 4 Mandarin words pronounced in the same (congruent) or different (incongruent) emotional tones. Trials where the prime emotion was congruent with the target emotion showed faster responses and higher accuracy in identifying the target emotion. In Experiment 2, 60 undergraduate students participated and the target was presented dichotically instead of binaurally. Primes congruent with the left ear produced a large left ear advantage, whereas right congruent primes produced a right ear advantage. These results indicate that unimodal priming produces stronger effects than those observed under cross-modal priming. The findings suggest that priming should likely be considered a strong top-down influence on laterality effects.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.031
GPT teacher head0.297
Teacher spread0.267 · 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 designBench or experimental
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
Published2017
Admission routes2
Has abstractyes

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