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Record W4289845727 · doi:10.1037/emo0001119

Effects of emotion and emotional ambiguity on early visual event-related potentials.

2022· article· en· W4289845727 on OpenAlexfundno aff
Édith Léveillé, Martine Desjardins, Gaëlle Dumel, Caroline Blais, Dave Saint‐Amour, Peter Scherzer, Louis De Beaumont

Bibliographic record

VenueEmotion · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsnot available
FundersFonds de Recherche du Québec - Santé
KeywordsPsychologyFacial expressionAmbiguityEvent-related potentialPsycINFOElectroencephalographyEmotional expressionCognitive psychologyFace perceptionDevelopmental psychologyEmotion recognitionPerceptionCommunicationNeuroscience

Abstract

fetched live from OpenAlex

The modulation of early sensory event-related potentials such as the P1, N1, and N170 by emotion and emotional ambiguity is still controversial. Some studies have found a modulation of one or all of these components by one or both of these factors, whereas others have failed to show such results. The objective of this study was to investigate the effects of emotion and ambiguity on the behavioral and electrophysiological responses to a morphed emotion recognition task. Thirty-seven healthy participants (19 men) completed an emotion recognition task where photographs of a male face expressing the six basic emotions morphed with another emotion (in a proportion ranging from 26% to 74%) were randomly presented while electroencephalography was recorded. After each face presentation, participants were asked to identify the facial emotion. We found an emotional effect on the P1, N1, and N170, with greater amplitudes for some emotional facial expressions than for others. However, we found no significant emotional ambiguity effect or interaction between emotion and ambiguity for any of these components. These findings suggest that computation of emotional facial expressions (regardless of their ambiguity) occurs from the early stages of brain processing. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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)0.000
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 score0.762
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.040
GPT teacher head0.332
Teacher spread0.291 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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