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Record W2119679233 · doi:10.1080/02699931.2013.878687

Confusion of fear and surprise: A test of the perceptual-attentional limitation hypothesis with eye movement monitoring

2014· article· en· W2119679233 on OpenAlexaff
Annie Roy‐Charland, Mélanie Perron, Olivia Beaudry, Kaylee Eady

Bibliographic record

VenueCognition & Emotion · 2014
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsLaurentian University
Fundersnot available
KeywordsSurprisePsychologyOptimal distinctiveness theoryCognitive psychologyPerceptionEye movementFacial expressionCommunicationSocial psychologyNeuroscience

Abstract

fetched live from OpenAlex

Of the basic emotional facial expressions, fear is typically less accurately recognised as a result of being confused with surprise. According to the perceptual-attentional limitation hypothesis, the difficulty in recognising fear could be attributed to the similar visual configuration with surprise. In effect, they share more muscle movements than they possess distinctive ones. The main goal of the current study was to test the perceptual-attentional limitation hypothesis in the recognition of fear and surprise using eye movement recording and by manipulating the distinctiveness between expressions. Results revealed that when the brow lowerer is the only distinctive feature between expressions, accuracy is lower, participants spend more time looking at stimuli and they make more comparisons between expressions than when stimuli include the lip stretcher. These results not only support the perceptual-attentional limitation hypothesis but extend its definition by suggesting that it is not solely the number of distinctive features that is important but also their qualitative value.

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)0.003
metaresearch head score (Gemma)0.026
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 score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.046
GPT teacher head0.250
Teacher spread0.204 · 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

Citations78
Published2014
Admission routes1
Has abstractyes

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