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Record W4229448604 · doi:10.1121/10.0011337

Facial posture as a phonetic prime for memory retrieval

2022· article· en· W4229448604 on OpenAlexaff
Arian Shamei, Sijia Zhang, Noah Luntzlara, Yangshuying Zhou, Sonja Frazier, Gillian de Boer, Bryan Gick

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2022
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversity of British Columbia Hospital
Fundersnot available
KeywordsRecallVowelPrime (order theory)Facial expressionPsychologySpeech recognitionPerceptionCognitive psychologyAudiologyComputer scienceCommunicationMathematicsNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Facial postures influence the perception of speech towards sounds with similar motor configurations [Yeung and Scott, J. Exper. Psych. 150, 983 (2021)]. However, it remains unknown whether facial posture can serve as a phonetic prime for the recall of speech. The present study tests whether maintaining a smile improves the recall of speech sounds with similar kinematics and somatosensory input (e.g., high front vowel /i/) [Ogane etal., J. Acoust. Soc. Am. 148, EL279–EL284 (2020)]. In the training phase, a list of monosyllabic words containing /i/, /a/, and /u/ vowels is presented aurally to participants maintaining a neutral expression. In the recall phase, participants are asked to recall these words in one of two conditions, maintaining either a smile or a neutral expression. Improved recall for tokens containing /i/ would suggest facial postures act as phonetic primes for memory retrieval. The experiment is ongoing; results will be presented and discussed. [Work by the NIH and NSERC].

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.026
GPT teacher head0.326
Teacher spread0.300 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueThe Journal of the Acoustical Society of AmericaSame topicMultisensory perception and integrationFrench-language works237,207