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Record W4281491615 · doi:10.3390/brainsci12060684

Social Priming in Speech Perception: Revisiting Kangaroo/Kiwi Priming in New Zealand English

2022· article· en· W4281491615 on OpenAlexaff
Gia Hurring, Jennifer Hay, Katie Drager, Ryan G. Podlubny, Laura Manhire, Alix Ellis

Bibliographic record

VenueBrain Sciences · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsUniversity of Alberta
FundersUniversity of CanterburyNorthwestern University
KeywordsKiwiPriming (agriculture)PsychologyPerceptionSpeech perceptionCognitive psychologyCommunicationBiologyEcologyNeuroscienceBotany

Abstract

fetched live from OpenAlex

We investigate whether regionally-associated primes can affect speech perception in two lexical decision tasks in which New Zealand listeners were exposed to an Australian prime (a kangaroo), a New Zealand prime (a kiwi), and/or a control animal (a horse). The target stimuli involve ambiguous vowels, embedded in a frame that would result in a real word with a KIT or a DRESS vowel and a nonsense word with the alternative vowel; thus, lexical decision responses can reveal which vowel was heard. Our pre-registered design predicted that exposure to the kangaroo would elicit more KIT-consistent responses than exposure to the kiwi. Both experiments showed significant priming effects in which the kangaroo elicited more KIT-consistent responses than the kiwi. The particular locus and details of these effects differed across experiments and participants. Taken together, the experiments reinforce the finding that regionally-associated primes can affect speech perception, but also suggest that the effects are sensitive to experimental design, stimulus acoustics, and individuals' production and past experience.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.363
Teacher spread0.303 · 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.

Study designQualitative
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

Citations26
Published2022
Admission routes1
Has abstractyes

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