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Record W3025383229 · doi:10.1101/2020.05.11.083931

Processing of a spoken narrative in the human brain is shaped by family cultural background

2020· preprint· en· W3025383229 on OpenAlexaff
Maria Hakonen, Arsi Ikäheimonen, Annika Hultén, Janne Kauttonen, Miika Koskinen, Fa‐Hsuan Lin, Anastasia Lowe, Mikko Sams, Iiro P. Jääskeläinen

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsNarrativePsychologyIdentity (music)NeuroimagingSimilarity (geometry)LinguisticsSentence processingCognitive psychologyCognitionNeuroscienceComputer scienceAestheticsArt

Abstract

fetched live from OpenAlex

ABSTRACT Using neuroimaging, we studied influence of family cultural background on processing of an audiobook in human brain. The audiobook depicted life of two young Finnish men, one with the Finnish and the other with the Russian family background. Shared family cultural background enhanced similarity of narrative processing in the brain at prelexical, word, sentence, and narrative levels. Similarity was also enhanced in brain areas supporting imagery. The cultural background was further reflected as semantic differences in word lists by which the subjects described what had been on their minds when they heard the audiobook during neuroimaging. Strength of social identity shaped word, sentence, and narrative level processing in the brain. These effects might enhance mutual understanding between persons who share family cultural background and social identity and, conversely, deteriorate between-group mutual understanding in modern multicultural societies wherein native speakers of a language may assume highly similar understanding.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.039
GPT teacher head0.296
Teacher spread0.257 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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