MétaCan
Menu
Back to cohort
Record W3184867101 · doi:10.1016/j.bandl.2021.104985

The neural basis of compound word processing revealed by varying semantic transparency and morphemic neighborhood size

2021· article· en· W3184867101 on OpenAlexaff
Hsin-Ju Lee, Shih-kuen Cheng, Chia‐Ying Lee, Wen‐Jui Kuo

Bibliographic record

VenueBrain and Language · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersMinistry of Science and Technology, Taiwan
KeywordsMorphemePsychologyCognitive psychologyNatural language processingSemantic memoryTransparency (behavior)Artificial intelligenceCommunicationComputer scienceCognitionNeuroscience

Abstract

fetched live from OpenAlex

This study investigated the neural basis of compound word processing by using fMRI and Chinese two-character compounds for lexical decision. Semantic transparency and morphemic neighborhood size were manipulated to augment the processing profile for measurement. The behavioral results disclosed a semantic transparency effect and its interaction with the neighborhood size, which supported existence of a mechanism for compound processing. The fMRI results located a neural substrate in the left inferior prefrontal cortex (BA 45) which reacted in an interactive manner to the two variables. While its activities were lower when their neighborhood size was larger for processing transparent compounds, its activities became higher when their neighborhood size was larger for processing opaque compounds. When scaling to a larger scope, the function of this mechanism fitted well with the theoretical account of unification function of the left inferior frontal cortex for language processing.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.014
GPT teacher head0.262
Teacher spread0.248 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueBrain and LanguageSame topicNeurobiology of Language and BilingualismFrench-language works237,207