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Record W4405444751 · doi:10.1075/ml.24019.ste

Tense and agreement processing in native Spanish speakers with aphasia

2024· article· en· W4405444751 on OpenAlexaff
Camila Stecher, María Elina Sánchez, Julia Roberta Carden, Virginia Jaichenco

Bibliographic record

VenueThe Mental Lexicon · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of WindsorBrock University
Fundersnot available
KeywordsGrammaticalityInflectionLinguisticsSentenceAgreementSentence processingAphasiaPsychologyComprehensionVerbPast tenseMorphemeContrast (vision)Artificial intelligenceComputer scienceCognitive psychologyPhilosophyGrammar

Abstract

fetched live from OpenAlex

Abstract Several cross-linguistic studies have reported an impairment of verbal morphosyntax in People with Aphasia (PWA), highlighting a greater impact on tense morphology in contrast to agreement-inflectional morphology ( Benedet, Christiansen & Goodglass, 1998 ; Friedmann & Grodzinsky, 1997 , 2000 ; Gavarrò & Martínez-Ferreiro, 2007 ; Kok et al., 2007 ; Wenzlaff & Clahsen, 2004 ). The Diacritical Encoding and Retrieval Hypothesis (DERH, Faroqi-Shah & Thompson, 2007 ) postulates that this deficit is due to a specific difficulty projecting the semantic time information to the morphosyntax of verbs, related to processing limitations in PWA. This study aimed to assess the processing of the tense and agreement inflection in Spanish-speaking PWA. A group of 9 PWA completed four tasks designed to study verbal inflection production and comprehension in Spanish: Sentence Completion, Sentence Elicitation, Grammaticality Judgements and Sentence-Picture Matching. As expected, PWA showed a significatively greater impairment in tense inflection compared to agreement inflection. Furthermore, the analysis showed that this differential impairment manifested when PWA were asked to encode semantic time information and retrieve the corresponding verb morphology. These findings are consistent with the postulates of the DERH.

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)0.000
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 score0.026
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.023
GPT teacher head0.284
Teacher spread0.262 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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