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Record W3043911948 · doi:10.1177/1747021820940297

Morphological and visual cues in compound word reading: Eye-tracking evidence from Hebrew

2020· article· en· W3043911948 on OpenAlexafffund
Victor Kuperman, Avital Deutsch

Bibliographic record

VenueQuarterly Journal of Experimental Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsMcMaster University
FundersLady Davis Fellowship Trust, Hebrew University of JerusalemSocial Sciences and Humanities Research Council of Canada
KeywordsSuffixNounHebrewLinguisticsPsychologyCompoundReading (process)Computer scienceArtificial intelligenceCommunicationNatural language processing

Abstract

fetched live from OpenAlex

Hebrew noun-noun compounds offer a valuable opportunity to study the long-standing question of how morphologically complex words are processed during reading. Specifically, in some morpho-syntactic environments, the first (head) noun of a compound carries a suffix-a clear orthographic marker of being part of a compound-whereas in others it is homographic with a stand-alone noun. In addition to this morphological cue, Hebrew occasionally employs hyphenation as a visual signal that two nouns, which are typically separated by a space, are combined in a compound. In a factorial design, we orthogonally manipulated the morphological and the visual cues and recorded eye movements of 75 proficient Hebrew readers while they read sentences with embedded compounds. The effect of hyphenation on reading times was inhibitory. This slow-down was significantly weaker in compounds where the syntactic relation between constituents was overtly marked by a suffix compared with compounds without a morphological marker. We interpret these findings as evidence that hyphenation is largely a redundant cue but morphological markers of compounding are psychologically valid cues for semantic integration of compounds. We discuss the implications of this finding for accounts of morphological 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.069
GPT teacher head0.419
Teacher spread0.350 · 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 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

Citations21
Published2020
Admission routes2
Has abstractyes

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