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Investigating Orthographic Versus Auditory Cross-Situational Word Learning With Online and Laboratory-Based Testing

Bibliographic Data

ID4501848
AuthorsPaola Escudero (0000-0002-8071-7663, Western Sydney University, corresponding author), Eline A Smit (0000-0003-0250-378X, Western Sydney University), Anthony J Angwin (0000-0003-4547-9433, Australian Research Council Centre of Excellence for the Dynamics of Language)
Year2023
Volume73
Issue2
Pages543-577
Publication date2023-06-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueLanguage Learning (JOURNAL)
Journal identifiersISSN: 0023-8333 • E-ISSN: 1467-9922
PublisherWiley (PUBLISHER • GB)
DOI10.1111/lang.12550
OpenAlexW4311070353
LanguageEN
Citations received2
References cited100

Research has shown that novel words can be learned through the mechanism of statistical or cross-situational word learning (CSWL). So far, CSWL studies using adult populations have focused on the presentation of spoken words. However, words can also be learned through their written form. This study compared auditory and orthographic presentations of novel words with different degrees of phonological overlap using CSWL in a laboratory-based and an online-based approach. In our analyses, we first compared accuracy across modalities, with our findings showing more accurate recognition performance for CSWL when novel words were presented through their written forms (orthographic condition) rather than through their spoken forms (auditory condition). Bayesian modeling suggested that accuracy for the orthographic condition was higher in the laboratory compared to online, whereas performance in the auditory condition was similar across both experiments. We discuss the implications of our findings for presentation modality and the benefits of our online testing protocol for future research

Cognitive psychology · Linguistics · Natural language processing · Orthographic projection · Presentation (obstetrics) · Situational ethics · Speech recognition · Word (group theory) · Artificial Intelligence · Computer Science · Natural Language Processing Techniques · Psychology · Speech and dialogue systems · Topic Modeling

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Unique citing works2
Citations per year2
Citation span2026 - 2026 (1)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 2

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