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Direct linkPatience Stevens; David Plaut – Grantee Submission, 2020
The statistical structure of a given language likely drives our sensitivity to words' morphological structure. The current work begins to investigate to what degree morphological processing effects observed in visual word recognition can be attributed to statistical regularities between orthography and semantics in English, without any prior…
Descriptors: Reading Processes, Word Recognition, Semantics, Written Language
Eun Seon Chung – Language Learning & Technology, 2024
While previous investigations on online machine translation (MT) in language learning have analyzed how second language (L2) learners use and post-edit MT output, no study as of yet has investigated how the learners process MT errors and what factors affect this process using response and reading times. The present study thus investigates L2…
Descriptors: English (Second Language), Korean, Language Processing, Translation
Ferman, Sara; Shmuel, Sapir Amira; Zaltz, Yael – Language Learning and Development, 2022
The acquisition of a new morphological rule can be influenced by numerous factors, including the type of feedback provided during learning. The present study aimed to test the effect of different feedback types on children's ability to learn and generalize an artificial morphological rule (AMR). Two groups of eight-year-olds learned to judge and…
Descriptors: Morphology (Languages), Feedback (Response), Error Correction, Learning Processes
Kenett, Yoed N.; Levi, Effi; Anaki, David; Faust, Miriam – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2017
Semantic distance is a determining factor in cognitive processes, such as semantic priming, operating upon semantic memory. The main computational approach to compute semantic distance is through latent semantic analysis (LSA). However, objections have been raised against this approach, mainly in its failure at predicting semantic priming. We…
Descriptors: Semantics, Priming, Cognitive Processes, Language Processing
Ansarin, Ali Akbar; Manesh, Solmaz Saeeidi – Journal of Pan-Pacific Association of Applied Linguistics, 2015
Representation of languages in a bilingual mind in general and the way bilinguals restore words from their mental lexicon, and the way they retrieve words have been explored by many researchers resulting in varied findings. The more information regarding bilingual memory is obtained, the better image would be constructed about this knowledge…
Descriptors: Reaction Time, Semantics, Priming, Bilingualism
Feng, Mingyu, Ed.; Käser, Tanja, Ed.; Talukdar, Partha, Ed. – International Educational Data Mining Society, 2023
The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for…
Descriptors: Information Retrieval, Data Analysis, Computer Assisted Testing, Cheating
Roelofs, Ardi; Piai, Vitoria; Schriefers, Herbert – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2011
E. Dhooge and R. J. Hartsuiker (2010) reported experiments showing that picture naming takes longer with low- than high-frequency distractor words, replicating M. Miozzo and A. Caramazza (2003). In addition, they showed that this distractor-frequency effect disappears when distractors are masked or preexposed. These findings were taken to refute…
Descriptors: Attention Control, Attention, Experiments, Semantics
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
