Investigating lexical categorization in reading based on joint diagnostic and training approaches for language learners
Abstract Efficient reading is essential for societal participation, so reading proficiency is a central educational goal. Here, we use an individualized diagnostics and training framework to investigate processes in visual word recognition and evaluate its usefulness for detecting training responder...
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-04-01
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| Series: | npj Science of Learning |
| Online Access: | https://doi.org/10.1038/s41539-024-00237-7 |
| _version_ | 1797209521866670080 |
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| author | Benjamin Gagl Klara Gregorová |
| author_facet | Benjamin Gagl Klara Gregorová |
| author_sort | Benjamin Gagl |
| collection | DOAJ |
| description | Abstract Efficient reading is essential for societal participation, so reading proficiency is a central educational goal. Here, we use an individualized diagnostics and training framework to investigate processes in visual word recognition and evaluate its usefulness for detecting training responders. We (i) motivated a training procedure based on the Lexical Categorization Model (LCM) to introduce the framework. The LCM describes pre-lexical orthographic processing implemented in the left-ventral occipital cortex and is vital to reading. German language learners trained their lexical categorization abilities while we monitored reading speed change. In three studies, most language learners increased their reading skills. Next, we (ii) estimated, for each word, the LCM-based features and assessed each reader’s lexical categorization capabilities. Finally, we (iii) explored machine learning procedures to find the optimal feature selection and regression model to predict the benefit of the lexical categorization training for each individual. The best-performing pipeline increased reading speed from 23% in the unselected group to 43% in the machine-selected group. This selection process strongly depended on parameters associated with the LCM. Thus, training in lexical categorization can increase reading skills, and accurate computational descriptions of brain functions that allow the motivation of a training procedure combined with machine learning can be powerful for individualized reading training procedures. |
| first_indexed | 2024-04-24T09:56:02Z |
| format | Article |
| id | doaj.art-070e0cb590c4441eb4828209de9e472d |
| institution | Directory Open Access Journal |
| issn | 2056-7936 |
| language | English |
| last_indexed | 2024-04-24T09:56:02Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Science of Learning |
| spelling | doaj.art-070e0cb590c4441eb4828209de9e472d2024-04-14T11:08:18ZengNature Portfolionpj Science of Learning2056-79362024-04-019111210.1038/s41539-024-00237-7Investigating lexical categorization in reading based on joint diagnostic and training approaches for language learnersBenjamin Gagl0Klara Gregorová1Self-learning Systems Laboratory, Department of Special Education and Rehabilitation, University of CologneDepartment of Psychology and Sports Sciences, Goethe UniversityAbstract Efficient reading is essential for societal participation, so reading proficiency is a central educational goal. Here, we use an individualized diagnostics and training framework to investigate processes in visual word recognition and evaluate its usefulness for detecting training responders. We (i) motivated a training procedure based on the Lexical Categorization Model (LCM) to introduce the framework. The LCM describes pre-lexical orthographic processing implemented in the left-ventral occipital cortex and is vital to reading. German language learners trained their lexical categorization abilities while we monitored reading speed change. In three studies, most language learners increased their reading skills. Next, we (ii) estimated, for each word, the LCM-based features and assessed each reader’s lexical categorization capabilities. Finally, we (iii) explored machine learning procedures to find the optimal feature selection and regression model to predict the benefit of the lexical categorization training for each individual. The best-performing pipeline increased reading speed from 23% in the unselected group to 43% in the machine-selected group. This selection process strongly depended on parameters associated with the LCM. Thus, training in lexical categorization can increase reading skills, and accurate computational descriptions of brain functions that allow the motivation of a training procedure combined with machine learning can be powerful for individualized reading training procedures.https://doi.org/10.1038/s41539-024-00237-7 |
| spellingShingle | Benjamin Gagl Klara Gregorová Investigating lexical categorization in reading based on joint diagnostic and training approaches for language learners npj Science of Learning |
| title | Investigating lexical categorization in reading based on joint diagnostic and training approaches for language learners |
| title_full | Investigating lexical categorization in reading based on joint diagnostic and training approaches for language learners |
| title_fullStr | Investigating lexical categorization in reading based on joint diagnostic and training approaches for language learners |
| title_full_unstemmed | Investigating lexical categorization in reading based on joint diagnostic and training approaches for language learners |
| title_short | Investigating lexical categorization in reading based on joint diagnostic and training approaches for language learners |
| title_sort | investigating lexical categorization in reading based on joint diagnostic and training approaches for language learners |
| url | https://doi.org/10.1038/s41539-024-00237-7 |
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