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Benjamin Gagl Jun 27, 2024

🚨 New Preprint Alert 🚨Non-Human Recognition of Orthography: How is it implemented and how does it differ from Human orthographic processing https://www.biorxiv.org/content/10.1101/2024.06.25.600635v1 Combining behavioral data from Humans/Baboons/Pigeons with a computational model for neuro-cognitive phenotyping

Benjamin Gagl Jun 12, 2024

The German Word Nerd 🤓Network got DFG funding! 🎉 We plan to build a Transparent, Transferable, and Sustainable Foundation for Psycholinguistic Reading Studies in German (TRUST). More info here: https://sites.google.com/view/gewonn/home If you want to get involved, get in contact!

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Benjamin Gagl Jun 11, 2024

Jana Hasenäcker Jun 11, 2024

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Benjamin Gagl May 10, 2024

Remember, if you want to join, there are only a few days left to register for the International Meeting on Quantifying Semantic-Orthographic Regularities Across Languages. Spots are filling up fast! https://sites.google.com/view/gewonn/events/quasemo-2024

QuaSemO 2024

Join us in Munich — October 7–9, 2024Registration is now open! (Deadline: 15th of May, 2024)

Benjamin Gagl May 10, 2024

Jana Hasenäcker Apr 11, 2024

Our International Meeting on Quantifying Semantic-Orthographic Regularities Across Languages (QuaSemO), happening from 7th-9th October 2024 in Munich, is open for registration! We have an amazing line-up of speakers! Check out our webpage: https://sites.google.com/view/gewonn/events/quasemo-2024

QuaSemO 2024

Join us in Munich — October 7–9, 2024Registration is now open! (Deadline: 15th of May, 2024)

Benjamin Gagl Apr 21, 2024

idw_online Apr 18, 2024

Das Training von Gehirnprozessen macht Lesen effizienter
Studie zeigt, dass eine Kombination von Computermodellierung und Künstlicher Intelligenz die Leseleistung beim Erlernen von Sprachen verbessert / Veröffentlichung in „npj Science of Learning“...
https://nachrichten.idw-online.de/2024/04/18/das-training-von-gehirnprozessen-macht-lesen-effizienter

Das Training von Gehirnprozessen macht Lesen effizienter

Benjamin Gagl Apr 19, 2024

Universität zu Köln Apr 19, 2024

🧠📚 Neue Studie: Gehirntraining verbessert Lesefähigkeit!
💬 Aktuelle Forschung der Uni Würzburg und @bg #UniKoeln zeigt, wie Kombinationen von Computermodellierung und KI das Sprachenlernen revolutionieren 🚀
Die Unterscheidung von bekannten und unbekannten Wörtern kann trainiert werden, was zu effizienterem Lesen führt.

Mehr dazu ➡️ https://uni.koeln/YCHPU

Benjamin Gagl Apr 19, 2024

Universität zu Köln Apr 19, 2024

🧠📚 New Study: Brain Training Enhances Reading Ability!
Recent research from @bg #UniCologne and the University of Würzburg reveals how combinations of computer modelling and AI are revolutionizing language learning. 🚀 Distinguishing between familiar and unfamiliar words can be trained, leading to more efficient reading.

Read more ➡️ https://uni.koeln/QFFER

Benjamin Gagl Apr 12, 2024

It's out now—so much work combining years of work! The project started with a hunch and grew into a huge project, including multiple training studies and a diagnostic approach that is likely applicable in the real world. Thanks to Klara and everyone else involved.

#reading #langage #learning

https://link.springer.com/article/10.1038/s41539-024-00237-7?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20240410&utm_content=10.1038/s41539-024-00237-7

Investigating lexical categorization in reading based on joint diagnostic and training approaches for language learners - npj Science of Learning

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.

SpringerLink

Benjamin Gagl Apr 11, 2024

Benjamin Gagl Jul 21, 2023

🚨🚨NEW PREPRINT 🚨🚨 "Investigating lexical categorization in visual word recognition based on a joint diagnostic and training approach for language learners." https://psyarxiv.com/rs6gy/
We designed a training program including a machine learning-based diagnostic approach to train lexical categorization optimally, i.e., the process most likely implemented in the visual word form area (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009995). 1/N