🚨New Preprint🚨 We developed a new version of the orthographic prediction error (oPE) and found that the representation increases in precision over time. We found that the newly developed oPE representation better explained late EEG time windows and behavior. Also, allowed better ResNet18 performance.
https://www.biorxiv.org/content/10.1101/2024.02.29.582776v1.full.pdf#ccn #ccn23 Poster presentation! Our poster on predictive processes in visual word recognition is up. Find the conference paper here:
https://2023.ccneuro.org/proceedings/0000745.pdf The best predicting model resulted in a correlation of .69 predicted vs. observed reading skill increase. When applied, the training effect on the group level increased from 23% to 43%.
6/N
Interestingly, when inspecting the results of the feature selection process, features based on the central model parameter of the Lexical Categorization Model (LCM) have been the essential word-level features for the predictive models.
5/N
In addition, we developed a diagnostic procedure intending to detect the responders to the training based on explainable machine learning. Here we investigated various variants (i.e., changing feature creation procedures or applying different ML algorithms) to detect the optimal combination—most investigated procedures produced a medium-to-high correlation of predicted vs. observed training effects.
4/N
We found significant training effects on reading skills across three studies, two of which were RTCs, and we preregistered the final study.
3/N
We trained language learners of German from various language backgrounds with a lexical decision task, including feedback.
2/N
🚨🚨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
