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Self-Learning Systems Lab (PI: Benjamin Gagl)

Here we are interested in investigating, describing, and improving the neuro-cognitive processes implemented in reading (e.g., vision, orthographic codes, and lexical access) and language (e.g., temporal sampling). We use computational modeling for theory development in the mechanistic description of the neuro-cognitive processes in reading. Our computational models are then evaluated based on behavioral (e.g., eye-tracking) and brain imaging data (e.g., M/EEG, fMRI). Moreover, computational descriptions allow us to improve applied measurement methods and statistical solutions for individualized diagnostics and interventions of reading skills. Here we implement statistical and machine-learning methods.