Publications by authors named "Dorothee B Hoppe"
Article Synopsis
- Error-driven learning algorithms adjust expectations based on prediction errors and are foundational for various computational models in brain and cognitive sciences, spanning from simple psychology models to complex deep learning applications.
- Despite their widespread use, comprehensive analyses of error-driven learning's basic mechanics, devoid of pre-existing theories, are rare in scholarly literature.
- This paper simplifies the concept of error-driven learning, connects it to its historical development, and emphasizes its discriminative nature, while also providing practical guidance through example simulations in an accompanying tutorial.
Linguistic category learning has been shown to be highly sensitive to linear order, and depending on the task, differentially sensitive to the information provided by preceding category markers (premarkers, e.g., gendered articles) or succeeding category markers (postmarkers, e.
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