This study addresses a series of methodological questions that arise when modeling
inflectional morphology with Linear Discriminative Learning. Taking the semi-productive …

NDL and LDL are simple computational algorithms for lexical learning and lexical
processing. Both NDL and LDL assume that learning is discriminative, driven by prediction …

The brain is not a glorified digital computer. It does not store information in registers, and it
does not mathematically transform mental representations to establish perception or …

ABSTRACT A computational model for the comprehension of single spoken words is
presented that builds on an earlier model using discriminative learning. Real-valued …

Word frequency is a strong predictor in most lexical processing tasks. Thus, any model of
word recognition needs to account for how word frequency effects arise. The Discriminative …

Since its first adoption as a computational model for language learning, evidence has
accumulated that Rescorla–Wagner error‐correction learning (Rescorla & Wagner, 1972) …

Trial-to-trial effects have been found in a number of studies, indicating that processing a
stimulus influences responses in subsequent trials. A special case are priming effects which …

The pyndl package implements Naïve Discriminative Learning (NDL) in Python. NDL is an
incremental learning algorithm grounded in the principles of discrimination learning …

Full-fledged grammatical article systems as attested in Germanic and Romance languages
are rather uncommon from a typological perspective. The frequency with which articles …

This study addresses whether there is anything special about learning a third language, as
compared to learning a second language, that results solely from the order of acquisition …