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

Naive discriminative learning (NDL) and linear discriminative learning (LDL) are simple
computational algorithms for lexical learning and lexical processing. Both NDL and LDL …

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 …

This chapter first provides an overview of Indonesian morphology. It then proceeds to show
how the morphology of Indonesian can be modeled using a computational error-driven …

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

Effects of word frequency on spoken word duration are well documented and have long
informed theories of the mental lexicon. In this study, we discuss the two theoretical …

This chapter presents three case studies of modeling aspects of lexical processing with
Linear Discriminative Learning (LDL), the computational engine of the Discriminative …

Many theories of word structure in linguistics and morphological processing in cognitive
psychology are grounded in a compositional perspective on the (mental) lexicon in which …

Closely related languages show linguistic similarities that allow speakers of one language to
understand speakers of another language without having actively learned it. Mutual …

We investigated the processing of morphologically complex words adopting an approach
that goes beyond estimating average effects and allows testing predictions about variability …