1 code implementation • 19 Jun 2023 • Maria Heitmeier, Yu-Ying Chuang, Seth D. Axen, R. Harald Baayen
So far, the mappings can either be obtained incrementally via error-driven learning, a computationally expensive process able to capture frequency effects, or in an efficient, but frequency-agnostic solution modelling the theoretical endstate of learning (EL) where all words are learned optimally.
1 code implementation • 1 Jul 2022 • Maria Heitmeier, Yu-Ying Chuang, R. Harald Baayen
This demonstrates the potential of the DLM to model behavioural data and leads to the conclusion that trial-to-trial learning can indeed be detected in unprimed lexical decision.
no code implementations • 8 Jul 2021 • Yu-Ying Chuang, Mihi Kang, Xuefeng Luo, R. Harald Baayen
This paper presents three case studies of modeling aspects of lexical processing with Linear Discriminative Learning (LDL), the computational engine of the Discriminative Lexicon model (Baayen et al., 2019).
no code implementations • 15 Jun 2021 • Maria Heitmeier, Yu-Ying Chuang, R. Harald Baayen
This study addresses a series of methodological questions that arise when modeling inflectional morphology with Linear Discriminative Learning.