Error-driven learning

Teachers: Jacolien van Rij and Dorothée Hoppe (University of Groningen)

Error-driven learning (also called discrimination learning) allows to simulate the time course of learning. It is based on the Rescorla-Wagner model (Rescorla & Wagner, 1972) for animal cognition, which assumes that learning is driven by expectation error, instead of behaviorist association (Rescorla, 1988). The equations formulated by Rescorla and Wagner have been used to investigate different aspects of cognition, including language acquisition (e.g., Hsu, Chater, and Vitányi, 2011; St. Clair, Monaghan, and Ramscar, 2009), second language learning (Ellis, 2006), and reading of  complex words (Baayen et al, 2011). Although error-driven learning can be applied for all domains in cognitive science, in this course we will focus on how it could be used for modeling language processing and language learning.

In this course we will start with simple simulations in R to illustrate the basic assumptions and mechanisms of error-driven learning. Several phenomena, such as order effects in language and Kamin’s blocking effect (Kamin, 1996), will be discussed from an error-driven learning perspective. At the second half of the week, we will discuss how to use large networks to investigate structure in language. Requirements: We assume that participants are familiar with R (

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theme by teslathemes adapted by Jelmer Borst
theme by teslathemes

adapted by Jelmer Borst