The suitability of the artificial grammar learning (AGL) paradigm to capture relevant aspects of the acquisition of linguistic structures has been empirically tested in a number of EEG studies. Some have shown a syntax-related P600 component, but it has not been ruled out that the AGL P600 effect is a response to surface features (e.g.,…

Children acquiring languages with noun classes (grammatical gender) have ample statistical information available that characterizes the distribution of nouns into these classes, but their use of this information to classify novel nouns differs from the predictions made by an optimal Bayesian classifier. We use rational analysis to investigate the…

Across languages of the world, some grammatical patterns have been argued to be more common than expected by chance. These are sometimes referred to as (statistical) "language universals." One such universal is the correlation between constituent order freedom and the presence of a case system in a language. Here, we explore whether this…

In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language-learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners' input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized…

Language acquisition may be one of the most difficult tasks that children face during development. They have to segment words from fluent speech, figure out the meanings of these words, and discover the syntactic constraints for joining them together into meaningful sentences. Over the past couple of decades, computational modeling has emerged as…