In order to provide better insight into the processes underlying language acquisition, this project investigates the properties of statistical- and rule-learning mechanisms in relation to the acquisition and evolution of language. We ask to what extent these mechanisms are unique to humans - or to human language - by comparing the acquisition of vocal structure in two species: humans (infants) and songbirds (zebra finches). This will be done by ways of a series of carefully constructed, comparable artificial language learning experiments.

In addition, we will develop computational models of artificial language learning that, on the one hand, predict optimal learning behavior and, on the other hand, test how different factors - perceptual biases, computational constraints, memory limitations, etc. - influence learning. The project will not only provide a better insight into the processes underlying language acquisition, but also into the linkage between linguistic and more general cognitive mechanisms and their evolution.

• computational linguistics
• language acquisition
• language evolution
• rule learning
• statistical learning