PhD student: Jun Lai
Supervisors: Fenna Poletiek and Niels O. Schiller
How do children acquire the highly complex grammatical rules of their language? Linguistic theories (Chomsky, 1980) claim that children master natural grammar by means of an inborn language device.
Empirical psychological studies and computational studies, however, have indicated that grammar induction could be achieved from experience (Reber 1967; Elman 1991). The latter indication was further supported by studies of statistical learning and information sampling.
In the present project we look at how simple sample characteristics of the linguistic stimulus environment might help inducing grammar knowledge. Using the traditional Artificial Grammar Learning paradigm (Reber, 1967), we manipulate aspects of the input sample, such as the ordering of the stimuli (Lai & Poletiek, 2011), their frequency distribution, and the sample size. Though the effect of such sample characteristics is theoretically very straightforward, as we show, it is the first time that these sample characteristics are systematically studied in AGL-experiments. Their effects shown in the context of artificial language, may contribute to the understanding of natural grammar acquisition occurring under similar input sample conditions.