Generalization in Artificial Language Learning: Modelling the Propensity to Generalize

摘要

Experiments in Artificial Language Learning have revealed much about the cognitive mechanisms underlying sequence and language learning in human adults, in infants and in non-human animals. This paper focuses on their ability to generalize to novel grammatical instances (i.e., instances consistent with a familiarization pattern). Notably, the propensity to generalize appears to be negatively correlated with the amount of exposure to the artificial language, a fact that has been claimed to be contrary to the predictions of statistical models (Pena et al. (2002); Endress and Bonatti (2007)). In this paper, we propose to model generalization as a three-step process, and we demonstrate that the use of statistical models for the first two steps, contrary to widespread intuitions in the ALL-field, can explain the observed decrease of the propensity to generalize with exposure time.