Generalization in Artificial Language Learning: Modelling the Propensity to Generalize

Alhama, R. G., & Zuidema, W. (2016). Generalization in Artificial Language Learning: Modelling the Propensity to Generalize. In Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning (pp. 64-72). Association for Computational Linguistics. doi:10.18653/v1/W16-1909.
Experiments in Artificial Language Learn-
ing have revealed much about the cogni-
tive mechanisms underlying sequence and
language learning in human adults, in in-
fants and in non-human animals. This pa-
per focuses on their ability to generalize
to novel grammatical instances (i.e., in-
stances consistent with a familiarization
pattern). Notably, the propensity to gen-
eralize appears to be negatively correlated
with the amount of exposure to the artifi-
cial language, a fact that has been claimed
to be contrary to the predictions of statis-
tical models (Pe
̃
na et al. (2002); Endress
and Bonatti (2007)). In this paper, we pro-
pose 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.
Publication type
Proceedings paper
Publication date
2016

Share this page