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

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