TY - GEN

T1 - Infinite use of finite means?

T2 - 43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021

AU - McCoy, R. Thomas

AU - Culbertson, Jennifer

AU - Smolensky, Paul

AU - Legendre, Géraldine

N1 - Funding Information: For helpful comments, we are grateful to Grusha Prasad, Najoung Kim, Tal Linzen, Robert Frank, the JHU Neurosymbolic Computation Lab, and the NYU CAP Lab. This research was supported by NSF GRFP No. 1746891.

PY - 2021

Y1 - 2021

N2 - Human language is often assumed to make “infinite use of finite means”—that is, to generate an infinite number of possible utterances from a finite number of building blocks. From an acquisition perspective, this assumed property of language is interesting because learners must acquire their languages from a finite number of examples. To acquire an infinite language, learners must therefore generalize beyond the finite bounds of the linguistic data they have observed. In this work, we use an artificial language learning experiment to investigate whether people generalize in this way. We train participants on sequences from a simple grammar featuring center embedding, where the training sequences have at most two levels of embedding, and then evaluate whether participants accept sequences of a greater depth of embedding. We find that, when participants learn the pattern for sequences of the sizes they have observed, they also extrapolate it to sequences with a greater depth of embedding. These results support the hypothesis that the learning biases of humans favor languages with an infinite generative capacity.

AB - Human language is often assumed to make “infinite use of finite means”—that is, to generate an infinite number of possible utterances from a finite number of building blocks. From an acquisition perspective, this assumed property of language is interesting because learners must acquire their languages from a finite number of examples. To acquire an infinite language, learners must therefore generalize beyond the finite bounds of the linguistic data they have observed. In this work, we use an artificial language learning experiment to investigate whether people generalize in this way. We train participants on sequences from a simple grammar featuring center embedding, where the training sequences have at most two levels of embedding, and then evaluate whether participants accept sequences of a greater depth of embedding. We find that, when participants learn the pattern for sequences of the sizes they have observed, they also extrapolate it to sequences with a greater depth of embedding. These results support the hypothesis that the learning biases of humans favor languages with an infinite generative capacity.

KW - artificial language learning

KW - center embedding

KW - extrapolation

KW - inductive biases

KW - language acquisition

M3 - Conference contribution

AN - SCOPUS:85139425006

VL - 43

T3 - Proceedings of the Annual meetuing of the Cognitive Science Society

SP - 2225

EP - 2231

BT - Proceedings of the 43rd Annual Meeting of the Cognitive Science Society

PB - The Cognitive Science Society

Y2 - 26 July 2021 through 29 July 2021

ER -