Elicited imitation is a widely used method for testing a child's knowledge of a language for scientific or clinical purposes. A child hears an utterance and is asked to repeat what they have heard. While it is assumed that their fluency or speed in doing so is contingent on their linguistic competence, little is known about the cognitive mechanisms and/or representations involved. To explore this, we train an encoder-decoder model, consisting of recurrent neural networks, to encode and reproduce a corpus of child-directed speech and then test its performance on the experimental task of Bannard and Matthews (2008). In that study pre-school children were asked to repeat high- and low-frequency four-word sequences in which the first three words were identical (e.g., sit in your chair and sit in your truck) and the final words and bigrams were closely matched for frequency. We find that like those children our model makes more errors on the initial three words when they are part of a low-frequency than a high-frequency sequence, despite the fact that the words being repeated are identical. We explore why this might be and pinpoint some possible similarities between the model and child language processing.

Modelling Children's Sentence Recall using an Encoder-Decoder Network

We present a computational (LSTM) model that learns to produce English (3sg and -bare) verb inflection when trained on English child-directed speech (CDS). The model is trained on input containing morphemized verbs and learns to predict the next word given a preceding sequence of words. The model produces the type of error (-bare for -3s) shown by English-learning children while avoiding errors that children do not often make (-3s for -bare). The model also shows the same type of sensitivity to input statistics that has been reported in English-learning children. Finally, we manipulated the length of the sequences the model is trained on, and show that this results in the delayed acquisition of -3sg forms that is characteristic of English-learning children with Developmental Language Disorder (DLD). Taken together these results suggest that input-driven learning is a major determinant of both typical and impaired acquisition of English verb inflection.

Simulating children’s verb inflection errors in English using an LSTM language model

FT40: Language Acquisition 2

poster

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CogSci 2022

The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





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CogSci 2023

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Daniel Freudenthal

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Modelling Children's Sentence Recall using an Encoder-Decoder Network

Simulating children’s verb inflection errors in English using an LSTM language model

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