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From language to language-ish: How brain-like is an LSTM representation of nonsensical language stimuli?

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Kaufeld,  Greta
Psychology of Language Department, MPI for Psycholinguistics, Max Planck Society;
International Max Planck Research School for Language Sciences, MPI for Psycholinguistics, Max Planck Society;

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Martin,  Andrea E.
Language and Computation in Neural Systems, MPI for Psycholinguistics, Max Planck Society;
Donders Institute for Brain, Cognition and Behaviour, External Organizations;

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Citation

Hashemzadeh, M., Kaufeld, G., White, M., Martin, A. E., & Fyshe, A. (2020). From language to language-ish: How brain-like is an LSTM representation of nonsensical language stimuli? In Findings of the Association for Computational Linguistics: EMNLP 2020 (pp. 645-655).


Cite as: http://hdl.handle.net/21.11116/0000-0007-91BD-0
Abstract
The representations generated by many mod- els of language (word embeddings, recurrent neural networks and transformers) correlate to brain activity recorded while people read. However, these decoding results are usually based on the brain’s reaction to syntactically and semantically sound language stimuli. In this study, we asked: how does an LSTM (long short term memory) language model, trained (by and large) on semantically and syntac- tically intact language, represent a language sample with degraded semantic or syntactic information? Does the LSTM representation still resemble the brain’s reaction? We found that, even for some kinds of nonsensical lan- guage, there is a statistically significant rela- tionship between the brain’s activity and the representations of an LSTM. This indicates that, at least in some instances, LSTMs and the human brain handle nonsensical data similarly.