Probabilistic language models in cognitive neuroscience: Promises and pitfalls

NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS

卷:83

Review

Armeni, Kristijan¹  Willems, Roel M.^1,2,3  Frank, Stefan L.³ 

[1] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, Nijmegen, Netherlands

[2] Max Planck Inst Psycholinguist, Nijmegen, Netherlands

[3] Radboud Univ Nijmegen, Ctr Language Studies, Nijmegen, Netherlands

关键词: Cognitive neuroscience of language;    Computational linguistics;    EEG;    MEG;    fMRI;    Probabilistic language models;    Information theory;    Surprisal;    Entropy;   

DOI  :  10.1016/j.neubiorev.2017.09.001

来源: Elsevier

【 摘 要 】

Cognitive neuroscientists of language comprehension study how neural computations relate to cognitive computations during comprehension. On the cognitive part of the equation, it is important that the computations and processing complexity are explicitly defined. Probabilistic language models can be used to give a computationally explicit account of language complexity during comprehension. Whereas such models have so far predominantly been evaluated against behavioral data, only recently have the models been used to explain neurobiological signals. Measures obtained from these models emphasize the probabilistic, information-processing view of language understanding and provide a set of tools that can be used for testing neural hypotheses about language comprehension. Here, we provide a cursory review of the theoretical foundations and example neuroimaging studies employing probabilistic language models. We highlight the advantages and potential pitfalls of this approach and indicate avenues for future research.

【 授权许可】

Free