Can Computers Understand Words Like Humans Do? Comparable Semantic Representation in Neural and Computer Systems
Linmin Zhang, Lingting Wang, Jinbiao Yang, Peng Qian, Xuefei Wang, Xipeng Qiu, Zheng Zhang, Xing Tian
January 2023
 

Semantic representation has been studied independently in neuroscience and computer science. A deep understanding of human neural computations and the revolution to strong artificial intelligence appeal for a joint force in the language domain. To investigate comparable representational formats of lexical semantics between these two complex systems, we used fine temporal resolution neural recordings to create a novel open dataset and innovated analysis methods. Specifically, we evaluated three natural language processing (NLP) models with electroencephalography (EEG) recordings under a semantic priming paradigm. With our novel single-trial analysis method, we found semantic representations generated from computational models significantly correlated with EEG responses at an early stage of a typical semantic processing time window in a two-word semantic priming paradigm. Moreover, three representative computational models differentially predicted EEG responses along the dynamics of word processing. Our study thus developed an objective biomarker for assessing human-like computation in computational models. Our novel framework trailblazed a promising way to bridge across disciplines in the investigation of higher-order cognitive functions in human and artificial intelligence.
Format: [ pdf ]
Reference: lingbuzz/007584
(please use that when you cite this article)
Published in: Journal of Cognitive Science 23(4): 439-466, https://doi.org/10.17791/jcs.2022.23.4.439
keywords: semantic priming, word embedding models, n400, electroencephalography, semantics
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