Can computers understand word meanings like the human brain does? Comparable semantic representation in neural and computer systems

Semantic representation, a crucial window into human cognition, has been studied independently in neuroscience and computer science. A deep understanding of neural computations in the human brain and the revolution to a strong artificial intelligence appeal for a necessity of joint force in the language domain. We investigated the representational formats of comparable lexical semantic features between these two complex systems with fine temporal resolution neural recordings. 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 selected computational models differentially predicted EEG responses along the dynamics of word processing in the human brain. Our study provided a finer-grained understanding of the neural dynamics underlying semantic processing and 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.