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In recent years, deep neural language models have made strong progress invarious NLP tasks. This work explores one facet of the question whetherstate-of-the-art NLP models exhibit elementary mechanisms known from humancognition. The exploration is focused on a relatively primitive mechanism forwhich there is a lot of evidence from various psycholinguistic experiments withinfants. The computation of "abstract sameness relations" is assumed to play animportant role in human language acquisition and processing, especially inlearning more complex grammar rules. In order to investigate this mechanism inBERT and other pre-trained language models (PLMs), the experiment designs fromstudies with infants were taken as the starting point. On this basis, wedesigned experimental settings in which each element from the original studieswas mapped to a component of language models. Even though the task in ourexperiments was relatively simple, the results suggest that the cognitivefaculty of computing abstract sameness relations is stronger in infants than inall investigated PLMs.

Is the Computation of Abstract Sameness Relations Human-Like in Neural Language Models?