An emerging line of work uses psycholinguistic methods to evaluate the syntactic generalizations acquired by neural language models (NLMs). While this approach has shown NLMs to be capable of learning a wide range of linguistic knowledge, confounds in the design of previous experiments may have obscured the potential of NLMs to learn certain grammatical phenomena. Here we re-evaluate the performance of a range of NLMs on reflexive anaphor licensing. Under our paradigm, the models consistently show stronger evidence of learning than reported in previous work. Our approach demonstrates the value of well-controlled psycholinguistic methods in gaining a fine-grained understanding of NLM learning potential
