The successes and failures of artificial neural networks (ANNs) highlight the importance of innate linguistic priors for human language acquisition.

Artificial neural networks (ANNs) equipped with general learning algorithms, but no linguistic knowledge, can learn to associate words with objects in naturalistic scenes when trained on head-mounted video recordings from a single child's first-person experience. Similarly, ANNs can master syntax when trained on a similar amount of linguistic data a child experiences in a few years. These findings have been taken to challenge the view that innate linguistic priors play a role in child language acquisition. Here I show that the training environments and learning resources of ANNs and humans are poorly matched, and accordingly, conclusions regarding human language priors are not merited. I also review three sets of findings that strongly suggest ANNs are missing human inductive biases: (a) children (but not ANNs) create new well-structured languages when only exposed to degraded ones; (b) ANN (but not humans) learn impossible and possible human languages in similar ways with similar facility; and (c) humans (but not ANNs) show a critical period for language learning. In this last case, adding an "innate" inductive prior to an ANN resulted in better ANN-human alignment. Just as is the case regarding claims of ANN-human alignment in the domain of vision, conclusions regarding ANN-human alignment in the domain of language are characterized by a lack of severe testing of hypotheses. (PsycInfo Database Record (c) 2026 APA, all rights reserved).