the Spanish models for the stimuli from @fernadez2003bilingual, contrary to the human results. For the extended set of stimuli, the results were significant ($p < 10^{-5}$, BF $> 100$) with a mean difference greater than zero ($\mu = 0.211$). Thus, rather than a HIGH bias, as we would expect, the RNN LMs once again had a LOW bias.

Discussion
==========

In this work, we explored the ability of RNN LMs to prioritize multiple simultaneous valid interpretations in a human-like way (as in *John met the student of the teacher that was happy*). While both LOW attachment (i.e. *the teacher was happy*) and HIGH attachment (i.e. *the student was happy*) are equally semantically plausible without a disambiguating context, humans have interpretation preferences for one attachment over the other (e.g., English speakers prefer LOW attachment and Spanish speakers prefer HIGH attachment). Given the recent body of literature suggesting that RNN LMs have learned abstract syntactic representations, we tested the hypothesis that these models acquire human-like attachment preferences. We found that they do not.

We first used a synthetic language experiment to demonstrate that RNN LMs are capable of learning a HIGH bias when HIGH attachment is at least as frequent as LOW 