attachment in the training data. These results suggest that any recency bias in RNN LMs is weak enough to be easily overcome by sufficient evidence of HIGH attachment. In English, the RNNs exhibited a human-like LOW bias, but this preference persisted even in cases where LOW attachment was ungrammatical. To test whether the RNNs were over-learning a general LOW bias of English, we tested whether Spanish RNNs learned the general HIGH bias in that language. Once again, RNN LMs favored LOW attachment over HIGH attachment. The inability of RNN LMs to learn the Spanish HIGH attachment preference suggests that the Spanish data may not contain enough HIGH examples to learn human-like attachment preferences.

In post-hoc analyses of the Spanish Wikipedia training corpus and the AnCora Spanish newswire corpus [@ancora], we find a consistent production bias towards LOW attachment among the RCs with unambiguous attachment. In Spanish Wikipedia, LOW attachment is 69% more frequent than HIGH attachment, and in Spanish newswire data, LOW attachment is 21% more frequent than HIGH attachment.[^13] This distributional bias in favor of LOW attachment does not rule out a subsequent HIGH RC bias in the models. It has been established in the psycholinguistic literature that attachment 