was conducted to see if the mean difference in surprisal differed from zero. The results were statistically significant ($p < 10^{-5}$, BF $> 100$). The mean difference in surprisal was 1.15, however, suggesting that the models still had a LOW bias when the lower nominal was syntactically unavailable for attachment. This is in stark contrast to what one would expect if these models had learned the relationship between syntactic constituents and relative clause attachment. A possible alternative to the recency bias explanation is that RNN LMs might learn that there is a general LOW attachment bias in English and overgeneralize this pattern even in cases where one of the nominals is syntactically unavailable.

The case of default HIGH bias: Spanish {#es_replication}
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Our English analyses suggest that RNN LMs either learn a general English LOW attachment preference that they apply in all contexts, or that they have a ‘default’ recency bias that prevents them from learning HIGH attachment preferences with more complex, naturalistic training data. In the case of the former, we would expect that models trained on a language whose speakers generally prefer HIGH attachment should be able to learn HIGH attachment. Spanish has a well-attested HIGH bias in 