model behavior on a wider range of stimuli. For space considerations, we only report here results of the <span style="font-variant:small-caps;">Extended Data</span> (the larger set of stimuli), but similar results hold for the @fernadez2003bilingual stimuli (see Supplemental Materials).

In order to compare the model results with the mean human interpretation results reported by @fernadez2003bilingual, we categorically coded the model response to each item for HIGH/LOW attachment preference. If model surprisal for LOW attachment was less than model surprisal for HIGH attachment, the attachment was coded as LOW. See Figure \[fig:en\_full\] for the comparison between RNNs and humans in English.

Statistical robustness for our RNN results was determined using the original distribution of surprisal values. Specifically, a two-tailed t-test was conducted to see if the mean difference in surprisal differed from zero (i.e. the model has some attachment bias). This revealed a highly significant ($p < 10^{-5}$, BF $> 100$) mean difference in surprisal of 0.77. This positive difference indicates that the RNN LMs have a consistent LOW bias, similar to English readers, across models trained with differing random seeds.

![Proportion HIGH vs LOW attachment in English. Human results from the original @fernadez2003bilingual experiment and RNN LM results from <span style="font-variant:small-caps;">Extended Data</span> 