humans [@carreiras1993relative; @carreiras1999esenparsing; @fernadez2003bilingual] offering a way to distinguish between competing recency bias and over-generalization accounts. That is, if the models can learn a HIGH bias when trained on Spanish data, we should be able to conclude that the general LOW bias in English is being overgeneralized by the RNNs to corner cases where HIGH bias should be preferred.

![Proportion HIGH vs LOW attachment in Spanish. Human results from the original @fernadez2003bilingual experiment and RNN LM results from the <span style="font-variant:small-caps;">Extended Data</span> (derived from @fernadez2003bilingual and @carreiras1993relative).[]{data-label="fig:es_full"}](figures/ES_extension_HIGH_LOW.png){width="8cm"}

Results
-------

As before, the differences in surprisal were calculated for each pair of experimental items. If the difference was greater than zero, the attachment was coded as LOW. Two sample t-tests were conducted to see if the mean difference in surprisal differed significantly from zero for both the direct simulation of @fernadez2003bilingual and the <span style="font-variant:small-caps;">Extended Data</span> that included the stimuli derived from @carreiras1993relative. The results categorically coded for HIGH/LOW attachment for the extended stimulus set are given in Figure \[fig:es\_full\], alongside the human results reported in @fernadez2003bilingual.

For the direct simulation, the mean did not differ significantly from 0 (BF $< 1/3$). This suggests that there is no attachment bias for 