particular, we probe the biases of neural networks for ambiguous relative clause (RC) attachments, such as the following:

. \[ambigrel\] Andrew had dinner yesterday with the of the *that was divorced*. [from @fernadez2003bilingual]

In \[ambigrel\], there are two nominals (*nephew* and *teacher*) that are available for modification by the RC (*that was divorced*). We refer to attachment of the RC to the syntactically higher nominal (i.e. the nephew is divorced) as HIGH and attachment to the lower nominal (i.e. the teacher is divorced) as LOW.

As both interpretations are equally semantically plausible when no supporting context is given, we might expect that humans choose between HIGH and LOW at chance. However, it has been widely established that English speakers tend to interpret the relative clause as modifying the lower nominal more often than the higher nominal [i.e. they have a LOW bias;[^1] @carreiras1993relative; @frazier1996construal; @carreiras1999esenparsing; @fernadez2003bilingual]. LOW bias is actually typologically much rarer than HIGH bias [@brysbaert1996modifier]. A proto-typical example of a language with HIGH attachment bias is Spanish [see @carreiras1993relative; @carreiras1999esenparsing; @fernadez2003bilingual].

A growing body of literature has shown that English linguistic structures conveniently overlap with non-linguistic biases in neural language models leading to performance advantages for models of 