Recency {#synthetic}
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We begin with a proof of concept. It has been noted that RNN LMs have a strong recency bias [@ravfogeletal2019]. As such, it could be possible that only one type of attachment, namely LOW attachment, is learnable. To investigate this possibility, we followed the methodology in @mccoyetal2018 and constructed a synthetic language to control the distribution of RC attachment in two experiments. Our first experiment targeted the question: if all RC attachment is HIGH, how many RCs have to be observed in training in order for a HIGH bias to generalize to unseen data? Our second experiment targeted the question: what proportion of HIGH and LOW attachment is needed in training to learn a bias?

Our synthetic language had RC attachment sentences and filler declarative sentences. The filler sentences follow the phrase structure template given in \[phraseA\], while RC attachment sentences follow the phrase structure template given in \[phraseB\].

. \[phrase\] . D N (P D N) (Aux) V (D N) (P D N) \[phraseA\] . D N Aux V D N ‘of’ D N ‘that’ ‘was/were’ V \[phraseB\]

Material in parentheses was optional and so was not present in all filler stimuli. That is to say, 