(11) Bonus from the appendix 💰💰💰
Prompted by reviewers, we investigated the effect of label choice in binary acceptability rating (acceptable, good, grammatical, natural), and found that participants’ behavior is invariant to the specific labels that the study uses.

(8) We then replaced verb frames with adjective frames (e.g., “What was Mary glad that John bought?”), finding that both P(adjective) and P(that | adjective) predicted acceptability ratings in a similar way. Thus, a broader range of constructions is governed by word and construction probabilities.

(7) We conducted a replication of LR, adding fillers, removing catch trials, and making slight changes in the critical materials, and arrived at the same results. We learn that these effects are robust and replicable, despite the changes in materials and study platform (MTurk -> Prolific).

(6) P(verb) and P(that | verb) were largely independent; this justified using them and their interaction in an alternative model to that of LR, which only used the bigram probability of ‘{verb} that’. Both parameters and their interaction were significant and positive!

(5) We first extracted the probabilities of the verbs, P(verb), from the Corpus of Contemporary American English, and syntactically parsed the sentences therein to extract the argument structure of each verb, finding the probability with which each verb takes a sentence complement, P(that | verb).