As an exploratory analysis, we refit the model with the addition of several variables reflecting structural features of the stimuli. Following10, which found that participants use flawed heuristics based on grammar and vocabulary cues to identify AI-generated texts, we examined whether participants look to structural and grammatical features of the poems to determine authorship. To test this, we added to the previous model stimulus word count (scaled), stimulus line count (scaled), stimulus all-lines-rhyme (a binary variable indicating whether or not all lines in the poem end with a rhyme), stimulus quatrain (a binary variable indicating whether the poem was formatted entirely in four-line stanzas, i.e., “quatrains”), and stimulus first person (a variable reflecting whether or not the poem was written in first person, with 3 values: “I” if written in singular first person, “we” if written in plural first person, and “no” if not written in first person).