We used a general linear mixed model logistic regression analysis (fit to a binomial distribution) to predict participant responses (“written by a human” or “generated by AI”) with poem’s authorship (human or AI), the identity of the poet, and their interaction as fixed effects. We used a sum coding for the identity of the poet, to interpret more easily the main effect of authorship across poets. As specified in our pre-registration, we initially included three random effects: random intercepts for participants (since we took 10 repeated measurements, one per poem, for each participant), random intercepts for poems, and random slopes for the identity of the poet for each poem. Following19, we used principal component analysis (PCA) to check for overparameterization, and determined that the model was indeed overparameterized.
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