Confidence was scaled, and we treated poet familiarity, poetry background, read frequency, liking poetry, and education level as ordered factors. We used this model to predict not whether participants answered “AI” or “human,” but whether participants answered the question correctly (e.g., answered “generated by AI” when the poem was actually generated by AI). As specified in our pre-registration, we predicted that participant expertise or familiarity with poetry would make no difference in discrimination performance. This was largely confirmed; the explanatory power of the model was low (McFadden’s R2 = 0.012), and none of the effects measuring poetry experience had a significant positive effect on accuracy. Confidence had a small but significant negative effect (b = -0.021673, SE = 0.003986, z = -5.437, p < 0.0001), indicating that participants were slightly more likely to guess incorrectly when they were more confident in their answer.
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