“…Recently, a QNA-based predictive approach was successfully applied to account for eye tracking parameters in the reading of three of Shakespeare's sonnets (sonnet 27, 60, and 66) using multiple psycholinguistic features . In the study of Xue et al, 2019, seven surface psycholinguistic features, a combination of well-studied (word length, word frequency, and higher frequent neighbors) and less-studied and novel features (orthographic neighborhood density, orthographic dissimilarity, consonant vowel quotient, and sonority score), were computed based on the Neurocognitive Poetics Model (NCPM, Jacobs, 2011Jacobs, , 2015aWillems and Jacobs, 2016;Nicklas and Jacobs, 2017) and recent proposals about QNA (e.g., Jacobs, 2017Jacobs, , 2018a. In addition, two non-linear interactive approaches, i.e., neural nets and bootstrap forests, were compared with a general linear approach (standard least squares regression), to look for the best way to predict three eye tracking parameters (first fixation duration, total reading time, and fixation probability) using the seven above mentioned features.…”