The E-Z Reader model (Reichle et al. 1998; 1999) provides a theoretical framework for understanding how word identification, visual processing, attention, and oculomotor control jointly determine when and where the eyes move during reading. In this article, we first review what is known about eye movements during reading. Then we provide an updated version of the model (E-Z Reader 7) and describe how it accounts for basic findings about eye movement control in reading. We then review several alternative models of (...) eye movement control in reading, discussing both their core assumptions and their theoretical scope. On the basis of this discussion, we conclude that E-Z Reader provides the most comprehensive account of eye movement control during reading. Finally, we provide a brief overview of what is known about the neural systems that support the various components of reading, and suggest how the cognitive constructs of our model might map onto this neural architecture. Key Words: attention; eye-movement control; E-Z Reader; fixations; lexical access; models; reading; regressions; saccades. (shrink)

ABSTRACTIn this article we investigate structural differences between “literary” metaphors created by renowned poets and “nonliterary” ones imagined by non-professional authors from Katz et al.’s 1988 corpus. We provide data from quantitative narrative analyses of the altogether 464 metaphors on over 70 variables, including surface features like metaphor length, phonological features like sonority score, or syntactic-semantic features like sentence similarity. In a first computational study using machine learning tools we show that Katz et al.’s literary metaphors can be successfully discriminated (...) from their nonliterary ones on the basis of response measures, in particular the ratings for familiarity, ease of interpretation, semantic relatedness, and comprehensibility. A second computational study then shows that the classifier can reliably detect and predict between-group differences on the basis of five QNA features generalizing from a... (shrink)

Two main goals of the emerging field of neurocognitive poetics are the use of more natural and ecologically valid stimuli, tasks and contexts and providing methods and models allowing to quantify distinctive features of verbal materials used in such tasks and contexts and their effects on readers responses. A natural key element of poetic language, metaphor, still is understudied insofar as relatively little empirical research looked at literary or poetic metaphors. An exception is Katz et al.’s corpus of 204 literary (...) metaphors by authors such as Shakespeare or Dylan Thomas, for which various rating data are available. We reanalyzed their corpus using a combination of quantitative narrative analysis, latent semantic analysis, and machine learning in order to identify relevant features of the metaphors that influenced the ratings. The combined application of computational tools sheds light on surface and affective-semantic features that co-determine the reception of poetic metaphors and successfully predicted the period of origin, authorship and goodness ratings of the metaphors. The present results can be used for generating quantitative hypotheses or selecting and matching verbal stimuli in empirical studies of literature and neurocognitive poetics. (shrink)