We report a study examining the role of linguistic context in modulating the influences of individual differences in fluid and crystalized intelligence on comprehension of literary metaphors. Three...

In this paper we examine the effect of two cognitive variables, Semantic Neighborhood Density and Interpretive Diversity, in first, distinguishing between literary (poetic) and nonliterary metaphor, and second, in determining what makes for a good metaphor. Analyses of items taken from a widely used set ofmetaphor norms indicated that while literary and nonliterary metaphor did not differ in many ways, the poetic items tended to 1) contain concepts that came from a more dense semantic space, 2) contain topic and vehicles (...) that came from equally dense semantic space, 3) suggest a greater number of possible interpretations as the topic and vehicle became more semantically dissimilar, and 4) evoke more emergent interpretations (i.e., less likely to be a characteristic of the topic or vehicle when considered separately). In addition, we found one way that the two variables were related to metaphor goodness: better metaphors were those with vehicles that came from increasingly less dense semantic space. This correlation was only reliable for literary, poetic items, presumably because these items were taken from a richer semantic environment suggesting many more alternative possibilities. (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)

I consider poetry composition from both the “inside” view of a poet and the “outside” view of a cognitive psychologist. From the perspective of a psychologist, I review behavioral and neural studies of the reception and generation of poetry, with emphasis on metaphor and symbolism. Taking the perspective of a poet, I discuss how the seeds for a poem may arise. Finally, I consider the prospects for future developments in a field of computational neurocognitive poetics.