The processing of metaphors and idioms has been the subject of neuroscientific research for several decades. However, results are often contradictory, which can be traced back to inconsistent terminology and stimulus control. In this systematic review of research methods, we analyse linguistic aspects of 116 research papers which used EEG, fMRI, PET, MEG, or NIRS to investigate the neural processing of the two figurative subtypes metaphor and idiom. We critically examine the theoretical foundations as well as stimulus control by performing (...) a systematic literature synthesis according to the PRISMA guidelines. We explicitly do not analyse the findings of the studies but instead focus on four primary aspects: definitions of figurative language and its subtypes, linguistic theory behind the studies, control for factors influencing figurative language processing, and the relationship between theoretical and operational definitions. We found both a lack and a broad variety in existing definitions and operationalisation, especially in regard to familiarity and conventionality. We identify severe obstacles in the comparability and validation potential of the results of the papers in our review corpus. We propose the development of a consensus in fundamental terminology and more transparency in the reporting of stimulus design in the research on figurative language processing. (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)

If the words of natural human language possess a universal positivity bias, as assumed by Boucher and Osgood’s (1969) famous Pollyanna hypothesis and computationally confirmed for large text corpora in several languages (Dodds et al., 2015), then children and youth literature (CYL) should also show a Pollyanna effect. Here we tested this prediction applying a vector space model- based sentiment analysis tool called SentiArt (Jacobs, 2019) to two CYL corpora, one in English (372 books) and one in German (500 books). (...) Pitching our analysis at the sentence level, and assessing semantic as well as lexico-grammatical information, bBoth corpora show the Pollyanna effect and thus add further evidence to the universality hypothesis. The results of our multivariate sentiment analyses provide interesting testable predictions for future scientific studies of literature. (shrink)

Cognitive Science, Volume 46, Issue 9, September 2022.