Bodo Winter – författare

Linguistic synesthesias combine different senses, as in English smooth melody (touch→sound). For nearly a century, researchers have gathered data that has been interpreted as supporting the notion of a hierarchical ordering of the senses. According to this proposal, expressions map the presumed-to-be 'lower' senses of touch, taste, and smell onto the presumed-to-be 'higher' senses of sound and sight. Here, this proposal is tested in the first-ever meta-analysis of linguistic synesthesias, combining thirty-eight datasets from fourteen different languages. The authors demonstrate that clear patterns emerge from the data, but many such patterns are inconsistent with the notion of a linear hierarchical order or a simple lower/higher divide of the senses. This calls for a shift in what theories are considered to be viable for explaining asymmetries between the senses in linguistic synesthesia.

Linguistic synesthesias combine different senses, as in English smooth melody (touch→sound). For nearly a century, researchers have gathered data that has been interpreted as supporting the notion of a hierarchical ordering of the senses. According to this proposal, expressions map the presumed-to-be 'lower' senses of touch, taste, and smell onto the presumed-to-be 'higher' senses of sound and sight. Here, this proposal is tested in the first-ever meta-analysis of linguistic synesthesias, combining thirty-eight datasets from fourteen different languages. The authors demonstrate that clear patterns emerge from the data, but many such patterns are inconsistent with the notion of a linear hierarchical order or a simple lower/higher divide of the senses. This calls for a shift in what theories are considered to be viable for explaining asymmetries between the senses in linguistic synesthesia.

Statistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual issues and avoiding excessive mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this text is the ideal main resource for graduate and advanced undergraduate students of Linguistics statistics courses as well as those in other fields, including Psychology, Cognitive Science, and Data Science.