Linear models and linear mixed effects models in R with linguistic applications

This text is a conceptual introduction to mixed effects modeling with linguistic applications, using the R programming environment. The reader is introduced to linear modeling and assumptions, as well as to mixed effects/multilevel modeling, including a discussion of random intercepts, random slopes and likelihood ratio tests. The example used throughout the text focuses on the phonetic analysis of voice pitch data.Comment: 42 pages, 17 figure

Can co-speech gestures alone carry the mental time line?

Time and space have been shown to be interlinked in people’s minds. To what extent can co-speech gestures influence thinking about time, over and above spoken language? In this study, we use the ambiguous question “Next Wednesday’s meeting has been moved forward two days, what day is it on now?” to show that people either respond “Monday” or “Friday,” depending on gesture. We manipulated both language (using either the adverb “forward”, or the adverb “backward”) and gesture (forward and backward movement), thus creating matches and mismatches between speech and gesture. Results show that the speech manipulation exerts a stronger influence on people’s temporal perspectives than gesture. Moreover, the effect of gesture disappears completely for certain hand shapes and if non-movement language is used (“changed by two days” as opposed to “moved by two days”). We additionally find that the strength of the gesture effect is moderated by likeability: when people like the gesturer, they are more prone to assuming their perspective, which completely changes the meaning of forward and backward gestural movements. Altogether, our results suggest that gesture does play a role in thinking about time, but this role is auxiliary when compared to speech, and the degree to which gesture matters depends on one’s social relation to the gesturer