Effects of Participant Engagement on Prosodic Prominence

Abstract

It is generally assumed that prosodic cues that provide linguistic information, like discourse status, are driven primarily by the information structure of the conversation. This article investigates whether speakers have the capacity to adjust subtle acoustic-phonetic properties of the prosodic signal when they find themselves in contexts in which accurate communication is important. Thus, we examine whether the communicative context, in addition to discourse structure, modulates prosodic choices when speakers produce acoustic prominence. We manipulated the discourse status of target words in the context of a highly communicative task (i.e., working with a partner to solve puzzles in the computer game Minecraft) and in the context of a less communicative task more typical of psycholinguistic experiments (i.e., picture description). Speakers in the more communicative task produced prosodic cues to discourse structure that were more discriminable than those in the less communicative task. In a second experiment, we found that the presence or absence of a conversational partner drove some, but not all, of these effects. Together, these results suggest that speakers can modulate the prosodic signal in response to the communicative and social context.

Funding

We thank Dominique Simmons, Luis Paneque, Samantha Jensen, and Kelsey Mills for assistance with data collection and coding and Cheyenne Munson Toscano for help creating the Minecraft map. ABL was supported by National Institutes of Health grant T32-HD055272. JCT was supported by a Postdoctoral Fellowship from the Beckman Institute. DGW is supported by R01 DC008774 and the James S. McDonnell foundation.

Notes

¹ A visual inspection of the data revealed that the two conditions have similar cue values in both communicative contexts. For this reason we collapse the two categories and focus on the differences between the focus and nonfocus conditions as a function of context.

² We also examined models with both by-subject and by-item (i.e., color word) random effects; these revealed the same pattern of results for the critical analyses (i.e., the interaction and main effect of information status within each task). Since there were only six different color words in the critical position in the lists, an item analysis likely does not have sufficient power to draw major conclusions. Thus, we present the by-subject models here.

³ The reliability metric given in Toscano and McMurray (2010) also includes terms for the likelihood of each category (to handle the fact that in their mixture model simulations, some categories had likelihoods near zero and, thus, should contribute little to the reliability estimates). Here, we simplify the equation by assuming that each category is equally likely and drop the likelihood terms.