Interlocutor modelling in comprehending speech from interleaved interlocutors of different dialectic backgrounds.

It has been shown that, in language comprehension, listeners model certain attributes of their interlocutor (e.g., dialectic background, age, gender) and interpret speech against that model; for example, they understand cross-dialectally ambiguous words such as flat and gas for their American English (AE) meanings more often when listening to an AE interlocutor than a British English (BE) interlocutor. This study further investigated whether listeners construct concurrent interlocutor models when communicating with interleaved interlocutors of different dialectic backgrounds, and, if they do, how they choose between concurrent models to interpret words. In two experiments, participants heard a word (e.g., flat) spoken by a BE or AE interlocutor and provided a word associate (indicating which meaning of the word was accessed). When different interlocutors were encountered in separate blocks, participants accessed more AE meanings when listening to an AE rather than a BE interlocutor, and the accent effect was not larger for words pronounced more differently in BE and AE (e.g., fall sounds more distinctly British vs. American than flat does). These results suggest that participants constructed an interlocutor model (e.g., of a BE or an AE speaker) and used it (instead of accent details in a word) to guide word meaning access. When interlocutors were interleaved in the same block, we observed a comparable accent effect, which increased as a function of between-accent differences in pronunciation. These results suggest that participants constructed concurrent interlocutor models and used accent details in a word to select the appropriate interlocutor model. We also observed that the accent effect was comparable for two interleaved interlocutors of the same gender (e.g., a female BE interlocutor and a female AE interlocutor) and for two interleaved interlocutors of different genders (e.g., a female BE interlocutor and a male AE interlocutor). These results suggest that participants did not use gender-related voice details for model selection when accent details were sufficient for interlocutor model selection.