Costs of switching auditory spatial attention in following conversational turn-taking

Gaven Lin, Simon Carlile

Following a multi-talker conversation relies on the ability to rapidly and efficiently shift the focus of spatial attention from one talker to another. The current study investigated the listening costs associated with shifts in spatial attention during conversational turn-taking in 16 normally-hearing listeners using a novel sentence recall task. Three pairs of syntactically fixed but semantically unpredictable matrix sentences, recorded from a single male talker, were presented concurrently through an array of three loudspeakers (directly ahead and +/-30° azimuth). Subjects attended to one spatial location, cued by a tone, and followed the target conversation from one sentence to the next using the call-sign at the beginning of each sentence. Subjects were required to report the last three words of each sentence (speech recall task) or answer multiple choice questions related to the target material (speech comprehension task). The reading span test, attention network test, and trail making test were also administered to assess working memory, attentional control, and executive function. There was a 10.7 ± 1.3% decrease in word recall, a pronounced primacy effect, and a rise in masker confusion errors and word omissions when the target switched location between sentences. Switching costs were independent of the location, direction, and angular size of the spatial shift but did appear to be load dependent and only significant for complex questions requiring multiple cognitive operations. Reading span scores were positively correlated with total words recalled, and negatively correlated with switching costs and word omissions. Task switching speed (Trail-B time) was also significantly correlated with recall accuracy. Overall, this study highlights (i) the listening costs associated with shifts in spatial attention and (ii) the important role of working memory in maintaining goal relevant information and extracting meaning from dynamic multi-talker conversations.