The effect of preferred background music on task-focus in sustained attention

Abstract

Although many people listen to music while performing tasks that require sustained attention, the literature is inconclusive about its effects. The present study examined performance on a sustained-attention task and explored the effect of background music on the prevalence of different attentional states, founded on the non-linear relationship between arousal and performance. Forty students completed a variation of the Psychomotor Vigilance Task-that has long been used to measure sustained attention-in silence and with their self-selected or preferred music in the background. We collected subjective reports of attentional state (specifically mind-wandering, task-focus and external distraction states) as well as reaction time (RT) measures of performance. Results indicated that background music increased the proportion of task-focus states by decreasing mind-wandering states but did not affect external distraction states. Task-focus states were linked to shorter RTs than mind-wandering or external distraction states; however, background music did not reduce RT or variability of RT significantly compared to silence. These findings show for the first time that preferred background music can enhance task-focused attentional states on a low-demanding sustained-attention task and are compatible with arousal mediating the relationship between background music and task-performance.

Fig. 1

Inverted-U relationship between arousal, attentional states (mind-wandering, task-focus, external distraction) and performance, as hypothesised by Unsworth and Robison (2016) and in similar form Yerkes and Dodson (1908)

Fig. 2

Schematic representation of a single experimental trial with a thought-probe after the trial

Fig. 3

Thought-probe response proportions as a function of attentional-state category and music-present/absent (music/no music). Error bars represent ± 1 standard error of the mean (S.E.M.)

Fig. 4

Thought-probe response proportions as a function of time-on-task (block number). Error bars represent ± 1 S.E.M

Fig. 5

Mean RT as a function of attentional-state category. Error bars represent ± 1 S.E.M

Fig. 6

Mean RT as a function of time-on-task (block number) and music-present/absent (music/no music). Error bars represent ± 1 S.E.M

Fig. 7

Standard deviation of RT as a function of time-on-task (block number) and music-present/absent (music/no music). Error bars represent ± 1 S.E.M

Fig. 8

Each participant was assigned to the genre that made up the greatest proportion of time in their playlist. The graph represents the number of participants for each genre who listened to that genre for the greatest proportion of time. The ‘other’ category included tracks that could not be otherwise categorised, such as Japanese anime music, music for meditation, and some indie and folk tracks that did not clearly fit into a different main category. The ‘instrumental’ category included tracks that were primarily produced using musical instruments, such as remixes of original tracks, piano ‘lounge’ music, and ‘ambient’ music