Listening to music in the first, but not the last 1.5 km of a 5-km running trial alters pacing strategy and improves performance

We examined the effects of listening to music on attentional focus, rating of perceived exertion (RPE), pacing strategy and performance during a simulated 5-km running race. 15 participants performed 2 controlled trials to establish their best baseline time, followed by 2 counterbalanced experimental trials during which they listened to music during the first (M-start) or the last (M-finish) 1.5 km. The mean running velocity during the first 1.5 km was significantly higher in M-start than in the fastest control condition (p < 0.05), but there was no difference in velocity between conditions during the last 1.5 km (p > 0.05). The faster first 1.5 m in M-start was accompanied by a reduction in associative thoughts compared with the fastest control condition. There were no significant differences in RPE between conditions (p > 0.05). These results suggest that listening to music at the beginning of a trial may draw the attentional focus away from internal sensations of fatigue to thoughts about the external environment. However, along with the reduction in associative thoughts and the increase in running velocity while listening to music, the RPE increased linearly and similarly under all conditions, suggesting that the change in velocity throughout the race may be to maintain the same rate of RPE increase.

A quantitative approach to evolution of music and philosophy

The development of new statistical and computational methods is increasingly making it possible to bridge the gap between hard sciences and humanities. In this study, we propose an approach based on a quantitative evaluation of attributes of objects in fields of humanities, from which concepts such as dialectics and opposition are formally defined mathematically. As case studies, we analyzed the temporal evolution of classical music and philosophy by obtaining data for 8 features characterizing the corresponding fields for 7 well-known composers and philosophers, which were treated with multivariate statistics and pattern recognition methods. A bootstrap method was applied to avoid statistical bias caused by the small sample data set, with which hundreds of artificial composers and philosophers were generated, influenced by the 7 names originally chosen. Upon defining indices for opposition, skewness and counter-dialectics, we confirmed the intuitive analysis of historians in that classical music evolved according to a master apprentice tradition, while in philosophy changes were driven by opposition. Though these case studies were meant only to show the possibility of treating phenomena in humanities quantitatively, including a quantitative measure of concepts such as dialectics and opposition, the results are encouraging for further application of the approach presented here to many other areas, since it is entirely generic.