2021
DOI: 10.1037/xge0000903
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Hierarchical beat perception develops throughout childhood and adolescence and is enhanced in those with musical training.

Abstract: Most music is temporally organized within a metrical hierarchy, having nested periodic patterns that give rise to the experience of stronger (downbeat) and weaker (upbeat) events. Musical meter presumably makes it possible to dance, sing, and play instruments in synchrony with others. It is nevertheless unclear whether or not listeners perceive multiple levels of periodicity simultaneously, and if they do, when and how they learn to do this. We tested children, adolescents, and musically trained and untrained … Show more

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Cited by 41 publications
(54 citation statements)
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References 143 publications
(253 reference statements)
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“…To sum up, in both explicit and implicit perceptual tasks, humans have been shown to be able to detect a beat, rhythmic patterns, and hierarchical metrical structure. These rhythmic abilities were shown in both musically trained and untrained subjects, though especially for explicit tasks [40] and the perception of hierarchical structure [43], training seems to improve performance. Using discrimination tasks akin to explicit timing tasks in humans, some species of birds, rats, crickets, and frogs were shown to discern different rhythmic patterns, but several of these findings can be explained by simple discrimination of single absolute temporal intervals.…”
Section: Implicit Rhythm Perception Tasksmentioning
confidence: 89%
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“…To sum up, in both explicit and implicit perceptual tasks, humans have been shown to be able to detect a beat, rhythmic patterns, and hierarchical metrical structure. These rhythmic abilities were shown in both musically trained and untrained subjects, though especially for explicit tasks [40] and the perception of hierarchical structure [43], training seems to improve performance. Using discrimination tasks akin to explicit timing tasks in humans, some species of birds, rats, crickets, and frogs were shown to discern different rhythmic patterns, but several of these findings can be explained by simple discrimination of single absolute temporal intervals.…”
Section: Implicit Rhythm Perception Tasksmentioning
confidence: 89%
“…Humans can differentiate between non-isochronous rhythms with regular accents ("strictly metrical rhythms", with a strong beat, see Figure 2B-III for an example) and without regular accents ("weakly-or non-metrical rhythms", with a weaker beat, see Figure 2B-IV for an example), by explicitly rating expected ease of tapping along [40,41], and beat presence [42]. Also, human ratings of rhythmicity show sensitivity to hierarchical structure [43,44].…”
Section: Explicit Rhythm Perception Tasksmentioning
confidence: 99%
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“…In this respect, it should be noted that the perception of metrical structures in a rhythm strongly relies on subjective experiences and culture (Cameron et al, 2015;London et al, 2017;van der Weij et al, 2017;Bouwer et al, 2018;Nave-Blodgett et al, 2021) and could be shaped by passive exposure to music (Jacoby and McDermott, 2017). For instance, some of the complex rhythms adopted here are common in certain folk music from Balkan, east European, or in jazz music, but almost absent in other cultures (e.g., they are almost absent in the Italian folk music, the state where participants were recruited and tested).…”
Section: Discussionmentioning
confidence: 99%
“…Based on these perceptual rules we can generate simple rhythms (i.e., sequences with equally spaced perceptual accents); alternatively, we can generate complex rhythms (i.e., sequences with unequally spaced perceptual accents (London, 1995)). The ability to extract a temporal meter from rhythms relies on musical training and on musical exposure to those rhythms (Cameron et al, 2015;London et al, 2017;van der Weij et al, 2017;Bouwer et al, 2018;Nave-Blodgett et al, 2021). This means that certain rhythms may induce, or favor (particularly in participants not previously exposed to those rhythms), perceptual strategies based on non-metrical rules, such as chunking and serial grouping rules (non-metrical coding) (Grahn and Brett, 2007;Grube and Griffiths, 2009;Bouwer et al, 2018).…”
Section: Introductionmentioning
confidence: 99%