Dancers’ Auditory Perception of Microtiming Deviations Within Drum Grooves
The purpose of this descriptive study is to investigate the differences in microtiming discrimination – perception of rhythmic fluctuations at the millisecond level – between dancers, percussionists, and business students. Many popular musical genres have established grooves or feels, and experienced dancers express those grooves through movement. This study aims to produce correlative data that can provide insight for music educators when teaching music of different styles in their classroom. This study may help provide evidence supporting the use of popular music, multicultural music, and movement or dancing in the classroom so that students receive a well-rounded music education. I hypothesized that the discrimination accuracy of the dancers would be in between that of the percussionists and the business students. A sub-hypothesis included that experienced dancers will be more sensitive to microtiming deviations within their preferred musical style compared to inexperienced participants.
The research questions addressed in this study include:
- How do dancers’ auditory perception of microtiming deviations in drum grooves in various styles of music differ from percussionists and business students?
- To what extent does dancing experience in a specific musical style affect the temporal resolution of the participant?
Groove is defined as the innate feeling of wanting to move some part of your body along with the music (Davies, Madison, Silva, & Gouyon, 2013). Music cognition researchers have documented the neurological relationship between movement and musical rhythms (Bengtsson et al., 2009; Grahn & Brett, 2007; Leow, Parrott, & Grahn, 2014; Loehr & Palmer, 2009; Thaut, 2009; Thaut, Trimarchi, & Parsons, 2014; Trainor et al., 2009; Witek et al., 2014). Researchers have shown that percussionists have a higher rhythmic sensitivity than nonmusicians (Davies, Madison, Silva, & Gouyon, 2013; Rammsayer & Altenmüller, 2006; Rammsayer, Buttkus, & Altenmüller, 2012). However, research on the audiomotor entrainment of dancers is still in its infancy. Typically, dancers may or may not produce movements that synchronize with the overall pulse of the music (Kotz, Ravignani, & Fitch, 2018). It is the researcher’s interest to understand the rhythmic perception of dancers in order to make connections between movement, music cognition, and music education.
The National Association for Music Education (2014) have rhythmic components for all grade levels, and by the 8th grade, it recommends that music students be able to sight-read notation and understand the cultural and historical context of the music to inform with performance. Many of the popular approaches to music teaching include elements of learning rhythm through movement. Dalcroze Eurythmics uses body motion and rhythmic solfege to develop a student’s inner ear, to internalize rhythmic expression, and to develop a foundation for improvisation (Abramson, 1980; Anderson, 2011; Caldwell, 1993; Juntunen & Hyvönen, 2004; Mead, 1994). Orff Schulwerk can help develop students’ understanding of rhythm through group activities using rhythmical body movements, such as clapping or stamping (Shamrock, 1986). The Kodály method infuses games, dances, and play to represent musical lines, in addition to using body percussion (Bowyer, 2015). Music learning theory stresses the importance of using movement to develop rhythmic audiation (Gordon, 2012). Contemporary music education researchers can use cognitive and neurological music research to inform their instruction (Flohr, 2010).
In order to find participants with experience moving to specific musical styles, volunteers from various dance classes at a major university were recruited to participate in the study. A group of percussionists was recruited from the music school to help establish the upper bound for the auditory perception test created for this experiment. A group of business students served as a control group that would ideally have less experience in music or dance instruction.
The stimuli consisted of 48 four-measure drum samples: 12 unaltered drum samples, three samples in four different musical styles, plus three alterations of each drum sample. The altered drum samples differed only in the 16thnote swing ratio with microtiming deviations of plus or minus 10ms, 20ms, and 40ms. Musical styles included Latin, jazz, and hip-hop, as well as snare drum patterns.
To create the altered drum samples, the researcher spliced stylized drum loops from either Logic Pro X or Loopmasters.com at each 16thnote transient. All of the drum samples were time-shifted to 130BPM so that the 40ms deviations were sufficiently noticeable across the different musical styles. The snare drum patterns were recorded live, quantized to the metronomic grid, and then edited for the variations. Then, using Logic Pro X, the second and fourth 16thnote of each beat was moved plus or minus 10ms, 20ms, or 40ms, depending on whether the sample already contained swung or straight rhythms. The researcher did not edit the 8thnote swing variation within each stylized groove. In other words, only the upbeat 16thnotes of each beat moved, adding swing to straight samples and removing swing to already swung samples; the downbeats and upbeats remained the same. The audio edit points at each transient were crossfaded manually to remove any digital artifacts. A subtle 80ms reverberation sound effect was added to each sample to help conceal the edit points.
The subjects participated in an A-Not-AR with Sureness discrimination task with a familiarization process in order to reduce response bias when indicating the confidence in their answers (van Hout, 2014). The subjects compared the 12 unaltered drum samples with the three microtiming alterations of each sample in a random order within blocks based on different musical styles. For each sample pair, the original, unaltered sample was always presented first as a reminder, followed by either the identical sample or one with a microtiming deviation.
Subjects sat at individual computers and listened to stimuli via headphones. They registered their responses through an online program designed on www.surveygizmo.com. Subjects had to determine if the second audio sample was “A” or “Not A” and indicate their level of confidence by deciding if they were sure, not sure, or guessing their answer for a total of 6 ordinal variables. The subjects also answered background questions about their music listening preferences, musical instruction and performance experience, and dancing instruction and performance experience. As an incentive, the participants were able to enter in a drawing for a gift card. The overall procedure took approximately 25 minutes.
Results (see poster for now)
The analysis indicates that the percussion group was able to perceive the 40ms microtiming deviations with more confidence than the other two groups, supporting previous research on the superior rhythmic acuity of percussionists (Davies, Madison, Silva, & Gouyon, 2013). However, due to high variance and small sample size, a significant difference could not be established between the performance of the dancers and the other two groups. There is a statistically significant, positive, and moderate correlation between the dance group’s self-reported swing dancing experience and their composite mean of -10ms jazz variations. r(n=11) = .559, p = .001. It makes sense that the correlation weakens as the alterations become more obvious with the 20ms and 40ms microtiming deviations. However, the results were not consistent with the hip-hop and Latin samples.
Additional analysis is required to determine the difficulty of each drum sample. The snare drum and hip-hop samples may have been easier to perceive for some groups because of the clear separated attacks of the rhythms. The alterations in the jazz samples may have been difficult to perceive due to the constant ringing of the ride cymbal and that the fact that the swing feel was being subtracted rather than added. The Latin drum samples may have been difficult due to rhythmic density added from the congas and a shaker on top of the drum set. Future studies may want to compare only single instruments in different styles performing linear rhythm patterns for a more accurate comparison.
Tempo may also be a factor in the performance of the groups and the quality of the samples as 130BPM may not be the most ecologically valid tempo within each style and pattern. Future studies may want to test the effect tempo plays on the perception of microtiming deviations.
As research on beat perception and synchronization of dancers is still in its infancy, this pilot study helps lay the groundwork for future investigations on this topic. Auditory rhythmic perception of dancers is a complex topic since they may or may not produce movements that synchronize with the overall pulse of the music (Kotz, Ravignani, & Fitch, 2018). Similar studies may be expanded with a larger sample size and use different discrimination tasks. Future studies can add additional groups for comparison, such as “musician, non-percussionist” or “dancers with musical instruction experience,” as well as more styles of music that correspond with appropriate dancing styles. An experimental study can be designed to compare microtiming discrimination before and after dancing instruction in specific styles. Further research will be required to understand the differences in the cognitive decision strategy used by percussionists versus less rhythmically experienced groups, as well as the effect that audiation has on discrimination ability. Overall, this topic is ripe for exploration in the music perception and cognition realm and may produce data that is relevant to music teaching and learning.
Abramson, R. M. (1980). Dalcroze-based improvisation. Music Educators Journal, 66(2), 62-68.
Anderson, W. T. (2011). The dalcroze approach to music education: Theory and applications. General Music Today, 26(1), 27-33.
Bengtsson, S. L., Ullén, F., Ehrsson, H. H., Hashimo, T., Kito, T., Naito, E., . . . Sadato, N. (2009). Listening to rhythms activates motor and premotor cortices. Cortex, 2009(45), 62-71.
Bowyer, J. (2015). More than solfége and hand signs: Philosophy, tools and lesson planning in the authentic kodály classroom. Music Educators Journal, 102(2), 69-76.
Caldwell, T. (1993). A dalcroze perspective on skills for learning. Music Educators Journal, 79(7), 27-29.
Davies, M., Madison, G., Silva, P., & Gouyon, F. (2013). The effect of microtiming deviations on the perception of groove in short rhythms. Music Perception: An Interdisciplinary Journal, 30(5), 497-510.
Flohr, J. W. (2010). Best practices for young children’s music education: Guidance from brain research. General Music Today, 23(2), 13-19.
Gordon, E. E. (2012). Learning sequences in music: A contemporary music learning theory. Chicago, IL: GIA Publications.
Grahn, J. A., & Brett, M. (2007). Rhythm and beat perception in motor areas of the brain. Journal of Cognitive Neuroscience, 19(5), 893-906.
Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29-36.
Juntunen, M., & Hyvönen, L. (2004). Embodiment in musical knowing: How body movement facilitates learning within Dalcroze Eurhythmics. British Journal of Music Education, 21(2), 199-214.
Kotz, S. A., Ravignani, A., & Fitch, W. T. (2018) The evolution of rhythm processing. Trends in Cognitive Sciences, 22(10), 896-910.
Lee, H. S., & Van Hout, D. (2009). Quantification of sensory and food quality: The R‐index analysis. Journal of Food Science, 74(6), R57-R64.
Leow, L., Parrott, T., & Grahn, J. A. (2014). Individual differences in beat perception affect gait responses to low- and high-groove music. Frontiers in Human Neuroscience, 8(811), 1-12.
Loehr, J. D., & Palmer, C. (2009). Subdividing the beat: Auditory and motor contributions to synchronization. Music Perception: An Interdisciplinary Journal, 26(5), 415-425.
Mead, V. (1994). More than mere movement: Dalcroze eurhythmics. Music Educators Journal, 82(4), 38-41.
National Association for Music Education. (2014). 2014 Music Standards (PK-8 General Music). Retrieved from https://nafme.org/wp-content/files/2014/11/2014-Music-Standards-PK-8-Strand.pdf.
Rammsayer, T. H., & Altenmüller, E. (2006). Temporal information processing in musicians and nonmusicians. Music Perception: An Interdisciplinary Journal, 24(1), 37-48.
Rammsayer, T. H., Buttkus, F., & Altenmüller, E. (2012). Musicians do better than nonmusicians in both auditory and visual timing tasks. Music Perception: An Interdisciplinary Journal, 30(1), 85-96.
Shamrock, M. (1986). Orff schulwerk: An integrated foundation. Music Educators Journal, 72(6), 51-55.
Thaut, M. H., Stephan, K. M., Wunderlich, G., Schicks, W., Tellman, L., Herzog, H., . . . Hömberg, V. (2009). Distinct cortico-cerebellar activations in rhythmic auditory motor synchronization. Cortex, 45(2009), 44-53.
Thaut, M. H., Trimarchi, P. D., & Parsons, L. M. (2014). Human brain basis of musical rhythm perception: Common and distinct neural substrates for meter, tempo, and pattern. Brain Sciences, 4, 428-452.
Trainor, L. J., Gao, X., Lei, J.-j., Lehtovaara, K., & Harris, L. R. (2009). The primal role of the vestibular system in determining musical rhythm. Cortex, 45, 35-43.
van Hout, D. D. (2014). Measuring meaningful differences: Sensory testing based decision making in an industrial context; applications of Signal detection theory and Thurstonian modelling (No. EPS-2014-304-MKT). ERIM Ph.D. Series Research in Management. Erasmus Research Institute of Management.
Witek, M. A. G., Clarke, E. F., Wallentin, M., Kringelbach, M. L., & Vuust, P. (2014). Syncopation, body-movement and pleasure in groove music. PLoS ONE, 9(4), 1-12.