Dr Peter Lovatt

Dance
Psychologist

Experiment 1: Synchronization and Continuation Tapping Task

Overview of study written by Dr Dawn Rose


January 2017: This study has received ethical approval and is in the data collection phase.


Synopsis:

This study investigates how different modes of stimuli (audio, visual and audio-visual) effects entrainment to an isochronous rhythm (i.e. a steady beat) as measured using finger and toe tapping and marching up and down ‘on the spot’ as a proxy for dancing in people with and without Parkinson’s disease (PD). 

 

Background:

The coordination of rhythmic movement to an external rhythm is known as sensorimotor synchronisation (Repp & Su, 2013). In order to be able to do this, for example whilst dancing to music, one must be able to perceive an underlying beat (the tactus). There is evidence that beat perception and the ability to entrain to a beat has to be learned and changes during the lifetime (McAuley et al., 2006; van Noorden & de Bruyn, 2009). Beat perception appears to be specifically affected by PD (Grahn & Brett, 2009). This may be because the neurology of PD is focused in the basal ganglia, a subcortical area of the brain known to be involved in temporal processing and movement automation. Kotz & Schwartze (2010; 2011) have suggested a dedicated temporal processing subcortico-thalamo-cortical network as the alternative pathway. Paradoxically, music and dance-based activities and therapeutic interventions are being used to ameliorate a range of symptoms of PD including balance, gait, and mood (see e.g. Thaut, 2005, Nombela et al., 2012; Benoit et al., 2014; Lewis et al., 2014). In the early stages of the disease, it has been suggested that external auditory cues (such as music) can offer a compensatory system for people with PD (Eckhart et al., 2006; Lewis et al., 2007).

 

Introduction:

In order to test the effects of different modalities on the ability to synchronise, and whether this ability is impaired in people with PD, a series of experiments which includes audio, visual and audio-visual as stimuli as well as a number of specific testing conditions which include finger and toe tapping and marching on the spot (as a proxy for dancing) has been devised.

 

Study Design:

Typical paradigms for tapping experiments include a period of entrainment to stimuli (such as a metronome), then a period of continuous tapping without the stimuli (Repp, 2005). This study uses that paradigm and extends it by including an additional period of re-entrainment to a stimuli. This is included in order to understand how, for example, interventions utilising music and dance-based activities may help manage motor difficulties such as gait freeze, a symptom of PD. It would be useful to know how whether there is any difference in the way in which people without PD re-entrain to external stimuli in comparison to healthy, and/or age-matched volunteers. In application, this may be useful for practitioners and may help their choice of music and understanding of, for example, counting people back into a movement series. 

 

Methods:

The experimental paradigm will include 30 seconds of stimuli to entrain to (Time 1), 30 seconds of continuation tapping or marching (Time 2), then 30 seconds more of the stimuli to re-entrain to (Time 3). Typically the first few seconds are discounted whilst the participant ‘finds’ the beat. However, in these experiments, these data may be used in a repeated measures fashion in order to compare to the later T3 re-entrainment period.

 

The type of stimuli will compare audio, visual and audio-visual conditions in terms of effectiveness for accuracy of entrainment. The level of stimuli will compare the richness of the type of stimuli in terms of effectiveness of entrainment.

 

For the audio stimuli there will be two levels. 1) a metronome, and 2) instrumental music with a strong beat. Stimuli are still being pilot tested but two examples are sections of the extended version of Move On Up by Curtis Mayfield (138 BPM) and also How Soon Is Now by The Smiths (95 BPM).

 

For the visual stimuli there will be two levels. A) a blinking light, and B) a video of another person demonstrating the movements in real time

 

The combination of audio-visual conditions are operationalised as so:

 

1A – A metronome and a flashing light

1B – A metronome and a video of the action to be copied

 

2A – Instrumental music with a strong beat and a flashing light

2B – Instrumental music with a strong beat and a video of the action to be copied

 

Conditions 1 and 2 will be compared.

Conditions A and B will be compared.

Conditions 1A to 2B will be compared.

 

Measures:

Participants will be asked to complete a questionnaire (Appendix E). This will ask about general descriptive data such as age, gender and current state of health. Participants will also be asked how much musical and dance training they have had during their lifetimes in order to control for a possible expertise effect (e.g. Fujii et al., 2011; Repp, 2010). This is the Gold Musical Sophistication Index which will be adapted to include questions also relating to dance training and experience (Müllensiefen, Gingras, Musil & Stewart, 2014). Participants will also complete the Mini Mental State Examination (Folstein, Folstein, & McHugh, 1975 – Appendix C). People with PD will also be asked to complete the Unified Parkinson’s Disease Rating Scale (Appendix F), and the Hoehn & Yahr scale (Hoehn & Yahr, 1967 – Appendix A).

 

Participants will be asked to complete two pre-test measures. The first pre-test will ascertain your personal beat period (PBP) for finger and toe tapping, and also for marching up and down on the spot. For this you will be asked to tap (fingers/toes) and march at a steady speed you feel is natural for 30 seconds on two occasions.

 

The second pre-test will assess the participant’s beat perception ability. This is the Gold beat perception test which is part of a battery of tests devised to assess levels of musical sophistication in non-musically trained people (Müllensiefen et al., 2014). This is an online survey using real musical excerpts upon which a tactus has been superimposed. It usually takes 15 minutes to complete. The participant is asked to say whether they think the superimposed ‘beat’ is on the beat (in synchrony with the music) or off the beat (out of synchrony with the music) following priming examples, and how sure they are of their answer.

 

The three conditions of entrainment, finger tapping, toe tapping and marching have been chosen to consider naturalistic motor behaviours (people often tap their toes more than their fingers) and provide ecological validity (i.e. the marching as a proxy for dancing). The equipment chosen to measure the discrete motor entrainment (finger and toe tapping) has been chosen because of the ergonomic design on the box in which the contact microphone is housed. This wooden box (a musical stomp box – see Figure 2) has a gently curved edge enabling the participant to rest the heel of their hand or foot on a surface and use only the hand or foot without straining their wrists or ankles. Marching will be measured by using two BioPac heel and toe strike sensors (see Figure 1).  The wireless sensor pack is securely fastened to the participants’ ankle with Velcro whilst the sensors themselves are taped to the participants shoe underneath the ball and toe areas. Wrist worn accelerometers (the size of a wrist watch – see Figure 3) may also be worn as an additional measure of movement.

 

Pending the results of pilot testing of the musical excerpts, the tempo of the stimuli will include a examples of instrumental music including the posited 2Hz human resonance tempo (120 BPM) whereby the tactus falls every 500 milliseconds. Extending this range by up to approximately ±20% (Repp, 2005) the stimuli will be randomly presented for example at 96 BPM (625ms), 108 BPM (556ms), 120 BPM (500ms), 132 BPM (454ms) and 144 BPM (416ms).

 

Hypothesis and Analysis:

Within-subject comparisons will be considered based on the prediction that the richer the stimuli the more accurate the entrainment.

 

Between-subject comparisons between healthy volunteers and people with Parkinson’s will include age as a co-varying variable. The prediction that the richer the stimuli the more accurate the entrainment stands but it is further predicted that healthy volunteers will perform with more accuracy relative to people with PD with regard to entrainment to external stimuli.

 

An a priori power analysis for the healthy sample, based on 1 tailed independent means t tests (effect size = .5, alpha p = .05, power = .8) suggests n=27 with a critical t = 1.71. For the PD sample with age matched pairs estimating Wilcoxon signed ranked tests (effect size = .5, alpha p = .05, power = .8) the critical t = 1.71 but the sample size needed is n=28. However, it is noted that tapping data may require alternative methods of analysis. The total N=55.

 

The dependent variable will be a measure of accuracy of synchrony. This will be measured according to synchronisation errors (asynchronies). Repp and Su (2016) define these as “the difference between the time of the tap (the contact between the finger and a hard surface) and the tie of the corresponding event onset in the external rhythm”  (p. 404). Typically, mean asynchrony is negative (i.e. the tap precedes the beat) and is therefore referred to as negative mean synchrony (NMA). The index of stability most commonly used is the standard deviation of asynchronies (SDasy).

 

When synchrony is poor, other studies have used alternative statistical analysis such as circular variance (e.g. Moens et al., 2014 - see also Fisher, 1995). This type of analysis may be utilised in the studies of people with Parkinson’s and in order to establish potential patterns of re-entrainment in healthy as well as PD participants. Three patterns of re-entrainment have been considered. Firstly, it is predicted that some participants will race taps in order to catch with the stimulus (Racing Ahead/Galloping). Secondly, it is predicted that some participants will stop and wait before committing to tapping (Stop and Wait/Hedging). Thirdly it is predicted that some participants will attempt both patterns, thereby apparently producing a random pattern but in fact over then under compensating (Over/Under).

 

 

SPSS will be used to conduct descriptive as well as the principal MANCOVA analysis. Tempi and condition of the stimuli will form the multivariate variables. Co-varying variables may include beat perception, preferred beat period, age and gender should these prove to be significant predictors of the dependent variable. Multiple regression analysis may be used to ascertain whether the amount of musical or dance training/expertise is predictive of accuracy of entrainment.

 

MATLAB may be used to analyse data in regard to the potential use of circular statistics.

 

References:

Benoit, C.-E., Dalla Bella, S., Farrugia, N., Obrig, H., Mainka, S., & Kotz, S. A. (2014). Musically Cued Gait-Training Improves Both Perceptual and Motor Timing in Parkinson’s Disease. Frontiers in Human Neuroscience, 8, 494. http://doi.org/10.3389/fnhum.2014.00494


Eckert, T., Peschel, T., Heinze, H. –J., & Rotte, M. (2006). Increased pre–SMA activation in early PD patients during simple self–initiated hand movements. Journal of Neurology, 253(2), 199–207. http://doi.org/10.1007/s00415-005-0956-z


Fisher, N. I. (1995). Statistical analysis of circular data. Cambridge: Cambridge University Press.


Folstein, M., Folstein, S. E., & McHugh, P. R. (1975). “Mini-Mental State” a Practical Method for Grading the Cognitive State of Patients for the Clinician. Journal of Psychiatric Research, 12(3), 189–198.


Fujii, S., Hirashima, M., Kudo, K., Ohtsuki, T., Nakamura, Y., & Oda, S. (2011). Synchronization Error of Drum Kit Playing with a Metronome at Different Tempi by Professional Drummers. Music Perception: An Interdisciplinary Journal, 28(5).


Grahn, J. A., & Brett, M. (2009). Impairment of beat-based rhythm discrimination in Parkinson’s disease. Cortex, 45(1), 54–61. http://doi.org/10.1016/j.cortex.2008.01.005


Hoehn, M. M., & Yahr, M. D. (1967). Parkinsonism: onset, progression, and mortality. Neurology, 17, 427–442.


Kotz, S. A., & Schwartze, M. (2010). Cortical speech processing unplugged: a timely subcortico-cortical framework. Trends in Cognitive Sciences, 14(9), 392–399. http://doi.org/10.1016/j.tics.2010.06.005


Kotz, S. A., & Schwartze, M. (2011). Differential input of the supplementary motor area to a dedicated temporal processing network: functional and clinical implications. Frontiers in Integrative Neuroscience, 5, 86. http://doi.org/10.3389/fnint.2011.00086


Lewis, M. M., Slagle, C. G., Smith, A. B., Truong, Y., Bai, P., McKeown, M. J., … Huang, X. (2007). Task specific influences of Parkinson’s disease on the striato-thalamo-cortical and cerebello-thalamo-cortical motor circuitries. Neuroscience, 147(1), 224–35. http://doi.org/10.1016/j.neuroscience.2007.04.006


Lewis, C., Annett, L. E., Davenport, S., Hall, A. A., & Lovatt, P. (2014). Mood changes following social dance sessions in people with Parkinson’s disease. Journal of Health Psychology, 21(4), 483–92. http://doi.org/10.1177/1359105314529681


McAuley, J. D., Jones, M. R., Holub, S., Johnston, H. M., & Miller, N. S. (2006). The time of our lives: Life span development of timing and event tracking. Journal of Experimental Psychology: General, 135(3), 348–367. http://doi.org/10.1037/0096-3445.135.3.348


Moens, B., Muller, C., van Noorden, L., Franěk, M., Celie, B., Boone, J., … Gosselin, N. (2014). Encouraging Spontaneous Synchronisation with D-Jogger, an Adaptive Music Player That Aligns Movement and Music. PLoS ONE, 9(12), e114234. http://doi.org/10.1371/journal.pone.0114234


Müllensiefen, D., Gingras, B., Musil, J., & Stewart, L. (2014). The musicality of non-musicians: an index for assessing musical sophistication in the general population. PloS one, 9(2), e89642.


Nombela, C., Hughes, L. E., Owen, A. M., & Grahn, J. A. (2013). Into the groove: Can rhythm influence Parkinson’s disease? Neuroscience & Biobehavioral Reviews, 37(10), 2564–2570. http://doi.org/10.1016/j.neubiorev.2013.08.003


Repp, B. H. (2005). Sensorimotor synchronization: A review of the tapping literature. Psychonomic Bulletin & Review, 12(6), 969–992. http://doi.org/10.3758/BF03206433


Repp, B. H. (2010). Sensorimotor synchronization and perception of timing: Effects of music training and task experience. Human Movement Science, 29(2), 200–213. http://doi.org/10.1016/j.humov.2009.08.002


Repp, B. H., & Su, Y.-H. (2013). Sensorimotor synchronization: A review of recent research (2006–2012). Psychonomic Bulletin & Review, 20(3), 403–452. http://doi.org/10.3758/s13423-012-0371-2


Thaut, M. H. (2005). Rhythm, Music, and the Brain: Scientific Foundations and Clinical Applications. New York: Routledge.


Van Noorden, L., & De Bruyn, L. (2009). The development of synchronisation skills of children 3 to 11 years old. In Proceedings of ESCOM—7th Triennial Conference of the European Society for the Cognitive Sciences of Music. Finland: University of Jyväskylä

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