Floris T. van Vugt

Auditory Feedback in Motor learning

Motor learning studies investigate how we learn motor skills, such as, for example, throwing a ball, learning to play the violin or touch typing. There is consensus in the motor learning literature that this learning occurs based on visual, tactile and proprioceptive feedback. However, the role of auditory feedback remains less clear. Does auditory feedback support motor learning in a similar way to other sensory modalities? Can the brain use sound to improve movements?

Working with Barbara Tillmann in Lyon, I monitored participants as they were instructed to tap a sequence of keystrokes as regularly as possibly. Over time, participants who received sounds synchronously with the keystrokes were able to improve their regularity. Participants who received no keystroke-triggered sounds or received constant auditory noise were not able to make such improvements (van Vugt & Tillmann, in revision).

A further question is how such auditory-based learning influences basic auditory-motor processing. In order to answer into this question, we developed a novel test that establishes participants’ sensitivity to delays between movements and the resulting sounds (van Vugt & Tillmann, 2014).

How do humans learn to control the timing of their movements?
How do we use the different types of feedback in this process? For example, can we use auditory feedback such as provided by a musical instrument?

Collaborators

Using music in rehabilitation of fine motor skills

Following a stroke, many people experience movement impairments. The currently existing treatments aim at improving very large and rough movements only. This leaves the patients often frustrated and disabled in their everyday life, since they cannot perform daily tasks that require fine motor control. Learning to play a musical instrument was previously shown to improve this fine motor control.

However, the mechanism by which such music-supported therapy is effective remains unclear. During my PhD work in Hannover, we asked whether the improvement was due to auditory-feedback based motor learning (van Vugt, Kafczyk, Kuhn, Rollnik, Tillman & Altenmueller, submitted). Furthermore, we designed a novel implementation of this therapy in patient pairs, which allowed us to test for movement gains based on synchronised playing (van Vugt, Ritter, Rollnik & Altenmueller, 2014).

Collaborators

Meet the wonderful and dedicated Hannover stroke rehabilitation team!

Tom Kafczyk
Wolfgang Kuhn
Juliane Ritter
Britta Westner
Prof. Jens Rollnik
Prof. Eckart Altenmueller

Motor timing in experts: pianists’ scale playing

Musicians are experts at movement timing, since even very small timing differences are important for musical expression. You’d think that when these experts play a musical scale, they play completely regularly. That is, you would expert the temporal intervals between the notes would all be equal on average. But no, it turns out that the intervals are a few milliseconds too short or too long. Now what’s really exciting is that these deviations appear to be systematic. That is, some intervals are always played too long, and others always too short. I’m interested in why this is so.

This project has several parts:

How individual are these deviations? Using a machine learning study, we find that they are so individual that we can recognise 9 pianists with 100 percent accuracy! (van Vugt, Jabusch & Altenmueller, 2013)
We try to understand what these deviations in scale playing tell us about movement disorders such as musician’s dystonia (e.g. van Vugt, Boullet, Jabusch & Altenmueller, 2013)
How do these deviations vary across time? An exciting new study reveals that at faster tempi, biomechanical constraints become dominant, whereas at slower tempi, playing is more expressive (van Vugt, Furuya, Vauth & Altenmueller, 2014). Furthermore, small fluctuations in timing are an indicator of circadian fluctuations (van Vugt, Treutler, Jabusch & Altenmueller, 2013).
What do these deviations tell us about how the motor system chunks the scales into sub-parts? We know that when you learn to play a motor sequence, you divide it into units which you can execute automatically (e.g. Sakai et al, 2003 Exp Brain Res). But how can we recognise these chunks from your timing profile? (van Vugt, Jabusch & Altenmueller, 2012).