Learning movement sequences with music changes brain connectivity. Behavioural relevance pending…

Research Spotlight #2 Moore, E., Schaefer, R., Bastin, M., Roberts, N., & Overy, K. (2017). Diffusion tensor MRI tractography reveals increased fractional anisotropy (FA) in arcuate fasciculus following music-cued motor training. Brain and Cognition, 116, 40-46. 

A couple of weeks ago my mum emailed me a link to a BBC news article which she rightly knew would be of interest to me. It reported on a study by researchers mainly from the University of Edinburgh, and the title of the BBC article was: “Learning with music can change brain structure, says study“. This of course grabbed my attention, as I thought it could provide a neuroscientific counterpart to my PhD student John Dyer’s recent work, which had found that triggering a melody with one’s movements helped in learning and recalling a complex bimanual coordination task (tracing a diamond with one hand and a triangle with the other to create a 4:3 rhythm). The study by Moore et al. on which the BBC article was based is indeed interesting, but probably creates more questions than it answers. It also ties in neatly with an ongoing issue in neuroscience – trying to draw conclusions from neural measures when the behavioural framework within which they should fit is incomplete.

The study involved people learning different patterns of finger movements on their left (non-dominant hand), e.g. index-ring-middle-pinkie-ring-index-ring-middle. You can try yourself and see that it is not that easy – hence room for some learning to happen. Everyone in the study learned the task with a kind of Guitar Hero style video to show them which order the fingers had to be pressed. Importantly, one group practiced with just the video (‘control group’), while the other additionally heard musical pitches (presumably like keys on a piano) to indicate the finger order of the sequence to press. This was the key difference between the groups – one had a ‘musical’ version of the task while the other didn’t. They learned the sequences at home, and the task sped up as they got more confident at producing the sequences. They were tested at performing the rehearsed sequences as well as some sequences that they hadn’t rehearsed at three stages: before training, midway through training and after training. MRI scans of their brains were also recorded before and after training. These were used to measure changes in the organisation of fibres which connects neural cells between the auditory and motor regions of the right-hemisphere of the brain (i.e. the side of the brain which would be responsible for sending muscle signals to the left hand). The hypothesis was that learning with the musical sounds may entail greater auditory-motor linkages in the brain hemisphere involved in coordinating the task-to-be-learned.

The results of the study showed that both groups got better at the task, that is, they completed a greater number of correct sequences as a result of training. However, there were no differences in how much the music and control groups improved, that is both groups were getting pretty much the same number of correct sequences by the end of training. This is rightly taken to show that the musical tones did not influence learning (at least in terms of correct sequences). There was also evidence of a training-dependent change in the organisational structure of the axons connecting auditory and motor regions of the brain for the musical group in the right hemisphere, but not in the left hemisphere or at all in the control group. It could be noted that the changes in neural organisation were small (4% change in the main measure, all measures hovering around the 0.05 p-value), but for such a ‘light’ intervention, this may be all the more compelling – if just adding a little music to the task can change the brain, imagine what you could do if you really went to town with musical movement training!

As I said earlier, these results are certainly interesting. The lack of difference in the learning measure between the group, when one might expect the sound to be helpful, points to a need to understand better if and how sound could lead to enhanced learning of the task (e.g. through movement sonification rather than just as a guide). Also, the apparent change in neural fibres in the music group, although small, may represent a fairly surprising amount of neural plasticity in such a short space of time, more than would be expected (although I cannot comment on whether a 4% difference is that surprising or not as I am not up on what counts as small/large changes in neuroplasticity measures).

In spite of these interesting aspects, there are some limitations on what can be taken from this paper and a number of further questions raised. Firstly, it is a shame that the performance of the task was only examined using correct sequences performed. While it is always a good idea to have a primary learning measure, other measures like timing accuracy or movement kinematics may have revealed group differences which would help with understanding what the neural changes correspond to. If the neural changes are not functional for the movements involved, but rather underpin some audio-motor mapping or association which is simultaneously being acquired by the learners, then this might have been examined by looking for disruptions in performance for the music group when an incongruous finger-tone mapping is introduced following training. It is also possible that using movement sonification (fingers trigger the sounds), rather than sounds as mere cues, may have led to enhanced learning for the music group. Each of these additional measures or experimental conditions might have led to observable behavioural effects, through which the neural changes could be interpreted. As it is, however, it cannot be determined yet whether the observed changes in brain have some functional significance, or are merely an artifact, comparable to an indentation in one’s middle finger following a period of intensive handwriting.

To be fair, the authors do acknowledge that the lack of measured learning differences between the two groups limits the conclusions that can be made with regard to the neural data. Nevertheless, they do propose that the study may be valuable for clinical practice, e.g. for movement rehabilitation in Stroke survivors. The mismatch between the excitement over the seeming neural transformation (less in the paper itself but more in its media coverage) and the lack of corresponding behavioural differences between the learning conditions is telling. Recently, a group of pretty eminent neuroscientists published an excellent critique of a trend in the neurosciences to focus disproportionately on the activity and structures of individual neural cells, or networks of cells, without giving due attention to the situated task and behaviour of the organism that such neural activity is supposed to subserve. Studying the properties of stomach acid molecules is supposed to help us understand digestion; studying the brain should help us understand behaviour.

One might ask – if the researchers had carried out the study as a purely behavioural experiment, would they have then been motivated to repeat it with MRI analysis? My guess is that the behavioural component would need to have been more convincing, in which case the cart may be in front of the horse on this one.


Different kinds of ‘skill transfer’

When I was a PhD student, I learned that there are 3 main topics of investigation in skill learning research: skill acquisition, skill retention, and skill transfer. The goal of researchers interested in skill ought to be to understand the processes by which these things happen, and to develop ways to enhance them.

The first two are fairly easy to define. Skill acquisition is the improvement of performance at a previously unlearned task, usually through repetition and training. Researchers might ask how to accelerate this process, or how to ensure that training/practice conditions ensure the most adaptive and long-lasting learning. Skill retention is the ability to perform well at the now-learned task after extended time periods, or following interference of another task/skill. ‘You never forget how to ride a bike’ = skill retention. In the research literature, retention is generally used as a metre stick to evaluate how successful the acquisition phase has been. Both acquisition and retention are (reasonably) well-defined and have been extensively researched.

Skill transfer is far less easy to define than acquisition or retention, and, while the most elusive, it is perhaps the most desirable phenomenon. It is something like the ability to perform an unrehearsed skill as the result of having previously acquired and retained a different-but-related skill. This definition has many problems and does not capture the idea of transfer fully. Examples might be better. Imagine a proficient Gaelic football player switching to Rugby – we might expect that many of the sub-skills required to do well in Gaelic would transfer to rugby, even if not all. Or a cello player taking up the viola. While the new instrument is a fraction of the size of the former, and played in a completely different posture, we might expect some of the learned skill to carry over. Of more general relevance, how can practice of one thing transfer to unpracticed situations? How can set-piece drills transfer to a real match against another team? How can rehearsing jazz scales transfer to a live improvisation during a gig? These are the kinds of phenomena and questions that skill transfer as a concept is supposed to capture.

An idea that I have been wondering about recently (and haven’t yet had the time to fully research) is about the different ways we could conceptualise skill transfer. Typically, the idea is that ‘acquisition of the skill to perform Task A will reliably result in better performance in Task B’ (assuming that Task A and B are similar enough in the relevant ways¹). However, I think there is another way of thinking about transfer. This is that ‘acquisition of the skill to perform Task A will reliably result in faster/better acquisition of the skill to perform Task B’. This may reveal itself independently of initial performance at Task B. Rather, by having tuned into the information that allowed Task A to be learned, and assuming that such information is meaningfully present in Task B, the learner will more readily be able to attune their attention to this in learning Task B and show accelerated acquisition. I am inspired here by the common anecdote² that having learned one instrument to a proficient level, it is easier to learn a second, and then easier still to learn a third, and so on. Thus, one view of transfer is improved performance of the unpractised task, while another would be improved learning of the unpractised task. The distinction may relate in part to a contrast between between ‘pick-up of information for action’ and ‘pick-up of information for learning’, but I am not yet in a position to formulate this idea fully yet.

I am sure that there is research relevant to this question out there. However, I have certainly not come across an explicit distinction between these two possible (and not mutually exclusive) ways that learning one skill could benefit another skill. I hope to look into this question further soon and report back with what I find.


  1. What counts as ‘the relevant ways’ is hugely important, and probably at the heart of the question of skill transfer, but something I will leave alone for now.
  2. I will look for proper evidence of this idea.