Thursday, 3 January 2013

Using coordination to study learning across the lifespan

What happens to our ability to learn new movement skills as we age? There is surprisingly little research on this topic; a relatively recent review (Voelcker-Rehage, 2008) found only 25 articles about learning in old age, and no systematic programme of work. The answer to this question matters a lot; rehabilitation after events such as a stroke pretty much always entail (re)learning movement skills, and if our ability to learn gets worse with age, rehabilitation faces an uphill struggle. 

I have been studying coordinated rhythmic movement for some time now, and now we have a good handle on the task dynamic my colleagues at Indiana and I have begun using it to study the process of learning more generally. We decided to use it to look at learning in old age, to see what we could see.

This project grew out of a grant I had from Remedi when I was a post-doc in Aberdeen. I wanted to use coordination to look at learning post-stroke. One of the problems with studying this is finding useful novel tasks to learn - you need to give the stroke patients something they've never done before so you can be sure that any improvement is about learning, and not simply recovery of function. My thought at the time was that I could use any changes at 180° to assess recovery and changes at 90° to assess learning. We tested a huge number of patients and age matched controls, but the project didn't pan out because neither group (all aged around 65) couldn't learn to move at 90°. The question remained, what was going on? We now have the first of three papers on this question out in press. 

Coats, Snapp-Childs, Wilson & Bingham (2012) trained 3 groups of people (people in their 20s, 70s and 80s) to produce a unimanual coordinated rhythmic movement at 90°. We used my coordination feedback method and had people move one joystick to control a dot on a screen to move at 0°, 90° and 180° relative to a computer controlled dot. We then trained each group to move at 90°, and we quantified both improvement (the difference between Baseline and Post Training) and learning rate (our paper is the first to look at the latter in older adults). 

We assessed coordination stability using the proportion of time on target, which is the average proportion of the trial spent at the target relative phase, +/- an error bandwidth of 20°. We introduced this measure to replace the more commonly used absolute error and standard deviation of relative phase measures. These numbers have a problem, namely that you cannot interpret one without the other. When people try to do, say, 90°, they often produce very stable behaviour because they are actually failing to do what you asked and have slipped into the easier 0°. If you look at the variability data, it looks like people are doing quite well. The proportion measure is a valid measure of how well people are doing what you asked them to do, and we've shown that it successfully resolves the kind of trouble the other variability measures can get you into (Snapp-Childs, Wilson & Bingham, 2011).

We therefore asked three questions

1. Can older adults learn to move at 90°?
The answer is 'a little'. We did see some improvement, but much less than the younger adult group.
Figure 1. Performance at 90° for the three groups before and after training
 2. How did the learning rates compare?
We fit the proportion time on task data from each session with exponential functions. The fits were all very good, confirming that people improve quickly to begin with and then improve steadily and more slowly over the rest of training. We computed the slope of the exponential function at Baseline to quantify the learning rate. The two older groups were the same and about half the rate of the younger adults.

Figure 2. Learning rate data for the three groups
 3. Maybe the older adults just can't use joysticks
One problem you run into using lab tasks with older adults is that they are often not familiar with joysticks, PCs, etc. We examined the 0° and 180° data to see how the older adult performance compared at these baseline conditions.

Figure 3 tells us two interesting things; first, there was no difference between the groups at 0°, suggesting they could do the basic task. Second, there was a difference between the young and older adults at 180°; the older adults were worse. This is our first clue as to why older adults are performing worse.

Figure 3. 0° and 180° performance for the three groups
What have we learned?
The advantage of this task is that we have a detailed understanding of the mechanisms that produce the various observed effects (formalised in Geoff's model; see our recent Avant paper for an overview). This theoretically motivated and empirically supported account proposes that coordinated rhythmic movement looks the way it does because of how we perceive relative phase; the information for relative phase is relative direction of motion.

We used the unimanual version of the task, where people control one dot and are coupled via vision only to the other dot. To perform the coordination, people must visually perceive relative phase by detecting the relative direction of motion. We can see in our data that the older adults can do 0° (which is easy to perceive for several reasons, including the ability to simply match the positions of the dots). However, when the only information that supports good performance is coordination information (i.e. at 90° and 180°) performance drops. The bottleneck seems to be the visual perception of relative motion information (which matches a recent literature on changes to visual perception with typical ageing, e.g. Anderson, 2012). 

This study used a well understood task to quantify learning rate and amount in younger and older adults, and found that the older adults (aged in their 70s and 80s) were only learning at about half the rate. The apparent cause is age related changes in the visual perception of motion information. 

This now opens up a useful line of inquiry: when do these age related perceptual changes begin to have functional consequences? Can you reduce the negative effects with training? What about other modalities? We have data and plans around answering these questions and more, and the key to our ability to answer these questions is the theory driven account of the mechanisms in this task.

ResearchBlogging.orgAnderson, G. J. (2012). Aging and vision: changes in function and performance from optics to perception. WIREs Cognitive Science 3(3), 403–410.

Coats, R. O., Snapp-Childs, W., Wilson, A. D., & Bingham, G. P. (2012). Perceptuo-motor learning rate declines by half from 20s to 70/80s Experimental Brain Research DOI: 10.1007/s00221-012-3349-4 Download 
Snapp-Childs, W., Wilson, A. D., & Bingham, G. P. (2011). The stability of rhythmic movement coordination depends on relative speed: The Bingham model supported. Experimental Brain Research, 215, 89-100. Download 
Voelcker-Rehage C (2008) Motor-skill learning in older adults—a review of studies on age-related differences. European Review of Aging and Physical Activity, 5, 5–16.

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