Research Article: Skill acquisition as a function of age, hand and task difficulty: Interactions between cognition and action

Date Published: February 7, 2019

Publisher: Public Library of Science

Author(s): Rachael K. Raw, Richard M. Wilkie, Richard J. Allen, Matthew Warburton, Matteo Leonetti, Justin H. G. Williams, Mark Mon-Williams, Welber Marinovic.


Some activities can be meaningfully dichotomised as ‘cognitive’ or ‘sensorimotor’ in nature—but many cannot. This has radical implications for understanding activity limitation in disability. For example, older adults take longer to learn the serial order of a complex sequence but also exhibit slower, more variable and inaccurate motor performance. So is their impaired skill acquisition a cognitive or motor deficit? We modelled sequence learning as a process involving a limited capacity buffer (working memory), where reduced performance restricts the number of elements that can be stored. To test this model, we examined the relationship between motor performance and sequence learning. Experiment 1 established that older adults were worse at learning the serial order of a complex sequence. Experiment 2 found that participants showed impaired sequence learning when the non-preferred hand was used. Experiment 3 confirmed that serial order learning is impaired when motor demands increase (as the model predicted). These results can be captured by reinforcement learning frameworks which suggest sequence learning will be constrained both by an individual’s sensorimotor ability and cognitive capacity.

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The study of cognition lies at the centre of psychological research. Nevertheless, there is no ‘standard model’ of cognition, and psychology lacks consensus on the very nature of this broad construct. Indeed, there are diametrically opposing views on how we should understand cognition. On the one hand, there is a long tradition of conceptualising ‘cognition’ as a closed system that is concerned purely with abstract information processing; a system which is independent of perceptual-motor functions. This entrenched view of the inconsequential nature of motor control within studies of cognition is particularly evident in the most popular approaches used to administer computerised cognitive test batteries (e.g. the CANTAB, NEPSY-II, NIH Toolbox). These batteries either: (i) ignore the motor aspects of performance once a baseline motor task has been ‘passed’ or (ii) treat motor performance as a separate entity that can be dissociated from other cognitive functions. The CANTAB [1] adopts approach (i) and assays visuomotor control in an initial ‘induction’ test before the core battery of tasks is presented. The core tests are designed to assess specific ‘cognitive’ abilities (e.g. attention, working memory and decision making) but necessarily require a variety of task-specific manual responses. This approach is problematic because it assumes that an individual’s sensorimotor performance does not contribute to variability in any of the subsequent, explicitly ‘cognitive’, assessments (provided motor performance exceeds a minimum threshold on one specific ‘motor’ task at the beginning of the assessment). The alternative approach taken in other test batteries (e.g. the NEPSY-II [2] and the NIH Toolbox [3]) is to treat motor skill as a separate function that can be measured independently. Thus, the NEPSY-II and the NIH Toolboxincorporate comprehensive sub-tests explicitly intended to assess various aspects of motor function. However, they indicate that these are taxonomically distinct from cognitive sub-tests (e.g. the NIH Toolbox includes a sub-battery of ‘Cognitive’ tests and a separate sub-battery of ‘Motor’ tests), and this gives rise to ontological questions with no clear answers–what defines a specific sub-test with ‘cognitive’ but no ‘motoric’ demands, or vice versa?

The results of the first experiment are consistent with the hypothesis that there is a relationship between motoric performance level and sequence learning–older participants were found not only to recall fewer moves (than the younger adults) at test, but also moved more slowly during the training trials. Two possible explanations are: (i) that encoding a movement sequence into memory has an influence over the speed of movement (i.e. learning alters motor performance, in this case movement duration), or (ii) that less skilled movements have a causal role in impairing motor sequence learning (i.e. movement performance level affects learning). To distinguish between these explanations a second experiment was conducted on a new set of Older and Younger participants, this time measuring learning when using both the preferred and non-preferred hands. Explanation (i) would predict impaired recall in the Older adults compared to Younger, but no differences between which hand was used to perform the task. Explanation (ii) would predict impaired recall in the Older adults, but also for both age-groups when using the non-preferred hand (i.e. superior motor performance is usually expected in the preferred hand when completing basic motor coordination tasks; see Raw et al. [36]). The same motor learning task was used as in Experiment 2, but because testing needed to be carried out with each hand, the number of movements to be learnt was reduced to keep overall experiment testing time equivalent, to avoid participant fatigue.

The findings of Experiment 2 suggest that there is a relationship between an individual’s baseline level of motor performance and their ability to learn a novel sequence. When Older and Younger participants used their non-preferred hand to complete the task, we found reduced quality of movements (i.e. longer trajectories and slower movements, during training), and poorer learning (i.e. moves recalled at a slower pace at test, and for the young at least fewer items recalled). This suggests that reduced motor performance can directly inhibit motor sequence learning, over and above the influences of cognitive capacity. An alternative explanation for our results, however, is that learning may be influenced at a neurological level by hemispheric specialisation. According to hemispheric specialisation theory [57], the left side of the brain is predominant in analytical skills such as tasks that involve breaking down problems into parts, reasoning, and logical thinking–the hemisphere said to possess “sequential, analytic, time-dependent mechanisms” [58]. In contrast, the right hemisphere is said to specialise in subjective functions such as intuition, creativity and emotion [59–60]. It might therefore be argued that because participants in Experiment 2 were right-handed, and the contralateral side of the brain that controls right-side movement specialises in skills that are vital for motor sequence learning, participants could have been at an advantage when completing the task with their preferred hand. There are hence two alternative plausible explanations for the finding of reduced motor sequence learning in the non-preferred hand; (i) participants were able to learn more of the sequence when using the hand that is controlled by the left hemisphere because it specialises in the processes involved in motor sequence learning; or (ii) a poor baseline level of motor performance inhibits motor sequence learning.




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