Many children have difficulty producing movements well enough to improve in perceptuo-motor learning. We have developed a training method that supports active movement generation to allow improvement in a 3D tracing task requiring good compliance control. We previously tested 7–8year old children who exhibited poor performance and performance differences before training. After training, performance was significantly improved and performance differences were eliminated. According to the Dynamic Systems Theory of development, appropriate support can enable younger children to acquire the ability to perform like older children. In the present study, we compared 7–8 and 10–12year old school children and predicted that younger children would show reduced performance that was nonetheless amenable to training. Indeed, the pre-training performance of the 7–8year olds was worse than that of the 10–12year olds, but post-training performance was equally good for both groups. This was similar to previous results found using this training method for children with DCD and age-matched typically developing children. We also found in a previous study of 7–8year old school children that training in the 3D tracing task transferred to a 2D drawing task. We now found similar transfer for the 10–12year olds.

Motor coordination impairments frequently co-occur with other developmental disorders and mental health problems in clinically referred populations. But does this reflect a broader dimensional relationship within the general population? A clearer understanding of this relationship might inform improvements in mental health service provision. However, ascertainment and referral bias means that there is limited value in conducting further research with clinically referred samples. We, therefore, conducted a cross-sectional population-based study investigating children’s manual coordination using an objective computerised test. These measures were related to teacher-completed responses on a behavioural screening questionnaire [the Strength and Difficulties Questionnaire (SDQ)]. We sampled 298 children (4–11 years old; 136 males) recruited from the general population. Hierarchical (logistic and linear) regression modelling indicated significant categorical and continuous relationships between manual coordination and overall SDQ score (a dimensional measure of psychopathology). Even after controlling for gender and age, manual coordination explained 15 % of the variance in total SDQ score. This dropped to 9 % after exclusion of participants whose SDQ responses indicated potential mental health problems. These results: (1) indicate that there is a clear relationship between children’s motor and mental health development in community-based samples; (2) demonstrate the relationship’s dimensional nature; and (3) have implications for service provision.

Many children have difficulty producing movements well enough to improve in sensori-motor learning. Previously, we developed a training method that supports active movement generation to allow improvement at a 3D tracing task requiring good compliance control. Here, we tested 7-8 year old children from several 2nd grade classrooms to determine whether 3D tracing performance could be predicted using the Beery VMI. We also examined whether 3D tracing training lead to improvements in drawing. Baseline testing included Beery, a drawing task on a tablet computer, and 3D tracing. We found that baseline performance in 3D tracing and drawing co-varied with the visual perception (VP) component of the Beery. Differences in 3D tracing between children scoring low versus high on the Beery VP replicated differences previously found between children with and without motor impairments, as did post-training performance that eliminated these differences. Drawing improved as a result of training in the 3D tracing task. The training method improved drawing and reduced differences predicted by Beery scores.

Current methods of measuring gross motor abilities in children involve either high-cost specialist apparatus that is unsuitable for use in schools, or low-cost but nonoptimal observational measures. We describe the development of a low-cost system that is capable of providing high-quality objective data for the measurement of head movements and postural sway. This system is based on off-the-shelf components available for the NintendoWii: (1) The infrared cameras in a pair of WiiMotes are used to track head movements by resolving the position of infrared-emitting diodes in three dimensions, and (2) center-of-pressure data are captured using the WiiFit Balance board. This allows the assessment of children in school settings, and thus provides a mechanism for identifying children with neurological problems affecting posture. In order to test the utility of the system, we installed the apparatus in two schools to determine whether we could collect meaningful data on hundreds of children in a short time period. The system was successfully deployed in each school over a week, and data were collected on all of the children within the school buildings at the time of testing (N = 269). The data showed reliable effects of age and viewing condition, as predicted from previous small-scale studies that had used specialist apparatus to measure childhood posture. Thus, our system has the potential to allow screening of children for gross postural deficits in a manner that has never previously been possible. It follows that our system opens up the possibility of conducting large-scale behavioral studies concerning the development of posture.

Manual dexterity and postural control develop throughout childhood, leading to changes in the synergistic relationships between head, hand and posture. But the postural developments that support complex manual task performance (i.e. beyond pointing and grasping) have not been examined in depth. We report two experiments in which we recorded head and posture data whilst participants simultaneously performed a visuomotor task. In Experiment 1, we explored the extent to which postural stability is affected by concurrently performing a visual and manual task whilst standing (a visual vs. manual-tracking task) in four age groups: 5-6 years (n = 8), 8-9 years (n = 10), 10-11 years (n = 7) and 19-21 years (n = 9). For visual tracking, the children's but not adult's postural movement increased relative to baseline with a larger effect for faster moving targets. In manual tracking, we found greater postural movement in children compared to adults. These data suggest predictive postural compensation mechanisms develop during childhood to improve stability whilst performing visuomotor tasks. Experiment 2 examined the extent to which posture is influenced by manual activity in three age groups of children [5-6 years (n = 14), 7-8 years (n = 25), and 9-10 years (n = 24)] when they were seated, given that many important tasks (e.g. handwriting) are learned and performed whilst seated. We found that postural stability varied in a principled manner as a function of task demands. Children exhibited increased stability when tracing a complex shape (which required less predictive postural adjustment) and decreased stability in an aiming task (which required movements that were more likely to perturb posture). These experiments shed light on the task-dependant relationships that exist between postural control mechanisms and the development of specific types of manual control.

To what degree does being male or female influence the development of manual skills in pre-pubescent children? This question is important because of the emphasis placed on developing important new manual skills during this period of a child's education (e.g. writing, drawing, using computers). We investigated age and sex-differences in the ability of 422 children to control a handheld stylus. A task battery deployed using tablet PC technology presented interactive visual targets on a computer screen whilst simultaneously recording participant's objective kinematic responses, via their interactions with the on-screen stimuli using the handheld stylus. The battery required children use the stylus to: (i) make a series of aiming movements, (ii) trace a series of abstract shapes and (iii) track a moving object. The tasks were not familiar to the children, allowing measurement of a general ability that might be meaningfully labelled 'manual control', whilst minimising culturally determined differences in experience (as much as possible). A reliable interaction between sex and age was found on the aiming task, with girls' movement times being faster than boys in younger age groups (e.g. 4-5 years) but with this pattern reversing in older children (10-11 years). The improved performance in older boys on the aiming task is consistent with prior evidence of a male advantage for gross-motor aiming tasks, which begins to emerge during adolescence. A small but reliable sex difference was found in tracing skill, with girls showing a slightly higher level of performance than boys irrespective of age. There were no reliable sex differences between boys and girls on the tracking task. Overall, the findings suggest that prepubescent girls are more likely to have superior manual control abilities for performing novel tasks. However, these small population differences do not suggest that the sexes require different educational support whilst developing their manual skills.

The neural systems responsible for postural control are separate from the neural substrates that underpin control of the hand. Nonetheless, postural control and eye-hand coordination are linked functionally. For example, a stable platform is required for precise manual control tasks (e.g. handwriting) and thus such skills often cannot develop until the child is able to sit or stand upright. This raises the question of the strength of the empirical relationship between measures of postural stability and manual motor control. We recorded objective computerised measures of postural stability in stance and manual control in sitting in a sample of school children ( n = 278) aged 3–11 years in order to explore the extent to which measures of manual skill could be predicted by measures of postural stability. A strong correlation was found across the whole sample between separate measures of postural stability and manual control taken on different days. Following correction for age, a significant but modest correlation was found. Regression analysis with age correction revealed that postural stability accounted for between 1 and 10 % of the variance in manual performance, dependent on the specific manual task. These data reflect an interdependent functional relationship between manual control and postural stability development. Nevertheless, the relatively small proportion of the explained variance is consistent with the anatomically distinct neural architecture that exists for ‘gross’ and ‘fine’ motor control. These data justify the approach of motor batteries that provide separate assessments of postural stability and manual dexterity and have implications for therapeutic intervention in developmental disorders.

Old age is associated with reduced mobility of the hand. To investigate age related decline when reaching-to-lift an object we used sophisticated kinematic apparatus to record reaches carried out by healthy older and younger participants. Three objects of different widths were placed at three different distances, with objects having either a high or low friction surface (i.e. rough or slippery). Older participants showed quantitative differences to their younger counterparts - movements were slower and peak speed did not scale with object distance. There were also qualitative differences with older adults showing a greater propensity to stop the hand and adjust finger position before lifting objects. The older participants particularly struggled to lift wide slippery objects, apparently due to an inability to manipulate their grasp to provide the level of precision necessary to functionally enclose the object. These data shed light on the nature of age related changes in reaching-to-grasp movements and establish a powerful technique for exploring how different product designs will impact on prehensile behavior.

Introduction: Children with Developmental Coordination Disorder (DCD) must overcome a ‘catch-22’ to achieve sensori-motor learning. They cannot produce movements well enough to improve. Snapp-Childs, Mon-Williams and Bingham (2012) developed a method that supports active movement generation to allow practice with improvement of good compliance control. They showed that the method allowed children with DCD to improve at a 3D tracing task to become as proficient as typically developing children who had also trained. In the present study, we examined the effect of this training on handwriting, specifically figure copying, in 7-8 year olds. Methods: Twenty-three children were tested with the Beery VMI (including tests of Visual Perception (VP) and Motor Coordination (MC)), the 3D tracing task, and a 2D letter-like figure copying task. The children then trained on the 3D tracing task until they all reached comparably good proficiency. The 3D tracing and copying tasks were tested again following training. Results: Performance on the Beery varied widely. Age referenced percentile scores ranged from 4-92 for VMI, 2-96 for VP, 0.7-73 for MC. Performance on the 3D tracing task at baseline varied as a function of the level of difficulty. After training, these differences were dramatically reduced. Baseline performance in both the 3D tracing and 2D copying tasks co-varied with VP scores (not VMI or MC scores) indicating that the ability to visually discriminate pattern detail predicts complex 3D path tracing and 2D line form copying. Again, following training, these relationships disappeared. For figure copying, the extent to which copied forms were larger than the target related inversely to error scores. Conclusions: Figure copying improved as a result of training on the 3D tracing task, reducing differences in size and error that co-varied with VP scores. In conclusion, the training method improved handwriting and reduced differences indicated by Beery scores Meeting abstract presented at VSS 2013

Introduction: Manual dexterity requires that the head and body are stable so that vision can be used to generate movement and correct errors. Manual dexterity and postural control improve with age so the development of manual skill must involve changes in the relationship between head, body and hand. Nevertheless, this relationship has not been well investigated whilst participants undertake a manual task, probably because of the technical difficulties in simultaneous recording. Methods: We created a system capable of presenting visuomotor tasks whilst objectively measuring manual skill, head movements and postural sway. We explored performance in four conditions: (i) Stationary fixation; (ii) Eyes closed; (iii) Visual tracking of moving targets at slow, medium and fast speeds; (iv) Tracking the moving targets with a handheld stylus. 514 children were recruited aged 3-11 years. The strengths and difficulties questionnaire provided an index of autistic traits and these data were combined with a range of educational measures. Results: A relationship was found between postural sway with stationary fixation and manual dexterity performance. Adults were able to visually track the moving target with minimal head movements and postural sway. The younger children showed large head movements with associated postural adjustments. A clear trend towards adult behaviour was observed as a function of age. These effects were magnified when children tracked the moving target with the handheld stylus. A composite ‘ASD trait’ score was found to significantly correlate with manual dexterity, postural stability, and head movement. Conclusions: These results suggest our system has potential as a population level tool for objectively measuring posture and detecting developmental disorders. These results will be discussed with regard to ongoing data collection in a cohort of 13,500 children (Born in Bradford) where performance on our task can be related to genetic, health and educational data on the children and parents. Meeting abstract presented at VSS 2013

Reach-to-grasp movements change quantitatively in a lawful (i.e. predictable) manner with changes in object properties. We explored whether altering object texture would produce qualitative changes in the form of the precontact movement patterns. Twelve participants reached to lift objects from a tabletop. Nine objects were produced, each with one of three grip surface textures (high-friction, medium-friction and low-friction) and one of three widths (50 mm, 70 mm and 90 mm). Each object was placed at three distances (100 mm, 300 mm and 500 mm), representing a total of 27 trial conditions. We observed two distinct movement patterns across all trials—participants either: (i) brought their arm to a stop, secured the object and lifted it from the tabletop; or (ii) grasped the object ‘on-the-fly’, so it was secured in the hand while the arm was moving. A majority of grasps were on-the-fly when the texture was high-friction and none when the object was low-friction, with medium-friction producing an intermediate proportion. Previous research has shown that the probability of on-the-fly behaviour is a function of grasp surface accuracy constraints. A finger friction rig was used to calculate the coefficients of friction for the objects and these calculations showed that the area available for a stable grasp (the ‘functional grasp surface size’) increased with surface friction coefficient. Thus, knowledge of functional grasp surface size is required to predict the probability of observing a given qualitative form of grasping in human prehensile behaviour.

Humans are expert decision makers, capable of assimilating information rapidly and tailoring behaviour optimally, according to task constraints and context. Thus, individuals can produce an effective response to a visual stimulus in a short time frame. Nevertheless, the mechanisms that evaluate evidence and reach decisions can sometimes select sub-optimal behaviours, with decision-making appearing to become ‘stuck in a rut’. We developed a model of learning that revealed this inertia is a naturally emergent feature of a learning system. To test the model, 30 participants (16 female, 14 male, mean age 26.8 years) completed an aiming task designed using specialised software presented on a digitizing tablet (Toshiba Portege M700-13P). A handheld stylus was used as an input device to move a cursor between two points displayed on a computer screen without hitting an obstacle blocking the route. In ‘sequential’ conditions, the obstacle was displaced to either to the left or the right of the screen and then incrementally moved 15 times away and then towards the starting positions. In the ‘random’ condition, the obstacle appeared randomly in one of the 15 positions (twice per session). Post-hoc interviews showed participants were unaware that the obstacle moved from trial-to-trial. In the random condition, participants showed a high bias towards selecting the shortest route between the points. In the sequential conditions, participants showed a bias towards the previous selected route (a phenomenon that can be termed hysteresis) even though this was a sub-optimal route avoided in the random condition. The learning model predicted precisely this qualitative pattern of decision-making and demonstrated that the emergent hysteretic effect evident during sequential conditions does not develop with randomisation of the motor task sequence. These results suggest that an understanding of responses to visual stimuli requires a consideration of the learning mechanisms underpinning skilled behaviours.