The general objective of the subprogramme is to understand, for various perceptual and motor tasks, why they are performed the way they are performed, and how they are controlled. This understanding is ultimately sought for humans, but some studies are also directed at animal motion, for various reasons (because insight can be gained from comparative functional anatomy, because animals are more specialised for certain tasks, because animal studies allow for invasive experiments, etc.).
Although the primary objective is to gain fundamental insights, the results are translated whenever possible into recommendations for rehabilitation programs in health care, and training programs and equipment used in sports. In the study of perceptual and motor tasks, the emphasis lies on psychophysical and biophysical methods of analysis, and experiments on subjects are intertwined with computer simulations using mathematical models of the systems under study.
In the study of discrete movement tasks, experiments on humans (sometimes animals) performing the tasks of interest are conducted to obtain descriptive information on movement performance and execution (kinematics, kinetics and electromyograms), and study their dependence on available sensory information, initial conditions (e.g. starting position), external constraints (e.g. fixation of subject to the environment), or musculo-skeletal or neural pathology. The description is not restricted to the average behaviour, but includes the statistical properties such as variability and correlation measures. Moreover, experiments are conducted to estimate the properties of the human musculo-skeletal system (such as force-length-velocity relationships of muscles and excitation dynamics).
For maximally fast goal directed movements, forward models of the musculoskeletal system are used to find the optimal performance given the available information and the properties of the action system, and to explore how it may be realized through neural control strategies. Special care is taken to incorporate in the models the true mechanical and physiological properties of muscles. The reason is that in fast movements, the inevitable delays in neural feedback loops restrict the use of neural feedback, so that robustness of the movement depends to a large extent on the stabilizing properties of muscles. Control strategies that seem conceptually sound and biologically plausible are compared to strategies used by subjects; alternatively, strategies used by subjects are tested for success using the simulation model.
For some goal-directed tasks (e.g. movements at lower velocities), perceptual limitations play a more important role than the limitations of the musculoskeletal systems. Our modelling efforts for understanding such movements are based on models that are restricted to the behaviour level. Again, finding optimal behaviour is one of the goals, but now the limitations of the perceptual mechanisms are the key aspect of the models.
In the study of cyclic movement tasks, experiments on subjects performing the tasks of interest are also conducted to obtain descriptive information about movement performance and execution (kinematics, kinetics, electromyograms, rate of oxygen uptake, blood lactate levels) and study their dependence on external constraints (for example air pressure, altitude), equipment (e.g. type of skates, properties of swimsuits, properties of bicycle), and pacing strategies used. Also, experiments are conducted to gain insight into the mechanisms underlying training effects. Moreover, experiments are conducted to estimate the properties of the system (such as, aerobic capacity, dynamics of aerobic and anaerobic power supply).
Again, computer simulations are used to find optimal performance that can be realized given the properties of the action system, and to explore control strategies for realizing it. Several types of models are used. To study a cyclic motion like running, for example, each of the individual steps can be studied with the simulation models used for discrete movements. If steady state running is of interest, however, limit cycle behaviour needs to be achieved in the model. Moreover, metabolic energy expenditure needs to be incorporated in the optimisation criterion. In case the maximal performance in an endurance race is of interest, pacing strategies become important as well. Optimal pacing strategies are currently studied using models based on balance of external power losses and power supply by the aerobic and anaerobic systems. Solutions found using these power balance models are put to the test by top athletes.
For the study of perception we generally use computer-based experiments in which we ask human subjects to answer simple multiple-choice questions about stimuli, or to set stimuli according to a certain instruction. In some experiments, we use real scenes to be judged by the subjects. Also for the analysis of the behaviour in perceptual tasks, mathematical modelling is an essential tool. The models we use here are generally based on a simple (but biologically plausible) description of information processing. An important aspect is that we take into account the statistics of behaviour and of the outside world, using optimal cue combination and Bayesian inference