Optimal control predicts human performance on objects with internal degrees of freedom.

On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.

Understanding jet noise.

Jets are one of the most fascinating topics in fluid mechanics. For aeronautics, turbulent jet-noise modelling is particularly challenging, not only because of the poor understanding of high Reynolds number turbulence, but also because of the extremely low acoustic efficiency of high-speed jets. Turbulent jet-noise models starting from the classical Lighthill acoustic analogy to state-of-the art models were considered. No attempt was made to present any complete overview of jet-noise theories. Instead, the aim was to emphasize the importance of sound generation and mean-flow propagation effects, as well as their interference, for the understanding and prediction of jet noise.