Numerous studies showed decreased performance in situations that require multiple tasks or actions relative to appropriate control conditions. Because humans often engage in such multitasking activities, it is important to understand how multitasking affects performance. In the present article, we argue that research on dual-task interference and sequential task switching has proceeded largely separately using different experimental paradigms and methodology. In our article we aim at organizing this complex set of research in terms of three complementary research perspectives on human multitasking. One perspective refers to structural accounts in terms of cognitive bottlenecks (i.e., critical processing stages). A second perspective refers to cognitive flexibility in terms of the underlying cognitive control processes. A third perspective emphasizes cognitive plasticity in terms of the influence of practice on human multitasking abilities. With our review article we aimed at highlighting the value of an integrative position that goes beyond isolated consideration of a single theoretical research perspective and that broadens the focus from single experimental paradigms (dual task and task switching) to favor instead a view that emphasizes the fundamental similarity of the underlying cognitive mechanisms across multitasking paradigms. (PsycINFO Database Record

A central ability of the motor system is to achieve goals with great reliability, although never with zero variability. It is argued that variability is reduced with practice by 3 separate means: reduction of stochastic noise (N), exploitation of task tolerance (T), and covariation (C) between central variables. A method is presented that decomposes variability into these components in relation to task space that is defined by the execution variables. Successful variable combinations form the solution manifold. In a virtual skittles task, it is demonstrated that participants' improvement over repetitions, indicated by increasing accuracy, is accounted for by N, T, and, to a lesser degree, C. The relative contribution of these components changes over the course of practice and task variations.

Although variability is a fundamental and ubiquitous feature of movement in all biological systems, skilled performance is typically associated with a low level of variability and, implicitly, random noise. Hence, during practice performance variability undergoes changes leading to an overall reduction. However, learning manifests itself through more than just a reduction of random noise. To better understand the processes underlying acquisition and control of movements we show how the examination of variability and its changes with practice provides a suitable window to shed light on this phenomenon. We present one route into this problem that is particularly suited for tasks with redundant degrees of freedom: task performance is parsed into execution and result variables that are related by some function which provides a set of equivalent executions for a given result. Variability over repeated performances is analyzed with a view to this solution manifold. We present a method that parses the structure of variability into four conceptually motivated components and review three methods that are currently used in motor control research. Their advantages and limitations are discussed.

In motor tasks with redundancy neuromotor noise can lead to variations in execution while achieving relative invariance in the result. The present study examined whether humans find solutions that are tolerant to intrinsic noise. Using a throwing task in a virtual set-up where an infinite set of angle and velocity combinations at ball release yield throwing accuracy, our computational approach permitted quantitative predictions about solution strategies that are tolerant to noise. Based on a mathematical model of the task expected results were computed and provided predictions about error-tolerant strategies (Hypothesis 1). As strategies can take on a large range of velocities, a second hypothesis was that subjects select strategies that minimize velocity at release to avoid costs associated with signal- or velocity-dependent noise or higher energy demands (Hypothesis 2). Two experiments with different target constellations tested these two hypotheses. Results of Experiment 1 showed that subjects chose solutions with high error-tolerance, although these solutions also had relatively low velocity. These two benefits seemed to outweigh that for many subjects these solutions were close to a high-penalty area, i.e. they were risky. Experiment 2 dissociated the two hypotheses. Results showed that individuals were consistent with Hypothesis 1 although their solutions were distributed over a range of velocities. Additional analyses revealed that a velocity-dependent increase in variability was absent, probably due to the presence of a solution manifold that channeled variability in a task-specific manner. Hence, the general acceptance of signal-dependent noise may need some qualification. These findings have significance for the fundamental understanding of how the central nervous system deals with its inherent neuromotor noise.