In order to find out whether the movements of single digits are controlled in a special way when grasping, we compared the movements of the digits when grasping an object with their movements in comparable single-digit tasks: pushing or lightly tapping the same object at the same place. The movements of the digits in grasping were very similar to the movements in the single-digit tasks. To determine to what extent the hand transport and grip formation in grasping emerges from a synchronised motion of individual digits, we combined movements of finger and thumb in the single-digit tasks to obtain hypothetical transport and grip components. We found a larger peak grip aperture earlier in the movement for the single-digit tasks. The timing of peak grip aperture depended in the same way on its size for all tasks. Furthermore, the deviations from a straight line of the transport component differed considerably between subjects, but were remarkably similar across tasks. These results support the idea that grasping should be regarded as consisting of moving the digits, rather than transporting the hand and shaping the grip.

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Nine years after proposing our "new view on grasping", we re-examine the support for the approach that we proposed. This approach consisted of two steps. The first step was to formulate three assumptions that made it possible to model grasping in the same way as one would model movements of a single digit. The second step was to implement an existing model for movements of a single digit (minimum jerk model) in accordance with these assumptions. In both cases we applied Occam's razor: we used as few entities as possible to explain as many phenomena as possible. Here we evaluate both steps in the light of recent experimental results. We show that there is ample support for assuming that the movement of the fingertip is controlled in the same way in a reach-to-grasp movement as in other movements performed to interact with objects. The predictions based on the implementation of the minimum jerk model were surprisingly good in many situations, although they were clearly wrong in some other situations. Since more complicated models do not perform better, we conclude that currently our approach gives the best description of grasping.

We present Intermittent Control (IC) models as a candidate framework for modelling human input movements in Human–Computer Interaction (HCI). IC differs from continuous control in that users are not assumed to use feedback to adjust their movements continuously, but only when the difference between the observed pointer position and predicted pointer positions becomes large. We use a parameter optimisation approach to identify the parameters of an intermittent controller from experimental data, where users performed one-dimensional mouse movements in a reciprocal pointing task. Compared to previous published work with continuous control models, based on the Kullback–Leibler divergence from the experimental observations, IC is better able to generatively reproduce the distinctive dynamical features and variability of the pointing task across participants and over repeated tasks. IC is compatible with current physiological and psychological theory and provides insight into the source of variability in HCI tasks.

Intermittent control combines open-loop trajectories with feedback at discrete time instances determined by events. Among other applications, it has recently been used to model quiet standing in humans where the system was assumed to be time-invariant. This article expands this work to the time-variant case by introducing an adaptive intermittent controller that exploits the well-known self-tuning architecture of adaptive control with a Kalman filter to perform online state and parameter estimation. Simulation and experimental results using a rotational inverted pendulum show advantages of the intermittent controllers compared to continuous feedback control since the former can provide persistent excitation due to their internal triggering mechanism, even when no external reference changes or disturbances are applied. Moreover, the results show that the event thresholds of intermittent control can be used to adjust the degree of responsiveness of the adaptation in the system, becoming a tool to balance the trade-off between steady-state performance and flexibility against parametric changes, addressing the stability–plasticity dilemma of adaptation and learning in control.