Selecting a graphical item by pointing with a computer mouse is a ubiquitous task in many graphical user interfaces. Several techniques have been suggested to facilitate this task, for instance, by reducing the required movement distance. Here we measure the natural coordination of eye and mouse pointer control across several search and selection tasks. We find that users automatically minimize the distance to likely targets in an intelligent, task dependent way. When target location is highly predictable, top-down knowledge can enable users to initiate pointer movements prior to target fixation. These findings question the utility of existing assistive pointing techniques and suggest that alternative approaches might be more effective.

Driver distraction strongly contributes to crashrisk. Therefore, assistance systems that warn the driver if her distraction poses a hazard to road safety, promise a great safety benefit. Current approaches either seek to detect critical situations using environmental sensors or estimate a driver's attention state solely from her behavior. However, this neglects that driving situation, driver deficiencies and compensation strategies altogether determine the risk of an accident. This work proposes to use inverse suboptimal control to predict these aspects in visually distracted lane keeping. In contrast to other approaches, this allows a situation-dependent assessment of the risk posed by distraction. Real traffic data of seven drivers are used for evaluation of the predictive power of our approach. For comparison, a baseline was built using established behavior models. In the evaluation our method achieves a consistently lower prediction error over speed and track-topology variations. Additionally, our approach generalizes better to driving speeds unseen in training phase.

Before initiating a saccade to a moving target, the brain must take into account the target’s eccentricity as well as its movement direction and speed. We tested how the kinematic characteristics of the target influence the time course of this oculomotor response. Participants performed a step-ramp task in which the target object stepped from a central to an eccentric position and moved at constant velocity either to the fixation position (foveopetal) or further to the periphery (foveofugal). The step size and target speed were varied. Of particular interest were trials that exhibited an initial saccade prior to a smooth pursuit eye movement. Measured saccade reaction times were longer in the foveopetal than in the foveofugal condition. In the foveopetal (but not the foveofugal) condition, the occurrence of an initial saccade, its reaction time as well as the strength of the pre-saccadic pursuit response depended on both the target’s speed and the step size. A common explanation for these results may be found in the neural mechanisms that select between oculomotor response alternatives, i.e., a saccadic or smooth response.

Maximum Causal Entropy (MCE) Inverse Optimal Control (IOC) has become an effective tool for modeling human behavior in many control tasks. Its advantage over classic techniques for estimating human policies is the transferability of the inferred objectives: Behavior can be predicted in variations of the control task by policy computation using a relaxed optimality criterion. However, exact policy inference is often computationally intractable in control problems with imperfect state observation. In this work, we present a model class that allows modeling human control of two tasks of which only one be perfectly observed at a time requiring attention switching. We show how efficient and exact objective and policy inference via MCE can be conducted for these control problems. Both MCE-IOC and Maximum Causal Likelihood (MCL)-IOC, a variant of the original MCE approach, as well as Direct Policy Estimation (DPE) are evaluated using simulated and real behavioral data. Prediction error and generalization over changes in the control process are both considered in the evaluation. The results show a clear advantage of both IOC methods over DPE, especially in the transfer over variation of the control process. MCE and MCL performed similar when training on a large set of simulated data, but differed significantly on small sets and real data.

ZusammenfassungAufgrund des limitierten Sehvermögens des Menschen muss sich ein Anwender vor großen, hochauflö-senden Displays frei bewegen können, um kleinste Details pixelgenau wahr zu nehmen oder einen Überblick über die gesamte Darstellungsfläche von mehreren Quadratmetern zu erhalten. Im Gegensatz zu konventionellen Eingabegeräten wie Maus und Tastatur beschränken Laserpointer den Anwender nicht in seiner Bewegungsfreiheit, sondern ermöglichen unabhängig von der Position zum Display eine sehr intuitive und direkte Art der Interaktion. In diesem Beitrag wird eine Interaktionsbibliothek vorgestellt, welche im Hinblick auf Präzision und verzögerungsfreier Steuerung erstmals auch den Einsatz von Laserpointer-Tracking bei großen, hochauflösenden Displays ermöglicht. In einem Vergleichsexperiment wurde die Interaktionsbibliothek in Kombination mit einem Infrarotlaserpointer gegenüber einer klassischen Maus als Standardeingabegerät bei unterschiedlichen Distanzen evaluiert. Der signifikante Performancevorteil der Maus von 12,5% scheint in Anbetracht der gewonnenen Bewegungsfreiheit und der unmittelbaren Interaktionsweise mit dem Laserpointer eher gering ins Gewicht zu fallen. Im Vergleich zu bisherigen Systemen konnte der Rückstand des Laserpointers um über 50% reduziert werden, was größtenteils auf die geringe Bewegungslatenz, das präzise Tracking und die wirksame Kompensation des Zitterns der Hand zurückzuführen ist. Ferner wurde bei der Studie ein signifikanter Distanzeffekt beim Laserpointer hinsichtlich Performance und Fehlerrate festgestellt.