Hendrik Koesling

Saccadic eye movements in a high-speed bimanual stacking task: changes of attentional control during learning and automatization.

Principles of saccadic eye movement control in the real world have been derived by the study of self-paced well-known tasks such as sandwich or tea making. Little is known whether these principles generalize to high-speed sensorimotor tasks and how they are affected by learning and automatization. In the present study, right-handers practiced the speed-stacking task in 14 consecutive daily training sessions, while their eye movements were recorded. Speed stacking is a high-speed sensorimotor task that requires grasping, moving, rotating, and placing of objects. The following main results emerged. Throughout practice, the eyes led the hands, displayed by a positive eye-hand time span. Moreover, visual information was gathered for the subsequent manual sub-action, displayed by a positive eye-hand unit span. With automatization, the eye-hand time span became shorter, yet it increased when corrected by the decreasing trial duration. In addition, fixations were mainly allocated to the goal positions of the right hand or objects in the right hand. The number of fixations decreased while the fixation rate remained constant. Importantly, all participants fixated on the same task-relevant locations in a similar scan path across training days, revealing a long-term memory-based mode of attention control after automatization of a high-speed sensorimotor task.

Saccadic eye movements in the dark while performing an automatized sequential high-speed sensorimotor task

Saccades during object-related everyday tasks select visual information to guide hand movements. Nevertheless, humans can perform such a task in the dark provided it was automatized beforehand. It is largely unknown whether and how saccades are executed in this case. Recently, a long-term memory (LTM)-based direct control mode of attention during the execution of well-learned sensorimotor tasks, which predicts task-relevant saccades in the dark, was proposed (R. M. Foerster, E. Carbone, H. Koesling, & W. X. Schneider, 2011). In the present study, participants performed an automatized speed-stacking task in the dark and in the light while their eye movements were recorded. Speed stacking is a sequential high-speed sensorimotor object manipulation task. Results demonstrated that participants indeed made systematic eye movements in the dark. Saccadic scan paths and the number of fixations were highly similar across illumination conditions, while fixation rates were lower and fixation durations were longer in the dark. Importantly, the eye reached a location ahead of the hands even in the dark. Finally, neither eye-hand dynamics nor saccade accuracy correlated with hand movement durations in the dark. Results support the hypothesis of an LTM-based mode of attention selection during the execution of automatized sequential high-speed sensorimotor tasks.

Visual search in the (un)real world: how head-mounted displays affect eye movements, head movements and target detection

Head-mounted displays (HMDs) that use a see-through display method allow for superimposing computer-generated images upon a real-world view. Such devices, however, normally restrict the users field of view. Furthermore, low display resolution and display curvature are suspected to make foveal as well as peripheral vision more difficult and may thus affect visual processing. In order to evaluate this assumption, we compared performance and eye-movement patterns in a visual search paradigm under different viewing conditions: participants either wore an HMD, had their field of view restricted by blinders or could avail themselves of an unrestricted field of view (normal viewing). From the head and eye-movement recordings we calculated the contribution of eye rotation to lateral shifts of attention. Results show that wearing an HMD leads to less eye rotation and requires more head movements than under blinders conditions and during normal viewing.

With a Flick of the Eye: Assessing Gaze-Controlled Human-Computer Interaction

Gaze-controlled user interfaces appear to be a viable alternative to manual mouse control in human-computer interaction. Eye movements, however, often occur involuntarily and fixations do not necessarily indicate an intention to interact with a particular element of a visual display. To address this so-called Midas-touch problem, we investigated two methods of object/action selection using volitional eye movements, fixating versus blinking, and evaluated error rates, response times, response accuracy and user satisfaction in a text-typing task. Results show significantly less errors for the blinking method while task completion times do only vary between methods when practice is allowed. In that case, the fixation method is quicker than the blinking method. Also, participants rate the fixation method higher for its ease of use and regard it as less tiring. In general, blinking appears more suited for sparse and non-continuous input (e.g., when operating ticket vending machines), whereas fixating seems preferable for tasks requiring more rapid and continuous selections (e.g., when using virtual keyboards). We could demonstrate that the quality of the selection method does not rely on efficiency measures (e.g., error rate or task completion time) alone: user satisfaction measures must certainly be taken into account as well to ensure user-friendly interfaces and, furthermore, gaze-controlled interaction methods must be adapted to specific applications.

Entropy-based correction of eye tracking data for static scenes

In a typical head-mounted eye tracking system, any small slippage of the eye tracker headband on the participants head leads to a systematic error in the recorded gaze positions. While various approaches exist that reduce these errors at recording time, only few methods reduce the errors of a given tracking system after recording. In this paper we introduce a novel correction algorithm that can significantly reduce the drift in recorded gaze data for eye tracking experiments that use static stimuli. The algorithm is entropy-based and needs no prior knowledge about the stimuli shown or the tasks participants accomplish during the experiment.