Analyzing learning, interaction, and automatization in speed stacking

2008-11 till 2011-11
Research Areas: 

Based on the ‘natural task’ approach and on basic attention research, ALIAS investigates visual selection processes in the bimanual, high-speed, sensorimotor task of speed stacking. The project asks how saccadic eye movement control (where-to-look-next?) changes during learning and which new characteristics arise at the level of international championship. Results point to the possibility that automatization leads to an increasing degree of long-term memory based control of attentional selection.

Methods and Research Questions: 

Humans move their eyes via saccades to informative locations of the environment while performing natural tasks. Researchers have intensively investigated eye movements and their characteristics in natural tasks. However, few studies addressed the development of attention control in such well-learned tasks. Only a few studies have investigated eye movements during learning of a novel task, although it is of particular interest how gaze patterns may change with practice. Insights into visual selection processes during skill acquisition can help to understand and improve the learning process. Becoming more effective at a task resembles the change from conscious to unconscious execution, which is an important feature of automatization. Traditionally, automatization was considered as processing without attention control, while recent concepts define automatization by a change in attention control. With regard to these contrasting positions, the project investigates how attention control – measured via the allocation of gaze - changes during learning and automatization of a high-speed sensorimotor task. Given a high degree of task automatization we asked how obligatory the link between hand and eye movement control is. This question can be answered by studying eye movements in the automatized speed-stacking task when it is performed without visual input in the dark. Performance measurements (execution time and accuracy) and eye movements are recorded while performing the bimanual, high-speed, sensorimotor stacking task in different conditions. Gaze videos are recorded based on mobile eye tracking and analyzed frame-by-frame. The speed-stacking task (also known as sport stacking) requires grasping, moving, rotating, and placing of objects. It consists of a fixed sequence of stacking up and down pyramids of plastic cups as fast as possible. Number, order, and direction of the stacking movements are predetermined (for an illustrative example see Movie or visit Studying eye movements in speed stacking has several advantages. The fact that speed stacking is largely unknown allows recruiting naïve participants. In addition, it is fast and easy to learn and to automatize. These facts allow for investigating the whole learning process, from first contact with the task until a high degree of automatization has been achieved. Furthermore, it is a task which can be executed at an amazingly high velocity. Finally, the task involves simultaneous movements of the two hands on different objects.


The comparison of eye movement patterns in the beginning of the learning process and after automatization reveals that the eyes lead the hands with a shorter delay and that fewer fixations are made with increasing automatization. In addition, international champions made even fewer fixations than trained students, while fixation duration and fixation rate did not differ. At the same time, all participants looked at task-relevant locations in a similar order across training days. Results indicate that sequences of saccades are transferred to long-term memory (LTM) during learning and are retrieved from LTM after automatization. In support of this assumption, the comparison between normal vision and darkness revealed lower fixation rates and longer fixation durations in the dark, while scanpaths and eye-hand dynamics were very similar between illumination conditions. A further study has revealed that a well-learned speed-stacking task recruits spatial working-memory resources even after automatization. In sum, results point to a gradual transition from a more sensory-based to a more long-term memory-based visual selection during learning and automatization of a high-speed sensorimotor task.


"Hochstapelei im Dienste der Wissenschaft - Rebecca Förster erforscht Bewegungs-Automatisierung", von Sabine Schulze, Westfalen-Blatt Nr. 229.pdf

"Fliegende Becher - Rebecca Förster schreibt ihre Doktorarbeit über das Speedstacking", von Hendrik Uffmann, Westfalen-Blatt Nr. 133.pdf

"Speed stacken - wie lernen wir?", von Melanie Heßler, WDR5_Leonardo.mp3

"Was passiert, wenn unsere Sinne verrückt spielen?", von Markus Erwig, ARTE+7 X:enius