Research at LME

Foto: Torsten Silz

The lab combines basic and applied research from a cognitive psychology perspective with special emphasis on psychological processes relevant for digital education techniques

Learning and deciding with data graphs

We are investigating how graphs can support learning in psychology (e.g., Blech & Gaschler, 2017, 2018) and in other domains (e.g., Zhao, Schnotz, Wagner, & Gaschler, 2014).

We develop and evaluate teaching tools for psychology such as the collection of experiment demos or tools to support teaching quantitative theories in cognitive psychology (e.g., Rescorla-Wagner-app).

Data graphs allow us to use the powerful computational capabilities of our visual system to with little effort grasp relevant aspects of data patterns. Yet, automatic perceptual processes lead to biased outcomes in some combinations of tasks and graph formats (e.g., Godau, Vogelgesang, & Gaschler, 2016; Kemper, Gaschler, & Schubert, 2017). Accordingly, in our experiments we test potential pitfalls of different formats. Research and communication related to sustainability and flexible usage of variable energy resources from renewable sources is one domain where data graph quality and fit of format to task are of high importance. We work on improving data graph-based communication in this domain (see e.g. MAXFAB project).

While researchers in many disciplines contribute to methods of assessing the fit between quantitative theories and data, relatively little is known about how we visually gauge this fit (for instance between a prediction line and data points in a scatter-plot). Yet in science and science communication, this visual fit seems to have substantial weight. Our experiments target processes and characteristics of visual fit computation and the resulting recommendations for data graph design.

Contributing research topics

The lab members can contribute different backgrounds (in part in basic research) to the work on psychological processes relevant for digital education techniques.

Dr. Christine Blech has been working on complex problem solving. In her doctoral thesis and her PhD at the University of Heidelberg she investigated cognitive aspects (e.g., knowledge acquisition) and motivational processes (e.g., balancing multiple goals) in this field.

M.Sc. Anna Conci has a research focus on visual search. She uses eyetracking experiments to investigate the processes that determine whether people detect vs. miss targets. This is relevant for many applications as well as for a better understanding of attention and memory.

Prof. Robert Gaschler has been working on (1) the formation and impact of expectations, (2) implicit learning, and (3) how people change task representations with practice. In his doctoral thesis at Humboldt-Universität he investigated how people with practice ignore irrelevant aspects of visual stimuli.

Christoph Naefgen has been working on the difference between task processing with pre-determined vs. free-choice actions and on processes relevant in multitasking at the University of Tübingen. In his work within the multitasking research cluster funded by the German Research Foundation, he is focusing on the role auf automatic prediction processes in multitasking.

Dr. Nadine Nett is interested in the way that irrelevant information (distractors) influences behavior and decisions. She investigates the mechanism of the distractor response binding effect and the influence of gender stereotypes on information processing.

Miriam Bettenhausen is working in the field of history of psychology using the according archive at FernUniversität. She addresses how societal context leads to changes in social networks, contents and methods of the discipline and how psychologists reflect on change in the discipline.

In her doctoral thesis and later work, Dr. Fang Zhao has been using eyetracking to investigate how students process text and pictures to understand teaching materials in order to answer questions.



Blech, C., & Gaschler, R. (2018). Assessing students’ knowledge about learning and forgetting curves with a free production technique: Measures and implications for the development of learning aids. Psychology Learning & Teaching, 17(3), 308–322. doi: 10.1177/1475725718779684

Blech, C. & Gaschler, R. (2017). Developing a drawing task to differentiate group average time course vs. dynamics in the individual. Psychology Learning & Teaching, 16(2), 212–231. doi: 10.1177/1475725717700516

Godau, C., Vogelgesang, T., & Gaschler, R. (2016). Perception of bar graphs -- A biased impression? Computers in Human Behavior, 59, 67-73. doi: 10.1016/j.chb.2016.01.036

Kemper, M., Gaschler, R., & Schubert, T. (2017). Stronger effects of self-generated than cue-induced expectations when verifying predictions in data graphs. Journal of Cognitive Psychology, 29(5), 626–641. doi: 10.1080/20445911.2017.1291644

Zhao, F., Schnotz, W., Wagner, I., & Gaschler, R. (2014). Eye tracking indicators of reading approaches in text-picture comprehension. Frontline Learning Research, 2(4), 46-66. doi: 10.14786/flr.v2i4.98



Allgemeine Psychologie: LME | 12.08.2021