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Factors of Narrative Understanding

WorkgroupPerception and Action  Lab
FundingIWM budget resources
Project description

The comprehension of different types of narratives, such as text, pictures, or comics, is important for societal participation. This dissertation project investigates how narrative comprehension changes with age and which factors contribute positively or negatively to narrative comprehension. These findings are essential for the development of interventions for different age groups and thus for greater societal participation.

An aging society and an increased level of everyday stress caused by recent drastic global changes also raise new questions about cognitive abilities such as narrative comprehension. Factors such as aging, low well-being, depression, and stress can negatively affect cognitive abilities. At the same time, factors such as sport, mental fitness, and stable social contacts can play a preventive role. However, it is not yet clear how they affect narrative comprehension. A series of experiments in this project have already shown that narrative comprehension is a fairly stable skill. However, it was found that this comprehension can decrease under stress. Interestingly, the effect of stress was different in different age groups.

The processes of visual narrative comprehension have been the least studied so far, thus we measure visual narrative comprehension using self-developed structured picture stories. To measure stress induction, we use both classical psychological scales (e.g., PANAS) and assess biological parameters (e.g., cortisol levels).

The results of this project will improve our understanding of the narrative comprehension of comics in different groups. We will also gain knowledge about the effects of age and stress and about factors, that may positively and negatively affect narrative comprehension. This will aid in the development of interventions and a more targeted use of comics to convey and communicate information to different groups.

  • Apl. Prof. Dr. Gerhard W. Eschweiler, Senior Physician at the University Clinic for Psychiatry and Psychotherapy, Tübingen
  • TREND-Study
  • M. Sc. Robert Richer, PhD Candidate & Group Head, Machine Learning and Data Analytics (MaD) Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg


Ekaterina Varkentin Ekaterina Varkentin
Tel.: +49 7071 979-267

Project team

Prof. Dr. Markus Huff