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EEG-based neural signatures of different types of working-memory load

Working group Multimodal Interaction Lab
Duration 01/2013 - 12/2016
Funding Leibniz-WissenschaftsCampus Tübingen
Project description

The project is part of Cluster01 in the ScienceCampus Tuebingen "Informational Environments". The cluster aims at bridging the gap between neuroscience and instructional psychology via informatics by developing an online adaptive learning environment based on physiological load-measures. Specifically, the online adaption grounds on specific types of working memory load via neural signatures in the electroencephalogram (EEG) of learners by means of advanced braincomputer interface (passive BCI) methodologies.

As a prerequisite of the Cluster's goal, the project at the IWM studies EEG frequency bandpower correlates of load on working memory and attention processes that are hypothesized to be elementary in complex learning tasks. Additionally the project focuses on eye-tracking measures like the pupil dilation (i.e., the increase in pupil diameter under increasing cognitive load). The goal of the project is to gain insight in the specificity and sensitivity of EEG and eye-tracking measures for these elementary cognitive processes and to develop a task that allows the combined manipulation of load on these cognitive processes. This task will be used to train specific BCI-classifiers (i.e., computational pattern-recognition algorithms). By means of cross-task-classification the hypotheses that elementary cognitive processes are building blocks of complex processes will then be examined. Cross-task classification means that BCI-classifiers are trained and validated on simple, well-controlled working memory tasks and then used on learning material to detect the specific working memory load signatures. A potential learning environment might use this technology to assess the cognitive load of learners and to adapt the learning material accordingly (i.e., by increasing or decreasing task difficulty) to maintain an optimal level of cognitive for each learner.


Wilhelm-Schickard-Institute for Informatics, University of Tübingen


Scharinger, C., Kammerer, Y., & Gerjets, P. (2015). Pupil dilation and EEG alpha frequency band power reveal load on executive functions for link-selection processes during text reading. PLoS ONE, 10, e0130608.

Scharinger, C., Soutschek, A., Schubert, T., & Gerjets, P. (2015). When flanker meets the n-back: What EEG and pupil dilation data reveal about the interplay between the two central-executive working memory functions inhibition and updating. Psychophysiology, 52, 1293-1304.

Gerjets, P., Walter, C., Rosenstiel, W., Bogdan, M., & Zander, T. O. (2014). Cognitive state monitoring and the design of adaptive instruction in digital environments: Lessons learned from cognitive workload assessment using a passive brain-computer interface approach. Frontiers in Neuroscience, 8:385. doi:10.3389/fnins.2014.00385.


Project Website: Leibniz-WissenschaftsCampus Cluster01