My name is Benedikt Ehinger. I’m a Tenure-Track Professor for Computational Cognitive Sciences at the SimTech and VIS at University of Stuttgart

Send me a message: science@benediktehinger.de

My studies and interests involve cognitive psychology, visual perception, categorization and cortocortical interactions. I am working with EEG, Bayesian statistics, eye tracking and slowly diving into VR/mobile EEG.


2018: Doktor rer. nat. / PhD Cognitive Science .
PhD Thesis Predictions, Decisions and Learning in the visual sense

2013: Master of Science in Cognitive Science.
Masterthesis „Filling in Blind-Spots: A psychophysical and an EEG study.“

2012: Research-Project: Mobile EEG: Comparing Real Environments with Laboratory Settings

2011: Bachelor of Science in Cogntive Science
Bachelorthesis: „Electrophysiological Correlates of Category Learning

2010: Abroad at Norwegian University of Technology, Trondheim.


Yan, C. et al. (2023) ‘Humans predict the forest, not the trees: statistical learning of spatiotemporal structure in visual scenes’, Cerebral Cortex, p. bhad115. Available at: https://doi.org/10.1093/cercor/bhad115. Cite
Skukies, René and Ehinger, Benedikt V. (2023) ‘‪The effect of estimation time window length on overlap correction in EEG data‬’, in. Computational Cognitive Neuroscience. Available at: https://scholar.google.de/citations?view_op=view_citation&hl=de&user=VKDX28YAAAAJ&sortby=pubdate&citation_for_view=VKDX28YAAAAJ:ns9cj8rnVeAC (Accessed: 24 June 2023). Cite
Bonasch, Hannes and Ehinger, Benedikt V. (2023) ‘‪Decoding accuracies as well as ERP amplitudes do not show between-task correlations‬’, in. Computational Cognitive Neuroscience. Available at: https://scholar.google.de/citations?view_op=view_citation&hl=de&user=VKDX28YAAAAJ&sortby=pubdate&citation_for_view=VKDX28YAAAAJ:GnPB-g6toBAC (Accessed: 24 June 2023). Cite
Nikolaev, A.R. et al. (2023) ‘Before the second glance: neural correlates of refixation planning in precursor fixations’. Available at: https://doi.org/10.1101/660308. Cite Download
Frömer, R. et al. (2023) ‘Common neural choice signals emerge artifactually amidst multiple distinct value signals’. bioRxiv. Available at: https://doi.org/10.1101/2022.08.02.502393. Cite Download
Chiossi, F. et al. (2022) ‘Adapting visualizations and interfaces to the user’, it - Information Technology, 64(4–5), pp. 133–143. Available at: https://doi.org/10.1515/itit-2022-0035. Cite Download
Govaart, G. et al. (2022) ‘EEG ERP preregistration template’. Available at: https://doi.org/10.31222/osf.io/4nvpt. Cite
Gert, A.L. et al. (2022) ‘WildLab: A naturalistic free viewing experiment reveals previously unknown electroencephalography signatures of face processing’, European Journal of Neuroscience, 56(11), pp. 6022–6038. Available at: https://doi.org/10.1111/ejn.15824. Cite Download
Pavlov, Y.G. et al. (2021) ‘#EEGManyLabs: Investigating the replicability of influential EEG experiments’, Cortex [Preprint]. Available at: https://doi.org/10.1016/j.cortex.2021.03.013. Cite
Dimigen, O. and Ehinger, B.V. (2021) ‘Regression-based analysis of combined EEG and eye-tracking data: Theory and applications’, Journal of Vision, 21(1), pp. 3–3. Available at: https://doi.org/10.1167/jov.21.1.3. Cite
Czeszumski, A. et al. (2021) ‘Coordinating With a Robot Partner Affects Neural Processing Related to Action Monitoring’, Frontiers in Neurorobotics, 15. Available at: https://doi.org/10.3389/fnbot.2021.686010. Cite Download
Gert, A.L. et al. (2020) ‘Faces strongly attract early fixations in naturally sampled real-world stimulus materials’, in ACM Symposium on Eye Tracking Research and Applications. New York, NY, USA: Association for Computing Machinery (ETRA ’20 Short Papers), pp. 1–5. Available at: https://doi.org/10.1145/3379156.3391377. Cite
Bosch, E. et al. (2020) ‘Opposite effects of choice history and stimulus history resolve a paradox of sequential choice bias’, Journal of Vision, p. 2020.02.14.948919. Available at: https://doi.org/(accepted). Cite
Heilbron, M. et al. (2019) ‘Tracking Naturalistic Linguistic Predictions with Deep Neural Language Models’, arXiv:1909.04400 [q-bio] [Preprint]. Available at: https://doi.org/10.32470/CCN.2019.1096-0. Cite
Ehinger, B.V. et al. (2019) ‘A new comprehensive eye-tracking test battery concurrently evaluating the Pupil Labs glasses and the EyeLink 1000’, PeerJ, 7, p. e7086. Available at: https://doi.org/10.7717/peerj.7086. Cite
Czeszumski, A. et al. (2019) ‘The Social Situation Affects How We Process Feedback About Our Actions’, Frontiers in Psychology, 10. Available at: https://doi.org/10.3389/fpsyg.2019.00361. Cite Download
Ehinger, B.V. (2019) ‘Unmixed: Linear Mixed Models combined with Overlap Correction for M/EEG analyses. An Extension to the unfold Toolbox’, in 2019 Conference on Cognitive Computational Neuroscience. 2019 Conference on Cognitive Computational Neuroscience, Berlin, Germany: Cognitive Computational Neuroscience. Available at: https://doi.org/10.32470/CCN.2019.1102-0. Cite
Ehinger, B.V. and Dimigen, O. (2019) ‘Unfold: An integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis’, peerJ [Preprint]. Available at: https://doi.org/https://doi.org/10.7717/peerj.7838. Cite
Sütfeld, L.R. et al. (2019) ‘How does the method change what we measure? Comparing virtual reality and text-based surveys for the assessment of moral decisions in traffic dilemmas’. Available at: https://doi.org/10.31234/osf.io/h2z7p. Cite
Ehinger, B.V., Kaufhold, L. and König, P. (2018) ‘Probing the temporal dynamics of the exploration–exploitation dilemma of eye movements’, Journal of Vision, 18(3), pp. 6–6. Available at: https://doi.org/10.1167/18.3.6. Cite
Benedikt V. Ehinger (2018) Decisions, Predictions, and Learning in the visual sense. Osnabrück University. Available at: https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-20181116806. Cite
Ehinger, B.V. (2018) EEGVIS Toolbox. Available at: https://doi.org/10.5281/zenodo.1312813. Cite
Ehinger, B.V. et al. (2017) ‘Humans treat unreliable filled-in percepts as more real than veridical ones’, eLife. Edited by P. Latham, 6, p. e21761. Available at: https://doi.org/10.7554/eLife.21761. Cite
Kietzmann, T.C. et al. (2016) ‘Extensive training leads to temporal and spatial shifts of cortical activity underlying visual category selectivity’, NeuroImage, 134, pp. 22–34. Available at: https://doi.org/10.1016/j.neuroimage.2016.03.066. Cite
Spoida, K. et al. (2016) ‘Melanopsin Variants as Intrinsic Optogenetic On and Off Switches for Transient versus Sustained Activation of G Protein Pathways’, Current Biology, 26(9), pp. 1206–1212. Available at: https://doi.org/10.1016/j.cub.2016.03.007. Cite
König, P. et al. (2016) ‘Eye movements as a window to cognitive processes’, Journal of Eye Movement Research, 9, pp. 1–16. Available at: https://doi.org/https://doi.org/10.16910/jemr.9.5.3. Cite
Ehinger, B.V. et al. (2016) ‘Understanding melanopsin using bayesian generative models- an Introduction’, bioRxiv [Preprint]. Available at: https://doi.org/10.1101/043273. Cite
Ehinger, B.V., König, P. and Ossandón, J.P. (2015) ‘Predictions of Visual Content across Eye Movements and Their Modulation by Inferred Information’, Journal of Neuroscience, 35(19), pp. 7403–7413. Available at: https://doi.org/10.1523/JNEUROSCI.5114-14.2015. Cite
Ehinger, B.V. et al. (2014) ‘Kinesthetic and vestibular information modulate alpha activity during spatial navigation: a mobile EEG study’, Frontiers in Human Neuroscience, 8. Available at: https://doi.org/10.3389/fnhum.2014.00071. Cite


2019 (single Lecture) Introduction to ERP analysis (EEG)
2019 (Workshop)
Statistics (Linear Model) & Deconvolution
Statistical rethinking
2018 Applied Generalized Linear Mixed Models
2017 Generalized Linear Mixed Models
2017 (Workshop) Combined EEG/Eyetracking at the ECEM
2016 Bayesian Data Analysis
2015 Advanced Methods for M/EEG Data Analysis
2013 Basic and Advanced MatLab Exercises in Data Analysis
Basic and Advanced MatLab Exercises in Data Analysis