My current research focus is on the temporal dynamics of human eye movements. Particularly, I am interested in how humans perceive and learn temporal regularities in the environment and adapt their eye movement behavior accordingly in order to optimize the outcome of their actions. I use behavioral experiments with human subjects as well as computational modelling involving machine learning, specifically Bayesian and reinforcement learning models to describe and understand the characteristics of human visual behavior.

• Hoppe, D., Helfmann, S., & Rothkopf, C. A. (2018). Humans quickly learn to blink strategically in response to environmental task demands. Proceedings of the National Academy of Sciences, 201714220.

• Hoppe, D., & Rothkopf, C. A. (2019). Multi-step planning of eye movements in visual search. Scientific Reports, 9(1), 144.

• Hoppe, D., & Rothkopf, C. A. (2016). Learning rational temporal eye movement strategies. Proceedings of the National Academy of Sciences, 201601305.

• Nazemi, K., Burkhardt, D., Hoppe, D., Nazemi, M., & Kohlhammer, J. (2015). Web-based evaluation of information visualization. Procedia Manufacturing, 3, 5527-5534.

• Kampmann, E., Diana, T., Kanitz, M., Hoppe, D., & Kahaly, G. J. (2015). Thyroid stimulating but not blocking autoantibodies are highly prevalent in severe and active thyroid-associated orbitopathy: a prospective study. International journal of endocrinology, 2015.

• Hoppe, D., Helfmann, S., & Rothkopf, C.A.: 'Learning when to blink: Environmental statistics guide blinking behavior.' Poster at the annual meeting of the Vision Science Society, May 19-24, 2017, St. Pete Beach, Florida, USA.

• Zhao, H., Hoppe, D. & Rothkopf, C.A.: 'Eye movement and steering control in locomotor interception.' Poster at the annual meeting of the Vision Science Society, May 13-18, 2016, St. Pete Beach, Florida, USA.

• Hoppe, D., & Rothkopf, C.A.: 'Modelling the dynamics of visual attention under uncertainty.' Talk at the annual meeting of the Vision Science Society, May 15-20, 2015, St. Pete Beach, Florida, USA.

• Umbach, N., Naumann, K., Hoppe, D., Brandt, H., Kelava, A., & Schmitz, B. (2015). Introducing nlsem: An R Package for Estimating Nonlinear Structural Equation Mixture Models. Talk at SEM meeting 2015, February 26-27, Berlin, Germany

• GK Best Paper Award (2016-2018) for the best publication in cognitive science by a young investigator (awarded by the Gesellschaft für Kognitionswissenschaft e.V.)

• Umbach, N., Naumann, K., Hoppe, D., Brandt, H., Kelava, A., Schmitz, B., & Umbach, M. N. (2017). Package ‘nlsem’.

• WS 2017-2018 Multivariate Methods (Exercise)
• SS 2017 General Psychology (Exercise) and Cognitive Psychology (Exercise)
• WS 2016-2017 Latent variable models (Exercise and Seminar)
• SS 2016 General Psychology (Exercise) and Cognitive Psychology (Exercise)
• WS 2015-2016 Latent variable models (Exercise and Seminar)
• SS 2015 General Psychology (Exercise)
• WS 2014-2015 Latent variable models (Exercise and Seminar)
• SS 2014 General Psychology (Exercise)
• WS 2013-2014 Latent variable models (Exercise and Seminar)