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Research Methods

The Multisensory Research Group uses various research methods. The methods include stimulation devices, recording techniques, and analysis tools.
 

Equipment

We have a range of specialised equipment and methods at our disposal in the laboratory for our investigations into multisensory perception. We work with the following equipment, among others:

  • Magnetic resonance imaging (Siemens MAGNETOM Prisma, 3 T): we collect structural, functional and diffusion-weighted MRI data - for example to investigate visual-vestibular self-movement stimuli, the underlying functional networks and fibre connections (fibre tracking) between sensory areas.
  • Eye movement measurement (LiveTrack Lightning, CRS Ltd.): We use this eye tracker to record eye movements and pupillometry with a temporal resolution of 500 Hz. The system is primarily used in studies on eccentric vision, crowding effects and in patients with central scotoma.
  • Neck muscle vibration (home-made): We use applied vibrations of the neck muscles to generate targeted proprioceptive stimuli. In combination with visual, vestibular or acoustic stimuli, we use this to investigate multisensory integration in self-motion perception.
  • Caloric vestibular stimulation (own design, MR-compatible): Using warm and cold stimulus irrigation of the external auditory canals, we specifically induce vestibular activity to investigate illusory self-motion perception and its cortical correlates.
  • Olfactory stimulation: We use an airflow-driven olfactometer to present odours precisely and reproducibly. This allows us to record olfactory contributions to multisensory perception and attention processes.
  • Virtual reality (HTC VIVE Focus, head-mounted display with motion tracking): We use the VR system to realise immersive, multisensory environments and simultaneously record movement and gaze data of the participants.
  • Psychophysical paradigms: To quantitatively record perception thresholds and decision-making processes, we use classic methods - such as the method of constant stimuli, adaptive staircase procedures and two-alternative forced-choice tasks - supplemented by analyses based on signal detection theory.
  • Questionnaires: In addition, we use standardised questionnaires to assess, among other things, self-motion perception, lateral preferences (e.g. handedness), motion sickness (kinetosis) and symptoms of dizziness.

Software and Analysis Methods

For stimulus presentation, data acquisition and analysis, we use a combination of established and self-developed tools:

  • Stimulus presentation: Psychtoolbox (MATLAB) and PsychoPy for time-critical visual, auditory and multisensory paradigms; for immersive scenes, we use Unity in conjunction with the VIVE system.
  • Data acquisition and synchronisation: Trigger-controlled acquisition via the respective device interfaces (MRI, eye tracker, VR) as well as cross-modality synchronisation so that stimuli, physiological signals and behavioural data are precisely timed in relation to each other.
  • Analysis: We analyse imaging data primarily with FreeSurfer; we also use MNE-Python for event-related analyses. We analyse behavioural and psychophysical data in R and MATLAB. For statistical modelling, we mainly use linear mixed models, Bayesian hierarchy models and signal detection theory approaches.
  • Reproducibility: We share study materials, analysis scripts and - where possible under data privacy law - raw data via OSF, GitHub and Zenodo.

Further Reading

A selection of papers that describe or build on our methodological approaches:

  • Beer, A. L., Plank, T., & Greenlee, M. W. (2011). Diffusion tensor imaging shows white matter tracts between human auditory and visual cortex. Experimental Brain Research, 213(2-3), 299-308. https://doi. org/10.1007/s00221-011-2715-y
  • Castellotti, S., Soldo, M., Plank, T., Viva, M. M. D., & Greenlee, M. W. (2025). Visual search performance depends on the congruency of olfactory sensations. Scientific Reports, 15(1), 38116. https://doi. org/10.1038/s41598-025-25995-1
  • Frank, S. M., & Greenlee, M. W. (2014). An MRI-compatible caloric stimulation device for the investigation of human vestibular cortex. Journal of Neuroscience Methods, 235, 208-218. https://doi. org/10.1016/j.jneumeth.2014.07.008
  • Malania, M., Lin, Y.-S., Hörmandinger, C., Werner, J. S., Greenlee, M. W., & Plank, T. (2024). Training-induced changes in population receptive field properties in visual cortex: Impact of eccentric vision training on population receptive field properties and the crowding effect. Journal of Vision, 24(5), 7. https://doi. org/10.1167/jov.24.5.7
  • Wein, S., Malloni, W. M., Tomé, A. M., Frank, S. M., Henze, G.-I., Wüst, S., Greenlee, M. W., & Lang, E. W. (2021). A graph neural network framework for causal inference in brain networks. Scientific Reports, 11(1), 8061. https://doi.org/10.1038/s41598-021-87411-8

 

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