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Neural correlates of the processing of visually simulated self-motion
Successful interaction with our environment requires the perception of our surroundings. For coping with everyday challenges our own movements in this environment are important. In my thesis, I have investigated the neural correlates of visually simulated self-motion. More specifically, I have analy...
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| Format: | Dissertation |
| Sprache: | Englisch |
| Veröffentlicht: |
Philipps-Universität Marburg
2019
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| Zusammenfassung: | Successful interaction with our environment requires the perception of our surroundings. For coping with everyday challenges our own movements in this environment are important. In my thesis, I have investigated the neural correlates of visually simulated self-motion. More specifically, I have analyzed the processing of two key features of visual self-motion: the self-motion direction (heading) and the traveled distance (path integration) by means of electroencephalogram (EEG) measurements and transcranial magnetic stimulation (TMS). I have focused on investigating the role of prediction about the upcoming sensory event on the processing of these self-motion features. To this end, I applied the approach of the predictive coding theory. In this context, prediction errors induced by the mismatch between predictions and the actual sensory input are used to update the internal model responsible for creating the predictions. Additionally, I aimed to combine my findings with the results of previous studies on monkeys in order to further probe the role of the macaque monkey as an animal model for human sensorimotor processing.
In my first study, I investigated the processing of different self-motion directions using a classical oddball EEG measurement. The frequently presented self-motion stimuli to one direction were interspersed with a rarely presented different self-motion direction. The headings occurred with different probabilities which modified the prediction about the upcoming event and allowed for the formulation of an internal model. Unexpected self-motion directions created a prediction error. I could prove this in my data by detecting a specific EEG-component, the mismatch negativity (MMN). This MMN-component does not only reveal the influence of predictions on the processing of visually simulated self-motion directions according to the predictive coding theory, but is also known to indicate the preattentive processing of the analyzed feature, here the heading. EEG data from monkeys was recorded with identical equipment during the presentation of the previously described stimulus by colleagues from my lab in order to test for the similarities in monkey and human processing of visually simulated self-motion. Remarkably, data showing a MMN-component similar to the human data was recorded. This led us to suggest that the underlying processes are comparable across human and non-human primates.
In my second study, the objective was to causally link the human functional equivalent of macaque medial superior temporal area (hMST) to the perception of self-motion directions. In previous studies this area has been shown to be important for the processing of self-motion. Applying TMS to right hemisphere area hMST resulted in an increase in variance when participants were asked to estimate heading to the left, i.e. to the direction contraversive to the stimulation site. The results of this study were used to test a model developed by colleagues of my lab. They used findings from single cell recordings in macaque monkeys to create it. Simulating the influence of lateralized TMS pulses on one hemisphere hMST this model hypothesized an increase in variance for estimation of headings contraversive to the TMS stimulated hemisphere. This is exactly what I observed in data of my TMS experiment. In this second study I verified the finding of previous studies that hMST is important for the processing of self-motion directions. In addition, I showed that a model based on recordings from macaque monkeys can predict the outcome of an experiment with human participants. This indicates the similarity of the processing of visually simulated self-motion in humans and macaque monkeys.
The third study focused on the representation of traveled distance using EEG recordings in human participants. The goal of this study was two-fold: First, I analyzed the influence of prediction on the processing of traveled distance. Second, I aimed to find a neural correlate of subjective traveled distance. Participants were asked to passively observe a forward self-motion. The movement onset and offset could not be predicted by them. In a next step participants reproduced double the distance of the previously observed self-motion. Since they actively modulated the movement to reach the desired distance, the resulting self-motion onset and offset could be predicted. Comparing the visually evoked potentials (VEPs) after self-motion onset and offsets of the predicted and unpredicted self-motion, I found differences supporting the predictive coding theory. Amplitudes for self-motion onset VEPs were larger in the passive condition. For self-motion offset, I found larger latencies for the VEP-components in the passive condition. In addition to these results I searched for a neural correlate of the subjective estimation of the distance presented in the passive condition. During the active reproduction of double the distance obviously the single distance was passed. I assumed that half of the reproduced double distance would be the subjective estimation of the single distance. When passing this subjective single distance, an increase in the alpha band activity was detected in half of the participants. At this point in time prediction about the upcoming movement changed since participants started reproducing the single distance again. In context of the predictive coding theory these prediction changes are considered to be feedback processes. It has been shown in previous studies that these kinds of feedback processes are associated with alpha oscillations. With this study, I demonstrated the influence of prediction on self-motion onset and offset VEPs as well as on brain oscillations during a distance reproduction experiment.
In conclusion, with this thesis I analyzed the neural correlates of the processing of self-motion directions and traveled distance. The underlying neural mechanisms seem to be very similar in humans and macaque monkeys, which suggests the macaque monkey as an appropriate animal model for human sensorimotor processing. Lastly, I investigated the influence of prediction on EEG-components recorded during the processing of self-motion directions and traveled distances. |
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| Umfang: | 148 Seiten |
| DOI: | 10.17192/z2019.0470 |