Titel:	Decisions, Predictions, and Learning in the visual sense
Autor(en):	Ehinger, Benedikt V.
ORCID des Autors:	https://orcid.org/0000-0002-6276-3332
Erstgutachter:	Prof. Dr. Peter König
Zweitgutachter:	Prof. Dr. Martin Rolfs Prof. Dr. Gordon Pipa
Zusammenfassung:	We experience the world through our senses. But we can only make sense of the incoming information because it is weighted and interpreted against our perceptual experience which we gather throughout our lives. In this thesis I present several approaches we used to investigate the learning of prior-experience and its utilization for prediction-based computations in decision making. Teaching participants new categories is a good example to demonstrate how new information is used to learn about, and to understand the world. In the first study I present, we taught participants new visual categories using a reinforcement learning paradigm. We recorded their brain activity before, during, and after prolonged learning over 24 sessions. This allowed us to show that initial learning of categories occurs relatively late during processing, in prefrontal areas. After extended learning, categorization occurs early during processing and is likely to occur in temporal structures. One possible computational mechanism to express prior information is the prediction of future input. In this thesis, I make use of a prominent theory of brain function, predictive coding. We performed two studies. In the first, we showed that expectations of the brain can surpass the reliability of incoming information: In a perceptual decision making task, a percept based on fill-in from the physiological blind spot is judged as more reliable to an identical percept from veridical input. In the second study, we showed that expectations occur between eye movements. There, we measured brain activity while peripheral predictions were violated over eye movements. We found two sets of prediction errors early and late during processing. By changing the reliability of the stimulus using the blind spots, we in addition confirm an important theoretical idea: The strength of prediction-violation is modified based on the reliability of the prediction. So far, we used eye-movements as they are useful to understand the interaction between the current information state of the brain and expectations of future information. In a series of experiments we modulated the amount of information the visual system is allowed to extract before a new eye movement is made. We developed a new paradigm that allows for experimental control of eye-movement trajectories as well as fixation durations. We show that interrupting the extraction of information influences the planning of new eye movements. In addition, we show that eye movement planning time follow Hick's law, a logarithmic increase of saccadic reaction time with increasing number of possible targets. Most of the studies presented here tried to identify causal effects in human behavior or brain-computations. Often direct interventions in the system, like brain stimulation or lesions, are needed for such causal statements. Unfortunately, not many methods are available to directly control the neurons of the brain and even less the encoded expectations. Recent developments of the new optogenetic agent Melanopsin allow for direct activation and silencing of neuronal cells. In cooperation with researchers from the field of optogenetics, we developed a generative Bayesian model of Melanopsin, that allows to integrate physiological data over multiple experiments, include prior knowledge on bio-physical constraints and identify differences between proteins. After discussing these projects, I will take a meta-perspective on my field and end this dissertation with a discussion and outlook of open science and statistical developments in the field of cognitive science.
URL:	https://osnadocs.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-20181116806
Schlagworte:	Active Vision; Decision; Learning; EEG; Eye tracking; Bayesian Statistics; Open Science
Erscheinungsdatum:	16-Nov-2018
Lizenzbezeichnung:	Namensnennung 3.0 Deutschland
URL der Lizenz:	http://creativecommons.org/licenses/by/3.0/de/
Publikationstyp:	Dissertation oder Habilitation [doctoralThesis]
Enthalten in den Sammlungen:	FB08 - E-Dissertationen

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