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In human neuroscientific research, there has been an increasing interest in how the brain computes the value of an anticipated outcome. However, evidence is still missing about which valuation related brain regions are modulated by the proximity to an expected goal and the previously invested effort to reach a goal. The aim of this dissertation is to investigate the effects of goal proximity and invested effort on valuation related regions in the human brain. We addressed this question in two fMRI studies by integrating a commonly used reward anticipation task in differential versions of a Multitrial Reward Schedule Paradigm. In both experiments, subjects had to perform consecutive reward anticipation tasks under two different reward contingencies: in the delayed condition, participants received a monetary reward only after successful completion of multiple consecutive trials. In the immediate condition, money was earned after every successful trial. In the first study, we could demonstrate that the rostral cingulate zone of the posterior medial frontal cortex signals action value contingent to goal proximity, thereby replicating neurophysiological findings about goal proximity signals in a homologous region in non-human primates. The findings of the second study imply that brain regions associated with general cognitive control processes are modulated by previous effort investment. Furthermore, we found the posterior lateral prefrontal cortex and the orbitofrontal cortex to be involved in coding for the effort-based context of a situation. In sum, these results extend the role of the human rostral cingulate zone in outcome evaluation to the continuous updating of action values over a course of action steps based on the proximity to the expected reward. Furthermore, we tentatively suggest that previous effort investment invokes processes under the control of the executive system, and that posterior lateral prefrontal cortex and the orbitofrontal cortex are involved in an effort-based context representation that can be used for outcome evaluation that is dependent on the characteristics of the current situation.
This dissertation connects two independent fields of theoretical neuroscience: on the one hand, the self-organization of topographic connectivity patterns, and on the other hand, invariant object recognition, that is the recognition of objects independently of their various possible retinal representations (for example due to translations or scalings). The topographic representation is used in the presented approach, as a coordinate system, which then allows for the implementation of invariance transformations. Hence this study shows, that it is possible that the brain self-organizes before birth, so that it is able to invariantly recognize objects immediately after birth. Besides the core hypothesis that links prenatal work with object recognition, advancements in both fields themselves are also presented. In the beginning of the thesis, a novel analytically solvable probabilistic generative model for topographic maps is introduced. And at the end of the thesis, a model that integrates classical feature-based ideas with the normalization-based approach is presented. This bilinear model makes use of sparseness as well as slowness to implement "optimal" topographic representations. It is therefore a good candidate for hierarchical processing in the brain and for future research.
Ziel des Seminars war, sportmedizinische und präventivmedizinische Aspekte zu Fitness und Wellness zu präsentieren und hierbei insbesondere auf die historische Entwicklung der Sportmedizin einzugehen sowie Techniken und Wirkungen verschiedener Massageformen und ganzheitliche asiatische Gesundheitsansätze und Bewegungskünste zu erörtern.
The dissertation deals with the general problem of how the brain can establish correspondences between neural patterns stored in different cortical areas. Although an important capability in many cognitive areas like language understanding, abstract reasoning, or motor control, this thesis concentrates on invariant object recognition as application of correspondence finding. One part of the work presents a correspondence-based, neurally plausible system for face recognition. Other parts address the question of visual information routing over several stages by proposing optimal architectures for such routing ('switchyards') and deriving ontogenetic mechanisms for the growth of switchyards. Finally, the idea of multi-stage routing is united with the object recognition system introduced before, making suggestions of how the so far distinct feature-based and correspondence-based approaches to object recognition could be reconciled.