Bayesian modeling of brain function and behavior

The application of Bayesian modeling techniques is increasingly common in neuroscience due to the coherent and principled way in which the paradigm deals with uncertainty. The Bayesian framework is particularly valuable in the context of complex, ill-posed generative problems, in which case the incorporation of prior knowledge is optimal and practical. If one wants to take full advantage of joint imaging and behavioral data, the need for comprehensive generative models is evident. Here, I exploited the versatility afforded by Bayesian modeling approaches to investigate a series of questions from clinical, systems, and cognitive neuroscience. First, I investigated the lateralization of the cortical motor network in Parkinson's disease patients on and off dopaminergic medication with fMRI and dynamic causal modeling (DCM). Group-level model comparison revealed that disease and medication effects differentially involve homotopic cortical motor connections, but medication does not appear to have a restorative effect at the systems level. Our findings suggest the presence of maladaptive mechanisms, resulting from disease progression and long-term dopamine replacement. In a second project, I considered the way in which humans handle uncertainty in the parameters of the generative model of a task at hand. Using a novel psychophysics paradigm, participants' responses to fitting a parabola to a set of noisy points were compared to the predictions of a set of regression models, including Bayesian regression and several sub-optimal models. Only Bayesian regression could explain participants' response distributions across various levels of sensory uncertainty, suggesting that humans process parameter uncertainty in accordance with Bayesian regression. This finding deepens our understanding of the way in which humans learn and generalize from sparse, ambiguous data. The topic investigated in the greatest depth in this thesis was visual crowding - the deterioration of target discrimination due to the presence of flanking objects. Traditionally, vision is modeled as a feedforward, hierarchical network. Thus, crowding is explained as a pooling of neighboring elements' features, leading to a "bottleneck" at the earliest stages of vision. However, additional flankers can paradoxically improve performance (uncrowding). First, I used DCM and fMRI to show that recurrent processing is crucial for crowding and un-crowding alike. Higher visual areas modulate the lower areas, suggesting that explicit object representation plays a key role. I then used population receptive field (pRF) mapping to investigate the effects of context on pRF size. In accordance with pooling model predictions, the pRF size in crowding is larger than in the case of no crowding. However, in uncrowding, the pRF size is smaller than in crowding and no crowding, providing further evidence against purely feedforward models. Overall, my findings provide strong empirical evidence of context-dependent f