The emergence of functional neuroimaging as a tool for cognitive neuroscience remains promising, yet is hampered by a number of technological obstacles. These obstacles can preclude collaboration, slow research, dictate experimental designs, encourage errors, and lead to the widespread unnecessary duplication of effort. In the present proposal, we describe software solutions to several of these obstacles, embedded in a flexible architecture for neuroimaging data analysis and presentation. For users of neuroimaging processing and analysis software, these tools will facilitate the interoperation of disparate software tools and data formats (thereby reducing the commitment in selecting a particular software package), improve computational efficiency, and aid in the design and analysis of functional neuroimaging experiments. For developers of neuroimaging methods, they will provide a framework for development of future neuroimaging tools. In concert with this, we also propose a number of discrete improvements to the available tools for experimental design and analysis, intended to improve existing software in terms of both generality and overall functionality. We also propose a project aimed at understanding the neural basis of executive control. The task-switching paradigm may be said to operationalize the concept of executive control, in that it requires subjects to willfully switch between two metacognitive sets. We here propose two studies aimed at understanding the neural correlates of different facets of task switching performance, intended to address the question of a distinct central executive. At the same time, the parametric nature of task switching experiments makes it easy (conceptually, if not practically) to assess a form of generality for any observed neural effect. This type of project, which essentially considers the paradigm itself a random effect, is facilitated jointly by the work presented here.
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