A Computational Theory of Rhythmic Attentional Sampling Functions

For every action, we see, hear, touch, smell and taste our surroundings. Our senses constantly receive a seemingly unmanageable flow of sensory signals. Attention is the mental function that selects relevant sensory information and ameliorate their processing by the brain to efficiently guide our actions.The recent years witnessed a paradigm shift in the field of attention research. Opposite to our impression that one can deploy attention continuously over short time periods, attention in fact processes perceptual information rhythmically. This rhythmic attentional sampling leads to phases of improved perceptual performance alternating with phases of diminished performance.So far, the vast majority of work focused on the description of these rhythmic behaviors and their brain correlates. We now need to go beyond mere description. We need to understand the mechanisms, meaning the neural operations, underlying rhythmic attentional sampling. My project SAMPLING will fill this knowledge gap.I will provide a computational theory of rhythmic attentional sampling functions to mechanistically explain rhythms in perceptual performance across different sensory modalities and attention systems. My theory will predict precisely and quantitatively various perceptual outcomes. My team and I will record an unprecedented range of behavioral and neurophysiological empirical data to test and either reject or refine the theory. A framework for open collaboration including open-source toolboxes and a comprehensive open-access database will allow the research community to challenge and improve the theory. It will also provide clear and tested experimental and analytic recommendations for future research endeavors.My project will develop and test a novel theory of attention that captures and explains rhythmic sampling, enabling a new era of basic and applied research and development that will optimize performance of healthy individuals and treat disorders of attention.