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Sensorimotor integration processes play a central role in daily life and require that different sources of sensory information become integrated: i.e. the information related to the object being under control of the agent (i.e. indicator) and the information about the goal of acting. Yet, how this is accomplished on a neurophysiological level is contentious. We focus on the role of theta- and beta-band activities and examine which neuroanatomical structures are involved. Healthy participants (n = 41) performed 3 consecutive pursuit-tracking EEG experiments in which the source of visual information available for tracking was varied (i.e. that of the indicator and the goal of acting). The initial specification of indicator dynamics is determined through beta-band activity in parietal cortices. When information about the goal was not accessible, but operating the indicator was required nevertheless, this incurred increased theta-band activity in the superior frontal cortex, signaling a higher need for control. Later, theta- and beta-band activities encode distinct information within the ventral processing stream: Theta-band activity is affected by the indicator information, while beta-band activity is affected by the information about the action goal. Complex sensorimotor integration is realized through a cascade of theta- and beta-band activities in a ventral-stream-parieto-frontal network.

In daily life, sensorimotor integration processes are fundamental for many cognitive operations. The pursuit-tracking paradigm is an ecological and valid paradigm to examine sensorimotor integration processes in a more complex environment than many established tasks that assess simple motor responses. However, the analysis of pursuit-tracking performance is complicated, and parameters quantified to examine performance are sometimes ambiguous regarding their interpretation. We introduce an open-source algorithm (TRACK) to calculate a new tracking error metric, the spatial error, based on the identification of the intended target position for the respective cursor position. The identification is based on assigning cursor and target direction changes to each other as key events, based on the assumptions of similarity and proximity. By applying our algorithm to pursuit-tracking data, beyond replication of known effects such as learning or practice effects, we show a higher precision of the spatial tracking error, i.e., it fits our behavioral data better than the temporal tracking error and thus provides new insights and parameters for the investigation of pursuit-tracking behavior. Our work provides an important step towards fully utilizing the potential of pursuit-tracking tasks for research on sensorimotor integration processes.