How visual information influences dual-task driving and tracking

Neural oscillations guiding action during effects imagery

Goal-directed acting requires the integration of sensory information but can also be performed without direct sensory input. Examples of this can be found in sports and can be conceptualized by feedforward processes. There is, however, still a lack of understanding of the temporal neural dynamics and neuroanatomical structures involved in such processes. In the current study, we used EEG beamforming methods and examined 37 healthy participants in two well-controlled experiments varying the necessity of anticipatory processes during goal-directed action. We found that alpha and beta activity in the medial and posterior cingulate cortex enabled feedforward predictions about the position of an object based on the latest sensorimotor state. On this basis, theta band activity seems more related to sensorimotor representations, while beta band activity would be more involved in setting up the structure of the neural representations themselves. Alpha band activity in sensory cortices reflects an intensified gating of the anticipated perceptual consequences of the to-be-executed action. Together, the findings indicate that goal-directed acting through the anticipation of the predicted state of an effector is based on accompanying processes in multiple frequency bands in midcingulate and sensory brain regions.

TRACK-a new algorithm and open-source tool for the analysis of pursuit-tracking sensorimotor integration processes

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.

The neurophysiology of continuous action monitoring

Monitoring actions is essential for goal-directed behavior. However, as opposed to short-lasting, and regularly reinstating monitoring functions, the neural processes underlying continuous action monitoring are poorly understood. We investigate this using a pursuit-tracking paradigm. We show that beta band activity likely maintains the sensorimotor program, while theta and alpha bands probably support attentional sampling and information gating, respectively. Alpha and beta band activity are most relevant during the initial tracking period, when sensorimotor calibrations are most intense. Theta band shifts from parietal to frontal cortices throughout tracking, likely reflecting a shift in the functional relevance from attentional sampling to action monitoring. This study shows that resource allocation mechanisms in prefrontal areas and stimulus-response mapping processes in the parietal cortex are crucial for adapting sensorimotor processes. It fills a knowledge gap in understanding the neural processes underlying action monitoring and suggests new directions for examining sensorimotor integration in more naturalistic experiments.

A dissociable functional relevance of theta- and beta-band activities during complex sensorimotor integration

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.

Investigating time-based expectancy beyond binary timing scenarios: evidence from a paradigm employing three predictive pre-target intervals

When the duration of a pre-target interval probabilistically predicts the identity of the target, participants typically form time-based expectancies: they respond faster to frequent interval-target combinations than to infrequent ones. Yet, previous research investigating the cognitive time-processing mechanisms underlying time-based expectancy assessed time-based expectancy always in situations with a binary set of intervals (i.e. short vs. long). Here we aim to test whether time-based expectancy transfers to more complex settings with three different predictive time intervals (short, medium, long) in which each predicts one of three different target stimuli with 80% probability. In three experiments we varied how the medium interval was computed (arithmetic mean, geometric mean, or in between both). Our results showed that participants were able to learn the time-event contingencies for the short and the long as well as for the medium interval, and were, thus able to flexibly redirect their target expectancy two times during the course of a trial. The evidence concerning the impact of the manipulation of the medium intervals’ absolute duration on time-based expectancy was, however, mixed, as time-based expectancy for the medium interval could only be observed in one of three reported experiments. In sum, the findings of the present study suggest a previously unknown cognitive flexibility underlying time-based expectancy and offer important theoretical implications, challenging future research on the timing mechanisms involved in time-based expectancy.

The impact of predictability on dual-task performance and implications for resource-sharing accounts

The aim of this study was to examine the impact of predictability on dual-task performance by systematically manipulating predictability in either one of two tasks, as well as between tasks. According to capacity-sharing accounts of multitasking, assuming a general pool of resources two tasks can draw upon, predictability should reduce the need for resources and allow more resources to be used by the other task. However, it is currently not well understood what drives resource-allocation policy in dual tasks and which resource allocation policies participants pursue. We used a continuous tracking task together with an audiomotor task and manipulated advance visual information about the tracking path in the first experiment and a sound sequence in the second experiments (2a/b). Results show that performance predominantly improved in the predictable task but not in the unpredictable task, suggesting that participants did not invest more resources into the unpredictable task. One possible explanation was that the re-investment of resources into another task requires some relationship between the tasks. Therefore, in the third experiment, we covaried the two tasks by having sounds 250 ms before turning points in the tracking curve. This enabled participants to improve performance in both tasks, suggesting that resources were shared better between tasks.