Adaptation of motor control strategies to environmental cues in a pursuit-tracking task

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.

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.

Perceptual-motor recalibration is intact in older adults

From an ecological perspective, perceptual-motor recalibration should be a robust and adaptable process, but there are suggestions that older adults may recalibrate slower. Therefore, this study investigated the age-related temporal effects in perceptual-motor recalibration after motor disturbances. In three experiments, we disturbed young and older adults' perception-action by fitting weights around their ankles and asking them to climb stairs or cross obstacles repeatedly. In Experiment 1, participants (n = 26) climbed stairs with different ankle weights. An innovative methodology was applied, identifying the timeline of recalibration as the point where a stable movement pattern emerged. Experiment 1 showed that older adults recalibrated slower than young adults in lighter (but not heavier) weight conditions. In Experiment 2, participants (n = 24) crossed obstacles with different ankle weights. Results showed that older adults recalibrated faster than young adults. Finally, in Experiment 3, participants (n = 24) crossed obstacles of unpredictable and varying heights with heavy ankle weights. Again, results showed that older adults recalibrated faster than young adults. Taken together these results show that although older adults had reduced muscle strength and flexibility, they recalibrated quickly, especially when the task was more challenging.

Model-Based Prediction of Operation Consequences When Driving a Car to Compensate for a Partially Restricted Visual Field by A-Pillars

The partial restriction of a driver's visual field by the physical structure of the car (e.g., the A-pillar) can lead to unsafe situations where steering performance is degraded. Drivers require both environmental information and visual feedback regarding operation consequences. When driving with a partially restricted visual field, and thus restricted visual feedback, drivers may predict operation consequences using a previously acquired internal model of a car. To investigate this hypothesis, we conducted a tracking and driving task in which visual information was restricted to varying degrees. In the tracking task, participants tracked a moving target on a computer screen with visible and invisible cursors. In the driving task, they drove a real car with or without the ability to see the distant parts of a visual field. Consequently, we found that the decrease in tracking performance induced by visual feedback restriction predicted the decrease in steering smoothness induced by visual field restriction, suggesting that model-based prediction was used in both tasks. These findings indicate that laboratory-based task performance can be used to identify drivers with low model-based prediction ability whose driving behavior is less optimal in restricted vision scenarios, even before they obtain a driver's license. However, further studies are required to examine the underlying neural mechanisms and to establish the generalizability of these findings to more realistic settings.

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.

Additive Effects of Prior Knowledge and Predictive Visual Information in Improving Continuous Tracking Performance

Visual information and prior knowledge represent two different sources of predictability for tasks which each have been reported to have a beneficial effect on dual-task performance. What if the two were combined? Adding multiple sources of predictability might, on the one hand, lead to additive, beneficial effects on dual-tasking. On the other hand, it is conceivable that multiple sources of predictability do not increase dual-task performance further, as they complicate performance due to having to process information from multiple sources. In this study, we combined two sources of predictability, predictive visual information and prior knowledge (implicit learning and explicit learning) in a dual-task setup. 22 participants performed a continuous tracking task together with an auditory reaction time task over three days. The middle segment of the tracking task was repeating to promote motor learning, but only half of the participants was informed about this. After the practice blocks (day 3), we provided participants with predictive visual information about the tracking path to test whether visual information would add to beneficial effects of prior knowledge (additive effects of predictability). Results show that both predictive visual information and prior knowledge improved dual-task performance, presented simultaneously or in absence of each other. These results show that processing of information relevant for enhancement of task performance is unhindered by dual-task demands.

How visual information influences dual-task driving and tracking

The study examined the impact of visual predictability on dual-task performance in driving and tracking tasks. Participants (N = 27) performed a simulated driving task and a pursuit tracking task. In either task, visual predictability was manipulated by systematically varying the amount of advance visual information: in the driving task, participants drove at night with low beam, at night with high beam, or in daylight; in the tracking task, participants saw a white line that specified the future target trajectory for 200, 400 or 800 ms. Concurrently with driving or tracking, participants performed an auditory task. They had to discriminate between two sounds and press a pedal upon hearing the higher sound. Results show that in general, visual predictability benefited driving and tracking; however, dual-task driving performance was best with highest visual predictability (daylight), dual-task tracking performance was best with medium visual predictability (400 ms). Braking/reaction times were higher in dual tasks compared to single tasks, but were unaffected by visual predictability, showing that its beneficial effects did not transfer to the auditory task. In both tasks, manual accuracy decreased around the moment the foot pressed the pedal, indicating interference between tasks. We, therefore, conclude that despite a general beneficial impact of predictability, the integration of visual information seems to be rather task specific, and that interference between driving and audiomotor tasks, and tracking and audiomotor tasks, seems comparable.

More Than Hitting the Correct Key Quickly

Many studies have documented that multitasking reduces Response Time (RT) indicators of implicit sequence learning as well as the expression of acquired sequence knowledge in RT benefits. In these tasks it is only relevant that the correct key is hit quickly, not where it is hit. We explored how variability in response location is influenced by (a) breaking a repeating sequence of target locations, (b) multitasking demands in the current trial, and (c) presence of multitasking in the block. Participants performed a Serial Reaction Time Task (SRTT) on a touchscreen while shutting down a beep tone by pressing the space bar with their non-dominant hand (throughout Experiment 1 and in the second half of Experiment 2). The first-order sequence of four response locations on the screen was broken by off-sequence deviants in 1/6th of the trials. Our results show a dissociation between RT and response location variability. While the effect of breaking the sequence on RT was larger under single- than under multitasking, breaking the sequence only led to an increase in response location variability under multitasking. Experiment 3 suggested that the impact of sequence knowledge on either aspect of performance in the SRTT is limited by interference from an additional task.

Implicit and Explicit Knowledge Both Improve Dual Task Performance in a Continuous Pursuit Tracking Task

The goal of this study was to investigate the effect of predictability on dual-task performance in a continuous tracking task. Participants practiced either informed (explicit group) or uninformed (implicit group) about a repeated segment in the curves they had to track. In Experiment 1 participants practices the tracking task only, dual-task performance was assessed after by combining the tracking task with an auditory reaction time task. Results showed both groups learned equally well and tracking performance on a predictable segment in the dual-task condition was better than on random segments. However, reaction times did not benefit from a predictable tracking segment. To investigate the effect of learning under dual-task situation participants in Experiment 2 practiced the tracking task while simultaneously performing the auditory reaction time task. No learning of the repeated segment could be demonstrated for either group during the training blocks, in contrast to the test-block and retention test, where participants performed better on the repeated segment in both dual-task and single-task conditions. Only the explicit group improved from test-block to retention test. As in Experiment 1, reaction times while tracking a predictable segment were no better than reaction times while tracking a random segment. We concluded that predictability has a positive effect only on the predictable task itself possibly because of a task-shielding mechanism. For dual-task training there seems to be an initial negative effect of explicit instructions, possibly because of fatigue, but the advantage of explicit instructions was demonstrated in a retention test. This might be due to the explicit memory system informing or aiding the implicit memory system.

Task Integration Facilitates Multitasking

The aim of this study was to investigate multi-task integration in a continuous tracking task. We were particularly interested in how manipulating task structure in a dual-task situation affects learning of a constant segment embedded in a pursuit-tracking task. Importantly, we examined if dual-task effects could be attributed to task integration by varying the structural similarity and difficulty of the primary and secondary tasks. In Experiment 1 participants performed a pursuit tracking task while counting high-pitched tones and ignoring low-pitched tones. The tones were either presented randomly or structurally 250 ms before each tracking turn. Experiment 2 increased the motor load of the secondary tasks by asking participants to tap their feet to the tones. Experiment 3 further increased motor load of the primary task by increasing its speed and having participants tracking with their non-dominant hand. The results show that dual-task interference can be moderated by secondary task conditions that match the structure of the primary task. Therefore our results support proposals of task integration in continuous tracking paradigms. We conclude that multi-tasking is not always detrimental for motor learning but can be facilitated through task-integration.

Validation of the Continuous Tracking Paradigm for Studying Implicit Motor Learning

A continuous pursuit-tracking task is the typical experimental paradigm to investigate implicit motor learning. Implicit motor learning is proven by a greater improvement in tracking of a repeated segment of a target path compared to random segments ( Pew, 1974 ). Recently, doubts about the validity of results obtained with this paradigm have been raised. Improved tracking of a repeated segment might simply be due to the characteristics of that particular segment. In response to these doubts, we seek to improve the continuous tracking task. Therefore, we computed a pool of 37 distinct target segments. Participants (N = 36) practiced the tracking task, each one with a unique repeated segment in the middle and varying outer segments, all taken from the pool of segments. After five practice blocks of 36 trials each, a test block was performed where the repeated middle segment was replaced with a random segment. The tracking performance on the repeated segment was better than on random segments. Furthermore, we assume that learning was implicit, because participants' answers to a posttest interview showed they were largely unaware of a repeated segment within the curves.

SMART-ER: a Situation Model of Anticipated Response consequences in Tactical decisions in skill acquisition - Extended and Revised

Situation Model of Anticipated Response consequences in tactical decisions (SMART) describes the interaction of top–down and bottom–up processes in skill acquisition and thus the dynamic interaction of sensory and motor capacities in embodied cognition. The empirically validated, extended, and revised SMART-ER can now predict when specific dynamic interactions of top–down and bottom–up processes have a beneficial or detrimental effect on performance and learning depending on situational constraints. The model is empirically supported and proposes learning strategies for when situation complexity varies or time pressure is present. Experiments from expertise research in sports illustrate that neither bottom–up nor top–down processes are bad or good per se but their effects depend on personal and situational characteristics.