Mental workload and motor performance dynamics during practice of reaching movements under various levels of task difficulty

Effect of Parallel Cognitive-Motor Training Tasks on Hemodynamic Responses in Robot-Assisted Rehabilitation

Objective: Previous studies suggest that the combination of robot-assisted training with other concurrent tasks may promote the functional recovery and improvement better than the single task. It is well-established that robot-assisted rehabilitation training is effective. This study aims to characterize the neural mechanisms and inter-regional connectivity changes associated with robot-assisted parallel interactive training tasks. Methods: Twenty-five healthy young adults (12 females and 13 males) participated in three number-related cognitive-motor parallel interactive training tasks categorized by difficulty: low difficulty (LD), medium difficulty (MD), and high difficulty (HD). Functional near-infrared spectroscopy was used to measure neural responses in the primary sensorimotor cortex (SM1), supplementary motor area (SMA), and prefrontal cortex (PFC). Activation maps and functional connectivity (FC) correlation matrix maps were applied to assess cortical response and connectivity among channels and regions of interest. Results: Significant differences were observed in both activation and connectivity results across the three training conditions. Stronger activation (p < 0.01) in oxy-hemoglobin was found in the MD conditions, with activation in the HD condition being stronger than in the LD condition. The FC in the PFC increased linearly with rising training difficulty. Trends in FC for SM1 and SMA were consistent with the activation results. Conclusions: In parallel training tasks of varying difficulty, MD stimulates more neural activity and promotes stronger network connections in the brain. This study enhances the understanding of the neurological processes involved in robot-assisted parallel interactive tasks and may inform more effective robot-assisted rehabilitation therapies.

Neurophysiology of ACL Injury

The neurophysiology of ACL injury extends beyond the mechanical rupture of the ligament to encompass profound alterations in the central and peripheral nervous systems, impacting sensorimotor integration and neuromuscular control. The ACL, densely populated with mechanoreceptors, plays a critical role in joint proprioception, dynamically regulating knee stability through complex neural circuits that connect to the spinal cord and brain. When disrupted by injury, these neural pathways contribute to delayed muscular activation, altered motor planning, and compromised joint stability. Such neuromechanical deficits increase the likelihood of reinjury and highlight the need for comprehensive neuroplastic rehabilitation. Neuroplastic therapy, employing tools like external focus strategies, stroboscopic glasses, smartboards, and virtual reality, aims to restore and enhance neural connectivity, sensory integration, and motor coordination. These advanced tools target distinct phases of motor learning, promoting automaticity and resilience in movement patterns. By integrating visual-cognitive, proprioceptive, and reflexive controls, this therapeutic approach not only accelerates recovery but also optimizes performance and reduces the risk of re-injury, representing a paradigm shift in ACL rehabilitation.

Relationship between Cognitive Demands and Biomechanical Indicators Associated with Anterior Cruciate Ligament Injury: A Systematic Review

BACKGROUND

Anterior cruciate ligament (ACL) injury during contact sports has a high incidence that has not been reduced despite the immense resources devoted to understanding its aetiology. A neurocognitive approach could increase knowledge of the mechanisms contributing to ACL injury enabling practitioners to address and minimise future risk.

OBJECTIVE

To systematically review the influence of manipulating cognitive demands during motor tasks (i.e. degree of uncertainty) on biomechanical variables associated with ACL injury risk.

METHODS

A systematic review was performed according to the Cochrane Handbook for Systematic Reviews of Interventions by searching the major sporting electronic databases. The search strategy included four groups of terms and was conducted by two authors independently. All studies were screened using unique inclusion criteria, with the included studies assessed for risk of bias.

RESULTS

Twenty-five studies were identified from 2031 records and included into the review process. During the experimental conditions where cognitive demands were higher, most biomechanical indicators associated with a greater risk of ACL injury during landing and cutting tasks were significantly enhanced compared with conditions with low or no cognitive demands.

CONCLUSIONS

An increase in task complexity through cognitive load significantly leads to changes in mechanisms associated with ACL injury during single-leg landings and cutting movements. Consequently, coaches and exercise professionals should consider inclusion of dual-task paradigms or uncertainty during injury risk assessment scenarios and injury prevention programs to help identify athletes at risk of ACL injury and reduce ACL injury frequency.

REGISTRATION

This protocol was registered in the PROSPERO database ( https://www.crd.york.ac.uk/PROSPERO ) in May 2022, with the registration number CRD42022315795.

Neurocognitive assessment under various human-robot teaming environments

Human-robot teaming has become increasingly important with the advent of intelligent machines. Prior efforts suggest that performance, mental workload, and trust are critical elements of human-robot dynamics that can be altered by the robot's behavior. Most prior human-robot teaming studies used behavioral analyses, but a limited number used neural markers, without the use of physical robots and complex tasks. Here we combine behavioral and EEG cortical dynamics to examine cognitive-motor processes when individuals complete a complex task under various team environments with a robot. The results revealed that altering the robot quality affected both behavioral and EEG dynamics. Task completion with an experienced robot led to greater team performance and human trust along with lower mental workload compared to an inexperienced teammate or when individuals performed alone. EEG changes suggest that different attentional processes were engaged when humans worked with the robot and performed alone, and that visual processing was more prominent when teaming with an inexperienced teammate. This work can inform human cognitive-motor processes and the design of robotic controllers in human-robot teams.

Hybrid Robotic and Electrical Stimulation Assistance Can Enhance Performance and Reduce Mental Demand

Combining functional electrical stimulation (FES) and robotics may enhance recovery after stroke, by providing neural feedback with the former while improving quality of motion and minimizing muscular fatigue with the latter. Here, we explored whether and how FES, robot assistance and their combination, affect users' performance, effort, fatigue and user experience. 15 healthy participants performed a wrist flexion/extension tracking task with FES and/or robotic assistance. Tracking performance improved during the hybrid FES-robot and the robot-only assistance conditions in comparison to no assistance, but no improvement is observed when only FES is used. Fatigue, muscular and voluntary effort are estimated from electromyographic recording. Total muscle contraction and volitional activity are lowest with robotic assistance, whereas fatigue level do not change between the conditions. The NASA-Task Load Index answers indicate that participants found the task less mentally demanding during the hybrid and robot conditions than the FES condition. The addition of robotic assistance to FES training might thus facilitate an increased user engagement compared to robot training and allow longer motor training session than with FES assistance.

Changes in Perceived Mental Load and Motor Performance during Practice-to-Learn and Practice-to-Maintain in Basketball

BACKGROUND

Attentional resource allocation during sports practice is associated with the players' perceived mental load. However, few ecological studies address this problem by considering the players' characteristics (e.g., practice experience, skill and cognition). Therefore, this study aimed to analyse the dose-response effect of two different types of practice, each with different learning objectives, on mental load and motor performance by using a linear mixed model analysis.

METHOD

Forty-four university students (age 20.36 ± 3.13 years) participated in this study. Two sessions were conducted, one based on a standard rules 1 × 1 basketball situation ("practice to maintain") and one with motor, temporal and spatial restrictions in 1 × 1 tasks ("practice to learn").

RESULTS

"Practice to learn" produced a higher perceived mental load (NASA-TLX scale) and a worse performance than "practice to maintain", but was moderated by experience and inhibition (p = 0.001). The same happens in the most demanding restriction (i.e., temporal, p < 0.0001).

CONCLUSION

The results showed that increasing the difficulty of 1 × 1 situations through restrictions harmed the player's performance and increased their perceived mental load. These effects were moderated by previous basketball experience and the player's inhibition capacity, so the difficulty adjustment should be based on the athletes themselves.

Towards a versatile mental workload modeling using neurometric indices

Researchers have been working to magnify mental workload (MWL) modeling for a long time. An important aspect of its modeling is feature selection as it interprets bulky and high-dimensional EEG data and enhances the accuracy of the classification model. In this study, a feature selection technique is proposed to obtain an optimized feature set with multiple domain features that can contribute to classifying the MWL at three distinct levels. The brain signals from thirteen healthy subjects were examined while they attended an intrinsic MWL of spotting differences in a set of similar pictures. The Recursive Feature Elimination (RFE) technique selects the robust features from the feature matrix by eliminating all the least contributing features. Along with the Support Vector Machine (SVM), the overall classification accuracy with the proposed RFE reached 0.913 from 0.791 surpassing the other techniques mentioned. The results of the study also significantly display the variation in the mean values of the selected features at the three workload levels (p<0.05). This model can become the principle for defining the workload level quantification applicable to diverse fields like neuroergonomics study, intelligent assistive devices (ADs) development, blue-chip technology exploration, cognitive evaluation of students, power plant operators, traffic operators, etc.

Practice-Not Task Difficulty-Mediated the Focus of Attention Effect on a Speed-Accuracy Tradeoff Task

External focus (attention to the movement effect) has been found effective in motor performance and learning. However, while some investigators have suggested that the effect of attentional focus varies with task difficulty, others reported external focus benefits regardless of difficulty. We hypothesized that attentional focus effects would vary with practice, due to changes in the individual's processing efficiency. We had three 20-person participant groups (external focus instructions, internal focus instructions, control) practice three difficulty levels of a Fitts reciprocal tapping task over two days. Participants in the external/internal focus groups were instructed to "mentally focus on moving the pen/your hand as fast and accurately as possible," while control participants were instructed to "mentally focus only on doing your best to achieve the task goal." We then analyzed the effect of attentional focus by task difficulty at the initial performance (the beginning of the practice) and after learning (the retention/transfer phase), using movement time (MT) and number of error taps (Err) as performance measures. The internal focus group made more errors than the control group only at the retention/transfer phase. We found no error differences between the external and internal focus groups, and there were no MT differences between any groups. Our primary hypothesis about the differential effect of attentional focus by practice was supported. The attentional focus effect on Err differed in the retention/transfer phase from the immediate phase, suggesting that practice mediated the attentional focus effect. We discuss how information theory may supplement understanding of attentional focus interventions in motor skill acquisition.

NeuroLISP: High-level symbolic programming with attractor neural networks

Despite significant improvements in contemporary machine learning, symbolic methods currently outperform artificial neural networks on tasks that involve compositional reasoning, such as goal-directed planning and logical inference. This illustrates a computational explanatory gap between cognitive and neurocomputational algorithms that obscures the neurobiological mechanisms underlying cognition and impedes progress toward human-level artificial intelligence. Because of the strong relationship between cognition and working memory control, we suggest that the cognitive abilities of contemporary neural networks are limited by biologically-implausible working memory systems that rely on persistent activity maintenance and/or temporal nonlocality. Here we present NeuroLISP, an attractor neural network that can represent and execute programs written in the LISP programming language. Unlike previous approaches to high-level programming with neural networks, NeuroLISP features a temporally-local working memory based on itinerant attractor dynamics, top-down gating, and fast associative learning, and implements several high-level programming constructs such as compositional data structures, scoped variable binding, and the ability to manipulate and execute programmatic expressions in working memory (i.e., programs can be treated as data). Our computational experiments demonstrate the correctness of the NeuroLISP interpreter, and show that it can learn non-trivial programs that manipulate complex derived data structures (multiway trees), perform compositional string manipulation operations (PCFG SET task), and implement high-level symbolic AI algorithms (first-order unification). We conclude that NeuroLISP is an effective neurocognitive controller that can replace the symbolic components of hybrid models, and serves as a proof of concept for further development of high-level symbolic programming in neural networks.

Acute Effect of Cognitive Compromise during Physical Exercise on Self-Regulation in Early Childhood Education

Self-regulation (SR) in pre-schoolers is a strong predictor of different aspects of mental health and wellbeing. However, SR only recently has been examined concerning physical activity and its effects on cognitive performance. In the present study, 49 preschool children aged 4-5 years were submitted to classroom movement breaks (CMBs) of 15-min with different degrees of difficulty. Before beginning the intervention, SR (i.e., head, toes, knees and shoulders test, HTKS) and skill levels were assessed for tasks demand adjustment to individual resources and the counterbalanced assignment of the participants to the groups. Similarly, after the intervention, the performance on the HTKS was re-evaluated. There was a general intervention effect on the SR of pre-schoolers, regardless of the difficulty level of the task [F (3) = 11.683, p-value < 0.001, η2p = 0.438]. Nevertheless, it seems that only when CMBs stimulate the children cognitively with optimal difficulty, is it possible to obtain benefits. We recommend providing teachers with professional support when implementing physical activity breaks in their daily program to generate an individualized level of cognitive load that would allow children to reach the optimal challenge point.

Failure to Engage Neural Plasticity through Practice of a High-difficulty Task is Accompanied by Reduced Motor Skill Retention in Older Adults

While the difficulty of a motor task can act as a stimulus for learning in younger adults, it is unknown how task difficulty interacts with age-related reductions in motor performance and altered brain activation. We examined the effects of task difficulty on motor performance and used electroencephalography (EEG) to probe task-related brain activation after acquisition and 24-h retention of a mirror star-tracing skill in healthy older adults (N = 36, 65-86 years). The results showed that the difficulty of the motor skill affected both the magnitude of motor skill learning and the underlying neural mechanisms. Behavioral data revealed that practicing a motor task at a high difficulty level hindered motor skill consolidation. The EEG data indicated that task difficulty modulated changes in brain activation after practice. Specifically, a decrease in task-related alpha power in frontal and parietal electrodes was only present after practice of the skill at the low and medium, but not the high difficulty level. Taken together, our findings show that a failure to engage neural plasticity through practice of a high-difficulty task is accompanied by reduced motor skill retention in older adults. The data help us better understand how older adults learn new motor skills and might have implications for prescribing motor skill practice according to its difficulty in rehabilitation settings.

Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning

This study attempted to multimodally measure mental workload and validate indicators for estimating mental workload. A simulated computer work composed of mental arithmetic tasks with different levels of difficulty was designed and used in the experiment to measure physiological signals (heart rate, heart rate variability, electromyography, electrodermal activity, and respiration), subjective ratings of mental workload (the NASA Task Load Index), and task performance. The indices from electrodermal activity and respiration had a significant increment as task difficulty increased. There were no significant differences between the average heart rate and the low-frequency/high-frequency ratio among tasks. The classification of mental workload using combined indices as inputs showed that classification models combining physiological signals and task performance can reach satisfying accuracy at 96.4% and an accuracy of 78.3% when only using physiological indices as inputs. The present study also showed that ECG and EDA signals have good discriminating power for mental workload detection. Practitioner summary: The methods used in this study could be applied to office workers, and the findings provide preliminary support and theoretical exploration for follow-up early mental workload detection systems, whose implementation in the real world could beneficially impact worker health and company efficiency. Abbreviations: NASA-TLX: the national aeronautics and space administration-task load index; ECG: electrocardiographic; EDA: electrodermal activity; EEG: electroencephalogram; LDA: linear discriminant analysis; SVM: support vector machine; KNN: k-nearest neighbor; ANNs: artificial neural networks; EMG: electromyography; PPG: photoplethysmography; SD: standard deviation; BMI: body mass index; DSSQ: dundee stress state questionnaire; ANOVA: analysis of variance; SC: skin conductance; RMS: root mean square; AVHR: the average heart rate; HR: heart rate; LF/HF: the ratio between the low frequencies band and the high frequency band; PSD: power spectral density; MF: median frequency; HRV: heart rate variability; BPNN: backpropagation neural network.

A Novel Application of Levenshtein Distance for Assessment of High-Level Motor Planning Underlying Performance During Learning of Complex Motor Sequences

Few studies have examined high-level motor plans underlying cognitive-motor performance during practice of complex action sequences. These investigations have assessed performance through fairly simple metrics without examining how practice affects the structures of action sequences. By adapting the Levenshtein distance (LD) method to the motor domain, we propose a computational approach to accurately capture performance dynamics during practice of action sequences. Practice performance dynamics were assessed by computing the LD based on the number of insertions, deletions, and substitutions of actions needed to transform any sequence into a reference sequence (having a minimal number of actions to complete the task). Also, combining LD-based performance with mental workload metrics allowed assessment of cognitive-motor efficiency dynamics. This approach was tested on the Tower of Hanoi task. The findings revealed that throughout practice this method could capture: i) action sequence performance improvements as indexed by a reduced LD (decrease of insertions and substitutions), ii) structural modifications of the high-level plans, iii) an attenuation of mental workload, and iv) enhanced cognitive-motor efficiency. This effort complements prior work examining the practice of complex action sequences in healthy adults and has potential for probing cognitive-motor impairment in clinical populations as well as the development/assessment of cognitive robotic controllers.

Motor Performance, Mental Workload and Self-Efficacy Dynamics during Learning of Reaching Movements throughout Multiple Practice Sessions

The human capability to learn new motor skills depends on the efficient engagement of cognitive-motor resources, as reflected by mental workload, and psychological mechanisms (e.g., self-efficacy). While numerous investigations have examined the relationship between motor behavior and mental workload or self-efficacy in a performance context, a fairly limited effort focused on the combined examination of these notions during learning. Thus, this study aimed to examine their concomitant dynamics during the learning of a novel reaching skill practiced throughout multiple sessions. Individuals had to learn to control a virtual robotic arm via a human-machine interface by using limited head motion throughout eight practice sessions while motor performance, mental workload, and self-efficacy were assessed. The results revealed that as individuals learned to control the robotic arm, performance improved at the fastest rate, followed by a more gradual reduction of mental workload and finally an increase in self-efficacy. These results suggest that once the performance improved, less cognitive-motor resources were recruited, leading to an attenuated mental workload. Considering that attention is a primary cognitive resource driving mental workload, it is suggested that during early learning, attentional resources are primarily allocated to address task demands and not enough are available to assess self-efficacy. However, as the performance becomes more automatic, a lower level of mental workload is attained driven by decreased recruitment of attentional resources. These available resources allow for a reliable assessment of self-efficacy resulting in a subsequent observable change. These results are also discussed in terms of the application to the training and design of assistive technologies.

Biomechanical and neurocognitive performance outcomes of walking with transtibial limb loss while challenged by a concurrent task

Individuals who have sustained loss of a lower limb may require adaptations in sensorimotor and control systems to effectively utilize a prosthesis, and the interaction of these systems during walking is not clearly understood for this patient population. The aim of this study was to concurrently evaluate temporospatial gait mechanics and cortical dynamics in a population with and without unilateral transtibial limb loss (TT). Utilizing motion capture and electroencephalography, these outcomes were simultaneously collected while participants with and without TT completed a concurrent task of varying difficulty (low- and high-demand) while seated and walking. All participants demonstrated a wider base of support and more stable gait pattern when walking and completing the high-demand concurrent task. The cortical dynamics were similarly modulated by the task demand for both groups, to include a decrease in the novelty-P3 component and increase in the frontal theta/parietal alpha ratio power when completing the high-demand task, although specific differences were also observed. These findings confirm and extend prior efforts indicating that dual-task walking can negatively affect walking mechanics and/or neurocognitive performance. However, there may be limited additional cognitive and/or biomechanical impact of utilizing a prosthesis in a stable, protected environment in TT who have acclimated to ambulating with a prosthesis. These results highlight the need for future work to evaluate interactions between these cognitive-motor control systems for individuals with more proximal levels of lower limb loss, and in more challenging (ecologically valid) environments.

Changes in Mental Workload and Motor Performance Throughout Multiple Practice Sessions Under Various Levels of Task Difficulty

The allocation of mental workload is critical to maintain cognitive-motor performance under various demands. While mental workload has been investigated during performance, limited efforts have examined it during cognitive-motor learning, while none have concurrently manipulated task difficulty. It is reasonable to surmise that the difficulty level at which a skill is practiced would impact the rate of skill acquisition and also the rate at which mental workload is reduced during learning (relatively slowed for challenging compared to easier tasks). This study aimed to monitor mental workload by assessing cortical dynamics during a task practiced under two difficulty levels over four days while perceived task demand, performance, and electroencephalography (EEG) were collected. As expected, self-reported mental workload was reduced, greater working memory engagement via EEG theta synchrony was observed, and reduced cortical activation, as indexed by progressive EEG alpha synchrony was detected during practice. Task difficulty was positively related to the magnitude of alpha desynchrony and accompanied by elevations in the theta-alpha ratio. Counter to expectation, the absence of an interaction between task difficulty and practice days for both theta and alpha power indicates that the refinement of mental processes throughout learning occurred at a comparable rate for both levels of difficulty. Thus, the assessment of brain dynamics was sensitive to the rate of change of cognitive workload with practice, but not to the degree of difficulty. Future work should consider a broader range of task demands and additional measures of brain processes to further assess this phenomenon.

Changes in motor performance and mental workload during practice of reaching movements: a team dynamics perspective

Few investigations have examined mental workload during motor practice or learning in a context of team dynamics. This study examines the underlying cognitive-motor processes of motor practice by assessing the changes in motor performance and mental workload during practice of reaching movements. Individuals moved a robotic arm to reach targets as fast and as straight as possible while satisfying the task requirement of avoiding a collision between the end-effector and the workspace limits. Individuals practiced the task either alone (HA group) or with a synthetic teammate (HRT group), which regulated the effector velocity to help satisfy the task requirements. The findings revealed that the performance of both groups improved similarly throughout practice. However, when compared to the individuals of the HA group, those in the HRT group (1) had a lower risk of collisions, (2) exhibited higher performance consistency, and (3) revealed a higher level of mental workload while generally perceiving the robotic teammate as interfering with their performance. As the synthetic teammate changed the effector velocity in specific regions near the workspace boundaries, individuals may have been constrained to learn a piecewise visuomotor map. This piecewise map made the task more challenging, which increased mental workload and perception of the synthetic teammate as a burden. The examination of both motor performance and mental workload revealed a combination of both adaptive and maladaptive team dynamics. This work is a first step to examine the human cognitive-motor processes underlying motor practice in a context of team dynamics and contributes to inform human-robot applications.