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

Accepted Manuscript Mental workload and motor performance dynamics during practice of reaching movements under various levels of task difficulty Isabelle M. Shuggi, Hyuk Oh, Patricia A. Shewokis, Rodolphe J. Gentili PII: DOI: Reference:

S0306-4522(17)30525-0 http://dx.doi.org/10.1016/j.neuroscience.2017.07.048 NSC 17925

To appear in:

Neuroscience

Received Date: Accepted Date:

4 November 2016 19 July 2017

Please cite this article as: I.M. Shuggi, H. Oh, P.A. Shewokis, R.J. Gentili, Mental workload and motor performance dynamics during practice of reaching movements under various levels of task difficulty, Neuroscience (2017), doi: http://dx.doi.org/10.1016/j.neuroscience.2017.07.048

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

Isabelle M. Shuggi1,2, Hyuk Oh1, Patricia A. Shewokis3,4, Rodolphe J. Gentili1,2,5 1

Department of Kinesiology, School of Public Health, University of Maryland, College Park, MD, USA.

2

Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD, USA.

3

School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA

19102, USA 4

Nutrition Sciences Department, College of Nursing and Health Professions, Drexel University,

Philadelphia, PA 19102, USA 5

Maryland Robotics Center, University of Maryland, College Park, MD, USA.

R.J. Gentili () 1

Cognitive Motor Neuroscience Laboratory, Department of Kinesiology, University of

Maryland, School of Public Health (Bldg #255), room #2144, College Park, Maryland, 20742, USA. Tel: (1) 301 405 2490, Fax: (1) 301 405 5578, e-mail: [email protected]

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Abstract The assessment of mental workload can inform attentional resource allocation during task performance that is essential for understanding the underlying principles of human cognitive-motor behavior. While many studies have focused on mental workload in relation to human performance, a modest body of work has examined it in a motor practice/learning context without considering individual variability. Thus, this work aimed to examine mental workload by employing the NASA TLX as well as the changes in motor performance resulting from the practice of a novel reaching task. Two groups of participants practiced a reaching task at a high and low nominal difficulty during which a group-level analysis assessed the mental workload, motor performance and motor improvement dynamics. A secondary cluster analysis was also conducted to identify specific individual patterns of cognitive-motor responses. Overall, both group- and cluster-level analyses revealed that: i) all participants improved their performance throughout motor practice, and ii) an increase in mental workload was associated with a reduction of the quality of motor performance along with a slower rate of motor improvement. The results are discussed in the context of the optimal challenge point framework and in particular it is proposed that under the experimental conditions employed here, functional task difficulty: i) would possibly depend on individuals’ information processing capabilities and ii) could be indexed by the level of mental workload which, when excessively heightened can decrease the quality of performance and more generally result in delayed motor improvements.

Keywords:

Motor

practice;

Mental

workload;

Motor

performance

Sensorimotor mapping; Reaching movements; Human-machine interaction.

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dynamics;

Introduction The human motor control system is very flexible since, through practice, it has the capability to learn new sensorimotor mappings allowing manipulation of novel tools and performance of new motor skills such as reaching movements under various constraints, which are essential when interacting with the environment (e.g., Casadio et al., 2012; Andrieux et al. 2016; Kagerer et al., 1997; Kagerer, 2016; Magill and Anderson, 2017; Weeks et al., 2003). Adaptive human motor performance of this type is largely dependent on efficient allocation of attentional resources along with the memory and information processing capabilities of the performer (e.g., Gentili et al., 2013; Gentili et al., 2015; Rietschel et al., 2014; Seidler et al., 2012; Shewokis et al., 2015; Stevens et al., 2015). In particular, an increase in task difficulty can affect attentional resources resulting in larger cognitive workload, which beyond a certain level, can overwhelm the informationprocessing system and thus compromise motor performance as well as the capability to handle additional tasks such as responding to unexpected events (Gentili et al., 2014; Guadagnoli and Lee, 2004; Marteniuk, 1976; Rietschel, 2011). Thus, a large body of work has examined cognitive workload through different metrics derived from various sources of information such as neuroimaging (e.g., electroencephalography (EEG), functional near infrared spectroscopy (fNIRS) (Ayaz et al., 2013; Harrison et al., 2014; Miller et al., 2011; Rietschel et al., 2014; Shewokis et al., 2015), physiological markers (e.g., heart rate variability, eye metrics (Hogervorst et al., 2014; Mehler et al., 2009)) and questionnaires (e.g., visual analog scales, National Aeronautics and Space Administration Task Load Index (NASA TLX); Akizuki and Ohashi, 2015; Bosse et al., 2015; Hu et al., 2016; Rendell et al., 2011; Ruiz-Rabelo et al., 2015; Young et al., 2008)) while a motor task was performed under various levels of difficulty. Although cognitive workload has been extensively studied in motor performance, its examination has been more limited during motor learning where various aspects of practice were manipulated (e.g., Akizuki and Ohashi, 2015; Bosse et al., 2015; Hu et al., 2016; Li and Wright, 2000; Rendell et al., 2011; Rietschel et al., 2014; Ruiz-Rabelo et al., 2015). Among those motor learning studies, a paucity examined the impact of challenge levels on the changes in mental workload and motor execution (Akizuki and Ohashi, 2015; Hu et al., 2016; Rendell et al., 2011). Recently, a study investigated the effect of

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three surgical tasks’ difficulty on mental workload and motor execution before and after training without however examining the changes in motor performance throughout the practice period (Hu et al., 2016). Another recent investigation examined the level of mental workload and the quality of motor performance during a postural control task that was executed under various levels of challenge. Although interesting, this effort focused on a gross motor task without examining i) fine motor control functions such as those employed to execute accurate reaching movements and ii) motor improvements dynamics throughout practice (Akizuki and Ohashi, 2015). Furthermore, these prior motor learning studies examined the effect of task difficulty on mental workload and motor performance by employing traditional group-level analyses via classical inferential statistics without consideration of data mining methods to examine individual variability and how individual differences affect the individual’s cognitive-motor performance. Since individual differences that arise from the dynamics between a motor task and a performer’s characteristics such as his/her information processing capabilities are important to determine the level of mental workload, it can be expected that beyond any group differences, specific individuals’ cognitive-motor responses can also be identified (Paas et al., 2003; Parasuraman and Jiang, 2012). Inspection of how the level of challenge affects mental workload and motor performance during motor practice could be considered by employing the optimal challenge point framework developed by Guadagnoli and colleagues (Guadagnoli and Lee, 2004) as well as cognitive load theory (Sweller, 2010; Wickens et al., 2013). Cognitive load theory suggests that the performance or learning of a task requires the recruitment of neural resources such as attention and working memory whose amount is inherently limited. Thus, if an excessive amount of those neural resources are consumed by the task, relevant information will not be fully processed due to inappropriate resource allocation, which will result in performance and learning decrements. As learning progresses, internal schemas are being built which allow individuals to perform more automatically (Kalyuga et al., 2003; Sweller, 2010; Wickens et al. 2013). Furthermore, by proposing the notion of nominal and functional task difficulty, the central idea of the optimal challenge point framework is that motor tasks can challenge individuals differently because of their unique combinations of motor abilities. In this framework,

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nominal task difficulty is related to the task features without considering the characteristics of the performer or the performance conditions, whereas, functional task difficulty represents the challenge level of a particular task with respect to the skills and capabilities of an individual as well as the performance conditions. Thus, low nominal task difficulty has little information available to process and individuals with poor skills (e.g., beginners) or with limited cognitive-motor processing capabilities are expected to experience a fairly low functional difficulty resulting in a limited mental workload and a satisfactory performance. Conversely, those individuals having limited skills or processing information capabilities who face a high nominal task difficulty may be overwhelmed by the amount of information to process and thus would experience a high functional task difficulty leading to poor performance and limited skill improvement during practice (Guadagnoli and Lee, 2004). In sum, for a given degree of expertise (e.g., beginners) and performance conditions, the functional task difficulty would depend on both the nominal task difficulty as well as an individual’s information processing capabilities, which together can influence the cognitive-motor responses (Guadagnoli and Lee, 2004). Therefore, this study aims to examine the dose-response of two different levels of challenge on the changes in mental workload and motor performance induced by the practice of a novel visuomotor reaching task while employing a traditional group-level analysis and a second exploratory assessment to identify any specific patterns at the individual level. When considering two groups of participants, where each group performs a novel task under a low or high nominal task difficulty, we predict that, as the level of challenge increases, a larger functional difficulty should be experienced by the participants who will be overwhelmed with the processing of the relevant information. Consequently, such increases in information processing demand should result in higher levels of observed mental workload, which when overly heightened, will translate into an overall motor performance decrement while negatively affecting the rate of motor enhancement dynamics resulting in a delayed performance improvement. In addition, a clustering analysis will be employed to assess the presence of unique individual patterns of cognitive-motor responses that may emerge relatively independently of the high and low level of nominal task difficulty imposed to each group. Before proceeding, it must be

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noted that this study focuses on motor practice and as such, the term learning, when occasionally employed, refers to the rate of performance improvements since the fairly permanent behavioral changes resulting from practice were not assessed (no retention or transfer test).

Experimental procedures Participants Thirty-six healthy (18 men, 18 women, age ranged between 20 and 24 years) participants without any neurological conditions and with normal or corrected-to-normal vision were recruited in the metropolitan Washington DC area and from the University of Maryland-College Park. Critical criteria to participate in this study included limited video game experience and no experience with head-guided systems. The participants were involved with this study after giving their informed consent. The Institutional Review Board at the University of Maryland, College Park approved all procedures.

Apparatus The apparatus was a human-body machine interface, which allowed performance of arm reaching movements with a simple virtual robotic arm having two degrees of freedom through limited head motion (i.e., head controlled reaching device). The head controlled reaching device was composed of a motion capture camera-based system (Optotrak™), which recorded in real-time the displacements of two infrared markers placed on the head (forehead and chin) of the participants. A separate computer then converted the head motion into the corresponding displacements of the robotic effector that moved within a two-dimensional (2D) workspace displayed on a computer screen placed in front of the participants at a distance of ~ 60 cm (Figure 1). The chin sensor was employed for target acquisition by slightly moving the jaw downward (slight opening of the mouth). Once the target was acquired, participants moved their head in the required direction to reach the target as fast and straight as possible with the end-effector of the virtual robotic arm. The head controlled reaching device employed was a relative reaching device, which works in a manner similar to a standard joystick. In the absence of head rotation, the position of the head sensors were located in a neutral area (or

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“deadband”) and, thus, the virtual robotic arm did not move. The neutral area was acquired through a simple calibration procedure. Once the participants rotated their head beyond the deadband limits, the spatial displacement of the arm, in the chosen direction, was initiated. The participants could move the robotic arm in the four cardinal directions (up, down, left and right) with limited angular head motions (yaw and pitch rotations) while the velocity of the robotic arm was kept constant throughout the entire task. A closed-form inverse kinematic algorithm was employed to control the end-effector based on the participant’s head movements. The interface was implemented by using a customized Matlab™ program. Through this human-machine interface, participants had to acquire a mapping (between angular head motion and spatial arm displacements) which, although relatively simple, required practice (e.g., Gentili et al. 2015; LoPresti and Brienza, 2004; Radwin et al., 1990; Williams and Kirsch, 2008, 2015). The advantage of employing such an experimental platform was to offer a task which was novel and fairly challenging to the participants due to the fact that: i) fine control of the robotic effector with neck muscles was unfamiliar (those muscles would not be employed to complete the same task under usual conditions) while also contributing to mitigating biases from previous motor experiences (Casadio et al., 2012; Mussa-Ivaldi et al., 2011) and ii) neck muscles have a reduced communication channel which is challenging since they are not naturally suited for a precise (joystick-based) motor control task in order to perform accurate reaching (Card et al., 1991; Jagacinski and Monk, 1985).

Experimental protocol and task The experimental procedures were thoroughly explained to all participants before starting the experiment who were then randomly and evenly assigned to two groups. Both groups followed exactly the same procedure, the only difference being that the first (n=18) and second (n=18) group moved the robotic arm throughout the workspace with a slow (~2.5 cm/s) and fast (~9 cm/s) velocity during the entire experiment. It must be noted that the term ‘reaching movements’ refers to the virtual robotic arm, which appears on the computer screen, but not necessarily to the head movements per-se. Moreover, since previous work has suggested that the frequency of knowledge of results could affect motor learning as well as the functional difficulty, in the present study the same

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frequency (100%) for knowledge of results was administrated to both groups (e.g., Winstein and Schmidt, 1990). Thus, the two groups only differed by the velocity of the robotic arm imposed to the participants who did not have any control over it. Although it is critical to note that there is not a specific manner to determine the nominal task difficulty, we defined its characterization based on the idea that it reflects a constant quantity of challenge independent of the performer and of the conditions of performance (Guadognoli and Lee, 2004). Keeping this definition in mind, from a control system theory standpoint, when a participant regulated the arm motion, there was an inherent sensorimotor delay between the perception of the performance via visual feedback and the generation of the neural command to correct the direction of the effector. At a slow velocity, these sensorimotor delays represented a fairly limited burden on the human control system since the corrective head motion, although slightly delayed with respect to arm motion, are still relevant to the current position of the arm, thus making its regulation fairly easy. However, at a fast velocity such delays become much more problematic since the corrections of movement direction occur too late to be efficient (i.e., during the same sensorimotor delay, the arm had moved much more) resulting in a much more challenging control of the effector. In other words, as the arm velocity increased, the task became more challenging since it reduced the time for the participants to regulate the arm movements and update their planning, which become even more problematic when performing with unusual muscles that are not well-suited for the task (Card et al., 1991; Jagacinski and Monk, 1985). The levels of challenge considered here may not have necessarily been different due to the amount of information to process, but related to the difficulty of detecting such information. Thus, we assumed that the slow and fast speed corresponded to a low and high nominal task difficulty since each represented a constant amount of task challenge independent of who was performing the task, which is consistent with the characterization of the nominal task difficulty previously proposed (Guadognoli and Lee, 2004). In addition, it is important to note that since the participants of both groups had to perform a completely novel task without having the possibility to rely too much on their previous motor experiences, it is reasonable to assume that they could be considered as beginners when facing the proposed challenge. Furthermore, other practice elements,

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which can influence the functional task difficulty such as the practice conditions (i.e., practice schedules, type of feedback, assisting the performer with a facilitation device) were kept exactly the same during the entire experiment and for all the participants. First, participants of both groups went through a calibration procedure where they had to stay still for about 25 secs while keeping a natural horizontal head position. Once the calibration procedure was complete it was ensured that the head movements could be performed comfortably; if needed the gain was adjusted and the calibration phase conducted again. Then, participants of both groups were instructed to perform reaching movements with the robotic end-effector as fast and as straight as possible towards various targets (n=40 per block), which were spatially distributed in a random manner while covering the entire 2D workspace. The targets were randomly presented one at a time in the workspace and the movements were self-initiated. A trial was composed of the following sequence. Once a given target appeared (red color), when ready (the participants had ample time to plan their movements), the participants acquired it (the target turned green) and immediately initiated the reaching movement with the robotic arm from its initial position (which corresponded to the final position reached in the previous trial). Once the target was reached, all visual stimuli were erased from the screen in preparation for the next trial and the participants moved their head back to the centered position (i.e., in the deadband). Participants were required to proceed immediately once the target was acquired and could control the virtual arm’s movements only from the target acquisition until it was reached. The targets appeared in the same order for all participants, regardless of group assignment. During the entire task (200 trials; 5 sets of 40 targets) the kinematics data of the virtual robotic arm were continuously collected and stored for further analysis.

Mental workload Among the various approaches that can be employed to assess the workload in participants, one possible simple technique is to employ questionnaires such as the NASA TLX, which has been widely recognized as a reliable and validated method for reporting perceptions of workload in various areas of research such as, piloting aircrafts, cognitive and psychomotor performances as well as medical procedures (Ayaz et al.,

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2012; Ayaz et al., 2013; Gentili et al., 2014; Hart and Staveland, 1988; Hart, 2006; Miller et al., 2011; Yurko et al., 2010; Zheng et al., 2012). The NASA TLX is a multifaceted tool that can be employed to assess subjectively the perceived workload while also providing results that are consistent with more objective metrics such as those derived from EEG or fNIRS (e.g., Ayaz et al., 2013; Gentili et al., 2014; Ke et al., 2014; Shewokis et al., 2015). While neural (e.g., EEG, fNIRS) and physiological (e.g., heart rate variability, eye movements) metrics can be considered more accurate and objective, tools such as the NASA TLX which subjectively assess the perceived workload offer the advantages of being less invasive, easier and cheaper to access, easily reproduced while having a good face validity (Hart, 2006; Young et al., 2008). NASA TLX provides an overall index of workload through relative contributions along six dimensions that are: mental, physical and temporal task demands as well as effort, frustration and perceived performance. To assess the effects resulting from the overall practice of the reaching task on the level of cognitive workload, task difficulty and more generally perceived effort, the NASA TLX was administrated to the participants after the practice session. We employed the scores (that range from 0 to 100) for each dimension with particular attention on the subscale that assessed the mental demand dimension since it was reported to likely correspond most to the mental effort (Akizuki and Ohashi, 2015; Ayaz et al., 2013; Bittner et al., 1989; Hart, 2006; Hendy et al., 1993; Kujala, 2012; Van Gog and Paas, 2008).

Kinematic performance Kinematic data of the robotic end-effector were analyzed to derive several performance metrics. Specifically, the following kinematic metrics were derived: i) number of control signals (CS) sent to the robotic end-effector which is defined as the number of times the head movement sent a command to the robotic arm to move in a given direction; ii) movement time (MT) which is defined as the time elapsed between two successive targets, iii) movement length (ML) which is defined as the distance travelled between two consecutive targets, iv) normalized jerk (NJ) which quantifies movement smoothness and is defined as the absolute jerk while accounting for differences in MT and path length of the robotic end-effector (Kitazawa et al. 1993), v)

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throughput (TP) which is defined as a measure of the amount of information a participant sends through a specific command source to drive the robotic end-effector (Williams and Kirsch, 2008) and vi) path efficiency (PE) which quantifies the straightness of the trajectory and is defined as the ratio between the ideal shortest distance over the real distance traveled by the robotic end-effector between two consecutive targets (100% represents perfect PE; Lau and O’Leary, 1993; Williams and Kirsch, 2008, 2015). It must be noted that due to variability in inter-target distances, for each movement between two successive targets the parameters CS, MT and ML (which do not account for such variations in distances) were normalized by the Euclidean inter-target distances. After computing these six metrics for each trial, the average performance (i.e., mean) and its variability (i.e., standard deviation) were obtained for each 20 trials/block, which were then averaged across participants for each block.

Statistical and data fitting analysis To assess any differences in workload resulting from the practice session, the scores for each of the six dimensions of the NASA TLX were compared between the participants of the two groups, who performed at a low and high level of nominal difficulty, by employing a t-test or a Mann-Whitney test depending on whether the assumption of normality was not met. When employed, the t-test was subjected to a Welch correction if the assumption of equal variance (tested with Levene's test) was not met. In addition, changes in both motor performance and movement variability during practice were examined by analyzing the kinematics during the early (i.e., two first blocks) and late (i.e., two last blocks) practice periods for individuals of both groups. Namely, for each kinematic metric separate mixed model ANOVAs 1 with 2 GROUP2 (low and high nominal difficulty) x 2 PERIOD (early, late practice period) with repeated measures on the last factor were conducted. Post-hoc analysis was conducted by

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The dependent measures that violated the assumption of unequal variance were subjected to a linear mixed effect model analysis to assess the differences in outcome. Since the results from the linear mixed effect model analysis and ANOVA were the same, the results obtained with the ANOVA were reported here. 2 Although it was informative to examine MT and TP when comparing the kinematic performance between the slow and fast groups, those two metrics were affected by the manipulation of the arm velocity and therefore the examination of the other four metrics were of primary interest for this particular case. 11

employing Tukey’s HSD and a significance criterion of =0.05 was employed for all the tests. Once this first step was completed, a second follow-up analysis was conducted to further explore the mental demand dimension within each group. First, the mental demand scores were found significantly larger for the group of individuals who performed at a high compared to low nominal difficulty (the other subscales did not reveal any between group differences; see result section for more details). Then, by further examining this mental demand dimension for each individual of both groups, the emergence of three subgroups, each having a different mental demand, was visually observed. A clustering analysis (K-means clustering method) was conducted and confirmed that three clusters of participants could be clearly identified, each representing a level of low, medium and high mental demand which spanned over the entire participant sample who performed either at a low or high level of nominal difficulty. Based on this clustering analysis, for each of the three identified clusters, the scores of the other dimensions of the NASA TLX as well as the kinematics data were re-examined. Since the number of participants in each clusters differed (nlow = 12; nmedium = 15 and nhigh = 9), Welch ANOVAs and linear mixed effect models were employed for analyzing the NASA TLX and the kinematics data, respectively. For the latter approach, the linear mixed effect model was determined by the Akaike information criterion (AIC). Specifically, the selected model included main and interaction fixed effects for the variables CLUSTER and PERIOD as well as random effects for the intercept. Whenever appropriate, multiple comparisons were conducted using Tukey’s HSD and a significance criterion of =0.05 was employed for all the tests. The false discovery rate (FDR) was employed to control the family-wise error rate for the multiple repeated measure ANOVAs conducted on the six kinematic metrics that indexed the performance and its variability for both the groups and the clusters-based analyses. Finally, to assess the motor improvement dynamics throughout practice, a data fitting analysis was conducted. At first a visual inspection of the data lead us to consider four possible fitting curves to model the improvement dynamics for CS, MT, ML and NJ that were: i) a single exponential (f(x) = ae-bx + c; {a, b, c} ∈ R); ii) a power function (f(x) = ax-b + c; {a, b, c} ∈ R), iii) a double exponential (f(x) = ae-bx + ce-dx + e; {a, b, c, d,

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e} ∈ R) and iv) a linear function (f(x) = ax + b; {a, b} ∈ R). For the two first models a, b and c represent the total quantity of improvement (number of trials x tends to infinity); the improvement rate and the asymptotic performance (number of trials x tends to infinity), respectively. For the third model, (a, c), (b, d) and e represent the total quantity of improvement; the improvement rate and the asymptotic performance, respectively. For the last model, a and b represent the improvement rate and the offset, respectively. While C, MT, ML and NJ decreased during practice, TP and PE increased and thus the data suggested to consider: i) a logarithmic function (f(x) = alog(x) + b; {a, b} ∈ R), ii) a rational function (f(x) = (a/x) + b; {a,b} ∈ R) and iii) a linear function (f(x) = ax + b; {a, b} ∈ R) to capture those changes throughout practice. For the first two models, a and b represent the asymptotic performance and the improvement rate, respectively. For the linear model, a and b represent the improvement rate and the offset, respectively. The coefficients of correlation, determination and the root mean squared error of the fit related to the fitting error as well as the complexity of the model (i.e., number of parameters employed) were considered to identify the best fit. Once the best model was selected for each kinematic metric, the parameters of the model were obtained for each participant. To address one of our hypotheses, which indicated that a heightened mental demand (which would reflect an elevation of functional task difficulty), would negatively affect the rate of performance enhancement leading to a delay of motor improvements, the parameter that represented the improvement rate for each of the selected fitted-models was examined. Then, the motor improvement rates of those models were statistically tested using a comparable group-based followed by a cluster-based analysis. Differences in the performance improvement rate between groups (or clusters) were examined by employing an independent t-test (one way ANOVA for clusters) or Mann-Whitney (Kruskal-Wallis test for clusters) depending if the normality assumption was met or not. When employed, the t-test was subjected to a Welch correction if the assumption of equal variance (tested with Levene's test) was not met.

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Results NASA TLX questionnaire Group-level analysis The statistical analysis conducted for the NASA TLX revealed that, compared to the low nominal difficulty group, the mental demand for the high nominal difficulty was significantly higher (t(34) = -2.107; p = 0.042; d = 0.700). The other dimensions of the NASA TLX did not reveal any significance (p > 0.05).

Cluster-level analysis The statistical analysis for the NASA TLX conducted with the clusters revealed a main effect for the performance (F(2, 19.259) = 7.279; p = 0.004), effort (F(2, 20.974) = 15.968, p < 0.001) and frustration (F(2, 17.897) = 11.633, p < 0.001) dimensions. The post-hoc analysis conducted on the performance dimension revealed that individuals of the low mental demand cluster perceived their performance as more successful compared to those of the medium mental demand cluster (p = 0.004, d = 1.396). The same analysis revealed that the perceived effort was higher as individuals of the cluster exhibited more elevated mental demand (low vs. medium cluster: p = 0.010, d = 1.119; low vs. high cluster: p < 0.001, d = 2.225; medium vs. high cluster: p = 0.036, d = 1.287). The posthoc analysis conducted on the frustration dimension also revealed a higher sense of discouragement and frustration for the participants of the medium (p = 0.029, d = 1.078) and high (p < 0.001, d = 2.118) mental demand clusters compared to those of the low mental demand cluster. It must be noted that the same analysis conducted on the mental demand revealed a main effect of the cluster (F(2, 19.049) = 89.779, p < 0.001) which confirmed that the three clusters identified were separated based on their mental demand (which is consistent with the idea that the algorithm classified individuals by maximizing the distance between clusters) (p < 0.001, d > 2.617 for all comparisons). When examining the distribution of the scores on the mental demand dimension between the two nominal task difficulty groups and the three mental demand clusters it appears that, individual differences in mental demand at a lower level of nominal task difficulty were attenuated since 94% of the individuals who performed at this level exhibited a mental workload that ranged between a low and a moderate level representing

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a relatively homogenous group. However, individuals who performed at a high nominal task difficulty revealed much higher inter-individual variations since their mental workload was distributed more equally over all three levels (28%, 28% and 44%, of the group samples was from the low, moderate and high mental demand clusters, respectively) representing thus a more heterogeneous group although almost half of the individuals (44%) who performed at a high nominal difficulty group were from the high mental demand cluster (Figure 2).

Kinematic performance Group-level analysis The findings revealed a GROUP x PERIOD interaction effect was observed for NJ (F(1,34) = 14.414, p < 0.001, ηP2 = 0.298). The post-hoc analysis disclosed that while the high nominal difficulty group had a significant reduction of smoothness from early to the late practice period (p < 0.001; d = 0.939); there was no difference for the low nominal difficulty group (p = 0.993). In addition, a main effect of GROUP was observed for CS, MT, ML, TP and PE (CS: F(1,34) = 96.747, p < 0.001, ηP2 = 0.740; MT: F(1,34) = 134.849, p < 0.001, ηP2 = 0.799; ML: F(1,34) = 90.761, p < 0.001, ηP2 = 0.727; TP: F(1,34) = 30.338, p < 0.001, ηP2 = 0.472; PE: F(1,34) = 81.320, p < 0.001, ηP2 = 0.705). Also, a main effect of PERIOD was identified for all the kinematic metrics except NJ (CS: F(1,34) = 19.262, p < 0.001, ηP2 = 0.362; MT: F(1,34) = 25.535, p < 0.001, ηP2 = 0.429; ML: F(1,34) = 25.964, p < 0.001, ηP2 = 0.433; TP: F(1,34) = 21.572, p < 0.001, ηP2 = 0.388; PE: F(1,34) = 10.144, p = 0.004, ηP2 = 0.230). The same analysis was conducted for the performance variability for these two groups which revealed that a GROUP x PERIOD interaction was also observed for MT, NJ, TP and PE (MT: F(1,34) = 4.892, p = 0.045, ηP2 = 0.126; NJ: F(1,34) = 23.832, p < 0.001, ηP2 = 0.412; TP: F(1,34) = 11.737, p = 0.003, ηP2=0.257; PE: F(1,34) = 10.185, p = 0.005, ηP2 = 0.231, respectively). Specifically, the MT variability from the early to the late period was further reduced for the low (p < 0.001; d = 2.034) compared to the high nominal difficulty group (p = 0.019; d = 0.840). Also, the same post-hoc comparison revealed that although the low nominal difficulty group significantly improved their consistency in producing smoother (NJ: p < 0.001, d = 2.087) and shorter (PE: p = 0.002,

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d = 1.058) movement paths from the early to the last practice stage, this difference was not observed for the high nominal difficulty group (p > 0.707 for both metrics). In addition, while the TP variability was significantly increased from early to late practice for the high nominal difficulty (p = 0.012; d = 0.662), the difference was not identified for the low nominal difficulty group (p > 0.413). Other findings include a main effect of GROUP for CS (F(1,34) = 98.061, p < 0.001, ηP2 = 0.743) and ML (F(1,34) = 138.338, p < 0.001, ηP2 = 0.803) as well as a main effect of PERIOD for CS (F(1,34) = 19.807, p < 0.001, ηP2 = 0.368). No other main or interaction effects reached the significance level (p > 0.05).

Cluster-level analysis There was a main effect of CLUSTER for CS, ML, and PE (CS: F(2,36) = 3.908, p = 0.047; ML: F(2,36) = 4.262, p = 0.037; PE: F(2,36) = 4.850 , p = 0.025). Tukey’s post-hoc analysis revealed that individuals of the high mental demand cluster exhibited larger CS, ML and PE compared to those from the low mental demand cluster (CS: p = 0.007, d = 1.270; ML: p = 0.003, d = 1.191; PE = 0.002, d = 1.317) and also higher than those from the medium mental demand cluster (CS: p = 0.023, d = 0.898; ML: p = 0.026, d = 1.077; PE = 0.026, d = 1.060). In addition, there was a main effect of the PERIOD for all the kinematic metrics (CS: F(1,36) = 22.320, p < 0.001; MT: F(1,36) = 27.421, p < 0.001; ML: F(1,36) = 30.025, p < 0.001; NJ: F(1,36) = 14.624, p < 0.001; TP: F(1,36) = 24.614, p < 0.001; and PE: F(1,36) = 11.294, p = 0.004). The same statistical analyses were applied to the performance variability for these three clusters that revealed a main effect of CLUSTER for the CS (F(2,36) = 4.982, p = 0.027) and ML (F(2,36) =7.582, p = 0.004). Post-hoc analyses revealed that individuals from the high mental demand cluster produced more variability for the CS and ML compared to those from the low (CS: p = 0.004, d = 1.339; ML: p = 0.002, d = 1.632) and medium (CS: p = 0.009, d = 0.998; ML: p = 0.003, d = 1.291) mental demand clusters. Moreover, a main effect of PERIOD for the CS (F(1,36) = 23.089, p < 0.001), MT (F(1,36) = 38.461, p < 0.001) and NJ (F(1,36) = 23.779, p < 0.001) was observed. No other main or interaction effect reached the significance level (p > 0.05).

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Data fitting and rate of motor improvement dynamics Generally, changes in the kinematic metrics C, MT, ML and NJ were best fitted with the single exponential function (R2 = 0.46 ± 0.08) where the performance improvement rate was represented by the parameter b in the model. Also, the metrics TP and PE were best modeled with the rational function (R2 = 0.33 ± 0.08) for which the motor improvement rate was represented by the parameter a in the fitted-model (Figure 7).

Group-level analysis Generally, the quality of the fit was better for the low (R2 = 0.61 ± 0.04) compared to the high nominal difficulty group (R2 = 0.38 ± 0.08). In addition, the comparison of the performance improvement rate for both groups revealed that the individuals of the high nominal difficulty group exhibited a lower improvement rate compared to those of the low nominal difficulty group for most of the kinematic metrics (CS: z = 5.031, p < 0.001, d = 0.738; MT: z = 4.240, p < 0.001, d = 1.174; ML: z = 3.860, p < 0.001, d = 0.826; NJ: z = 4.018, p < 0.001, d = 0.573 and PE: z = -2.721, p = 0.007, d = 0.919).

Cluster-level analysis The model could capture best the performance improvement dynamics for individuals from the medium mental demand cluster (R2 = 0.54 ± 0.07) and to a lesser extent those from the low (R2 = 0.23 ± 0.04) and high (R2 = 0.42 ± 0.04) mental demand clusters. There was a significant difference in the motor improvement rates between the clusters for MT (H(2) = 7.035, p = 0.029) and NJ (H(2) = 8.292, p = 0.014). We observed that the improvement rates for these metrics were smaller for the individuals of the high mental demand cluster compared to those from the low (MT: z = 2.274, p = 0.022, d = 1.074; NJ: z = 2.487, p = 0.012, d = 0.489) and medium (MT: z = 2.415, p = 0.015, d = 1.002; NJ: z = 2.593, p = 0.009, d = 0.406) mental demand clusters.

Discussion First, the group-level analysis revealed that although individuals of both the low and high nominal difficulty group improved their performance, those who performed

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under a high nominal difficulty exhibited larger mental demands which translated into an overall reduction of the quality of reaching movements (performance and its variability) over the entire practice stage while negatively affecting their performance improvement dynamics as indicated by a delay in motor improvement. Second, the clustering analysis revealed three (low, medium and high) mental workload clusters distributed over the two nominal difficulty groups. As the mental demand of clustered individuals increased, the perceived performance, task difficulty and frustration measures were negatively affected while the quality of their performance and its variability over the entire practice period was reduced. These groups also had a delay in motor improvements. Third, the clustered individuals were distributed across the two nominal task difficulty groups and as nominal difficulty increased, more pronounced individual variations in mental demand and more generally in cognitive-motor responses were observed leading to increased heterogeneous inter-individual behaviors.

Functional difficulty and individual characteristics during cognitive-motor reaching performance Overall, both the group- and cluster-level analyses revealed similar results regarding the changes in mental workload and motor production with: i) all participants improving their reaching movements throughout motor practice, and ii) an increase in mental workload, which when overly heightened, translated into an overall deterioration of reaching performance and of its variability which was associated with negative influences on performance dynamics as indicated by a delay in motor performance improvements. By employing the optimal challenge point framework and under the assumption that individuals had a specific skill level (i.e., novices) who performed under the same performance conditions (i.e., same practice schedule and feedback), these findings could be explained with a model where the functional task difficulty, to some degree, depends on the participant’s individual characteristics. First, when considering the findings at the group-level, an increase in nominal difficulty would have imposed an elevated level of challenge which generally would have overwhelmed most of the participants due to heighten demands to process relevant

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information in order to perform the reaching task. Such an increased effort to process information would contribute to augmenting the mental demand which, when highly elevated, would translate into a reduced quality of reaching movements (i.e., jerkier, longer and more variable) accompanied by a delay in the rate of motor improvements (Akizuki and Ohashi, 2015; Guadagnoli and Lee, 2004). It must be noted that although the mental demand dimension of the NASA TLX (which here revealed a significant difference) has often been considered as essential to index the mental workload since it would reflect the mental effort (e.g., Akizuki and Ohashi, 2015; Kujala, 2012), no difference for the other dimensions was observed. A possible explanation could be that some dimensions may be less relevant and/or sensitive to the task requirements. For instance, the physical demand dimension for young healthy individuals who used limited head motion to acquire a fairly simple sensorimotor map to control the robotic arm may have limited relevance. Also, an absence of differences for the other dimensions of the NASA TLX could be due to variations in an individual’s cognitive-motor responses to the task demand. Indeed, the functional difficulty experienced by the participants when facing the challenge would likely depend on the individual’s cognitive-motor capabilities (Guadagnoli and Lee, 2004; Paas et al., 2003; Parasuraman and Jiang, 2012). This notion of variations in individual cognitive-motor responses could provide a mechanism to explain the results at the cluster level by considering the optimal challenge point framework, which suggests that the functional difficulty depends on the expertise and performance conditions but also on additional elements (e.g., environmental conditions, individual processing characteristics) (Guadagnoli and Lee, 2004). We propose that an individual’s variations in information processing capabilities would be one of these elements. Thus, individuals with higher (lower) information processing capabilities, would have a smaller (higher) functional difficulty resulting in a smaller (larger) mental demand associated with higher (lower) reaching performance, with smaller (larger) performance variability, and faster (slower) rate of motor performance improvements. Also, it must be noted that even if individuals of the low nominal difficulty differed in their capabilities to process information, since they performed under a condition that required minimal information processing, their mental workload and

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motor production would tend to be more homogenous than those from the high nominal task difficulty group which is consistent with our results. The idea that individual variations would result into different cognitive-motor responses (for the same nominal difficulty) is conceivable since: i) the cohort of participants was fairly homogenous (all were young, healthy, educated and beginners at the proposed task; see method section), ii) prior studies identified individual differences in relevant cognitive-motor processes such as working memory and motor learning (Glabus et al., 2003; Paas et al., 2003; Parasuraman and Jiang, 2012; Tomassini et al., 2011). However, although plausible, this proposition should be further experimentally tested in the future by examining individual variations of the relevant neural functions (e.g., working memory, executive functions) in a context of motor learning. When considering our results as a whole, it must be noted that the group-level manipulation was, to some degree, effective since about half of individuals in the high nominal difficulty group exhibited the largest mental demand (44% of the individuals in this group were from the high mental demand cluster). However, the clusters provide a more refined perspective of these findings by identifying specific individual cognitivemotor patterns which did not necessarily correspond to what the imposed task difficulty was supposed to produce (individuals of the high nominal difficulty group were also found in the medium (28%) and low (28%) mental demand cluster suggesting that individuals responded differently to the same nominal task difficulty). Thus, overall the observed differences in individual cognitive-motor responses in the context of this study suggest that the functional difficulty may be mediated by variations in individual information processing capabilities when facing the challenge. Generally, our findings confirm and extend those from prior studies, which revealed that as the difficulty of the task to be learned increased, the resulting increase in mental workload was accompanied by an overall reduction of motor performance and an increase of its variability, which in some cases, could delay motor improvements (Akizuki and Ohashi, 2015; Bosse et al., 2015; Hu et al., 2016; Kovacs et al. 2009a; Rendell et al., 2011; Shea et al., 2011). For instance, a recent study that examined the motor performance and mental workload during learning of a novel postural control task, revealed that a high functional difficulty level led to an elevated mental demand

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associated with a reduction of gait stability while delaying motor learning (Akizuki and Ohashi, 2015). Our study also reinforces the notion that the measurement of mental workload can contribute to quantifying the functional difficulty experienced by the individuals (Akizuki and Ohashi, 2013, 2015). Taken together, our findings and other empirical work noted support the notion that while the mental workload was subjectively examined, there is a consistent relationship between mental workload and reaching performance for both group and cluster analyses that reinforces the validity of using the NASA TLX in a motor learning context (Akizuki and Ohashi, 2015; Bosse et al., 2015; Hu et al., 2016; Rendell et al., 2011). It must be noted that although we found that both mental workload and the quality of reaching movements were negatively related this is not necessarily the case since the mental workload can increase while the motor performance remains unchanged (Gentili et al., 2014; Hu et al., 2016). Thus, if mental workload reflects functional difficulty, similar coupling dynamics between the functional difficulty experienced by individuals and their observed behavioral performance may further inform the cognitive-motor processes. Interestingly, changes in performance throughout practice revealed that the motor performance dynamics were negatively altered. Namely, the performance improvement rates of the fitted-models, which captured the quality of the reaching performance (e.g., movement time, smoothness) during practice, were substantially reduced for the individuals who experienced a high functional difficulty as indicated by an elevated mental workload. In other words, although slower, performance improvements still occur but under higher mental efforts as indicated by heightened mental demands. These findings are in agreement with those from previous studies, which revealed that the motor performance improvement was negatively affected and even hindered when individuals performed under excessive functional difficulty (Akizuki and Ohashi, 2015; Schmidt et al., 1990). More generally, such delayed improvement dynamics is consistent with the optimal challenge point predictions where an extreme functional difficulty generates too much information to process for an individual, which in turn impedes motor learning (Guadagnoli and Lee, 2004). It must be noted that delayed motor improvements could also be due to a very low functional difficulty, however, by its novelty and unfamiliar nature, our reaching task was likely still challenging for the participants and consequently

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our results cannot be directly compared with those from prior work that also manipulated a very low nominal difficulty (Akizuki and Ohashi, 2015; Guadagnoli and Lee, 2004). Although the present findings are consistent with the optimal challenge point theory, it must be noted that this has not systematically been the case as suggested by previous studies, which found results that were not compatible with the predictions of this framework resulting in likely more complex interactions between elements of the experimental work (Ollis et al., 2005; Panchuk et al. 2013; Patterson and Lee, 2008; Rendell et al. 2010; Sanli and Lee, 2015). It must be noted that the results from this study could also be explained in the context of cognitive load theory. Namely, as the task difficulty increased (by augmenting the velocity of the arm to control) the mental workload also became elevated and when excessive resulted in the degradation of the performance as well as of its rate of improvement, which is consistent with this theoretical framework (Chandler and Sweller, 1991; Mousavi et al., 1995; Kalyuga et al., 2003; Sweller and Chandler, 1994; Sweller, 2010; Wickens et al., 2013). Specifically, based on this theory, a possible explanation would be that an elevation of the level of challenge would result in an increase in the intrinsic load via task difficulty which elevates the recruitment of attentional and working memory processes and, when excessive, relevant information cannot be processed appropriately anymore which leads to performance decrement and in the slowing down of performance improvements (Chandler and Sweller, 1991; Sweller and Chandler, 1994; Mousavi et al., 1995; Kalyuga et al., 2003; Sweller, 2010; Wickens et al., 2013). Consistent with cognitive load theory, there may be an increase in some extraneous load, which would also negatively impact the encoding, attentional and working memory processes (Sweller, 2010). Application of cognitive load theory to the understanding of task and extraneous influences on motor performance and learning is an important area for future study.

Applications to assistive technology and rehabilitation Although a head-controlled device was employed as an experimental platform to study mental workload and performance during motor practice of a novel reaching skill, by its nature this work can also inform head-controlled systems and more generally the

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practice, learning and design of human-machine interfaces as well as prostheses used for functional rehabilitation. Previous head-controlled device studies mainly focused on hardware design with little analysis of the cognitive-motor processes underlying practice and learning to operate such systems; although, due to their unfamiliar control nature they require practice to achieve a certain level of skillfulness (Card et al., 1991; Betke et al., 2002; Evans et al., 2000; LoPresti et al., 2002; Radwin et al., 1990; Williams and Kirsch, 2008, 2015). The few studies that assessed user performance during/after practice with head-controlled systems and more generally with assistive devices/prostheses mainly focused on the motor aspect with little or no analysis of the user’s cognitive states such as mental demand, which is essential for human performance (Gentili et al., 2014; Miller et al., 2011; Radwin et al., 1990; Rietschel et al., 2014; Weeks et al., 2003; Williams and Kirsch, 2008, 2015). Namely, while patients may exhibit appropriate motor responses, a more integrated assessment of their cognitive-motor performance could include the examination of mental workload, which when excessive, may prevent allocating attentional resources to other tasks (e.g., Abascal, 2008; Deeny et al., 2014; Felton et al., 2012; Gentili et al., 2015). Also, as far as we know, none of these previous studies examined individual cognitive-motor responses of the users during/after practice, which could inform individualized training approach of the users.

Limitations and future work As a first step to examine the relationship between cognitive workload and arm reaching performance in a motor practice/learning context while also accounting for individual differences, this study includes several limitations. First, although the NASA TLX is a well-established tool to assess the mental workload and more generally the effort generated for a given task, it does not necessarily allow for an objective measurement of the mental workload and also cannot continuously assess changes in the relevant neural processes throughout motor practice/learning. As such, future studies could employ more objective metrics derived from physiological signals (e.g., brain activity) to capture the dynamics of the cognitive-motor processes such as those underlying the engagement of attentional mechanisms (e.g., Murray and Janelle, 2007; Rietschel et al., 2014; Shewokis et al., 2015). Also, other behavioral performance metrics

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such as accuracy measurements could be employed to further inform the quality of the reaching performance as well as its improvement. Another limitation is that although this work evidenced individual differences in cognitive-motor responses, their specific origins and associated neural processes need to be further examined. A possibility would be to employ a mediation analysis including the motor performance and mental workload during the execution of the reaching task as well as the assessment of relevant cognitive functions (e.g., working memory, executive functions). Also, although this work mainly focused on information processing as a mechanism underlying changes in cognitivemotor performance, other elements, not assessed here, such as motivational factors also play a crucial role while manipulating the level of challenge and more generally during motor learning (Leiker et al., 2016; Lohse et al., 2013; Lohse et al., 2015). Finally, since it was suggested that the encoding of motor representations can be affected based on short-term or long-term learning effects (Boutin et al., 2012; Boutin et al., 2013), future work will not only focus on the practice period but also include retention and/or a transfer tests to further assess the underlying cognitive-motor processes during motor learning.

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NASA

TLX

and

blinks. Surg

Legends Figure 1: Experimental set-up to perform reaching movements towards spatially distributed targets through a human-machine interface. By employing a simple mapping, the angular head rotations (yaw (Φ) and pitch (Ψ)) are converted in real-time into spatial displacements depending if the head position is within the deadzone or not (see Materials and Methods section for more details). The computer embeds the controller of the virtual robotic arm and also records the corresponding kinematics of the reaching movements throughout practice which are stored for analysis.

Figure 2: Mental workload and repartition of individuals in the two difficulty groups and the three mental demand clusters obtained with the NASA TLX (for details about the six questions of this questionnaire (Q1-Q6), see Hart et al. 1988). (A) Mental workload for the low and high nominal difficulty group. (B) Mental workload for the low, medium and high mental demand clusters. (C) Proportion of individuals from the two nominal difficulty group classified into each clusters (left panel) and of each cluster into the low and high nominal difficulty groups (right panel) based no their scores on the NASA TLX mental demand dimension. (D) Individual distribution of the high and low nominal difficulty group in each mental demand cluster. LND: low nominal difficulty; HND: high nominal difficulty; LC: low mental demand cluster; MC: medium mental demand cluster; HC: high mental demand cluster. ***: p<0.001; **: p<0.01; *: p<0.05.

Figure 3: Kinematic performance during the early and late practice stage for the low (gray bars) and high (black bars) nominal difficulty groups. LND: low nominal difficulty; HND: high nominal difficulty; CS: number of control signals; MT: movement time; ML: movement length; NJ: normalized jerk; TP: throughput; PE: path efficiency. ***: p<0.001; **: p<0.01; *: p<0.05. Figure 4: Variability of the kinematic performance during the early and late practice period for the low (gray bars) and high (black bars) nominal difficulty groups. LND: Low nominal difficulty; HND: High nominal difficulty; CS: number of control signals; MT: movement time; ML: movement length; NJ: normalized jerk; TP: throughput; PE: path efficiency. ***: p<0.001; **: p<0.01; *: p<0.05.

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Figure 5: Kinematic performance from the early and late practice period for the low (white bars), medium (gray bars) and high (black bars) mental demand clusters. CS: number of control signals; MT: movement time; ML: movement length; NJ: normalized jerk; TP: throughput; PE: path efficiency. ***: p<0.001; **: p<0.01; *: p<0.05.

Figure 6: Variability of the kinematic performance during the early and late practice stage for the low (white bars), medium (gray bars) and high (black bars) mental demand clusters. CS: number of control signals; MT: movement time; ML: movement length; NJ: normalized jerk; TP: throughput; PE: path efficiency. ***: p<0.001; **: p<0.01; *: p<0.05.

Figure 7: Data fitting analysis of the kinematics performance for MT (left column) and NJ (right column) throughout practice. The data and fitted model for the low (thick empty circles) and high (black circles) nominal difficulty groups are represented in the first and second row, respectively. The data and fitted model for the low (white circle), medium (gray circles) and high (black circles) mental demand clusters are illustrated in the third, fourth and fifth row, respectively. The average learning rate for the fitted model (exponential for MT and NJ, see text for details) for the two nominal difficulty groups and the three clusters are depicted in the sixth and last row. For each fitted-model, the coefficient of determination (R2) indicate the quality of the fit. LND: low nominal difficulty; HND: high nominal difficulty; LC: low mental demand cluster; MC: medium mental demand cluster; HC: high mental demand cluster. ***: p<0.001; **: p<0.01; *: p<0.05.

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Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Highlights ● A high

level of task difficulty during motor practice increases the mental workload and

reduces the quality of performance ● An excessive level

of challenge during the acquisition of a new motor skill alters the

performance dynamics ● The level

of mental workload may reflect the degree of functional task difficulty during

motor practice ● The functional

difficulty depends on individual information processing capabilities

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