vary spontaneously, as it is determined by the speed of the motor driving the treadmill belt. An electronic walkway is a carpet 0.9 m wide and up to 7 m in length. Walking on such a walkway is like walking on a carpet, something familiar to most. Individuals can therefore vary their pace freely, thus allowing the inﬂuences of cog- nitive tasks on gait to be assessed. An electronic walkway is reliable and commonly used instrument to assess walking behavior under natural walking conditions. A wireless EEG system allows brain electrical activity to be recorded without the risk of a participant being impeded by wires. To relate brain activity to gait phase, the data from an electronic walkway and EEG need to be synchronized. This paper describes the procedure we developed to synchro- nize data from an electronic walkway with data from a wireless EEG-system. Our electronic walkway could not provide an electric pulse at a regular interval, something that would have enabled us to determine the accuracy of the synchronization. To assess synchro- nization, we performed a proof of concept measurement involving two elderly and two young participants. We looked for task-speciﬁc modulation of gait regularity and brain activity to different cog- nitive tasks in a motor-cognitivedual-task setting. We chose this approach, because in the elderly, the former has been shown to be modulated by the nature of the cognitivetask ( Beauchet et al., 2005 ) and act as a reliable indicator of cognitive-motor interfer- ence as well as for an elevated fall risk ( Bridenbaugh and Kressig, 2011 ). If synchronization between gait and EEG data was accurate, we expected to observe task-related changes in brain activity that mirrored gait regularity.
loosing balance and falling. Another approach states that priority of the motor is given over the cognitivetask (i.e., “posture first” strategy; Bloem et al., 2006; Yogev-Seligmann et al., 2012 ). Thus, there is no explicit limitation of motor performance during DT practice and the risk of loosing balance and falling is low. Our results are not in line with the first or with the second approach. Task prioritization did not differently favor task performance in the prioritized domain during DT balance performance in young adults. One might again argue that central capacity in healthy young adults is not stressed enough by challenging demands during DT situations. Thus, their cognitive capacity in terms of attentional resources is able to adequately handle both, the motor demand of the stabilometer task as well as the cognitive demand of serial three subtractions. More difficult and attention-demanding tasks during DT conditions might overstrain central capacity in young adults. For example, in older adults it has been shown that particularly visual demands cause greater reductions in DT motor performance compared to other (e.g., verbal or auditive) tasks ( Bock, 2008; Beurskens and Bock, 2012 ). Shumway-Cook et al. proposed that the allocation of attention during the performance of concurrent tasks is complex, depending on many factors including the nature of the cognitivetask, the postural task, the goal of the subject, and the instructions, implying that task prioritization is flexible and depends on a variety of individual, task, and environmental factors ( Shumway-Cook et al., 1997; Kelly et al., 2013 ). This finding might be used to explain our results, indicating that the motor and/or cognitivetask used itself did not affect prioritization. Consequently, other aspects, such as individual preconditions (e.g., physique, cognitive status and motivation) might affect DT performance following prioritized practice. However, this needs further clarification in terms of DT practice and task prioritization. Further, other studies showed immediate effects of task prioritization during DT conditions ( Schaefer et al., 2008; Kelly et al., 2010; Yogev-Seligmann et al., 2010 ). Our results did not show differences in motor and/or cognitive performance following specific test instructions, indicating that prioritization of one over another task is beneficial during task execution. This however did not lead to a specific learning effect during the retention/transfer phase of the experiment.
The reasons for impaired balance performance in chil- dren have been attributed to not fully developed struc- tures within the central nervous system . For example, Riach and Hayes  investigated age-related changes in postural sway in children and compared their findings to results from adult research. They were able to show that children predominately rely on visual information to con- trol balance, whereas grown-ups prioritize the propriocep- tive system. In this context, Peterson et al.  observed that children at the age of 12 years develop adult-like abil- ities to integrate proprioceptive feedback in balance con- trol. Children often encounter situations involving the concurrent performance of a cognitivetask while walking. For example, they may need to identify signs and signals on their way to school or talk to classmates and carry a book or physical education utilities while walking. Children aged 9 years show impaired motor performance when walking in DT situations compared to young adults . Especially, young children (4–6 years) decrease their stride
for the slow discrete repetitive movement (pegboard) in our experiment. Movement frequencies were clearly reduced under MCDT conditions in the pegboard task. The basic assumption underlying the explanation of this phenomenon is that both tasks are controlled by different types of control regime. This is confirmed by the observation that cortical load increases from STpeg to DTpeg but not from STpad to DTpad. In the latter case, one can even see a drop in prefrontal brain activation to the level of the cognitive ST. Actually this is exactly what you would expect, if the motortask runs automatically, that is, without any additional, prefrontally located cognitive processes being involved. By this, cortical activity is not upregulated to its limits in the paddleball task, even under MCDT conditions. Quite the contrary, in the pegboard task, adding the calculation task further increased cortical activity. As the correlational results show, this upregulation reaches a level where signs of saturation become visible. Surprisingly however, we also saw negative correlations between increase in prefrontal activity and motor performance in the ST–pegboard condition, where the absolute activation level was far from maximum. We do not know yet, how to explain this particular finding.
Attention can be defined as the information processing capacity of an individual, which is presumably limited (see e.g., Kahneman, 1973; Wickens, 1980, 1989). Usually, studies in the context of postural control used so called cognitive-motordual-tasks (e.g., Dault et al., 2003) to determine the attentional demand. In these cognitive-motordual-tasks a postural task (e.g., balancing on a balance board, standing, or walking) and a secondary cognitivetask (e.g., counting backward; see Yardley et al., 1999) are performed at the same time and performance is compared to performing only one task separately. According to the notion of limited attentional resources, a more demanding postural task should induce more interference with a cognitive tasks and vice versa (Woollacott and Shumway- Cook, 2002; Fraizer and Mitra, 2008; Boisgontier et al., 2013). However, empirical evidence is ambiguous, as some studies report interference between a motortask and a cognitivetask (e.g., Andersson et al., 1998), whereas others did not report an effect of postural control demands (whether participants were sitting or standing) on the performance in the cognitive tasks in general (Dault et al., 2001; see also Huxhold et al., 2006). Other studies tackled this issue but the majority focused on the question whether postural control suffers in terms of for example postural sway and sway velocity increases in cognitive-motordual tasks compared to single tasks (in this case if a cognitivetask is added vs. only a postural task is given; see e.g., Beurskens et al., 2016).
To date, limited information is available on cognitive- motor interference in aging and underlying neural processes using electroencephalography (EEG). In a previous study, Beurskens et al.  examined the gait pattern and brain activity of young adults while concurrently performing a cognitive or motor interference task. Besides an impaired walking performance in dual- compared with single-task condition, the authors reported lower alpha-band activity at frontal and central electrodes while walking and concurrently performing a cognitive or motor interference task. In con- trast, beta activity was increased during the secondary motor interference task at frontal sides, which was also linked to the additional recruitment of neural resources. In another study, Ozdemir et al.  compared cognitive and postural single and cognitive-postural dual-task performances in old and young adults at low and high task demands. Their partici- pants performed a one-back (low cognitivetask demand) or a two-back working memory task (high cognitivetask demand) and stood on a ﬁxed (low postural task demand) or a free-swinging platform (high postural task demand). Each task condition was either performed as a single task or in combination as a cognitive-postural dualtask. According to Beurskens et al. , postural de ﬁcits were only found at high cognitive and high postural task demands. With regard to the underlying neural mechanisms, the authors reported a higher theta activity over frontal, central-frontal, and central areas with increased cognitivetask diﬃculty as opposed to postural task diﬃculty. In addition, they found increased delta frequency synchronization over central-frontal, central, and central-parietal sites with increasing postural task demands. Furthermore, they found increased alpha fre- quency synchronization for increasing dual-tasking di ﬃ- culty, i.e., for the one-back and two-back tasks on the sway platform. The increase of delta and alpha frequencies was more pronounced in young compared with old adults. The results indicate a general cognitive impairment in old com- pared to young adults. Furthermore, high postural task demands require more cognitive resources in old compared to young adults, resulting in additionally impaired cognitive performance. Nevertheless, the interpretation of the study is limited by a small sample size of 10 young and 9 old adults.
obtain Euler angles on which the behavior variable brush_movement is set. In the integration process, small errors are accumulated over time. For a behavior such as brush_teeth which usually has a long duration compared to other behaviors, the accumulation of errors leads to misclassifications: brush_teeth was mixed up with paste_on_brush in 25.6% of the cases. The average classification rate over all user behaviors is 75.7% where noth- ing has the second lowest classification rate with 53.7%. Nothing is the only behavior which is confused with any other behavior in the recognition as shown in table 14 . Nothing serves as a transition behavior between user behaviors in the brushing task. For example, a user’s hands approach or leave an object which initializes or finalizes a behavior. Such phases are in- cluded in the training process of the Bayesian network (BN) responsible for nothing since we don’t explicitly model the initialization and finalization phases of the user behaviors in the according BNs. Hence, the recognition rate of nothing is low with 53.7% since the training data consists of initial- ization and finalization phases of various behaviors. If we drop the rate of nothing in the average calculation, we get a classification rate of 78.5% which is a good result with regard to the huge variance in task execution. An important measure of the system’s performance is the number of trials in which the user reaches the final state according to the system’s frame- work of action for performing the tooth brushing task. The performance of the TEBRA system in the collaborative scenario is excellent as depicted in column FSR(%) in table 15 . Each of the 13 users reached the final state in
Despite the commonalities between task-switching and dual-task- ing, there are only a few studies and theoretical considerations about how the performance costs in task-switching and dual-tasking might be related (e.g., Band, Jolicoeur, Akyürek, & Memelink, 2006 ; Oriet & Jolicoeur, 2003 ; Pashler, 2000 ; Sigman & Dehaene, 2006 ). For instance, by implementing T1-T2 switches and T1-T2 repetitions into dual-task trials, Band and van Nes ( 2006 ) as well as Lien et al. ( 2003 ) observed, in addition to a PRP effect, switch costs in T2. The cost was comparable across SOAs. Lien et al. ( 2003 ) concluded from the switch costs in T2 that time is needed for processes involved in the disengagement of T1 and the engagement of T2, and thus, that the shifting component is in- volved in dual-tasking (see also Liepelt, Strobach, Frensch, & Schubert, 2011 , for similar conclusions derived from studies on practice effects in dual-tasking; for a review, see, e.g., Strobach & Schubert, 2017 ). They argued that the missing absorption of switch costs in T2 during the waiting period for the availability of the response-selection bottleneck (i.e., additivity of SOA and T1-T2 transition) suggests that these proc- esses, at least partially, occur after response selection for T1 and before response selection for T2, and modified the response-selection bot- tleneck accordingly.
The novel finding of the present study is that task-set competition at the level of stimuli represents a factor decreasing the preparatory efficiency in older adults. There are three possible explanations for this deficit. First, the age-related task-preparation deficit might result from impaired task-set updating abilities in old age. Second, task-set maintenance might be more difficult for older adults than for young adults. This would indicate that age-related differences in task preparation are not primarily due to impaired task-set updating abilities, but are rather due to impaired maintenance abilities in old age. Third, there might be an age-related decline in both abilities. However, there are many studies providing evidence that maintenance abilities are relatively intact in old age, whereas processing components of working memory declines with age (e.g., Bopp & Verhaeghen, 2005; Craik, 1977; Dobbs & Rule, 1989; Park et al., 2002). Moreover, for predictable task sequences, there is no evidence for passive decay of task sets in preparation intervals up to 600 ms (e.g., Rogers & Monsell, 1995). Thus, working memory maintenance and task-set dissipation seem to play no important role when task preparation is assessed in predictable task sequences with relatively short preparation intervals. This suggests that in the present study, age-related task-set updating deficits are the source for the reduced preparatory efficiency in old age.
In any case, our findings suggest three main conclusions. First, they provide converging evidence for the idea that the current metacontrol state, which we argue implements a particular degree of persistence versus flexibility of cognitive control, can be systematically biased. This supports the general idea that control processes can vary in style (e.g., Goschke, 2003 ; Cools and D’Esposito, 2011 ; Hommel, 2015 ) and the assumption that inducing particular internal states provides an effective means to promote particular styles (e.g., Dreisbach and Goschke, 2004 ). Second, our findings provide converging evidence for the idea that binaural beats in the gamma range have an impact on the current metacontrol state. While the functional and neural mechanism underlying this impact is not yet entirely understood, the empirical link between the processing of rather low- level auditory stimuli and broadly operating control processes provides rather strong constraints on how this mechanism might work. The question how binaural beats affect brain rhythms related to cognitive control might be key in getting more insight on this issue. Third, together with our previous observations ( Reedijk et al., 2013, 2015 ), the present findings point to some interesting commonalities of, and functional overlap between the selection and consolidation of successive visual stimuli, the sequential search of verbal stimuli in memory, and the separation of sequentially performed tasks. These commonalities seem to support Hommel’s (2015) claim that metacontrol states operate on (i) the degree to which alternative representations compete with each other and (ii) the degree to which their mutual competition is top-down biased through the current goal. In particular, a tendency toward persistence would imply
The finding that only the tracking task was free of dual-task interference is in line with the idea that a certain amount of capacity was saved for better performance on this task, with performance decrements only visible in the auditory task, in line with general capacity theories (Tombu & Jolicœur, 2003). As stated before, this prioritization of the tracking task is likely to be due to its continuous nature and not overridable through instructions, predictability effects, or time on the task. On the other hand, the multiple resource theory by Wickens (2008), more readily explains the task-specific effects of predictability by proposing that predictability only increases the available resources in the modalities of the specific task. These tasks draw from separate pools of resources that are not shared between tasks, so freeing up resources within these pools will only benefit the task itself, as they cannot be transferred to the resource pool of the other task. In summary, the difference in dual-task cost effects can be explained by prioritization or resource-saving effects acting on a general resource, whereas the effects of predictability staying within their respective tasks suggests that these tasks draw from separate pools of resources that are not shared between tasks. It should be noted that the general resource theory and the multiple resource theory are not incompatible with each other, indeed Wickens (2008) does not deny the existence of a general resource (or effort), and Kahneman (1973) noted that occurrences of structural interference could not always be accounted for by an undifferentiated resource pool. Indeed, an overview of the dual-tasking literature by Wahn and König (2017) shows that for studies combining object based tasks with spatial orientation tasks the evidence points towards a partial sharing of resources.
improved performance in a complex motor skill learning task (three ball cascade juggling) after sleep in comparison to wakefulness. They observed an increase in slow oscillation, delta and sigma EEG spectral power during N3 after juggling. As these changes in EEG spectral power are well known to be critical for the consolidation of explicit knowledge, the authors discussed that even implicit tasks include initially the usage of explicit memory systems, and that in particular complex motor skill learning like juggling requires more time to automatize processes and thus may need a more extensive explicit process. Studies investigating sleep- related cerebral changes mediating memory consolidation of motor skills (Albouy et al., 2013b) revealed that the consolidation of new motor sequences that are known explicitly before practice begins seems to require a functional interaction between the basal ganglia (striatum) and limbic (hippocampus) system during post-training sleep. On the other hand, motor skills without explicit knowledge are related to a distinct neural network involving the cerebellum and associated cortical regions (e.g. posterior parietal region, premotor cortex) that revealed to be mostly independent of sleep (Doyon et al., 2009). In this vein, Robertson’s Aware- ness Theory (Robertson et al., 2004) hypothesizes that sleep beneﬁts off-line gains only when subjects have full explicit knowledge about the motor skill they have to learn. Given that our innovative gross motortask is considered to be mainly an implicit adaptation task, there might be a rather small impact of sleep especially during the early phase of learning. Riding a bicycle whether with normal or inverse steering is constrained by very short time periods that are available for correct steering adjustments (otherwise the rider is enforced to dismount from the bike). The high time pressure on information processing, in turn, mainly hinders the implementation of explicit learning strategies
parallel processing of all available information is not possible (Anderson, 2020). Technical systems that always assist each and every step of an activity may also lead to a high degree of dependence on the system and impede learning processes when users resort to mindlessly following a system’s in- structions. For example, Maguire, Woollett, and Spiers (2006) had shown that London taxi drivers’ acquisition of navigation knowledge increased their hippocampal 1 volume, whereas ten years later McKinlay (2016) warned that over-reliance on automatic wayfinding like GPS satellite-navigation systems erodes our natural abilities. Therefore, the amount of information presented to users should be restricted to the required minimum. This generally con- forms with established principles from disciplines such as human-centred de- sign (ISO 9241-110; ISO 14915), human-computer interaction (Shneiderman et al., 2016), ergonomics (ISO 15005), and usability engineering (Nielsen, 2005). To this end, it must be determined in which situations assistance is actually required. This may be the case when users are either unsure about what to do, or when they are about to do something wrong. In perilous or time-critical task sequences, these situations should obviously be anticipated beforehand to mitigate possible damage. In non-critical activities, feasible predictions could contribute to smoother task execution, better user experi- ence and better performance rather than waiting for human errors to occur and trying to correct them afterwards. Technical systems that incorporate such an “anticipatory module”, combined with effective assistance features, can induce a new level of learning processes. The subsequent chapters of Part I shall propose and evaluate new computational approaches for gener- ating such predictions on the basis of structural-dimensional analysis of mental representations (SDA-M; see Chapter 3 and Schack, 2012) related to specified tasks.
decreased muscle strength in this motor test. These data are in line with the reduced muscle strength and increased muscle tonus of the legs in HSP patients . When the same mutant mice were tested on the horizontal grid test, which assesses both coordination and muscle strength, they spent less time hanging onto the metallic grid compared to WT mice. Interestingly, when the same motor behavioral tests were performed in younger mice (8 months old), no motor phenotype was observed. Finally, spastin knockout (-/-), heterozygous (+/-) and wild type (+/+) mice were tested on the rotarod, a behavioral motortask that requires proper coordination, muscle strength, and endurance in order to maintain balance on a rod rotating at increasingly higher speeds. As expected, mutant (+/- and -/-) mice spent less time on the rotating rod when compared to wild type littermates, but this phenotype was only present in older mice (14 months of age).
Dual-task costs are calculated as the difference between Task 2 performance in dual-task
conditions and performance in single-task blocks [ 10 ]. The PRP effect reflects worse Task 2
performance in dual-task conditions with short SOAs than with long SOAs [ 9 ]. In contrast to the multitasking costs measured in task-switching contexts, there is so far no consensus about the underlying cognitive mechanisms (i.e., working memory updating, inhibition, and shift- ing) of performance costs arising in dual-task contexts. However, a recent study by Hirsch and colleagues (2018) suggests that dual-task costs reflect, like mixing costs, cognitive processes involved in maintaining and updating task sets in working memory [ 11 ]. Furthermore, this study provides first evidence indicating that the PRP effect might reflect at least partly pro- cesses related to the engagement and disengagement and/or inhibition of task sets.
Abstract: The performance of choice-reaction tasks during athletic movement has been demonstrated to evoke unfavorable biomechanics in the lower limb. However, the mechanism of this observation is unknown. We conducted a systematic review examining the association between (1) the biomechanical and functional safety of unplanned sports-related movements (e.g., jumps/runs with a spontaneously indicated landing leg/cutting direction) and (2) markers of perceptual–cognitive function (PCF). A literature search in three databases (PubMed, ScienceDirect and Google Scholar) identified five relevant articles. The study quality, rated by means of a modified Downs and Black checklist, was moderate to high (average: 13/16 points). Four of five papers, in at least one parameter, found either an association of PCF with task safety or significantly reduced task safety in low vs. high PCF performers. However, as (a) the outcomes, populations and statistical methods of the included trials were highly heterogeneous and (b) only two out of five studies had an adequate control condition (pre-planned movement task), the evidence was classified as conflicting. In summary, PCF may represent a factor affecting injury risk and performance during unplanned sports-related movements, but future research strengthening the evidence for this association is warranted.
A closer inspection of the response times of the alternaters revealed that every other switch trial was responded within 200ms or less. However, the mean IRI to the trial immediately preceding such a fast switch indicated that these times were more than just compensatory prolonged. That indicates a high interleaving of the two tasks, but without indications of overlapping processing. The resulting pattern resembles that of response grouping sometimes found in PRP studies (Pashler, 1994b; Ulrich & Miller, 2008). However, this strategy does not seem to be effective in reducing the overall costs involved in multitasking. Participants from this group showed a net cost effect of on average 177ms with every fast switch. That nearly reaches the time range of the mean switch costs (220ms) observed in the first experiment for participants of the non-preview condition who were forced to perform task switching in a strictly serial manner. This raises the question why an alternating strategy has been chosen at all. Similar to the blocking strategy constant alternating does not require online decision-making due to the fixed task sequence which can be planned in advance and thus might have reduced the cognitive effort at least subjectively. Additionally, participants who in principle prefer serial processing of multiple tasks might have chosen alternating instead of blocking because of a subjectively better compliance with the instruction to perform both tasks concurrently with equal priority.
Houpand Horoufchin 1 , Danilo Bzdok 2,3,4 , Giovanni Buccino 5 , Anna M. Borghi 6,7 &
Ferdinand Binkofski 1,3,8
Embodied and grounded cognition theories have assumed that the sensorimotor system is causally involved in processing motor-related language content. Although a causal proof on a single-cell basis is ethically not possible today, the present fMRI study provides confirmation of this longstanding speculation, as far as it is possible with recent methods, employing a new computational approach. More specifically, we were looking for common activation of nouns and objects, and actions and verbs, representing the canonical and mirror neuron system, respectively. Using multivariate pattern analysis, a resulting linear classifier indeed successfully generalized from distinguishing actions from objects in pictures to distinguishing the respective verbs from nouns in written words. Further, these action-related pattern responses were detailed by recently introduced predictive pattern decomposition into the constituent activity atoms and their relative contributions. The findings support the concept of canonical neurons and mirror neurons implementing embodied processes with separate roles in distinguishing objects from actions, and nouns from verbs, respectively. This example of neuronal recycling processing algorithms is consistent with a multimodal brain signature of human action and object concepts. Embodied language theory is thus merged with actual neurobiological implementation.
In applying state-of-the-art brain imaging methods involving rigorous statistical thresholds across three different imaging modalities, this study revealed that learning to ride a unicycle was associated with reductions of GM volume, especially in the right STG and in the left parahippocampus. Intriguingly, about five weeks later during which participants were no longer regularly engaged in unicycling, a re-increase in GM in the rSTG was found. At the same time, there was a continuous increase in CT in the left primary motor cortex across the time points of assessment, which, however, was significant only at the follow-up assessment relative to the post- and especially to the pre-test. In addition, diffusion-based analyses revealed increases in FA mainly in the right forceps major and in the right corticospinal fiber tract. These changes in GM and WM morphology were paralleled by signifi- cant increases in unicycling performance and by significant training-induced improvements in postural control, which diminished until the follow-up assessments. Taken together, the findings of this study clearly demonstrate that learning a highly dynamic balance task modulates brain structure in manifold and highly dynamic ways.
Materials and Methods
he present investigation leveraged a toolbox of data-driven machine learning techniques that optimally allowed us to automatically extract useful neural patterns from fMRI recordings. First, support vector classiication with recursive feature extraction in a predeined meta-analytic search space allowed for efective identiication of sub- tle neural activity changes in the putative human MNS and in canonical neurons. Second, the ensuing indings were detailed by predictive pattern decomposition to uncover underlying components of variation that gave rise to the whole-brain activity in the diferent experimental conditions. As a neurobiologically informed topograph-ical prior we beneitted from a previous coordinate-based meta-analysis 46 . his quantitative synthesis isolated the consistent neural activity increases during a variety of experimental tasks on “action observation” and “action imitation” – two cognitive processes with close relationship to the putative mirror neuron system. Functional MRI data was acquired from healthy participants. he data was then analyzed using Recursive feature extraction (RFE) that was applied on the data using a mask obtained from an ALE meta-analysis 46 . his way the most rele- vant voxels could be extracted. To demonstrate the neural recycling efect, a 2-fold cross-validation scheme was used: he training data consisted of the fMRI neural activity maps acquired during verb and noun trials from all subjects, while the testing data composed these trial-wise fMRI maps from the objects and objects-with-hand conditions. As such, we trained the classiication algorithm on one subset of the experimental conditions and evaluated the itted classiication algorithm on unseen, statistically independent brain images from the remaining conditions. We then added additional statistical analyses to corroborate the results. Following, a predictive pat- tern analysis was conducted, extracting ten whole brain activation patterns (components).