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Illustration of the center-out movement to a presented radial target (white) and the outcome parameters directional error and homing time. The figure is based on one trial of a young subject at the start (A) and at the end (B) of a practice session. The angle (α) between the two blue lines determines the directional error. The homing time is the time it takes from leaving the dotted circle to stabilizing on the radial target (red trajectory). At the start of the experiment the subject had a larger homing time and a larger directional error than at the end of the practice session (left panel vs. right panel).
Source publication
Sequence-specific postural motor learning in a target-directed weight-shifting task in 12 older and 12 young participants was assessed. In the implicit sequence learning condition participants performed a concurrent spatial cognitive task and in the two explicit conditions participants were required to discover the sequence order either with or wit...
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Purpose
We hypothesize that training older adults with a structured visual attention task will result in improved balance and mobility, potentially reducing their risk for falls.
Methods
Healthy older adults aged 70 + took part in the study (mean age 80.3 ± 6 years). In this randomised control trial (NCT02030743), 15 participants were randomly ass...
Some athletes utilize olfactory inhalation treatments using ammonia salt and aromatic oils to attain their peak performance or for physical and psychological relaxation. However, there is still a lack of clear evidence on olfactory inhalation treatment and scent types via precise experiments, and there is no research regarding fine motor control pe...
Balance can be a main factor contributing to success in many disciplines, and biathlon is a representative example. A more stable posture may be a key factor for shooting scores. The center of foot pressure (COP) is commonly recorded when evaluating postural control. As COP measurements are highly irregular and non-stationary, non-linear determinis...
Background and objective:
Walking is a complex human behavior, usually performed in a dynamic environment. Gait parameters are thought to reflect this complexity. Re-StepTM is a therapy system in the form of a computerized shoe, originally developed for the training and rehabilitation of individuals with brain damage. This system may be used as mo...
Dual-tasking charges the sensorimotor system with performing two tasks simultaneously. Center of pressure (COP) analysis reveals the postural control that is altered during dual-tasking, but may not reveal the underlying neural mechanisms. In the current study, we hypothesized that the minimal intervention principle (MIP) provides a concept by whic...
Citations
... Finally, we aimed to elucidate the training-related neural activation changes of VR-based weight-shift training using fNIRS. Based on earlier work, we expected that older adults would be able to improve weight-shifting, balance performance and limits of stability after a single training session 8 and that these changes would be maintained over 24 h 24 . We were uncertain about whether such improvements would hold when exposed to DT distraction 25 . ...
... These findings are in line with a systematic review on balance training in older adults 26 , and the effects sizes found after a 4-week 27 and 5-week 28 balance training program. Caljouw et al. 8 also found improvements in target-directed weight-shifting performance after a single-session of training. They revealed that an implicit training method, in which less focus was directed towards task details, led to better results than explicit training in older adults, possibly due to its relative independence of working memory 29 . ...
... Next, we could also demonstrate that weight-shift training effects were resilient to DT interference. Similar results were found in other studies investigating DT balance training 8,32 showing that DT performance remained intact at follow-up 25 . Even though participants in the current study received ST training, as no deliberate secondary task was added to the practice environment, the finding that DT performance improved may be explained by the fact that the VR wasp game combined several task components in the motor-cognitive domain. ...
Mediolateral weight-shifting is an important aspect of postural control. As it is currently unknown whether a short training session of mediolateral weight-shifting in a virtual reality (VR) environment can improve weight-shifting, we investigated this question and also probed the impact of practice on brain activity. Forty healthy older adults were randomly allocated to a training (EXP, n = 20, age = 70.80 (65–77), 9 females) or a control group (CTR, n = 20, age = 71.65 (65–82), 10 females). The EXP performed a 25-min weight-shift training in a VR-game, whereas the CTR rested for the same period. Weight-shifting speed in both single- (ST) and dual-task (DT) conditions was determined before, directly after, and 24 h after intervention. Functional Near-Infrared Spectroscopy (fNIRS) assessed the oxygenated hemoglobin (HbO2) levels in five cortical regions of interest. Weight-shifting in both ST and DT conditions improved in EXP but not in CTR, and these gains were retained after 24 h. Effects transferred to wider limits of stability post-training in EXP versus CTR. HbO2 levels in the left supplementary motor area were significantly increased directly after training in EXP during ST (change < SEM), and in the left somatosensory cortex during DT (change > SEM). We interpret these changes in the motor coordination and sensorimotor integration areas of the cortex as possibly learning-related.
... Six participants in the not-informed group had gained a considerable amount of explicit knowledge, as they could verbalize the entire fixed sequence of length four at the end of the experiment (see Section 3.1 for detailed results). Since it is uncertain at which time point they gained consciously accessible knowledge of the sequence and we aimed to retain group characteristics as distinct as possible, they were excluded from analyses (Caljouw et al., 2016;Hirano et al., 2017). Based on the same reasoning, two participants in the informed group were excluded. ...
Sequence learning in serial reaction time tasks (SRTTs) is usually inferred through the reaction time measured by a keyboard. However, this chronometric parameter offers no information beyond the time point of the button-press. We therefore examined whether sequence learning can be measured by muscle activations via electro-myography (EMG) in a dual-task paradigm. The primary task was a SRTT, in which the stimuli followed a fixed sequence in some blocks, whereas the sequence was random in the control condition. The secondary task stimulus was always random. One group was informed about the fixed sequence, and the other not. We assessed three dependent variables. The chronometric parameter premotor time represents the duration between stimulus onset and the onset of EMG activity, which indicates the start of the response. The other variables describe the response itself considering the EMG activity after response start. The EMG integral was analyzed, and additionally , tensor decomposition was implemented to assess sequence dependent changes in the contribution of the obtained subcomponents. The results show explicit sequence learning in this dual-task setting. Specifically, the informed group show shorter premotor times in fixed than random sequences as well as larger EMG integral and tensor contributions. Further, increased activity seems to represent response certainty, since a decrease is found for both groups in trials following erroneous responses. Interestingly, the sensitivity to sequence and post-error effects varies between the subcomponents. The results indicate that muscle activity can be a useful indicator of response behavior in addition to chronometric parameters.
... Although the older adults appeared to be less likely to acquire a conscious, explicit representation of the sequence of stimuli or responses, a statistically significant difference was not found. This observation contrasts with age-related differences in explicit sequence monitoring reported by Caljouw et al. [53] in a postural visuomotor sequence learning task involving body weight shifts. Therefore, it may be that the influence of age on the acquisition of explicit sequence knowledge may be specific to the type of movement task or that our experiment was not sufficiently sensitive to uncover an age effect in the acquired explicit knowledge. ...
Sequence learning in serial reaction time tasks (SRTT) is an established, lab-based experimental paradigm to study acquisition and transfer of skill based on the detection of predictable stimulus and motor response sequences. Sequence learning has been mainly studied in key presses using visual target stimuli and is demonstrated by better performance in predictable sequences than in random sequences. In this study, we investigated sequence learning in the context of more complex locomotor responses. To this end, we developed a novel goal-directed stepping SRTT with auditory target stimuli in order to subsequently assess the effect of aging on sequence learning in this task, expecting that age-related performance reductions in postural control might disturb the acquisition of the sequence. We used pressure-sensitive floor mats to characterise performance across ten blocks of trials. In Experiment 1, 22 young adults demonstrated successful acquisition of the sequence in terms of the time to step on the target mat and percent error and thus validated our new paradigm. In Experiment 2, in order to contrast performance improvements in the stepping SRTT between 27 young and 22 old adults, motion capture of the feet was combined with the floor mat system to delineate individual movement phases during stepping onto a target mat. The latencies of several postural events as well as other movement parameters of a step were assessed. We observed significant learning effects in the latency of step initiation, the time to step on the target mat, and motion parameters such as stepping amplitude and peak stepping velocity, as well as in percent error. The data showed general age-related slowing but no significant performance differences in procedural locomotor sequence learning between young and old adults. The older adults also had comparable conscious representations of the sequence of stimuli as the young adults. We conclude that sequence learning occurred in this locomotor learning task that is much more complex than typical finger-tapping sequence learning tasks, and that healthy older adults showed similar learning effects compared to young adults, suggesting intact locomotor sequence learning capabilities despite general slowing and normal age-related decline in sensorimotor function.
... 有研究考察了情景记忆编码和提取阶段脑激活的关系，发现成功编码和成功提取所激活的 脑区有高度重合，因而认为提取是编码过程的再现，有着相同的神经活动模式 [71,72] 。但是，另 一些研究发现编码和提取存在一定的差异，表现出不对称的关系。例如，左侧前额叶更多地参 与编码加工，而右侧前额叶更多地参与提取加工；海马的前部在编码阶段有较多的激活，而提 取阶段选择性激活海马的后部区域 [73,74] ；编码和提取阶段海马激活的差异在海马头部相比海马 体和海马尾部更大 [75] [91,92] ，主要采用人工语法任务、序列反应时任务和统计学习任务等 加以研究。在个体出生后的头一年，内隐学习就已出现并成为婴幼儿时期个体主要的学习方式 [93][94][95][96] 。研究发现，8 个月大的婴儿通过单纯地接触人工语言就可以很快地学会不同音节之间的 转换，表现出对语言音节信息的敏感性 [95] 。9 个月大的婴儿在接触多元素视觉场景时，会对元 素之间共同发生的条件概率关系表现出敏感性 [97] 。6 个月和 12 个月大的婴儿已经学会了根据 先前的听觉刺激预测视觉刺激 [98,99] 。甚至有研究显示，在跨通道的学习中，婴儿表现出比成人 更大的优势 [100] 。总之，婴幼儿具有很强的内隐学习能力，并且这种能力可能随着他们的年龄 增长而更加完善。 有研究者 [101] 考察了 4 岁到 85 岁个体的内隐概率序列学习。结果发现，4-12 岁年龄组表现 出最强的学习效果。在 12 岁左右，内隐学习的成绩有显著的下降，在老年阶段内隐学习的成 绩更低。研究者认为，在 12 岁之前，个体的学习方式主要是内隐学习。之后，内隐学习开始 退居二线，随着年龄的进一步增长，个体的学习方式主要依靠外显学习，并且可以很好地协调 内隐学习和外显学习之间的关系 [102] 。进入老年，个体的内隐学习能力一般不会削弱 [103] ，但是 有些形式的内隐学习可能随着年龄的增长而有所下降 [104] 。另外，老年人内隐学习的能力会受 到任务难度、外显学习和工作记忆等其他认知能力的影响 [105] 。 内隐记忆通常是指个体在不需要有意识回忆过去经历过的信息的测验中，相比新信息，对 经历过的信息做出反应的速度更快，正确率更高，常以启动效应表示 [106][107][108] 。研究显示，在出 生后的头 9 个月里，年龄较大的婴儿在内隐记忆上的表现比年龄较小的婴儿更好；但在 9 个月 之后的生命发展过程中，内隐记忆表现相对稳定 [109][110][111][112][113] 。追踪研究发现，出生 9 个月后内隐记 忆成绩可以预测 3 岁左右的内隐记忆表现 [112] 。不过，也有研究得到了不一致的结果。例如， Vakil 等人 [114] 比较了儿童中期(6.5-8.5 岁)、青少年中期(13-14.5 岁)和成年早期(20.5-24 岁)三个年龄组被试的内隐记忆和外显记忆，发现儿童中期组个体的内隐记忆表现不如其它两 个年龄组，可能意味着内隐记忆在儿童期内仍在继续发展。 内隐记忆可能并非单一的成分 [115,116] ，对儿童期到成年期内隐记忆的发展研究显示，知觉 内隐记忆大多没有表现出明显的年龄差异 [60,[117][118][119] ，但儿童和成人的概念内隐记忆存在差异 [120,121] 。也就是说，儿童期内隐记忆的发展可能存在知觉和概念之间的分离。不过，也有研究 显示出知觉和概念内隐记忆有类似的发展轨迹 [114] 。另外，多数研究认为，成人外显记忆随年 龄增长有所下降 [122,123] ，65 岁后下降更加明显，但内隐记忆却在老化过程中保持完好 [124][125][126][127][128][129] 。 然而，最近有部分证据显示，发现内隐记忆不受老化影响的研究主要采用的是识别式启动，而 老年人相比年轻人在产生式启动上的成绩明显降低 [130][131][132][133] 。可见，内隐记忆的老化可能在识别 式和产生式启动之间存在分离。 ...
... Therefore, adequate visual feedback is keyed to postural skill transfer in older adults, who already have an increased reliance on the visual system for postural control. The behavioral results highlight the fact that visual cues can improve plan-based posture control in older adults when explicit awareness of posture-relevant features is desirable under environmental constraints (Caljouw et al., 2016). ...
Skill transfer from trained balance exercises is critical to reduce the rate of falls in older adults, who rely more on vision to control postural responses due to age-dependent sensory reweighting. With an electroencephalography (EEG) minimum spanning tree (MST) structure, the purpose of this study was to compare the organization of supraspinal neural networks of transfer effect after postural training using full and intermittent visual feedbacks for older adults. Thirty-two older adults were randomly assigned to the stroboscopic vision (SV) ( n = 16; age = 64.7 ± 3.0 years) and control (16; 66.3 ± 2.7 years) groups for balance training on a stabilometer (target task) with on-line visual feedback. Center-of-pressure characteristics and an MST-based connectome of the weighted phase-lag index during the bilateral stance on a foam surface (transfer task) were compared before and after stabilometer training. The results showed that both the SV and control groups showed improvements in postural stability in the trained task ( p < 0.001). However, unlike the control group ( p = 0.030), the SV group who received intermittent visual feedback during the stabilometer training failed to reduce the size of postural sway in the anteroposterior direction of the postural transfer task (unstable stance on the foam surface) in the post-test ( p = 0.694). In addition, network integration for the transfer task in the post-test was absent in the SV group ( p > 0.05). For the control group in the post-test, it manifested with training-related increases in leaf fraction in beta band ( p = 0.015) and maximum betweenness in alpha band ( p = 0.018), but a smaller diameter in alpha ( p = 0.006)/beta ( p = 0.021) bands and average eccentricity in alpha band ( p = 0.028). In conclusion, stabilometer training with stroboscopic vision impairs generalization of postural skill to unstable stance for older adults. Adequate visual information is a key mediating factor of supraspinal neural networks to carry over balance skill in older adults.
... The relation between implicit sequence learning [typically measured by reaction time (RT)] and WM capacity has been studied extensively (e.g., Bo, Jennett, & Seidler, 2012;Caljouw, Veldkamp, & Lamoth, 2016;Feldman, Kerr, & Streissguth, 1995;Guzmán, 2018;Kaufman et al., 2010;Unsworth & Engle, 2005;Weitz, O'Shea, Zook, & Needham, 2011;Yang & Li, 2012). However, previous studies reported mixed results regarding the relation between the two systems, potentially because researchers used different WM capacity tests (i.e., visuospatial, verbal, or numerical), but also different measures of learning in the SRT task (e.g., the difference in average RT between blocks with a training sequence and blocks with a random sequence, or the rate of RT improvement across blocks of the SRT task; for a review, see . ...
... For example, a number of previous studies found no relation between implicit sequence learning and WM capacity (Caljouw et al., 2016;Guzmán, 2018;Jimenez & Vazquez, 2005;Jongbloed-Pereboom, Nijhuis-van der Sanden, & Steenbergen, 2019;Masters, 1992;Meissner, Keitel, Südmeyer, & Pollok, 2016;Unsworth & Engle, 2005;Yang & Li, 2012). Unsworth and Engle (2005) reported that there were no differences in implicit learning in a manual version of the SRT between high and low WM capacity individuals. ...
... Based on a number of previous studies (e.g., Caljouw et al., 2016;Guzmán, 2018;Kaufman et al., 2010;Unsworth & Engle, 2005), a relation between WM capacity and implicit sequence learning would not be expected. However, if WM capacity and implicit learning at least partly rely on shared mechanisms, we should expect that the two systems are related to some extent . ...
We investigated the relation between implicit sequence learning and individual differences in working memory (WM) capacity. Participants performed an oculomotor version of the serial reaction time (SRT) task and three computerized WM tasks. Implicit learning was measured using anticipation measures only, as they represent strong indicators of learning. Our results demonstrate that anticipatory behavior in the SRT task changes as a function of WM capacity, such that it increases with decreased WM capacity. On the other hand, WM capacity did not affect the overall number of correct anticipations in the task. In addition, we report a positive relation between WM capacity and the number of consecutive correct anticipations (or chunks), and a negative relation between WM capacity and the overall number of errors, indicating different learning strategies during implicit sequence learning. The results of the current study are theoretically important, because they demonstrate that individual differences in WM capacity could account for differences in learning processes, and ultimately change individuals’ anticipatory behavior, even when learning is implicit, without intention and awareness.
... It should be noted that in our study explicit knowledge was gained by instructing participants that the middle segment was always the same, rather than offering knowledge of what the repeating segment looked like beforehand. As such our methods are more in line with Caljouw et al. (2016) who instructed participants to look for the sequence in an SRT task in the explicit condition and found that the younger group, similar in age as the participants in our study, performed comparable to the implicit condition while the older group was worse compared to the implicit condition. The finding that explicit instructions do not benefit motor learning when compared with implicit instructions concurs with findings in whole body movement tracking tasks (Shea et al., 2001) and a catching task on the computer (Green and Flowers, 1991). ...
The goal of this study was to investigate the effect of predictability on dual-task performance in a continuous tracking task. Participants practiced either informed (explicit group) or uninformed (implicit group) about a repeated segment in the curves they had to track. In Experiment 1 participants practices the tracking task only, dual-task performance was assessed after by combining the tracking task with an auditory reaction time task. Results showed both groups learned equally well and tracking performance on a predictable segment in the dual-task condition was better than on random segments. However, reaction times did not benefit from a predictable tracking segment. To investigate the effect of learning under dual-task situation participants in Experiment 2 practiced the tracking task while simultaneously performing the auditory reaction time task. No learning of the repeated segment could be demonstrated for either group during the training blocks, in contrast to the test-block and retention test, where participants performed better on the repeated segment in both dual-task and single-task conditions. Only the explicit group improved from test-block to retention test. As in Experiment 1, reaction times while tracking a predictable segment were no better than reaction times while tracking a random segment. We concluded that predictability has a positive effect only on the predictable task itself possibly because of a task-shielding mechanism. For dual-task training there seems to be an initial negative effect of explicit instructions, possibly because of fatigue, but the advantage of explicit instructions was demonstrated in a retention test. This might be due to the explicit memory system informing or aiding the implicit memory system.
Predictability is increasingly recognized as an important principle in perception and motor learning. The pursuit of increased predictability seems to one of the main goals that the human system pursues. Therefore, providing predictability in one of the most challenging situations that humans face, namely multitasking, a promising line of research. In this thesis the impact of predictability was systematically investigated in five experiments. In the first four experiments predictability was achieved by implementing a repeating pattern in one task, or both tasks. Participants acquired knowledge of these patterns either explicitly or implicitly in several training sessions, under single-task or dual-task conditions. We tested whether this increased predictability helped dual-task performance after the training sessions. The results suggest that predictability is helpful for dual-task performance, although the benefits are confined to the predictable task itself. In a fifth experiment we focused on providing between task predictability, which led to a large performance improvement in both tasks, prompting the discussion about what constitutes a task, in the sense of when can two tasks be perceived as a single task comprising both, a theoretical problem we tried to tackle in one of the articles. Explanations for the findings, theoretical implications, methodological issues and suggestions for future research are given in the general discussion
Motor sequence learning in persons with multiple sclerosis (pwMS) and healthy controls (HC) under implicit or explicit learning conditions has not yet been investigated in a stepping task. Given the prevalent cognitive and mobility impairments in pwMS, this is important in order to understand motor learning processes and optimize rehabilitation strategies. Nineteen pwMS (the Expanded Disability Status Scale = 3.4 ± 1.2) and 18 HC performed a modified serial reaction time task by stepping as fast as possible on a stepping tile when it lit up, either with (explicit) or without (implicit) knowledge of the presence of a sequence beforehand. Motor sequence learning was studied by examining response time changes and differences between sequence and random blocks during the learning session (acquisition), 24 h later (retention), and in three dual‐task (DT) conditions at baseline and retention (automaticity) using subtracting sevens, verbal fluency, and vigilance as concurrent cognitive DTs. Response times improved and were lower for the sequenced compared with the random blocks at the post‐ and retention tests (P's < 0.001). Response times during DT conditions improved after learning, but DT cost improved only for the subtracting sevens DT condition. No differences in learning were observed between learning conditions or groups. This study showed motor sequence learning, by acquisition and retention, in a stepping task in pwMS with motor impairments, to a similar degree as HC and regardless of learning conditions. Whether automaticity increased remains unclear.
