这项研究的支持是由马里兰大学运动学研究生研究倡议基金提供给悦杜。

该议定书是按照马里兰大学帕克分校机构审查委员会核准的准则进行的。
1. 实验设置
<ol><li>设置运动捕获系统, 如图 1a所示。将八台照相机放在一个圆圈中, 半径为4米。 注: 相机的数量和位置可以变化, 只要所有的摄像头都适当定位, 以获得一个清晰的视野, 所有反射标记附加到参与者的身体。</li>
	<li>在圆圈中心设置一个步进站。定位在步进站中心的深蓝色毡垫覆盖的 "home 位置", 并放置六个步进目标, 覆盖在家庭位置周围的浅蓝色毡垫 (图 1b)。根据个人的步进距离, 确定目标和主位置之间的距离 (请参见步骤 3, 在脚步完成的任务过程中)。</li>
	<li>要控制任务起搏条件, 请放置两个电橡胶传感器, 在接触时产生模拟信号, 在 home 位置下检测脚返回的时间。</li>
	<li>将 23 "显示器2米放置在主位置前面。六视觉刺激与地面上的六步目标空间匹配。</li>
	<li>使用安装在笔记本电脑中的计算机程序控制视觉刺激的外观顺序。</li>
	<li>使用数据输出和采集设备同步膝上型计算机和运动捕获系统。</li>
	<li>打开运动捕捉摄像头, 瞄准它们, 使每台摄像机都能查看步进站周围的音量。</li>
	<li>确定捕获卷 (e. g) 中是否存在不需要的反射对象 (从光线、地板或任何反光材料反射)。用织物材料覆盖这些被识别的反光物体, 这样它们在实验试验中不会被错误地收集成数据。</li>
	<li>使用运动捕捉系统提供的指令和设备, 校准运动捕获系统, 以确保从反射标记18中准确收集3维数据。</li>
	<li>对于动态校准, 通过在参与者执行该任务时所有反射标记都将移动的空间, 向运动捕捉系统提供的校准棒波形。收集2000帧的成像数据进行动态标定。</li>
	<li>对于静态标定, 将校准棒放在地板上, 位置和方向可以作为运动捕捉系统协调系统的起源。运行运动捕获系统以设置原点。</li>
	<li>根据学习的目的设计一个标记集。 注意: 其中一个示例显示在图 1b中, 其中使用了38标记设置。</li>
	<li>按照供应商提供的说明创建标签骨架模板, 可用于以后的数据获取和处理18中的重建和自动标记。具体地说, 请参与者站在步进站的主位置, 并附上所有标记。指示参与者尽可能静止地站立, 并确保所有标记都通过运动捕获系统可见。捕获试验 (持续约十年代)。在运动捕获系统中, 通过将标记连接在一起, 为每个标记指定一个名称并创建段。链接所有段以完成骨架模板 (如图 1c所示)。</li>
</ol>2. 参加者准备
<ol><li>在参观实验室前, 通知参加者穿戴适当的服装 (g)、短裤和 t恤衫)。</li>
	<li>抵达后, 请学员仔细阅读并签署同意书。屏幕为学习资格。 注: 筛查问卷可根据个别研究的目的而有所不同。这些调查表可能包括但不限于手优势调查表19、全球物理活动级别调查表20、神经系统健康调查表和儿童的运动评估电池21.</li>
	<li>请参加者脱掉鞋子和袜子, 然后附上38个球形反光标记, 每50毫米直径, 到皮肤在预先确定的重要骨标志使用双面, 低过敏性胶带和预包装胶带。此标记设置与图 1b中显示的自定义骨架模板相同。</li>
	<li>清除来自参与者身体的38个标记以外的所有不需要的反射 (请参见步骤 1.8)。</li>
	<li>指导参加者以 T 型姿势静静地站在家里的位置上。运行动作捕获系统以捕获十年代的所有标记 (i. e, 校准试验)。</li>
</ol>3. 踏脚作业程序
<ol><li>在每个参与者开始任务之前, 请设置参数, 包括但不限于: 参与者 id、组 ID、学习块数、刺激演示的时间长度以及刺激 (ISI) 或响应-刺激间隔之间的时间间隔 (RSI) 控制运动完成和下一次刺激开始之间的时间间隔 (在这种情况下, 需要电橡胶传感器在家庭位置下; 有关详细信息, 请参阅协议1节)。 注意: 根据研究的目的, ISI 可以多种多样 (e. g, 1300 毫秒或1000毫秒)。</li>
	<li>指导参与者站在主场位置, 调整主场位置的距离, 使参与者能够舒适地踏入地板上的所有六个目标。</li>
	<li>指导参与者对每个目标进行多次快速的步骤, 并在每个参与者的最舒适的步进长度上标记从主场位置到每个目标的距离。</li>
	<li>向参与者提供任务说明。<ol>
<li>指示参与者, 一旦刺激出现在显示器上显示的六个位置之一, 他们需要尽快和准确地在地板上的相应目标, 然后返回到主场位置。</li>
		<li>让参与者对位于右侧的三个目标 (即、目标1、2和 6) 执行右脚。图 1a), 左脚到其他三目标 (i. e, 目标3、4和 5;图 1a)。 注意: 在整个任务期间, 这些数字对参与者是不可见的。</li>
		<li>通知参与者每次运行后 (i. e, 学习块) 的任务都有3分钟的休息。根据实验需要修改断点的长度。设置时间警报, 提醒参与者休息结束。</li>
		<li>指导参与者保持他们的肘部在他们的身边, 并在九十度的角度弯曲, 当他们执行任务, 使相机可以看到标记放置在臀部。</li>
	</ol>
</li>
	<li>运行由36个步骤组成的练习块 (i. e), 刺激以1300毫秒的 ISI 显示36倍; 请参阅 "脚踩" 任务过程以了解详细信息), 以便参与者熟悉该任务。指示参与者, 刺激将持续出现在六个地点之一, 他们需要响应的刺激尽可能快, 准确, 因为他们可以。此块中的刺激以随机顺序出现。 注意: ISI 可以替换为一个 RSI (参见脚踩的任务过程为细节)。如果使用非常短的 ISI, 参与者可能无法响应一些刺激。这些步骤被视为错误。</li>
	<li>在练习块之后, 启动实验块。在该协议中, 有六个块, 每个实验块由100步/刺激组成。每块后给参与者一个强制性的3分钟休息。 注: 在 1300 ms ISI 情况下, 每个区块通常需要大约2.5 分钟。如果使用了 RSI, 每块的长度可能会因参与者对刺激反应的速度而变化。<ol>
<li>指导学员完成六个实验区块。根据实验目的设定视觉刺激的特定顺序。刺激跟随一个指定或随机序列。参与者对刺激秩序的表述是未知的。 注意: 实验块的数量可能会有所不同。在这里, 引入了一个6块的设计, 其中指定的序列 a 在 1-4 和6块中给出, 并且在5块中显示了一个新的序列 B。具体和随机序列也可以变化。在本协议中, 序列 A 遵循1423564215的顺序 (i. e, 1-右侧, 2-右前, 3 左前, 4-左, 5-左后, 6-右后) 和序列 B 遵循3615425214的顺序。</li>
		<li>在每个学习块之前, 指导参与者尽可能快速、准确地响应刺激。</li>
	</ol>
</li>
	<li>在所有学习块完成后, 请参与者完成一个前后, 该过程由文献13、14、22中描述的广泛使用的召回和识别测试组成。</li>
</ol>4. 数据处理和统计分析
<ol><li>对于每个参与者, 在运动捕获系统软件中打开收集的数据试验。根据供应商提供的说明18, 检查每个试用并填写试用数据中的任何空白。</li>
	<li>将每个数据试用导出为一个 ASCII 文件, 其中包含所有38个标记的三个坐标。</li>
	<li>从 ASCII 文件中派生变量 (i. e, 反应时间 (RT), 移动时间 (MT), 响应时间, 以及 COM 的轨迹), 以下步骤如下:<ol>
<li>将 ASCII 数据文件输入到数据分析软件中。使用具有 10 Hz 的截止频率的第八阶北海过滤器筛选数据3。 注意: 派生 COM 运动轨迹的方法取决于标记的设置。在图 1b中显示的38标记设置中, 可以使用 De 利瓦23描述的方法和人体测量参数。你也可以跟踪在第五腰椎水平的一个标记集测量的近似 COM 的运动24。</li>
		<li>根据下面的描述导出响应时间、RT 和 MT:<ol>
<li>使用脚跟上的标记、大脚趾和 5th跖骨来描述数据分析软件中足部运动的轨迹。</li>
			<li>沿垂直方向 (垂直于地面) 绘制这三标记的轨迹。在水平平面 (平行于地面) 上绘制脚趾标记的轨迹, 以确定每个步骤是否正确地执行到正确的目标。在以后的统计分析中排除了指向错误目标的步骤。</li>
			<li>在每个步骤前后标记每个标记高度的基线。</li>
			<li>当标记达到最大高度的10% 时, 将每个标记的运动开始标识为第一个样本。</li>
			<li>由于每个主题都可以使用不同的策略来触摸目标 (使用脚趾或 5th跖骨), 所以使用最早达到峰值的标记来定义运动开始。</li>
			<li>标识当使用的标记下降到与起始位置相同的高度时, 步进的终点。</li>
			<li>连续运行数据分析程序, 直到处理100个步骤。</li>
			<li>对于所有步骤, 计算和输出响应时间作为刺激开始和运动结束之间的时间差异, RT 作为刺激和运动发病的时间差异, MT 作为运动开始的时间差异和它的终结点。将输出文件保存为. xls 格式。</li>
			<li>使用这些. xls 文件, 计算每个块和参与者之间的这些变量的方法, 随后将用于统计分析的数据。</li>
			<li>由于在实验设计中通常有一个内部因素 (i. e, 学习块), 使用混合效应 ANOVAs 分析数据 (重复的测量方差可以谨慎地使用球形假设)。用 Akaike 的信息标准 (AIC) 确定混合效应方差分析中使用的协方差矩阵。使用特定的多个比较更正过程 (取决于实验设计), 将方差分析中的显著结果分解为特殊的多对比校正步骤。在p = 0.05 中设置统计意义级别。</li>
		</ol>
</li>
	</ol>
</li>
</ol>

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P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Postural+responses+to+specific+types+of+working+memory+tasks.">Postural responses to specific types of working memory tasks.</a> Gait Posture. 25 (3), 368-373 (2007).</li><li>Riley, M. A., Baker, A. A., Schmit, J. M., Weaver, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+visual+and+auditory+short-term+memory+tasks+on+the+spatiotemporal+dynamics+and+variability+of+postural+sway.">Effects of visual and auditory short-term memory tasks on the spatiotemporal dynamics and variability of postural sway.</a> J Mot Behav. 37 (4), 311-324 (2005).</li><li>Stins, J. F., Michielsen, M. E., Roerdink, M., Beek, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sway+regularity+reflects+attentional+involvement+in+postural+control:+Effects+of+expertise,+vision+and+cognition.">Sway regularity reflects attentional involvement in postural control: Effects of expertise, vision and cognition.</a> Gait Posture. 30 (1), 106-109 (2009).</li><li>Nougier, V., Vuillerme, N., Teasdale, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+a+reaction+time+task+on+postural+control+in+humans.">Effects of a reaction time task on postural control in humans.</a> Neurosci. Lett. 291 (2), 77-80 (2000).</li><li>Robertson, E. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+serial+reaction+time+task:+Implicit+motor+skill+learning?.">The serial reaction time task: Implicit motor skill learning?.</a> J Neurosci. 27 (38), 10073-10075 (2007).</li></ol>

作者没有什么可透露的。

本协议描述了修改后的任务的实验设置和过程。修改后的任务与经典的工作任务共享其吸引力的简单性, 尽管修改后的任务要求使用一种运动捕获技术。与经典的工作任务一样, 许多参数可以在脚踩式工作任务中的特定研究问题上操作, 包括但不限于: 区间-刺激区间的长度或响应-刺激区间27, 序列的类型结构28和序列知识29的意识。
与经典的任务相比, 步进任务具有三优点。首先, 脚踩的工作需要移动腿部, 同时保持整个身体的站立姿势, 而不是简单地按四手指, 而坐在经典的工作任务要求。因此, 步进任务是一项任务的变体, 它涉及比手指按压更大的电机控制复杂性, 从而更好地接近隐式运动序列学习在日常测序活动。另外, 考虑体位控制和认知过程之间的互动关系30,31,32,33, 这个脚踩的任务也允许我们了解如何体位控制与总的运动序列学习互动, 特别是在人群中, 如发育协调紊乱的儿童6,7,8, 谁有困难整合体位控制和认知任务。这项研究将作为一个基础, 为儿童和成人的总运动技能学习障碍的最佳干预制定。
其次, 执行马达动作通常涉及多个阶段, 包括目标选择、马达规划和运动执行。由于经典的工作任务只需要按下键, 这不涉及移动到空间中的新位置, 因为手指总是在响应键上, 因此任务强调目标选择, 而不是移动执行9, 以及用于测量序列学习是目标选择和运动执行的混合体。步进式的任务提供了一个机会来检查目标选择和/或运动执行是否对运动序列学习有显著的促进作用。例如, 运动执行的一个特点, 运动时间 (MT), 可以在脚踩的任务中检查。虽然我们的代表性结果对隐式序列学习没有贡献, 但这里值得强调的一个事实是, 经典的任务和修改后步任务的代表性协议不需要精确的目标来响应目标。例如, 在脚踩任务的参与者被鼓励, 但不是严格的要求, 以准确地击中目标 (但朝正确的方向迈进是必要的), 因为他们可能会改变他们的寻位位置轻微。而在手指按压任务中的参与者总是将手指放在相应的键上, 这样就不需要精确的目标。然而, 当精确瞄准是必要的, 运动执行可能扮演一个关键的角色在序列学习10, 表明解剖多阶段马达性能的重要性 (i. e, 目标选择, 马达计划和运动执行) 进一步了解电机序列学习的基本机制。此外, 经典的任务缺乏它的能力, 以阐明的时间演变的认知过程中运行的序列学习。与此相反, 步进式的任务, 像其他涉及空间运动的任务 (e. g, 手臂达到和眼球运动)10,12, 允许我们检查连续运动轨迹。运动时间动力学的测量可以用来揭示未来序列学习研究中隐藏的认知过程11。例如, 在刺激出现之前使用 COM 运动, 我们可以确定哪些目标参与者在看到刺激之前, 以及在一致的预期发生时, 这在手指紧迫的任务中是不可行的。
这项工作的另一个突出的用途是在隐式序列学习过程中追求显性序列知识的逐步发展。通常称为隐式学习任务1,34。但是, 在该任务中, 序列学习通常涉及一个显式过程, 如在完成任务22之后召回和/或识别序列的能力所揭示的。由于这些召回和/或识别测试通常是在完成任务之后执行的, 所以它只测量整个工作过程中获得的显性知识的总数量。很难知道序列的显式记忆何时出现, 以及它如何通过学习逐步发展。我们的代表结果表明, 步进式任务在研究跨学习块的显式序列知识的时间演化过程中呈现出独特的能力。例如,图 5a显示, 有一半参与者开始从1和2块获取显式序列知识, 并且更了解块3和4中的序列。
总之, 本协议引入了一个修改后的任务, 涉及脚踩运动。这一改良的经典任务的变体增加了马达和体位要求, 是学习顺序技能在日常生活中必不可少的。此外, 步进式的任务允许分离的目标选择和运动执行, 两个组成部分, 可能会对隐性运动序列学习造成差异。步进训练任务还为研究运动序列学习中的显式和隐式过程的并行操作提供了一种新的方法。

隐式电机序列学习, 通常称为学习序列不知道序列, 是至关重要的日常活动, 并已进行了良好的研究由一个聚合的任务, 称为串行反应时间 (尼森) 任务设计的 Bullemer1. 在这个经典的任务中, 参与者按下按键, 快速准确地响应视觉刺激。为了检查序列学习 , 视觉刺激的外观纵 , 遵循一个预先结构或随机序列 , 这是未知的参与者。对预先结构化序列 (e. g) 的响应时间比随机或另一种预先结构化序列1、2更快, 这就证明了学习的速度。虽然经典的工作任务通常需要双手动手指攻丝, 绝大多数的隐性运动序列学习日常活动, 如跳舞, 演奏乐器, 或运动, 涉及全身行动, 目前在经典的任务中找不到姿势和惯性挑战。因此, 我们建议序列学习任务需要更加多方面。此外, 前一项研究的重点几乎完全集中在任务的认知部分 (g、决策或行动选择), 忽略序列学习中涉及的马达控制问题 (e. g, 运动执行)。因此, 为了进一步了解隐式电机序列学习, 必须研究序列学习在整个身体或总马达任务, 更好地接近我们的日常运动活动。
在我们最近的研究中, 我们将经典的工作任务扩展到了一个修改后的任务, 即用脚踩来代替手指按压, 将体位控制纳入序列学习3,4,5。此修改后的任务提供了它自己的优势, 以补充经典的工作任务。首先, 总的电机序列学习任务更好地模仿每天的顺序活动, 其中涉及全身运动。到目前为止, 我们对电机序列学习的理解通常来自于经典的任务, 但是很少人知道在日常活动中学习时序运动技能时, 从经典的工作中学习的马达序列的知识是否仍然是真实的。因此, 修改后的工作任务允许我们检查系统报告的特征 (e. g), 在手指按压的工作中, 在指压的过程中是否存在与年龄无关的隐性序列学习. 体位控制是涉及。此外, 在有体位控制和总运动技能学习困难的人群中, 如发育协调障碍患儿6,7,8, 了解体位控制如何与总马达序列学习互动是帮助改进干预策略的关键, 从而优化学习顺序运动技能在日常生活中的有效性。
第二, 关于隐式序列学习的一个常见的概念是, 运动规划, 而不是运动执行, 在学习经典任务9中的序列过程中起着重要的作用。这是因为按键不涉及移动到空间中的新位置, 因为手指总是在响应键上。然而, 许多日常的顺序行为都涉及较大的空间运动。在需要大空间运动的情况下, 运动执行是否是电机序列学习中的关键角色, 知之甚少。在经典的任务中, 响应时间、反应时间 (RT) 和运动时间的总和 (MT) 是序列学习的指标。像其他涉及空间运动的范式10一样, 步进式的任务, 使得研究人员能够将隐式序列学习中的响应时间分解为 RT, 这反映了认知处理, 而 MT 则是运动的特点。本身。
第三, 除了 MT 之外, 脚踏式和运动捕捉技术的结合提供了关于连续全身运动 (e. g) 的丰富数据, 即质心或 COM 的运动。测量运动的持续变化具有揭示由 RT 或 MT11、12测量的离散响应所依据的认知过程的动力学的优点。特别是, 在 "已完成" 任务中的学习序列通常被解释为显式和隐式过程的混合。也就是说, 尽管将工作任务作为一种隐性学习任务共同使用, 但参与者往往显示出在完成任务后口头召回学习序列的能力, 这表明了隐含序列学习中涉及的一个显式组件。虽然显式组件可以通过在完成任务13、14之后进行的召回测试来评估, 但这些任务后测试缺乏在学习期间检查显式知识的时间演变的能力。我们建议, 有明确的序列知识, 个人会知道下一个刺激的位置, 从而产生预期体位调整15,16,17以前馈方式准备使踏脚移动到相应的目标。因此, 在刺激外观 (即、预期) 之前检查 COM 的移动将打开一个窗口来研究隐式序列学习过程中显式内存的逐步发展。
该协议说明了步进式任务的实验设置和程序。我们提供响应时间、RT 和 MT 的代表性结果。此外, 我们还研究了姿态控制与隐式运动序列学习隐含过程的关系。

<table>
	<tbody>
		<tr>
			<td>Vicon motion capture system</td>
			<td>Vicon</td>
			<td>Vicon T-40, T-160, calibration wand</td>
			<td>Alternative systems may be used</td>
		</tr>
		<tr>
			<td>50 mm reflective markers</td>
			<td>Vicon</td>
			<td>N/A</td>
			<td>Numbers of markers may be varied</td>
		</tr>
		<tr>
			<td>Labview software</td>
			<td>National Instruments</td>
			<td>N/A</td>
			<td>Control visual stimuli. Use together with DAQ board. Alternative software may be used</td>
		</tr>
		<tr>
			<td>DAQ board</td>
			<td>National Instruments</td>
			<td>BNC-2111; DAQCard-6024E</td>
			<td></td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>MathWorks</td>
			<td>N/A</td>
			<td>Alternative software may be used</td>
		</tr>
		<tr>
			<td>double sided hypo-allergenic adhesive tape</td>
			<td></td>
			<td>N/A</td>
			<td></td>
		</tr>
		<tr>
			<td>pre-wrapping tape</td>
			<td></td>
			<td>N/A</td>
			<td></td>
		</tr>
	</tbody>
</table>

the "motor" in implicit motor sequence learning: a foot-stepping serial reaction time task

本协议描述了一种用于研究隐式电机序列学习的改进的串行反应时间 () 任务。与经典的工作, 在坐的时候, 涉及手指按压动作, 修改后的任务要求参与者采取双脚, 同时保持站立姿势。这个步进任务需要全身的行动, 强加姿势挑战。脚踩任务以几种方式补充了经典的工作任务。步进式的任务是更好的代理日常活动, 需要持续的体位控制, 从而可以帮助我们更好地理解序列学习在现实生活中的情况。此外, 响应时间在经典的工作任务中是序列学习的指示器, 但不清楚反应时间、反应时间 (RT) 代表心理过程还是运动时间 (MT) 是否反映运动本身, 是电机中的关键角色。序列学习。步进式的任务使研究人员能够将响应时间分解为 RT 和 MT, 这可能说明如何在序列学习中参与马达规划和运动执行。最后, 体位控制与认知是交互作用的, 但对于体位控制与学习运动序列的交互作用知之甚少。利用运动捕捉系统, 可以记录整个身体的运动 (如 g), 即质量中心 (COM))。这些措施使我们能够揭示由 RT 和 MT 测量的离散反应所依据的动态过程, 并有助于阐明体位控制与序列学习中所涉及的显性和隐性过程之间的关系。介绍了实验设置、过程和数据处理的详细情况。有代表性的数据是通过我们以前的研究之一。结果与反应时间, RT, MT, 以及前瞻性体位反应的关系和显着的过程所涉及的隐性运动序列学习。

上述范例由杜和同事在一系列研究中实现3,4,5。我们使用从这些研究之一4中所采用的部分数据来表示脚踩式工作任务的使用情况。在这项研究中, 有6学习块和 700 ms 的 RSI 使用。视觉刺激跟随序列 A (i. e, 1423564215;图 1a)在块1到4和 6, 并遵循序列 B (i. e, 3615425214) 在5块。响应-刺激间隔设置为700毫秒.图 2a说明了12年轻人在六个学习块中的平均响应时间。在脚踩步进的任务中, 响应时间显示了相同的模式和可比的响应时间, 这是以前在经典的手指冲压的工作任务2,25,26.具体而言, 与4块 (差异 = 83.4 毫秒 13.19, 平均误差, 平均值等) 相比, 在5块中, 对一个新序列的响应时间明显慢。p < 0.001), 指示学习序列1,2。虽然在指压和脚步任务下的序列学习没有直接进行比较, 但响应时间的相似大小和模式表明, 内隐运动序列学习可能不会受到体位控制存在的影响。要求在典型地被开发的成人。
图 2b说明了响应时间的两个组件: rt 和 MT 平均 rt 与响应时间相同。特别是, 5 块中的 RT 比4块 (差异 = 93.19 毫秒 12.69) 慢;p < 0.001)。与响应时间和 RT 不同, MT 在4和5块之间是可比较的 (差异 =-7.73 毫秒 3.88;p = 0.072)。在我们的其他研究中报告了相同的 RT 和 MT 结果3,5。这些结果一起表明, 序列学习最有可能是反映的 RT, 一个代理的认知处理, 而不是 MT, 它的特点的运动本身。
图 3和图 4描述了在刺激出现之前 COM 移动100毫秒的方向的示例。COM 对于每个刺激的方向在开始时是非常不一致的 (i. e, 块 1), 这些看似随机的移动方向在一个参与者 (图 3) 中不会跨块改变。但是, 对于另一个参与者 (图 4), 随着跨块的学习进展, 这些随机移动方向变得更加一致。图 5a显示跨块 (F (555) = 3.07、 p < 0.05) 的移动方向变异性的显著变化。具体而言, 可变性从4块到 5 (p < 0.05) 中增加, 表明 COM 移动方向将是运动序列学习的一个明显标志。
更重要的是, 预期的群众运动中心可能会反映出在隐式运动序列学习中操作的显式过程。只有在参与者 (n = 6、 p < 0.05) 中, 至少部分地获得了序列的显式知识, 而在参与者 (n = 6、 p = 0.98) 中, 才显示出从4块到5的可变性增加的情况。知识;图 5b突出显示了此序列知识。此外, 从4块到5的可变性的变化与参与者获取的显式知识的数量显著相关 (图 5c)。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56483/56483fig1.jpg"> 图 1: 实验设置.(a)八摄像机适当定位, 以便收集所有标记的清晰数据。六在地面上的步进目标对应于显示器上显示的六视觉刺激。(b) 38 个直径为0.5 厘米的球形反射标记, 每个在重要的骨性地标上都附着在皮肤上。这些骨性地标包括顶点, 7th颈椎, 胸骨凹槽, acromions, 肘部 (侧面和内侧), 上臂, 手腕 (桡和尺), 3rd关节, 前髂上棘 (ASIS), 髂后上髂骨刺 (PSIS), 中心在两个 PSISs, 膝盖 (侧面和内侧), 胫骨, 脚踝 (侧面和内侧), 跟骨, 大脚趾, 和 5th跖骨。紫色标记: 从前视图可见的标记;红色标记: 后面的标记;白色标记: 静态试用后移除标记。(c)基于38个标记的设置的骨架模板。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56483/56483fig1large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56483/56483fig2.jpg"> 图 2: 将响应时间分解为 RT 和 MT.(a)跨块的平均响应时间。灰色区域代表5块, 其中刺激的外观遵循一个新的序列。序列学习发生在5块比4块中的响应时间慢的情况下。(b) RT 作为响应时间的一个组成部分, 呈现与响应时间相同的模式, 而 MT 不会从块4更改为5块。误差条: 平均值的标准误差。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56483/56483fig2large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56483/56483fig3.jpg"> 图 3: 不具有显式序列知识的一个参与者的 COM 移动方向.COM 移动方向显示为每个刺激 (刺激 1-6, 参见图 1a) 横跨块。com 可以从原点移动到虚线圆圈上的任意位置, 代表 com 可以移动的所有方向。空圆圈代表观察到的方向。实心箭头表示平均方向。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56483/56483fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/56483/56483fig4.jpg"> 图 4: 来自一个具有显式序列知识的参与者的 COM 移动方向.COM 移动方向显示为每个刺激 (刺激 1-6, 参见图 1a) 横跨块。com 可以从原点移动到虚线圆圈上的任何位置, 代表 com 可以移动的所有方向。空圆圈代表观察到的方向。实心箭头表示平均方向。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56483/56483fig4large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/56483/56483fig5.jpg"> 图 5: COM 移动方向的可变性及其与序列学习中涉及的显式和隐式过程的关系.移动方向由每个空圆圈之间的圆弧长度和在图 3和 图 4中的平均箭头指向的点进行量化。这相当于从平均运动方向到连接原点和每个空圆的线的角度 (以度为单位)。可变性计算为跨角度的标准偏差。(a)跨块的平均可变性: 灰色区域代表5块, 其中刺激的外观遵循一个新的序列。可变性从4块增加到5。(b) COM 移动方向可变性的这种更改仅在获取 (至少部分是对序列的显式知识) 的参与者中显示, 但不显示对序列的显式知识的参与者。(c)从4块到5的可变性的变化与参与者获得的显性知识的数量显著相关。误差条: 平均值的标准误差。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56483/56483fig5large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>

Watch this Scientific Journal Video about The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task at JoVE.com

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

隐式电机序列学习中的 "马达": 步进串行反应时间任务

我们介绍了步进串行反应时间 (工作) 任务。这一修改后的任务, 补充了经典的工作, 只涉及手指紧迫的运动, 更好地接近每天的排序活动, 并允许研究人员研究的动态过程中的离散反应措施和解开隐式序列学习中操作的显式过程。

This protocol describes a modified serial reaction time (SRT) task used to study implicit motor sequence learning. Unlike the classic SRT task that ...

the-motor-implicit-motor-sequence-learning-foot-stepping-serial

Nanyang Technological University

Research

JoVE Journal

Behavior

8.4K 次观看.  University of Maryland, College Park. 我们介绍了步进串行反应时间 (工作) 任务。这一修改后的任务, 补充了经典的工作, 只涉及手指紧迫的运动, 更好地接近每天的排序活动, 并允许研究人员研究的动态过程中的离散反应措施和解开隐式序列学习中操作的显式过程。

视频: The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

Study Motor Skill Learning by Single-pellet Reaching Tasks in Mice

Reaching for and retrieving objects require precise and coordinated motor movements in the forelimb. When mice are repeatedly trained to grasp and retrieve food rewards positioned at a specific location, their motor performance (defined as accuracy and speed) improves progressively over time, and plateaus after persistent training. Once such reaching skill is mastered, its further maintenance does not require constant practice. Here we introduce a single-pellet reaching task to study the acquisition and maintenance of skilled forelimb movements in mice. In this video, we first describe the behaviors of mice that are commonly encountered in this learning and memory paradigm, and then discuss how to categorize these behaviors and quantify the observed results. Combined with mouse genetics, this paradigm can be utilized as a behavioral platform to explore the anatomical underpinnings, physiological properties, and molecular mechanisms of learning and memory.

Persistent practice improves the precision of coordinated movements.&#160;Here we introduce a single-pellet reaching task, which is designed to assess the learning and memory of forelimb skill in mice.&#160;

学习运动技能通过单颗粒到达任务小鼠学习

Reaching for and retrieving objects require precise and coordinated motor movements in the forelimb. When mice are repeatedly trained to grasp and ...

科研

行为学

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Kinesthetic awareness is important to successfully navigate the environment. When we interact with our daily surroundings, some aspects of movement are deliberately planned, while others spontaneously occur below conscious awareness. The deliberate component of this dichotomy has been studied extensively in several contexts, while the spontaneous component remains largely under-explored. Moreover, how perceptual processes modulate these movement classes is still unclear. In particular, a currently debated issue is whether the visuomotor system is governed by the spatial percept produced by a visual illusion or whether it is not affected by the illusion and is governed instead by the veridical percept. Bistable percepts such as 3D depth inversion illusions (DIIs) provide an excellent context to study such interactions and balance, particularly when used in combination with reach-to-grasp movements. In this study, a methodology is developed that uses a DII to clarify the role of top-down processes on motor action, particularly exploring how reaches toward a target on a DII are affected in both deliberate and spontaneous movement domains.

It is unclear how top-down signals from the ventral visual stream affect movement. We developed a paradigm to test motor behavior towards a target on a 3D depth inversion illusion. Significant differences are reported in both deliberate, goal-directed movements and automatic actions under illusory and veridical viewing conditions.

方法探索自上而下视觉过程对电机性能的影响

Kinesthetic awareness is important to successfully navigate the environment. When we interact with our daily surroundings, some aspects of movement are ...

The 5-Choice Serial Reaction Time Task: A Task of Attention and Impulse Control for Rodents

This protocol describes the 5-choice serial reaction time task, which is an operant based task used to study attention and impulse control in rodents. Test day challenges, modifications to the standard task, can be used to systematically tax the neural systems controlling either attention or impulse control. Importantly, these challenges have consistent effects on behavior across laboratories in intact animals and can reveal either enhancements or deficits in cognitive function that are not apparent when rats are only tested on the standard task. The variety of behavioral measures that are collected can be used to determine if other factors (i.e., sedation, motivation deficits, locomotor impairments) are contributing to changes in performance. The versatility of the 5CSRTT is further enhanced because it is amenable to combination with pharmacological, molecular, and genetic techniques.

This protocol describes the 5-choice serial reaction time task, which is an operant based task used to study attention and impulse control in rodents. Test day challenges, which are modifications of the standard task, increase flexibility of the task and can be combined with other manipulations to more fully characterize behavior.

5选择序列反应时任务:注意的任务和脉冲控制鼠害

This protocol describes the 5-choice serial reaction time task, which is an operant based task used to study attention and impulse control in rodents. ...

Compensatory Limb Use and Behavioral Assessment of Motor Skill Learning Following Sensorimotor Cortex Injury in a Mouse Model of Ischemic Stroke

Mouse models have become increasingly popular in the field of behavioral neuroscience, and specifically in studies of experimental stroke. As models advance, it is important to develop sensitive behavioral measures specific to the mouse. The present protocol describes a skilled motor task for use in mouse models of stroke. The Pasta Matrix Reaching Task functions as a versatile and sensitive behavioral assay that permits experimenters to collect accurate outcome data and manipulate limb use to mimic human clinical phenomena including compensatory strategies (i.e., learned non-use) and focused rehabilitative training. When combined with neuroanatomical tools, this task also permits researchers to explore the mechanisms that support behavioral recovery of function (or lack thereof) following stroke. The task is both simple and affordable to set up and conduct, offering a variety of training and testing options for numerous research questions concerning functional outcome following injury. Though the task has been applied to mouse models of stroke, it may also be beneficial in studies of functional outcome in other upper extremity injury models.

Mouse models have become increasingly popular in studies of behavioral neuroscience. As models advance, it is important to develop sensitive behavioral measures specific to the mouse. This protocol describes the Pasta Matrix Reaching Task, which is a skilled motor task for use in mouse models of stroke.

补偿肢使用和运动技能学习行为评估以下感觉运动皮层损伤缺血性脑卒中的小鼠模型

Mouse models have become increasingly popular in the field of behavioral neuroscience, and specifically in studies of experimental stroke. As models ...

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning process. Here a method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process. This discovery-learning task uses an infant activated mobile that rotates and plays music based on specified leg action of infants. Supine infants activate the mobile by moving their feet vertically across a virtual threshold. This paradigm is unique in that as infants independently discover that their leg actions activate the mobile, the infants&#8217; leg movements are tracked using a motion capture system allowing for the quantification of the learning process. Specifically, learning is quantified in terms of the duration of mobile activation, the position variance of the end effectors (feet) that activate the mobile, changes in hip-knee coordination patterns, and changes in hip and knee muscle torque. This information describes infant exploration and exploitation at the interplay of person and environmental constraints that support task-specific action. Subsequent research using this method can investigate how specific impairments of different populations of infants at risk for movement disorders influence the discovery-learning process for task-specific action.

A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process.

量化学习婴幼儿:跟踪腿操作期间发现，学习任务

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning ...

Electrophysiological Motor Unit Number Estimation (MUNE) Measuring Compound Muscle Action Potential (CMAP) in Mouse Hindlimb Muscles

Compound muscle action potential (CMAP) and motor unit number estimation (MUNE) are electrophysiological techniques that can be used to monitor the functional status of a motor unit pool in vivo. These measures can provide insight into the normal development and degeneration of the neuromuscular system. These measures have clear translational potential because they are routinely applied in diagnostic and clinical human studies. We present electrophysiological techniques similar to those employed in humans to allow recordings of mouse sciatic nerve function. The CMAP response represents the electrophysiological output from a muscle or group of muscles following supramaximal stimulation of a peripheral nerve. MUNE is an electrophysiological technique that is based on modifications of the CMAP response. MUNE is a calculated value that represents the estimated number of motor neurons or axons (motor control input) supplying the muscle or group of muscles being tested. We present methods for recording CMAP responses from the proximal leg muscles using surface recording electrodes following the stimulation of the sciatic nerve in mice. An incremental MUNE technique is described using submaximal stimuli to determine the average single motor unit potential (SMUP) size. MUNE is calculated by dividing the CMAP amplitude (peak-to-peak) by the SMUP amplitude (peak-to-peak). These electrophysiological techniques allow repeated measures in both neonatal and adult mice in such a manner that facilitates rapid analysis and data collection while reducing the number of animals required for experimental testing. Furthermore, these measures are similar to those recorded in human studies allowing more direct comparisons.

Electrophysiological Motor Unit Number Estimation MUNE Measuring Compound Muscle Action Potential CMAP in Mouse Hindlimb Muscles

We present refined protocols that allow in vivo monitoring of motor unit function in the mouse. Techniques to measure compound muscle action potential (CMAP) and motor unit number estimation (MUNE) in the mouse hind limb muscles innervated by the sciatic nerve are described.

电电机组数估计（MUNE）在小鼠后肢肌肉测量复合肌肉动作电位（CMAP）

Compound muscle action potential (CMAP) and motor unit number estimation (MUNE) are electrophysiological techniques that can be used to monitor the ...

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Skilled reaching tasks are commonly used in studies of motor skill learning and motor function under healthy and pathological conditions, but can be time-intensive and ambiguous to quantify beyond simple success rates. Here, we describe the training procedure for reach-and-pull tasks with ETH Pattus, a robotic platform for automated forelimb reaching training that records pulling and hand rotation movements in rats. Kinematic quantification of the performed pulling attempts reveals the presence of distinct temporal profiles of movement parameters such as pulling velocity, spatial variability of the pulling trajectory, deviation from midline, as well as pulling success. We show how minor adjustments in the training paradigm result in alterations in these parameters, revealing their relation to task difficulty, general motor function or skilled task execution. Combined with electrophysiological, pharmacological and optogenetic techniques, this paradigm can be used to explore the mechanisms underlying motor learning and memory formation, as well as loss and recovery of function (e.g. after stroke).

A paradigm is presented for training and analysis of an automated skilled reaching task in rats. Analysis of pulling attempts reveals distinct subprocesses of motor learning.

调查运动技能的学习过程与机器人Manipulandum

Skilled reaching tasks are commonly used in studies of motor skill learning and motor function under healthy and pathological conditions, but can be ...

Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

Understanding the mechanisms underlying locomotor learning helps researchers and clinicians optimize gait retraining as part of motor rehabilitation. However, studying human locomotor learning can be challenging. During infancy and childhood, the neuromuscular system is quite immature, and it is unlikely that locomotor learning during early stages of development is governed by the same mechanisms as in adulthood. By the time humans reach maturity, they are so proficient at walking that it is difficult to come up with a sufficiently novel task to study de novo locomotor learning. The split-belt treadmill, which has two belts that can drive each leg at a different speed, enables the study of both short- (i.e., immediate) and long-term (i.e., over minutes-days; a form of motor learning) gait modifications in response to a novel change in the walking environment. Individuals can easily be screened for previous exposure to the split-belt treadmill, thus ensuring that all experimental participants have no (or equivalent) prior experience. This paper describes a typical split-belt treadmill adaptation protocol that incorporates testing methods to quantify locomotor learning and generalization of this learning to other walking contexts. A discussion of important considerations for designing split-belt treadmill experiments follows, including factors like treadmill belt speeds, rest breaks, and distractors. Additionally, potential but understudied confounding variables (e.g., arm movements, prior experience) are considered in the discussion.

We describe a protocol for investigating human locomotor adaptation using the split-belt treadmill, which has two belts that can drive each leg at a different speed. We specifically focus on a paradigm designed to test the generalization of adapted locomotor patterns to different walking contexts (e.g., gait speeds, walking environments).

用分体式跑步机评价人类运动适应的泛化

Understanding the mechanisms underlying locomotor learning helps researchers and clinicians optimize gait retraining as part of motor rehabilitation. ...

Using the FishSim Animation Toolchain to Investigate Fish Behavior: A Case Study on Mate-Choice Copying In Sailfin Mollies

Over the last decade, employing computer animations for animal behavior research has increased due to its ability to non-invasively manipulate the appearance and behavior of visual stimuli, compared to manipulating live animals. Here, we present the FishSim Animation Toolchain, a software framework developed to provide researchers with an easy-to-use method for implementing 3D computer animations in behavioral experiments with fish. The toolchain offers templates to create virtual 3D stimuli of five different fish species. Stimuli are customizable in both appearance and size, based on photographs taken of live fish. Multiple stimuli can be animated by recording swimming paths in a virtual environment using a video game controller. To increase standardization of the simulated behavior, the prerecorded swimming path may be replayed with different stimuli. Multiple animations can later be organized into playlists and presented on monitors during experiments with live fish.
In a case study with sailfin mollies (Poecilia latipinna), we provide a protocol on how to conduct a mate-choice copying experiment with FishSim. We utilized this method to create and animate virtual males and virtual model females, and then presented these to live focal females in a binary choice experiment. Our results demonstrate that computer animation may be used to simulate virtual fish in a mate-choice copying experiment to investigate the role of female gravid spots as an indication of quality for a model female in mate-choice copying.
Applying this method is not limited to mate-choice copying experiments but can be used in various experimental designs. Still, its usability depends on the visual capabilities of the study species and first needs validation. Overall, computer animations offer a high degree of control and standardization in experiments and bear the potential to &#39;reduce&#39; and &#39;replace&#39; live stimulus animals as well as to &#39;refine&#39; experimental procedures.

Using the FishSim Animation Toolchain to Investigate Fish Behavior: A Case Study on Mate-Choice Copying In Sailfin Mollies

Using the novel FishSim Animation Toolchain, we present a protocol for non-invasive visual manipulation of public information in the context of mate-choice copying in sailfin mollies. FishSim Animation Toolchain provides an easy-to-use framework for the design, animation and presentation of computer-animated fish stimuli for behavioral experiments with live test fish.

利用fishsim动画工具链调查鱼类行为--以鱼翅钼中的选物复制为例

Over the last decade, employing computer animations for animal behavior research has increased due to its ability to non-invasively manipulate the ...

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Multisensory integration research investigates how the brain processes simultaneous sensory information. Research on animals (mainly cats and primates) and humans reveal that intact multisensory integration is crucial for functioning in the real world, including both cognitive and physical activities. Much of the research conducted over the past several decades documents multisensory integration effects using diverse psychophysical, electrophysiological, and neuroimaging techniques. While its presence has been reported, the methods used to determine the magnitude of multisensory integration effects varies and typically faces much criticism. In what follows, limitations of previous behavioral studies are outlined and a step-by-step tutorial for calculating the magnitude of multisensory integration effects using robust probability models is provided.

The current study aims to provide a step-by-step tutorial for calculating the magnitude of multisensory integration effects in an effort to facilitate the production of translational research studies across diverse clinical populations.

使用种族模型不平等来量化行为多感官整合效应

Multisensory integration research investigates how the brain processes simultaneous sensory information. Research on animals (mainly cats and primates) ...