Name: the "motor" in implicit motor sequence learning: a foot-stepping serial reaction time task
Uploaded: 2018-05-03T12:30:00.000+00:00
Duration: 10 min 39 s
Description: Watch this Scientific Journal Video about The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task at JoVE.com

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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A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sequence+learning+under+dual-task+conditions:+alternatives+to+a+resource-based+account.">Sequence learning under dual-task conditions: alternatives to a resource-based account.</a> Psychol Res. 69 (5-6), 352-368 (2005).</li><li>Curran, T. <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+aging+on+implicit+sequence+learning:+Accounting+for+sequence+structure+and+explicit+knowledge.">Effects of aging on implicit sequence learning: Accounting for sequence structure and explicit knowledge.</a> Psychol Res. 60 (1-2), 24-41 (1997).</li><li>Ramenzoni, V. C., Riley, M. A., Shockley, K., Chiu, C. Y. 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。
<p class="jove...

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, ...

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

Universidad San Sebastian

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

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Rodents have been traditionally used as a standard animal model in laboratory experiments involving a myriad of sensory, cognitive, and motor tasks. Higher cognitive functions that require precise control over sensorimotor responses such as decision-making and attentional modulation, however, are typically assessed in nonhuman primates. Despite the richness of primate behavior that allows multiple variants of these functions to be studied, the rodent model remains an attractive, cost-effective alternative to primate models. Furthermore, the ability to fully automate operant conditioning in rodents adds unique advantages over the labor intensive training of nonhuman primates while studying a broad range of these complex functions.
Here, we introduce a protocol for operantly conditioning rats on performing working memory tasks. During critical epochs of the task, the protocol ensures that the animal's overt movement is minimized by requiring the animal to 'fixate' until a Go cue is delivered, akin to nonhuman primate experimental design. A simple two alternative forced choice task is implemented to demonstrate the performance. We discuss the application of this paradigm to other tasks.

A fully automated protocol for rodent operant conditioning is proposed. The protocol relies on precise temporal control of behavioral events to investigate the extent to which this control influences neural activity underlying sensorimotor integration and cognitive control experiments.

为感觉运动整合与认知控制实验完全自动化的啮齿动物调理协议

Rodents have been traditionally used as a standard animal model in laboratory experiments involving a myriad of sensory, cognitive, and motor tasks. ...

科研

行为学

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 ...

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. ...

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 ...

Acquisition of a High-precision Skilled Forelimb Reaching Task in Rats

Movements are the main measurable output of central nervous system function. Developing behavioral paradigms that allow detailed analysis of motor learning and execution is of critical importance in order to understand the principles and processes that underlie motor function. Here we present a paradigm to study movement acquisition within a daily session of training (within-session) representing the fast learning component and primary acquisition as well as skilled motor learning over several training sessions (between-session) representing the slow learning component and consolidation of the learned task. This behavioral paradigm increases the degree of difficulty and complexity of the motor skill task due to two features: First, the animal realigns its body prior to each pellet retrieval forcing renewed orientation and preventing movement execution from the same angle. Second, pellets are grasped from a vertical post that matches the diameter of the pellet and is placed in front of the cage. This requires a precise grasp for successful pellet retrieval and thus prevents simple pulling of the pellet towards the animal. In combination with novel genetics, imaging and electrophysiological technologies, this behavioral method will aid to understand the morphological, anatomical and molecular underpinnings of motor learning and memory.

A paradigm is presented to analyze the acquisition of a high-precision skilled forelimb reaching task in rats.

收购高精度熟练的前肢到达任务大鼠

Movements are the main measurable output of central nervous system function. Developing behavioral paradigms that allow detailed analysis of motor ...

A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance

Assessment of reactive balance traditionally imposes some type of perturbation to upright stance or gait followed by measurement of the resultant corrective behavior. These measures include muscle responses, limb movements, ground reaction forces, and even direct neurophysiological measures such as electroencephalography. Using this approach, researchers and clinicians can infer some basic principles regarding how the nervous system controls balance to avoid a fall. One limitation with the way in which these assessments are currently used is that they heavily emphasize reflexive actions without any need to revise automatic postural reactions. Such an exclusive focus on these highly stereotypical reactions would fail to adequately address how we can modify these reactions should the need arise (e.g., avoiding an obstacle with a recovery step). This would appear to be a glaring omission when one considers the enormous complexity of the environments we face daily. Overall, the status quo when evaluating the neural control of balance fails to truly expose how higher brain resources contribute to preventing falls in complex settings. The present protocol offers a way to require suppression of automatic, but inappropriate corrective balance reactions, and force a selection among alternative action choices to successfully recover balance following postural perturbation.

Here we offer a protocol that allows the user to selectively change affordances and/or constraints on movements that are relevant for recovering balance after postural perturbation.

改进的精益和释放技术，强调反应抑制和反应平衡中的动作选择

Assessment of reactive balance traditionally imposes some type of perturbation to upright stance or gait followed by measurement of the resultant ...

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

While the subject of learning has attracted immense interest from both behavioral and neural scientists, only relatively few investigators have observed single-neuron activity while animals are acquiring an operantly conditioned response, or when that response is extinguished. But even in these cases, observation periods usually encompass only a single stage of learning, i.e. acquisition or extinction, but not both (exceptions include protocols employing reversal learning; see Bingman&#160;et al.1 for an example). However, acquisition and extinction entail different learning mechanisms and are therefore expected to be accompanied by different types and/or loci of neural plasticity.
Accordingly, we developed a behavioral paradigm which institutes three stages of learning in a single behavioral session and which is well suited for the simultaneous recording of single neurons&#39; action potentials. Animals are trained on a single-interval forced choice task which requires mapping each of two possible choice responses to the presentation of different novel visual stimuli (acquisition). After having reached a predefined performance criterion, one of the two choice responses is no longer reinforced (extinction). Following a certain decrement in performance level, correct responses are reinforced again (reacquisition). By using a new set of stimuli in every session, animals can undergo the acquisition-extinction-reacquisition process repeatedly. Because all three stages of learning occur in a single behavioral session, the paradigm is ideal for the simultaneous observation of the spiking output of multiple single neurons. We use pigeons as model systems, but the task can easily be adapted to any other species capable of conditioned discrimination learning.

Learning new stimulus-response associations engages a wide range of neural processes which are ultimately reflected in changing spike output of individual neurons. Here we describe a behavioral protocol allowing for the continuous registration of single-neuron activity while animals acquire, extinguish, and reacquire a conditioned response within a single experimental session.

自由移动鸽子穿越三个阶段学习记录单个神经元&#39;动作电位

While the subject of learning has attracted immense interest from both behavioral and neural scientists, only relatively few investigators have observed ...

神经科学

An Open-Source, Fully Customizable 5-Choice Serial Reaction Time Task Toolbox for Automated Behavioral Training of Rodents

The 5-choice serial reaction time task (5-CSRTT) is a behavioral test often used to study visuospatial attention and impulsiveness in rodents. The task requires animals to allocate attention to a horizontal array of five small apertures equipped with light sources and, within a limited time window, nose-poke one illuminated target aperture to get a food reward at the food magazine located in the opposite wall of the chamber. The task considers behavioral control measures such as response accuracy and reaction times and allows to infer selective attention and impulsivity. Task difficulty can be controlled by modifying the stimulus duration and task design in general. Commercially available apparatus usually consists of an experimental chamber and particular software to specify task parameters, but due to fixed hard- and software, they pose many limitations on changes in the general experimental design and specific task requirements and the related data output. This article explains a fully customizable alternative based on an easy-to-use single-board microcontroller and standard electrotechnical components, an open-access Arduino script, and a Matlab-toolbox for hardware control and behavioral task specifications, respectively. The toolbox includes an optional staircase procedure, enabling automated behavioral training. The complete hardware setup, which can be installed in customized chambers, and the freely adaptable software encourage non-standardized task and chamber design. The design of the system and the open-source code for hardware control and experimental setup are described.

The present protocol describes the development of an open-source 5-choice serial reaction time task toolbox for rodent animal models, using Arduino and related hardware and a versatile Matlab toolbox, including an optional script for automated behavioral training. The scripts are customizable and facilitate the implementation of different trial-and-test designs.

一个开源的，完全可定制的5选择串行反应时间任务工具箱，用于啮齿动物的自动行为训练

The 5-choice serial reaction time task (5-CSRTT) is a behavioral test often used to study visuospatial attention and impulsiveness in rodents. The task ...

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first language development. While many studies of statistical learning are conducted within a single domain or modality, recent evidence suggests that this skill may differ based on the context in which the stimuli are presented. In addition, few studies investigate learning as it unfolds in real-time, rather focusing on the outcome of learning. In this protocol, we describe an approach for identifying the cognitive and neural basis of statistical learning, within an individual, across domains (linguistic vs. non-linguistic) and sensory modalities (visual and auditory). The tasks are designed to cast as little cognitive demand as possible on participants, making it ideal for young school-aged children and special populations. The web-based nature of the behavioral tasks offers a unique opportunity for us to reach more representative populations nationwide, to estimate effect sizes with greater precision, and to contribute to open and reproducible research. The neural measures provided by the functional magnetic resonance imaging (fMRI) task can inform researchers about the neural mechanisms engaged during statistical learning, and how these may differ across individuals on the basis of domain or modality. Finally, both tasks allow for the measurement of real-time learning, as changes in reaction time to a target stimulus is tracked across the exposure period. The main limitation of using this protocol relates to the hour-long duration of the experiment. Children might need to complete all four statistical learning tasks in multiple sittings. Therefore, the web-based platform is designed with this limitation in mind so that tasks may be disseminated individually. This methodology will allow users to investigate how the process of statistical learning unfolds across and within domains and modalities in children from different developmental backgrounds.

Presented here is a protocol introducing a set of child-friendly statistical learning tasks geared towards examining children&#8217;s learning of temporal statistical patterns across domains and sensory modalities. The developed tasks collect behavioral data using the web-based platform and task-based functional magnetic resonance imaging (fMRI) data for examining neural engagement during statistical learning.

通过在线平台和神经成像技术测量学龄儿童跨模式和领域的统计学习

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first ...

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low-cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel. The platform incorporates two common associative learning paradigms: eyeblink conditioning and delayed tactile startle conditioning. Behavior is tracked using a camera and the wheel movement by a detector. We describe the components and setup and provide a detailed protocol for training and data analysis. This platform allows the incorporation of optogenetic stimulation and fluorescence imaging. The design allows a single host computer to control multiple platforms for training multiple animals simultaneously.

We have developed a single platform to track animal behavior during two climbing fiber-dependent associative learning tasks. The low-cost design allows integration with optogenetic or imaging experiments directed towards climbing fiber-associated cerebellar activity.

用于监测小脑依赖性感觉联想学习的灵活平台

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in ...