Name: quantifying learning in young infants: tracking leg actions during a discovery-learning task
Uploaded: 2015-06-01T14:00:00.000+00:00
Duration: 11 min 18 s
Description: Watch this Scientific Journal Video about Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task at JoVE.com

这项研究是由博士研究的推广（PODS）支持的I和II奖项基金会的物理治疗，并从美国物理治疗协会芭芭拉萨金特教育处的认养文档奖学金。

该机构审查委员会在南加州大学批准了这项研究。 1.系统准备<ol><li>建立了动作捕捉系统。请注意:这些步骤是每个运动捕捉系统不同。 <ol><li>对准通过点击动作捕捉程序" 进行新注册"，进入30秒的采集时间，点击" 注册 "，并移动所述注册对象的捕获体积内的坐标的两个运动捕捉传感器，一个传感器的系统为30秒。当注册成功完成后，观察根的意思是在计算机屏幕上平方（RMS）注册错误。 </li><li>全球坐标系使用登记对象通过单击测试表对准动作捕捉程序" 执行新的对齐 "。 <ol><li>通过将注册OBJE定义原点克拉上的测试台和点击" 数字化 "，在动作捕捉程序的右上角。通过将登记对象的框的顶部并点击" 数字化 "定义的Z轴; Z轴是垂直于所述表中。 </li><li>通过移动包装盒上的登记对象沿该表的长度，并点击" 数字化 "定义在Z / Y +面; Y轴平行于该表和X轴的长度是平行于输送台的宽度。 </li></ol></li><li>堵塞指示灯成两个闸门口，进入运动捕捉系统程序（24选通端口1和20选通2端口）内的每个选通端口的LED数量。请参考图3，用于每个LED的数量和位置。选择丢失的数据，以提供一个长条图表状显示的发光二极管被跟踪的实时性。 <img alt="图3" src="/files/ftp_upload/52841/52841fig3.jpg" /><strong&gt;图3放置的发光二极管。选通1由刚性体（4个LED在每个刚体）为右和左大腿，小腿和脚的。频闪2由刚性体为右和左骨盆，胸骨标记，和各个LED的。 </li></ol></li><li>成立婴儿移动计算机程序。 <ol><li>输入分钟每个条件的数量。第1天，输入2阶段1（2分钟基线，非加固条件），6期2（6分钟采集，加固条件）; 0 3阶段（0分消，非加固条件）。 </li><li>对于第2天，输入2阶段1（基线），6期2（收购），2阶段3（消光），并选中" 使用按照Zmin为默认值 "，让第一天的基线中计算的门槛将门槛用于2日的收购条件。 </li><li>选择"StreamframesAllFrames"，然后点击" 发送 "，使手机程序使用的数据来自运动捕捉系统激活基于指定的标准婴儿移动。 </li></ol></li><li>设置在视频摄像机。 <ol><li>启动三个同步录像视频计算机程序（右侧卧位，左侧卧位，开销意见）。 </li><li>启动额外的视频摄像头在婴儿的头记录的面部表情和眼睛注视。 </li></ol></li></ol> 2.婴儿准备<ol><li>描述实验的父母，并告知他们互动尽可能少与他们的婴儿。 注意:给父母，如果婴儿不整个实验变得繁琐，父母应该婴儿坐在旁边的视图外，但是，如果婴儿变得模糊存在与婴儿相互作用的一个进展。 <ol><li>首先，在一个令人放心的语气问父母说，"一切都还好，我就在这里，"。 </li><li>二，问家长要站在婴儿的美景，一边安抚婴儿。 </li><li>三，问父母要么持有的婴儿的一只手或给婴儿奶嘴。 注:最少需要保持冷静婴儿和警觉父母的互动，并给出尽可能迅速结束越好。 </li></ol></li><li>脱衣服的婴儿，放置在婴儿移动的婴儿，并用尼龙搭扣带横放在后备箱固定婴儿表。 </li><li>后的婴儿被固定到该表中，将胸骨标记和骨盆，大腿，小腿，以及对婴儿的脚刚体。 </li></ol> 3.婴儿移动学习任务<ol><li>每一天，通过同步地开始运动捕获系统，移动计算机程序，和摄像机启动移动学习任务。 <ol><li>这两天从分0比2，基准条件下，观察婴幼儿自发踢。 </li><li>在2英里期间1天Ñ​​基线条件，观察作为婴儿移动程序连续计算的阈值基于从上刚体每只脚的LED中的一个的z数据移动活化。例如，右脚和标记21的左脚标记9。标记9是右脚上刚体圆圈黄色图1中的中心的LED。标记21是在左脚刚体的中心LED。 </li><li>在2分钟基线的端部，移动程序将在2分钟基线条件期间设定在一个标准偏差（SD）上述双脚的平均高度的高度的阈值。 </li><li>这两天从分2至8，收购条件，作为观察婴儿的移动和旋转播放音乐时，LED无论是放置在脚下穿过第一天的2分钟基线条件中计算的门槛。 注:移动激活会持续，只要脚高于虚拟阈至最多3秒。 3秒后，莫如果婴儿移动足下面的虚拟阈值，然后垂直移动的脚，并再次跨过阈值胆汁将只重新激活。这"3秒法则"鼓励积极探索腿动作对持有超过阈值的脚。 </li><li>第2天从8-10分钟，消光条件，作为观察婴儿自发地踢不移动加固。 </li></ol></li><li>后的婴儿与移动交互，收集静态校准试验来定义每个脚部一个局部坐标系，并定义一个参考配置为每个本体段中的空间。 <ol><li>修复十个单独的LED两侧使用双面EKG铤在下列地点婴幼儿的皮肤:下面的第十肋骨躯干外侧中线，髋关节，膝关节外侧关节线的大转子，脚踝外踝，和远端5 日跖骨。 </li><li>握住婴儿的下肢在对于5秒的延伸，解剖位置。所有关节角度在这个校正位置被定义为0°。 </li></ol></li><li>第2天，收集人体测量数据。 <ol><li>权衡在数字电子秤每个婴儿。 </li><li>看看下面的测量:婴幼儿的总长度;圆周在中间段大腿，小腿和脚的;宽度膝关节（在膝关节线），踝（在踝）和脚（在跖骨头）;和大腿（大粗隆膝关节线），小腿（膝关节行至外踝）和脚（内踝以第一跖趾关节）的长度。 </li></ol></li></ol> 4.数据分析<ol><li>通过使用自定义计算语言程序的实验如Matlab的每2分钟间隔期间计算RLA％的性能进行分析和学习。计算的时间的一个或两个，活化的移动高于阈值的每个脚的LED的持续时间。因为3秒的时间间隔后该移动不激活，减去的时间，其中一个或两个LED均高于阈值大于3秒的时间间隔的持续时间。 <ol><li>由统计分析期间三，2分钟采集间隔中的任一项的％RLA是否显著超过2分钟基线间隔3,4,7,9,10测量组的性能的每一天。 </li><li>归类单个婴儿为具有每天执行的任务，如果任何一个2分钟采集间隔期间的％RLA是等于或大于1.5倍的％RLA在2分钟基线间隔3,4,6,9,10。 </li><li>通过确定在整个6分钟取得条件第2天的RLA％ 统计是否超过在基准条件第1天3,6％RLA测量通过天组学习。 </li><li>个人归类为婴幼儿，如果学习者2日的全部6分钟采集状态期间％RLA等于或GRE亚特超过1天的3,6,11 1.5倍的基准条件。 </li></ol></li><li>通过实验的每2分钟间隔期间的编码录像带分析觉醒和注意力。觉醒规模定义为:犯困= 1，警惕和不活动= 2，警惕和积极= 3，挑剔= 4，和哭泣= 5 3,8,11。注意比例被定义为:0 =不看移动，1 =看着移动3,8。 </li><li>处理位置数据和使用自定义MATLAB程序提取踢。 <ol><li>运动捕捉系统输出到自定义的Matlab程序加载位置数据文件内插使用三次样条丢失的位置数据（最大连续20帧）。 </li><li>加载插文件到定制Matlab程序至（a），使用四阶巴特沃思5 Hz的截止频率滤波位置的数据作为从功率谱分析确定，和（b）计算如下关节角度:髋部弯曲/展期，髋关节外展/内收，髋部外/内旋，膝关节屈曲/伸展，踝关节反演/外翻，踝关节背屈/在12描述跖屈。 </li><li>加载角度文件到一个自定义的Matlab程序提取踢。定义一个踢作为发病连续腿部运动的，其中，髋关节或膝关节角度变化超过11.5°（0.2弧度）为任一屈曲或伸展3,9,13-15的开头。定义踢峰值延伸的以下一个屈曲运动或峰屈曲以下的延伸运动3,9,14帧的末尾。 </li></ol></li><li>对于所有的踢，计算使用自定义的MATLAB程序运动学参数。 <ol><li>计算位置方差中的LED在每只脚，活化的移动3的z方向（垂直，任务特定方向）。 </li><li>计算髋关节屈曲/延伸和膝关节屈曲/伸缩缝使用的相关性Pearson相关系数（R）在零滞后之间的髋关节和膝关节角度游览。比较相关（r）婴儿中，转换髋膝关节角度相关费舍尔Z值 3,9,15。 </li><li>时间正常化关节角度数据，然后计算髋关节屈曲/伸展和屈膝/从角位置/速度数据16,17延伸的连续相对相位（CRP）。在以下五个时间点分析的CRP计算结果:（1）开始时踢的，（二）峰的第一个段的速度，（三）关节反转，（d）该第二区段的峰值速度，以及（e踢3）结束。 </li></ol></li><li>对于所有的踢，通过查看同步的视频数据识别非接触踢腿。计算动力学参数使用自定义的MATLAB程序非接触式踢腿。 <ol><li>从修改从Hatze的成年人人体18婴儿模型计算公式的段质和中心的质量。计算的惯性力矩3D大腿，小腿和脚从公式修改段从Jensen的成年人人体19婴儿模型。 </li><li>计算术语在使用空间操作20的螺旋理论运动的下面的等式。 <img alt="式（1）" src="/files/ftp_upload/52841/52841equation1.jpg" /> 使用拉格朗日方法，其中，M（θ）为惯性矩阵，导出， <img alt="theta1" src="/files/ftp_upload/52841/theta1.jpg" />科里奥利和离心扭矩矩阵，N（θ）的引力（GRA），扭矩和T肌肉（毛里求斯）的扭矩。 </li><li>计算使用来自非接触踢，体段惯性参数和运动的生物力学方程三维运动学数据关节力矩。 </li><li>在分区各关节的净（NET）扭矩转换成运动有关（MDT），GRA和MUS扭矩贡献21。净扭矩成正比的加速度在每个接头处。MDT转矩涉及与肢体的运动相互关联的段之间的机械相互作用有关的被动扭矩。 GRA扭矩与重力向下的作用肢体的被动力。 MUS扭矩包括从有源肌肉收缩和肌肉的被动变形，肌腱，韧带和其它关节周围组织的力。 </li><li>用于髋关节和膝关节分开，计算扭矩脉冲作为各分配扭矩（MUS，GRA，MDT），以净转矩的贡献大小。计算过程中的时间间隔，其中，膝盖MUS扭矩作用在相同或相反的方向与膝盖NET扭矩相比正或负的转矩脉冲（转矩*时间）。执行此相同的计算膝关节GRA和MDT扭矩和臀部毛里求斯，GRA和MDT扭矩。用于髋关节和膝关节分开，总结所有正和负脉冲的每个转矩分量，得到的每个隔开的扭矩者脉冲的贡献的大小的量度E（MUS，GRA，MDT）以净扭矩冲动。 </li></ol></li></ol>

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Brain Res. 142 (2), 163-180 (2002).</li><li>Angulo-Kinzler, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exploration+and+selection+of+intralimb+coordination+patterns+in+3-month+old+infants.">Exploration and selection of intralimb coordination patterns in 3-month old infants.</a> J. Mot. Behav. 33, 363-376 (2001).</li><li>Fetters, L., Chen, Y. P., Jonsdottir, J., Tronick, E. Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kicking+coordination+captures+differences+between+full-term+and+premature+infants+with+white+matter+disorder.">Kicking coordination captures differences between full-term and premature infants with white matter disorder.</a> Hum. Mov. Sci. 22, 729-748 (2004).</li><li>Jeng, S., Chen, L., Yau, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kinematic+analysis+of+kicking+movements+in+preterm+infants+with+very+low+birth+weight+and+full-term+infants.">Kinematic analysis of kicking movements in preterm infants with very low birth weight and full-term infants.</a> Phys. Ther. 82, 148-159 (2002).</li><li>Jensen, J. L., Thelen, E., Ulrich, B. D., Schneider, K., Zernicke, R. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adaptive+dynamics+of+the+leg+movement+patterns+of+human+infants:+III.+age-related+differences+in+limb+control.">Adaptive dynamics of the leg movement patterns of human infants: III. age-related differences in limb control.</a> J. Mot. Behav. 27, 366-374 (1995).</li><li>Piek, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+quantitative+analysis+of+spontaneous+kicking+in+two-month-old+infants.">A quantitative analysis of spontaneous kicking in two-month-old infants.</a> Hum. Mov. Sci. 15, 707-726 (1996).</li><li>Thelen, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Developmental+origins+of+motor+coordination:+Leg+movements+in+human+infants.">Developmental origins of motor coordination: Leg movements in human infants.</a> Dev. Psychobiol. 18, 1-22 (1985).</li><li>Vaal, J., van Soest, A. J., Hopkins, B., Sie, L. T. L., van der Knaap, M. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+of+spontaneous+leg+movements+in+infants+with+and+without+periventricular+leukomalacia.">Development of spontaneous leg movements in infants with and without periventricular leukomalacia.</a> Exp. Brain Res. 135, 94-105 (2000).</li></ol>

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的发现学习任务的婴幼儿设计对于婴幼儿探索学习任务必须精心设计，以确保婴幼儿独立发现的偶然性。在的采集条件开始几个移动范例，婴儿或者表明该移动由所述移动7,22或每个婴儿的腿部一个非偶然活化激活由研究者被动移动到引入婴儿到应急9。此外，照顾者和实验者可以提供额外的加固，以支持婴儿的性能。具体的规则，在这里列出，是至关重要的，...

任务的具体行动，从自发运动在婴儿期出现。它已经提出，特定任务的动作通过发现学习过程1,2-出现。任务是发现了婴儿，因为他们自发地移动，并探讨其产生的环境影响的新行动。任务的具体行动，涌现作为婴儿利用他们的行动和他们对周围世界的影响之间的连接。然而，鲜为人知的是，精确的工艺，婴儿探索和利用学习来修改自己的自发动作来执行任务的具体行动。这里描述了一种方法，其中3-4个月婴幼儿学习任务被发现和他们的腿部动作捕捉，量化学习过程。 <img alt="图1" src="/files/ftp_upload/52841/52841fig1.jpg" /> 图1:婴儿踢激活的移动任务。 </强&gt;的中心的发光二极管（LED）安装于刚体每只脚（黄色圆圈）的激活的移动时，它穿过虚拟阈值（红色虚线）。重新印刷从萨金特等人的许可。3 这一发现学习任务使用婴儿激活的移动旋转和播放的基础上婴幼儿3的规定腿的动作音乐。移动下仰卧放置婴儿通过垂直移动他们的脚跨虚拟阈值（ 图1）激活它。这一模式的独特之处作为独立的婴儿发现自己的腿动作激活的移动，婴儿的腿部动作使用的动作捕捉系统，允许对学习过程的定量跟踪。 实验方案包括数据收集两天。第1天由一个2分钟的基准条件，其中一个婴儿自发地踢，但他的腿行动不能激活婴儿移动，随后6分钟采集条件，其中婴儿的腿部动作激活的婴儿移动，如果婴儿移动他的脚垂直交叉的虚拟阈值。该协议允许对婴儿自发腿动作的量化以及的运动的各个方面的婴儿探索其腿动作和活化婴儿移动之间的关系的定量。在第2天，除了2分钟基线条件和6分钟获取条件，一个2分钟的消光条件被添加在其中的婴儿的腿部动作不激活婴儿移动。这允许婴儿是如何改变自己的腿动作时，已经学会了环境响应中止量化。 在以前的婴幼儿移动范式，腿部运动4-6，具体的髋关节和膝关节的角频率7,8，或踢板9一直reinforc编着与移动的移动。每一天的性能相比于基线条件4-9被定义为增加在采集或消光条件期间这些腿的动作。跨越天学习被定义为天2或3的基线或取得条件期间增加这些腿动作和第1天5,6的基线条件。这些以前的移动范例表明，婴儿增加的腿的动作被与移动激活增强的频率，然而，它们不提供在运动学习任务的时候选择婴儿都提供给他们的信息。例如，如果踢球率是钢筋，婴演示性能和学习时，他们的蹬率增加或者与移动或者当移动不再激活交互时。这表明，婴儿能够提炼他们踢率，但它是未知的，如果婴儿能完善其腿部的协调模式或扭矩生产发电机密封Ë腿的动作是不是在他们的首选运动剧目。 这个移动范例是在婴儿唯一需要证明更精腿的动作，以激活移动比以往的移动范式。在这个移动范例，上表各支脚的高度使用从连接到每个脚的发光二极管（LED）的位置数据的2分钟基线条件期间被计算。虚拟阈值则设为平行于该表在一个高度，既英尺的高度的上限范围内的基线条件期间。在采集，移动和旋转播放音乐，如果任一只脚跨过的门槛。经过3秒时，移动站和重新激活仅当婴儿移动低于阈值的脚，然后垂直移动脚和再次跨过阈值。要激活手机的时间量最大，婴儿需要动脚高于阈值并维持其对GRAV性3秒，然后迅速移动脚低于阈值，并再次移动它上面的阈值，并在那里举行了3秒， 等等 。这就需要更精确的腿的动作不是简单地增加踢率。 <img alt="图2" src="/files/ftp_upload/52841/52841fig2.jpg" /> 图2:从婴儿代表末端效应（英尺）的未过滤的位置数据来自谁的基础上的个性化学习标准展示学习3个月大的婴儿2日未过滤的位置数据。红线是发光二极管的z坐标（LED）的放置右脚的位置数据。蓝线是从左脚的LED位置数据。粗黑线是表。虚线是虚拟阈放置14厘米以上表格作为单独确定的，根据其基线期间踢的高度每个婴儿第1天。X轴的条件时通过2分钟的间隔标记。请注意如何婴儿在基线移动他的脚在移动不激活前30秒采集1时，那么他始终保持双脚离桌子和移动他的脚对周围的门槛下一个5 1/2分钟，直到手机灭绝状态时不再激活。 这种移动模式的第二个特点是，每个婴儿的腿部动作是用一个国家的最先进的动作捕捉技术，以量化的婴儿是如何使用他们的运动选择学习任务跟踪。的每只脚激活从一个代表婴儿移动LED未过滤位置数据被包括在图2中。注意如何婴儿移动他的脚在不同高度上表基线和采集的第一部分期间，但然后移动两脚对周围的收购康迪的休息时的阈值直到重刑移动不再灭绝期间激活。这是许多潜在的移动战略，以实现发现学习的任务之一。该策略可以通过计算使用从运动捕捉系统获得的位置数据的三维运动学和动力学进行量化。具体而言，学习过程被量化的增强腿的动作（％RLA），它等于移动激活的持续时间，所述末端执行器（英尺），其激活移动，髋膝协调模式的位置方差的百分比和髋关节和膝关节扭矩。

<table><tbody><tr><td>Optotrak Certus Position Sensor, Far Focus, with stand</td><td>Northern Digital Inc</td><td>8800852</td><td></td></tr><tr><td>Optotrak Data Acquisition Unit II (ODAU II)</td><td>Northern Digital Inc</td><td>8800767</td><td></td></tr><tr><td>Optotrak Vinten Stand, Certus with Quick Fix Adapter</td><td>Northern Digital Inc</td><td>8800855.002</td><td></td></tr><tr><td>Certus S-Type, Standard Configuration</td><td>Northern Digital Inc</td><td>8800761</td><td></td></tr><tr><td>Marker (7 mm) pair, c/w RJII connector and 8 ft cable</td><td>Northern Digital Inc</td><td>8001029.001</td><td></td></tr><tr><td>AC Line Cord, Medical Grade, North America</td><td>Northern Digital Inc</td><td>7500010</td><td></td></tr><tr><td>Cubic Reference Emitter Kit - Certus</td><td>Northern Digital Inc</td><td>8800768</td><td></td></tr><tr><td>3 Pylon IEEE 1394 cameras</td><td>Basler</td><td>A6021c</td><td></td></tr><tr><td>Vixia HG10 camcorder</td><td>Canon</td><td>2183B001</td><td></td></tr><tr><td>Adhesive Disks</td><td>MVAP Medical Supplies</td><td>E401-500</td><td></td></tr><tr><td>Reversible head support</td><td>Eddie Bauer</td><td>52556</td><td></td></tr><tr><td>Softstrap Strap</td><td>Sammons Preston</td><td>A34960</td><td></td></tr><tr><td>Digital Pediatric Scale</td><td>Healthometer</td><td>Model 524KL</td><td></td></tr></tbody></table>

quantifying learning in young infants: tracking leg actions during a discovery-learning task

任务的具体行动，从自发运动在婴儿期出现。它已经提出，特定任务的动作通过发现学习的过程出现。这里描述了一种方法，其中3-4个月婴幼儿学习任务被发现和他们的腿部动作捕捉，量化学习过程。这一发现学习任务使用婴儿激活的移动，基于对婴幼儿指定的腿的动作和旋转播放音乐。仰卧婴儿激活移动通过垂直移动他们的脚跨虚拟门槛。这一模式的独特之处作为独立的婴儿发现自己的腿动作激活的移动，婴儿的腿部动作使用的动作捕捉系统，允许对学习过程的定量跟踪。具体而言，学习是量化移动激活的持续时间而言，末端执行器（脚），该激活的移动，改变髋膝coordina的位置方差化模式，并改变在髋关节和膝关节的肌肉力矩。此信息描述为支持特定任务行动的人，环境约束的相互影响婴幼儿的勘探和开采。使用这种方法可以研究婴儿的不同人群的风险如何具体障碍的运动障碍影响的发现学习的过程任务的具体行动的后续研究。

对婴幼儿的学习过程可以在％RLA方面进行量化，年底效应（英尺），髋膝角度相关系数，以及髋关节和膝关节力矩的位置变化。分析每个级别提供了有关在发现学习的过程如何婴儿探索他们的腿部动作和激活婴幼儿的移动之间关系的独特信息。 对于RL​​A％及髋膝关节角度相关系数，混合回归模型自回归协方差结构和群体（学生，非学生）作为学科之间的因子的统计分析被?...

Watch this Scientific Journal Video about Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task at JoVE.com

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

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 ...

quantifying-learning-young-infants-tracking-leg-actions-during

Universidad San Sebastian

Research

JoVE Journal

Behavior

10.6K 次观看.  University of Southern California. 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.

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

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental paradigms and analysis tools have been limited to constrained laboratory tasks and contexts due to technical limitations imposed by the available set of measuring and analysis techniques and the age of the subjects. These limitations severely limit the study of developmental neural dynamics and associated neural networks engaged in cognition, perception and action in infants performing &#8220;in action and in context&#8221;. This protocol presents a novel approach to study infants and young children as they freely organize their own behavior, and its consequences in a complex, partly unpredictable and highly dynamic environment. The proposed methodology integrates synchronized high-density active scalp electroencephalography (EEG), inertial measurement units (IMUs), video recording and behavioral analysis to capture brain activity and movement non-invasively in freely-behaving infants. This setup allows for the study of neural network dynamics in the developing brain, in action and context, as these networks are recruited during goal-oriented, exploration and social interaction tasks.

This protocol presents a novel methodology for the neural decoding of intent from freely-behaving infants during unscripted social interaction with an actor. Neural activity is acquired using non-invasive high-density active scalp electroencephalography (EEG). Kinematic data is collected with inertial measurement units and supplemented with synchronized video recording.

在自由，表现人类婴儿一种新的实验和分析方法意向的多模态神经解码期间社会互动

Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental ...

科研

行为学

A Method to Quantify Visual Information Processing in Children Using Eye Tracking

Visual problems that occur early in life can have major impact on a child's development. Without verbal communication and only based on observational methods, it is difficult to make a quantitative assessment of a child's visual problems. This limits accurate diagnostics in children under the age of 4 years and in children with intellectual disabilities. Here we describe a quantitative method that overcomes these problems. The method uses a remote eye tracker and a four choice preferential looking paradigm to measure eye movement responses to different visual stimuli. The child sits without head support in front of a monitor with integrated infrared cameras. In one of four monitor quadrants a visual stimulus is presented. Each stimulus has a specific visual modality with respect to the background, e.g., form, motion, contrast or color. From the reflexive eye movement responses to these specific visual modalities, output parameters such as reaction times, fixation accuracy and fixation duration are calculated to quantify a child's viewing behavior. With this approach, the quality of visual information processing can be assessed without the use of communication. By comparing results with reference values obtained in typically developing children from 0-12 years, the method provides a characterization of visual information processing in visually impaired children. The quantitative information provided by this method can be advantageous for the field of clinical visual assessment and rehabilitation in multiple ways. The parameter values provide a good basis to: (i) characterize early visual capacities and consequently to enable early interventions; (ii) compare risk groups and follow visual development over time; and (iii), construct an individual visual profile for each child.

A method is described to quantify the quality of visual information processing based on reflexive eye movements in response to specific visual modalities. Reaction times and fixation output parameters are used to characterize visual performance in children with and without visual impairments from 6 months of age.

一种方法来量化视觉信息加工儿童使用眼动追踪

Visual problems that occur early in life can have major impact on a child's development. Without verbal communication and only based on observational ...

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

This protocol describes a modified serial reaction time (SRT) task used to study implicit motor sequence learning. Unlike the classic SRT task that involves finger-pressing movements while sitting, the modified SRT task requires participants to step with both feet while maintaining a standing posture. This stepping task necessitates whole body actions that impose postural challenges. The foot-stepping task complements the classic SRT task in several ways. The foot-stepping SRT task is a better proxy for the daily activities that require ongoing postural control, and thus may help us better understand sequence learning in real-life situations. In addition, response time serves as an indicator of sequence learning in the classic SRT task, but it is unclear whether response time, reaction time (RT) representing mental process, or movement time (MT) reflecting the movement itself, is a key player in motor sequence learning. The foot-stepping SRT task allows researchers to disentangle response time into RT and MT, which may clarify how motor planning and movement execution are involved in sequence learning. Lastly, postural control and cognition are interactively related, but little is known about how postural control interacts with learning motor sequences. With a motion capture system, the movement of the whole body (e.g., the center of mass (COM)) can be recorded. Such measures allow us to reveal the dynamic processes underlying discrete responses measured by RT and MT, and may aid in elucidating the relationship between postural control and the explicit and implicit processes involved in sequence learning. Details of the experimental set-up, procedure, and data processing are described. The representative data are adopted from one of our previous studies. Results are related to response time, RT, and MT, as well as the relationship between the anticipatory postural response and the explicit processes involved in implicit motor sequence learning.

We introduce the foot-stepping serial reaction time (SRT) task. This modified SRT task, complementing the classic SRT task that involves only finger-pressing movement, better approximates daily sequenced activities and allows researchers to study the dynamic processes underlying discrete response measures and disentangle the explicit process operating in implicit sequence learning.

隐式电机序列学习中的 "马达": 步进串行反应时间任务

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

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Young children's visual environments are dynamic, changing moment-by-moment as children physically and visually explore spaces and objects and interact with people around them. Head-mounted eye tracking offers a unique opportunity to capture children's dynamic egocentric views and how they allocate visual attention within those views. This protocol provides guiding principles and practical recommendations for researchers using head-mounted eye trackers in both laboratory and more naturalistic settings. Head-mounted eye tracking complements other experimental methods by enhancing opportunities for data collection in more ecologically valid contexts through increased portability and freedom of head and body movements compared to screen-based eye tracking. This protocol can also be integrated with other technologies, such as motion tracking and heart-rate monitoring, to provide a high-density multimodal dataset for examining natural behavior, learning, and development than previously possible. This paper illustrates the types of data generated from head-mounted eye tracking in a study designed to investigate visual attention in one natural context for toddlers: free-flowing toy play with a parent. Successful use of this protocol will allow researchers to collect data that can be used to answer questions not only about visual attention, but also about a broad range of other perceptual, cognitive, and social skills and their development.

Young children do not passively observe the world, but rather actively explore and engage with their environment. This protocol provides guiding principles and practical recommendations for using head-mounted eye trackers to record infants' and toddlers' dynamic visual environments and visual attention in the context of natural behavior.

行动中的目光: 在自然主义行为中对儿童动态视觉注意力的头戴式眼动

Young children's visual environments are dynamic, changing moment-by-moment as children physically and visually explore spaces and objects and interact ...

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Assessing infant category learning is a challenging but vital aspect of studying infant cognition. By employing a familiarization-test paradigm, we straightforwardly measure infants&#8217; success in learning a novel category while relying only on their looking behavior. Moreover, the paradigm can directly measure the impact of different auditory signals on the infant categorization across a range of ages. For instance, we assessed how 2-year-olds learn categories in a variety of labeling environments: in our task, 2-year-olds successfully learned categories when all exemplars were labeled&#160;or the first two exemplars were labeled, but they failed to categorize when no exemplars were labeled or only the final two exemplars were labeled. To determine infants&#8217; success in such tasks, researchers can examine both the overall preference displayed by infants in each condition and infants&#8217; pattern of looking over the course of the test phase, using an eye-tracker to provide fine-grained time-course data. Thus, we present a powerful paradigm for identifying the role of language, or any auditory signal, in infants&#8217; object category learning.

Here we present a protocol for familiarization-test paradigms which provide a direct test of infant categorization and help to define the role of language in early category learning.

用眼动跟踪范式界定语言在婴儿对象分类中的作用

Assessing infant category learning is a challenging but vital aspect of studying infant cognition. By employing a familiarization-test paradigm, we ...

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 ...

神经科学

使用 Erasmus Ladder 自动评估小鼠的精细运动性能

Monitoring Fine and Associative Motor Learning in Mice Using the Erasmus Ladder

Behavior is shaped by actions, and actions necessitate motor skills such as strength, coordination, and learning. None of the behaviors essential for sustaining life would be possible without the ability to transition from one position to another. Unfortunately, motor skills can be compromised in a wide array of diseases. Therefore, investigating the mechanisms of motor functions at the cellular, molecular, and circuit levels, as well as understanding the symptoms, causes, and progression of motor disorders, is crucial for developing effective treatments. Mouse models are frequently employed for this purpose.
This article describes a protocol that allows the monitoring of various aspects of motor performance and learning in mice using an automated tool called the Erasmus Ladder. The assay involves two phases: an initial phase where mice are trained to navigate a horizontal ladder built of irregular rungs (&#34;fine motor learning&#34;), and a second phase where an obstacle is presented in the path of the moving animal. The perturbation can be unexpected (&#34;challenged motor learning&#34;) or preceded by an auditory tone (&#34;associative motor learning&#34;). The task is easy to conduct and is fully supported by automated software.
This report shows how different readouts from the test, when analyzed with sensitive statistical methods, allow fine monitoring of mouse motor skills using a small cohort of mice. We propose that the method will be highly sensitive to evaluate motor adaptations driven by environmental modifications as well as early-stage subtle motor deficits in mutant mice with compromised motor functions.

作者聚焦：通过使用 Erasmus Ladder 对运动学习和髓鞘可塑性研究进行自动评估来揭示神经机制 

This article presents a protocol that allows a non-invasive and automated assessment of fine motor performance, as well as adaptive and associative motor learning upon challenges, using a device called the Erasmus Ladder. Task difficulty can be titrated to detect motor impairment ranging from major to subtle degrees.

使用伊拉斯谟量程监测小鼠的精细和联想运动学习

Behavior is shaped by actions, and actions necessitate motor skills such as strength, coordination, and learning. None of the behaviors essential for ...

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Developing objective and quantitative methods of early gross motor assessment is essential to better understand neurodevelopment and to support early therapeutic interventions. Here, we&#160;present&#160;a method to quantify gross motor performance using a multisensor wearable, MAIJU (Motility Assessment of Infants with a JUmpsuit), which offers an automated, scalable,&#160;quantitative, and objective assessment using&#160;a fully automated cloud-based pipeline. This wearable suit is equipped with four movement sensors that record synchronized data to a mobile phone utilizing a low-energy Bluetooth connection. An offline analysis in the cloud server generates fully analyzed results within minutes for each recording. These results include a graphical report of the recording session and a detailed result matrix that gives second-by-second classifications for posture, movement, infant carrying, and free playtime. Our recent results show the virtue of such&#160;quantified motor assessment&#160;providing&#160;a potentially&#160;effective method for distinguishing variations in the infant&#39;s gross motor development.

This paper outlines the assessment of infants' gross motor performance with a multisensor wearable and its fully automated deep learning-based analysis pipeline. The method quantifies the posture and movement patterns of infants from lying supine until they master walking independently.

使用多传感器可穿戴设备对婴儿粗大运动能力进行量化评估

Developing objective and quantitative methods of early gross motor assessment is essential to better understand neurodevelopment and to support early ...

量化学习婴幼儿:跟踪腿操作期间发现，学习任务

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在自由，表现人类婴儿一种新的实验和分析方法意向的多模态神经解码期间社会互动

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隐式电机序列学习中的 "马达": 步进串行反应时间任务

行动中的目光: 在自然主义行为中对儿童动态视觉注意力的头戴式眼动

用眼动跟踪范式界定语言在婴儿对象分类中的作用

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

使用伊拉斯谟量程监测小鼠的精细和联想运动学习

使用伊拉斯谟量程监测小鼠的精细和联想运动学习

使用伊拉斯谟量程监测小鼠的精细和联想运动学习

使用多传感器可穿戴设备对婴儿粗大运动能力进行量化评估