Support for this research was provided by the University of Maryland Kinesiology Graduate Research Initiative Fund to Yue Du.

The protocol was performed in accordance with the guidelines approved by the Institutional Review Board at the University of Maryland, College Park.
1. Experimental Set-up
<ol>
	<li>Set up a motion capture system as shown in Figure 1a. Place eight cameras in a circle with a radius of 4 m. 
	NOTE: The number and positions of cameras can be varied, provided all cameras are appropriately positioned to obtain a clear vision of all reflective markers attached to a participant&#39;s body.</li>
	<li>Set up a stepping station in the center of the circle. Position a &#34;home position&#34; covered by dark blue felt mats in the center of the stepping station and place six stepping targets covered by light blue felt mats surrounding the home position as its front, back, and side (Figure 1b). Determine the distance between the targets and home position, according to an individual&#39;s stepping distance (see step 3 in the foot-stepping SRT task procedure).</li>
	<li>To control the task pacing condition, place two electric rubber sensors, which generate analog signals when touched, under the home position to detect the time when the feet return.</li>
	<li>Position a 23&#39;&#39; monitor 2 m in front of the home position. The six visual stimuli are spatially matched with those six stepping targets on the floor.</li>
	<li>Control the appearance order of the visual stimuli using a computer program installed in a laptop.</li>
	<li>Synchronize the laptop and the motion capture system using a data output and acquisition device.</li>
	<li>Turn on the motion capture cameras, and aim them so that each camera can view the volume surrounding the stepping station.</li>
	<li>Identify whether there are unwanted reflective objects from the capture volume (e.g., reflection from light, floor, or any reflective materials). Cover these identified reflective objects with fabric material, so that they are not mistakenly collected as data during experimental trials.</li>
	<li>Using the instructions and equipment supplied with the motion capture system, calibrate the motion capture system to ensure accurate collection of 3-D data from reflective markers18.</li>
	<li>For dynamic calibration, wave the calibration wand supplied with the motion capture system through the space where all reflective markers would move when participants perform the SRT task. Collect 2,000 frames of imaging data for dynamic calibration.</li>
	<li>For static calibration, place the calibration wand on the floor with a position and orientation which can be used as the origin of the coordination system of the motion capture system. Run the motion capture system to set the origin.</li>
	<li>Design a marker set depending on the purpose of study. 
	NOTE: One example is shown in Figure 1b where a 38-marker set-up is used.</li>
	<li>Follow the vendor-supplied instructions to create a labeling skeleton template that can be used for reconstruction and auto-labelling in later data acquisition and processing18. Specifically, ask a participant to stand on the home position of the stepping station with all markers attached. Instruct the participant to stand as still as possible and make sure all markers are visible through the motion capture system. Capture a trial (lasting about 10 s). In the motion capture system, assign each marker a name and create segments by connecting markers together. Link all segments to finalize the skeleton template (shown in Figure 1c).</li>
</ol>
2. Participant Preparation
<ol>
	<li>Inform participants to wear appropriate attire (e.g., shorts and a t-shirt) before visiting the lab.</li>
	<li>Upon arrival, ask participants to carefully read and sign the consent form. Screen for study eligibility. 
	NOTE: The screening questionnaires could be different based on the purpose of each individual study. These questionnaires may include, but are not limited to, the hand dominance questionnaire19, global physical activity level questionnaire20, neurological health questionnaire, and the Movement Assessment Battery for Children21.</li>
	<li>Ask participants to take off their shoes and socks, then attach 38 spherical reflective markers, each 50 mm in diameter, to the skin at pre-determined significant bony landmarks using double sided, hypo-allergenic adhesive tape and pre-wrapping tape. This marker set-up is the same as the customized skeleton template shown in Figure 1b.</li>
	<li>Clear all unwanted reflections other than those 38 markers from the participant&#39;s body (see step 1.8).</li>
	<li>Instruct participants to stand quietly on the home position in a T-pose. Run the motion capture system to capture all markers for 10 s (i.e., the calibration trial).</li>
</ol>
3. The Foot-stepping SRT Task Procedure
<ol>
	<li>Before each participant starts the task, set up the parameters, including, but not limited to: participant ID, group ID, number of learning block, the time length of stimulus presentation, and the time interval between stimuli (ISI) or response-stimulus interval (RSI) that controls the time interval between the completion of movement and the onset of the next stimulus (in this case, electric rubber sensors are needed under the home position; see protocol section 1 for details). 
	NOTE: The ISI could be varied (e.g., 1,300 ms or 1,000 ms) according to the purpose of the study.</li>
	<li>Instruct participants to stand on the home position and adjust the distance of the home position so that participants can comfortably step onto all six targets on the floor.</li>
	<li>Instruct participants to quickly step on each target several times, and mark the distance from the home position to each target at the most comfortable stepping length for each participant.</li>
	<li>Provide the task instructions to participants.
	<ol>
		<li>Instruct participants that once a stimulus appears at one of six locations shown on the monitor, they need to step as quickly and accurately as possible to the corresponding target on the floor and then return to the home position.</li>
		<li>Ask participants to step with the right foot to the three targets located on the right side (i.e., targets 1, 2, and 6; Figure 1a), and the left foot to the other three targets (i.e., targets 3, 4, and 5; Figure 1a). 
		NOTE: The numbers are invisible to participants during the entire task.</li>
		<li>Inform participants that there is a 3 min break after each run (i.e., learning block) of the task. Modify the length of the break based on experimental needs. Set a time alarm to remind participants of the end of the break.</li>
		<li>Instruct participants to keep their elbows by their side and bent at a ninety-degree angle when they perform the task so that the cameras can see the markers placed on the hip.</li>
	</ol>
	</li>
	<li>Run a practice block that consists of 36 steps (i.e., stimuli appear 36 times with a ISI of 1,300&#160;ms; see the foot-stepping SRT task procedure for details) so that participants are familiar with the task. Instruct participants that stimuli will continuously appear at one of six locations and they need to respond to stimuli as fast and accurately as they can. Stimuli in this block appear in a random order. 
	NOTE: The ISI could be replaced by an RSI (see the foot-stepping SRT task procedure for details). If a very short ISI is used, participants may not be able to respond to some stimuli. These steps are considered errors.</li>
	<li>After the practice block, start the experimental blocks. In this protocol, there are six blocks and each experimental block is comprised of 100 steps/stimuli. Give participants a mandatory 3 min break after each block. 
	NOTE: Under the 1,300 ms ISI condition, each block typically takes about 2.5 min. If a RSI is used, the length of each block may vary depending on how fast participants respond to stimuli.
	<ol>
		<li>Instruct participants to complete six experimental blocks. Set the specific order of visual stimuli according to experimental purposes. Stimuli follow either a specified or random sequence. The presentation of stimulus order is unknown to participants. 
		NOTE: The number of experimental blocks could vary. Here, a 6-block design is introduced where a specified sequence A is given in blocks 1 - 4 and 6 and a novel sequence B is presented in block 5. The specific and random sequence could also be varied. In this protocol, sequence A follows the order of 1423564215 (i.e., 1 - right side, 2 - right front, 3 - left front, 4 - left side, 5 - left back, and 6 - right back) and sequence B follows the order of 3615425214.</li>
		<li>Before each learning block, instruct participants to response to stimuli as quickly and accurately as they can.</li>
	</ol>
	</li>
	<li>Upon the completion of all learning blocks, ask participants to complete a posttest that consists of widely used recall and recognition tests described in the literature13,14,22.</li>
</ol>
4. Data Processing and Statistical Analysis
<ol>
	<li>For each participant, open collected data trials in the motion capture system software. Review each trial and fill in any gaps in the trial data according to the vendor-supplied instructions18.</li>
	<li>Export each data trial as an ASCII file that contains three coordinates for all 38 markers.</li>
	<li>Derive variables (i.e., reaction time (RT), movement time (MT), response time, and trajectory of the COM) from the ASCII files following steps below:
	<ol>
		<li>Input the ASCII data files into data analysis software. Use an eighth-order Butterworth filter with a cutoff frequency of 10 Hz to filter the data3. 
		NOTE: The way to derive the COM movement trajectory depends on the marker set-up. In the 38-marker set-up shown in Figure 1b, methods and anthropometric parameters described by De Leva23 may be employed. One can also track the movement of the approximate COM measured by a marker set at the level of the fifth lumbar vertebra24.</li>
		<li>Derive response time, RT, and MT following the descriptions below:
		<ol>
			<li>Use the markers attached on the heels, big toes, and the 5th metatarsals to characterize the trajectories of foot movements in data analysis software.</li>
			<li>Plot the trajectory of these three markers along the vertical direction (perpendicular to the floor). Plot the trajectory of the toe marker within the horizontal plane (parallel to the floor) to identify whether each step is correctly performed to the right target. Steps to a wrong target are excluded for later statistical analyses.</li>
			<li>Mark the baseline of each marker&#39;s height before and after each step.</li>
			<li>Identify the movement onset of each marker as the first sample when the marker reaches 10% of the maximum height.</li>
			<li>Since each subject may use different strategies to touch the target (using the toes or the 5th metatarsal), define the movement onset using the marker that reaches its peak the earliest.</li>
			<li>Identifies the end-point of stepping as the time point when the used marker drops to the same height as the onset.</li>
			<li>Continuously run the data analysis program until 100 steps are processed.</li>
			<li>For all steps, calculate and output the response time as the temporal difference between the stimulus onset and the end of movement, RT as the temporal difference between the stimulus and movement onsets, and MT as the temporal difference between the movement onset and its end point. Save the output files in the .xls format.</li>
			<li>Using these .xls files, calculate means of these variables for each block and across participants, data which will subsequently be used for statistical analyses.</li>
			<li>Since there is typically a within-subject factor (i.e., learning block) in the experimental design, use mixed-effect ANOVAs to analyze data (repeated measures ANOVA could be used with caution regarding the sphericity assumption). Determine the co-variance matrix used in the mixed-effect ANOVA by the Akaike&#39;s Information Criterion (AIC). Decompose the significant results from the ANOVA using post hoc tests with specific multiple comparison correction procedures (depending on the experimental design). Set the statistical significance level at p = 0.05.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Nissen, M. J., Bullemer, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Attentional+requirements+of+learning:+Evidence+from+performance+measures.">Attentional requirements of learning: Evidence from performance measures.</a> Cognit Psychol. 19 (1), 1-32 (1987).</li><li>Willingham, D. B., Nissen, M. J., Bullemer, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+the+development+of+procedural+knowledge.">On the development of procedural knowledge.</a> J Exp Psychol Learn Mem Cogn. 15 (6), 1047-1060 (1989).</li><li>Du, Y., Valentini, N. C., Kim, M. J., Whitall, J., Clark, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Children+and+adults+both+learn+motor+sequences+quickly,+but+do+so+differently.">Children and adults both learn motor sequences quickly, but do so differently.</a> Front Psychol. 8 (158), (2017).</li><li>Du, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Learning processes underlying implicit motor sequence acquisition in children and adults. , (2016).</li><li>Du, Y., Clark, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+insights+into+statistical+learning+and+chunk+learning+in+implicit+sequence+acquisition.">New insights into statistical learning and chunk learning in implicit sequence acquisition.</a> Psychon Bull Rev. , 1-9 (2016).</li><li>Gheysen, F., Van Waelvelde, H., Fias, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impaired+visuo-motor+sequence+learning+in+Developmental+Coordination+Disorder.">Impaired visuo-motor sequence learning in Developmental Coordination Disorder.</a> Res Dev Disabil. 32 (2), 749-756 (2011).</li><li>Wilson, P. H., Maruff, P., Lum, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Procedural+learning+in+children+with+developmental+coordination+disorder.">Procedural learning in children with developmental coordination disorder.</a> Hum Movement Sci. 22 (4-5), 515 (2003).</li><li>Cermak, S. A., Larkin, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Developmental coordination disorder. , (2002).</li><li>Taylor, J. A., Ivry, R. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Implicit+and+explicit+processes+in+motor+learning.">Implicit and explicit processes in motor learning.</a> Action science. , 63-87 (2013).</li><li>Moisello, C., et al. <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+revisited:+a+study+on+motor+sequence+learning+with+an+arm-reaching+task.">The serial reaction time task revisited: a study on motor sequence learning with an arm-reaching task.</a> Exp Brain Res. 194 (1), 143-155 (2009).</li><li>Song, J. H., Nakayama, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hidden+cognitive+states+revealed+in+choice+reaching+tasks.">Hidden cognitive states revealed in choice reaching tasks.</a> Trends Cogn Sci. 13 (8), 360-366 (2009).</li><li>Marcus, D. J., Karatekin, C., Markiewicz, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oculomotor+evidence+of+sequence+learning+on+the+serial+reaction+time+task.">Oculomotor evidence of sequence learning on the serial reaction time task.</a> Mem Cognition. 34 (2), 420-432 (2006).</li><li>Shanks, D. R., Johnstone, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluating+the+relationship+between+explicit+and+implicit+knowledge+in+a+sequential+reaction+time+task.">Evaluating the relationship between explicit and implicit knowledge in a sequential reaction time task.</a> J Exp Psychol Learn Mem Cogn. 25 (6), 1435-1451 (1999).</li><li>Destrebecqz, A., Peigneux, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methods+for+studying+unconscious+learning.">Methods for studying unconscious learning.</a> Prog Brain Res. 150, 69-80 (2005).</li><li>Massion, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Movement,+posture+and+equilibrium:+interaction+and+coordination.">Movement, posture and equilibrium: interaction and coordination.</a> Prog Neurobiol. 38 (1), 35-56 (1992).</li><li>MacKinnon, C. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Preparation+of+anticipatory+postural+adjustments+prior+to+stepping.">Preparation of anticipatory postural adjustments prior to stepping.</a> J Neurophysiol. 97 (6), 4368-4379 (2007).</li><li>Cordo, P. J., Nashner, L. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Properties+of+postural+adjustments+associated+with+rapid+arm+movements.">Properties of postural adjustments associated with rapid arm movements.</a> J Neurophysiol. 47 (2), 287-382 (1982).</li><li>Vicon Motion System Nexus Documentation. Available from: <a target="_blank" href="https://docs.vicon.com/display/Nexus25/Nexus+Documentation">https://docs.vicon.com/display/Nexus25/Nexus+Documentation</a> (2017)</li><li>Oldfield, R. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+assessment+and+analysis+of+handness:+The+edinburgh+inventory.">The assessment and analysis of handness: The edinburgh inventory.</a> Neuropsychologia. 9, 97-113 (1971).</li><li>Armstrong, T., Bull, F. <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+the+world+health+organization+global+physical+activity+questionnaire+(GPAQ).">Development of the world health organization global physical activity questionnaire (GPAQ).</a> J Public Health. 14 (2), 66-70 (2006).</li><li>Henderson, S. E., Sugden, D. A., Barnett, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Movement Assessment Battery for Children - Second edition (Movement ABC-2). , (2007).</li><li>Destrebecqz, A., Cleeremans, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Can+sequence+learning+be+implicit?+New+evidence+with+the+process+dissociation+procedure.">Can sequence learning be implicit? New evidence with the process dissociation procedure.</a> Psychon Bull Rev. 8 (2), 343-350 (2001).</li><li>De Leva, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adjustments+to+Zatsiorsky-Seluyanov's+segment+inertia+parameters.">Adjustments to Zatsiorsky-Seluyanov's segment inertia parameters.</a> J Biomech. 29 (9), 1223-1230 (1996).</li><li>Bair, W. -. N., Kiemel, T., Jeka, J. J., Clark, J. E. <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+multisensory+reweighting+for+posture+control+in+children.">Development of multisensory reweighting for posture control in children.</a> Exp Brain Res. 183 (4), 435-446 (2007).</li><li>Curran, T., Keele, S. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Attentional+and+nonattentional+forms+of+sequence+learning.">Attentional and nonattentional forms of sequence learning.</a> J Exp Psychol Learn Mem Cogn. 19 (1), 189-202 (1993).</li><li>Du, Y., Prashad, S., Schoenbrun, I., Clark, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Probabilistic+motor+sequence+yields+greater+offline+and+less+online+learning+than+fixed+sequence.">Probabilistic motor sequence yields greater offline and less online learning than fixed sequence.</a> Front Hum Neurosci. 10, (2016).</li><li>Destrebecqz, A., Cleeremans, A., Jim&#233;nez, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Attention and implicit learning. , 181-213 (2003).</li><li>Jimenez, L., Vazquez, G. 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>

The authors have nothing to disclose.

This protocol describes the experimental set-up and procedures for a modified SRT task. The modified SRT task shares its appealing simplicity with the classic SRT task, although the modified SRT task demands use of a motion capture technique. Like the classic SRT task, many parameters could be manipulated for specific research questions in the foot-stepping SRT task, including but not limited to: the length of interval-stimulus interval or response-stimulus interval27, the type of sequence structu...

Implicit motor sequence learning, generally known as learning a sequence without knowing the sequence, is critical to our daily activities and has been well studied by a paradigmatic task named the serial reaction time (SRT) task designed by Nissen and Bullemer1. In this classic SRT task, participants press keys to respond quickly and accurately to visual stimuli. To examine sequence learning, the appearance of visual stimuli is manipulated to follow either a pre-structured or random sequence, which is unknown to participants. Learning is evidenced by the faster response time to the pre-structured sequence (e.g., the training sequence) than that to the random or another pre-structured sequence1,2. While the classic SRT task typically requires bi-manual finger tapping, a vast majority of implicit motor sequence learning in everyday activities, such as dancing, playing musical instruments, or playing sports, involves whole body actions that present postural and inertial challenges not found in the classic SRT task. Thus, we proposed that sequence learning tasks need to be more multifaceted. In addition, the focus of the previous research has been almost exclusively on the cognitive component of the task (e.g., decision making or action selection), ignoring the motor control issues involved in sequence learning (e.g., movement execution). Thus, to further understand implicit motor sequence learning, it is essential to study sequence learning in a whole-body or gross motor task that better approximates our daily motor activities.
In our recent studies, we extended the classic SRT task to a modified SRT task where finger pressing was replaced by foot stepping to incorporate postural control into sequence learning3,4,5. This modified task presents its own advantages to complement the classic SRT task. First, the gross motor sequence learning task better mimics daily sequential activities where whole-body movement is involved. To date, our understanding of motor sequence learning typically comes from the classic SRT task, but little is known whether the knowledge of motor sequence learning from the classic SRT task remains to be true in learning sequential motor skills in daily activities. Thus, the modified SRT task allows us to examine whether the systematically reported characteristics (e.g., age-independent implicit sequence learning between children and adults) in the finger-pressing SRT task remain when postural control is involved. Additionally, in populations with posture control and gross motor skill learning difficulties, such as children with developmental coordination disorder6,7,8, understanding how posture control interacts with gross motor sequence learning is critical to help improve intervention strategies, and thus optimize the effectiveness of learning sequential motor skills in daily life.
Second, a common notion about implicit sequence learning is that motor planning, and not movement execution, plays an important role in learning a sequence in the classic SRT task9. This is because pressing keys does not involve moving to new locations in space, as the fingers are always on the response keys. However, many daily sequential behaviors involve large spatial movements. Little is known as to whether movement execution is a key player in motor sequence learning when large spatial movements are required. In the classic SRT task, response time, the summation of reaction time (RT) and movement time (MT), serves as an indicator of sequence learning. The foot-stepping SRT task, like other paradigms involving spatial movements10, allows the researcher to disentangle response time in implicit sequence learning into RT, which reflects cognitive processing, and MT, which characterizes the movement itself.
Third, in addition to MT, the combination of the foot-stepping SRT task and motion capture techniques provides rich data on the continuous whole-body movement (e.g., movement of the center of mass, or COM). Measuring the continuous change of movement has the advantage of revealing the dynamics of the cognitive processes underlying the discrete response measured by RT or MT11,12. In particular, learning sequences in the SRT task are typically explained as a mixture of explicit and implicit processes. That is, despite the common use of the SRT task as an implicit learning task, participants often show the capability to verbally recall the learned sequence after the SRT task, suggesting an explicit component involved in implicit sequence learning. Although the explicit component can be assessed by recall tests conducted after the SRT task13,14, these post-task tests lack the ability to examine the temporal evolution of explicit knowledge during learning. We propose that with explicit sequence knowledge, an individual would know the location of the next stimulus, and thus produce anticipatory postural adjustment15,16,17 in a feedforward manner to prepare for the stepping foot to move to the corresponding target. Therefore, examining the movement of the COM before the stimulus appearance (i.e., anticipation) opens a window to studying the progressive development of explicit memory during implicit sequence learning.
The protocol demonstrates the experimental set-up and procedure of the foot-stepping SRT task. We provide representative results of response time, RT, and MT. In addition, we present results regarding the relationship between posture control and the explicit processes underlying implicit motor sequence learning.

<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

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.

The above paradigm is implemented by Du and colleagues in a series of studies3,4,5. We use a part of data adopted from one of these studies4 to represent the usage of the foot-stepping SRT task. In this study, there are 6 learning blocks and a RSI of 700 ms is used. Visual stimuli followed sequence A (i.e., 1423564215; Figure 1a) in b...

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

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.

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

Research

JoVE Journal

Behavior

8.4K Views.  University of Maryland, College Park. 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.

Video: 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.

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

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;

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.

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.

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.

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.

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.

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

Neuroscience

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.

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.

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.

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