This research was supported by Promotion of Doctoral Studies (PODS) I and II awards from the Foundation for Physical Therapy and an Adopt-A-Doc Scholarship from the Education Section of the American Physical Therapy Association to Barbara Sargent.

The Institutional Review Board at the University of Southern California approved this study.
1.&#160;System Preparation
<ol>
	<li>Set up the motion capture system. Please note: these steps are different for each motion capture system.
	<ol>
		<li>Align the coordinate systems of the two motion capture sensors to that of one sensor by clicking &#8220;Perform New Registration&#8221; in the motion capture program, entering a collection time of 30 sec, clicking &#8220;Register,&#8221; and moving the registration object within the capture volume for 30 sec. When registration is successfully completed, observe a root mean square (RMS) registration error on the computer screen.</li>
		<li>Align the global coordinate system to the testing table using the registration object by clicking &#8220;Perform New Alignment&#8221; in the motion capture program.
		<ol>
			<li>Define the origin by placing the registration object on the upper right corner of the testing table and clicking &#8220;Digitize&#8221; in the motion capture program. Define the Z-axis by placing the registration object on top of a box and clicking &#8220;Digitize&#8221;; the Z-axis is perpendicular to the table.</li>
			<li>Define the Z/Y+ plane by moving the registration object on the box along the length of the table and clicking &#8220;Digitize&#8221;; the Y-axis is parallel to the length of the table and the X-axis is parallel to the width of the table.</li>
		</ol>
		</li>
		<li>Plug the LEDs into the two strobe ports and enter the number of LEDs per strobe port within the motion capture system program (24 for strobe port 1 and 20 for strobe port 2). Refer to Figure 3 for the number and location of each LED. Select missing data view to provide a strip chart-like display of LEDs being tracked in real time. 
		<img alt="Figure 3" src="/files/ftp_upload/52841/52841fig3.jpg" />Figure 3. Placement of LEDs. Strobe 1 consists of the rigid bodies (4 LEDs on each rigid body) for the right and left thigh, shank, and foot. Strobe 2 consists of the rigid bodies for the right and left pelvis, the sternum markers, and the individual LEDs.</li>
	</ol>
	</li>
	<li>Set up the infant mobile computer program.
	<ol>
		<li>Input the number of minutes for each condition. For Day 1, input 2 for phase 1 (2 min baseline, non-reinforcement condition), 6 for phase 2 (6 min acquisition, reinforcement condition), and 0 for phase 3 (0 min extinction, non-reinforcement condition).</li>
		<li>For Day 2, input 2 for phase 1 (baseline), 6 for phase 2 (acquisition), 2 for phase 3 (extinction), and check &#8220;Use Zmin as Default&#8221; so that the threshold computed during baseline of Day1 will be the threshold used for the acquisition condition of Day 2.</li>
		<li>Choose &#8220;StreamframesAllFrames&#8221; and click &#8220;Send&#8221; to enable the mobile program to use data from the motion capture system to activate the infant mobile based on specified criteria.</li>
	</ol>
	</li>
	<li>Set up the video cameras.
	<ol>
		<li>Initiate the video computer program for the three synchronized videos (right lateral, left lateral, overhead views).</li>
		<li>Start the additional video camera over the infant&#8217;s head to record facial expressions and eye gaze.</li>
	</ol>
	</li>
</ol>
2. Infant Preparation
<ol>
	<li>Describe the experiment to the parents and inform them to interact as little as possible with their infant. 
	NOTE: Tell the parents that if the infant does not become fussy throughout the experiment, the parents should sit beside the infant outside of their view, however, if the infant becomes fussy there is a progression of interaction with the infant.
	<ol>
		<li>First, ask the parent to say, &#8220;Everything is okay, I&#8217;m right here,&#8221; in a reassuring tone.</li>
		<li>Second, ask the parent to stand in the infant&#8217;s view while reassuring the infant.</li>
		<li>Third, ask the parent to either hold one of the infant&#8217;s hands or give the infant a pacifier. 
		NOTE: The least amount of parent interaction necessary to keep the infant calm and alert is given and is ended as quickly as possible.</li>
	</ol>
	</li>
	<li>Undress the infant, place the infant under the infant mobile, and secure the infant to the table using a Velcro band placed across the trunk.</li>
	<li>After the infant is secured to the table, place the sternum markers and pelvic, thigh, shank, and foot rigid bodies on the infant.</li>
</ol>
3. Infant Mobile Learning Task
<ol>
	<li>Each day, initiate the mobile learning task by synchronously starting the motion capture system, mobile computer program, and video cameras.
	<ol>
		<li>On both days from min 0 to 2, the baseline condition, observe the infant spontaneously kicking.</li>
		<li>On Day 1 during the 2 min baseline condition, observe as the infant mobile program continuously computes the threshold for mobile activation based on the z-data from one of the LEDs on the rigid body of each foot. Example, marker 9 on the right foot and marker 21 on the left foot. Marker 9 is the center LED on the right foot rigid body circled in yellow in Figure 1. Marker 21 is the center LED on the left foot rigid body.</li>
		<li>At the end of the 2 min baseline, the mobile program will set the threshold at a height of one standard deviation (SD) above the average height of both feet during the 2 min baseline condition.</li>
		<li>On both days from min 2 to 8, the acquisition condition, observe as the infant mobile rotates and plays music when the LED placed on either foot crosses the threshold computed during the 2 min baseline condition of Day 1. 
		NOTE: Mobile activation will continue for as long as the foot is above the virtual threshold to a maximum of 3 sec. After 3 sec, the mobile will reactivate only if the infant moves the foot below the virtual threshold, and then moves the foot vertically and again crosses the threshold. This &#8220;3 sec rule&#8221; encourages active leg exploratory movements versus holding the feet above the threshold.</li>
		<li>On Day 2 from min 8-10, the extinction condition, observe as the infant kicks spontaneously without mobile reinforcement.</li>
	</ol>
	</li>
	<li>After the infant interacts with the mobile, collect a static calibration trial to define a local coordinate system for each leg segment and define a reference configuration for each body segment in space.
	<ol>
		<li>Fix ten individual LEDs bilaterally to the infant&#8217;s skin using double-sided EKG collars at the following locations: lateral midline of the trunk below the tenth rib, greater trochanter of the hip, lateral knee joint line, ankle lateral malleolus, and distal end of the 5th metatarsal.</li>
		<li>Hold the infant&#8217;s lower extremity in an extended, anatomical position for 5 sec. All joint angles in this calibration position are defined as 0&#176;.</li>
	</ol>
	</li>
	<li>On Day 2, collect anthropometric data.
	<ol>
		<li>Weigh each infant on a digital electric scale.</li>
		<li>Take the following measurements: total length of the infant; circumference at mid-segment of thigh, shank, and foot; width of knee (at the knee joint line), ankle (at the malleoli), and foot (at the metatarsal heads); and length of the thigh (greater trochanter to knee joint line), shank (knee joint line to lateral malleolus), and foot (medial malleolus to first metatarsophalangeal joint).</li>
	</ol>
	</li>
</ol>
4. Data Analysis
<ol>
	<li>Analyze performance and learning by computing the %RLA during each 2 min interval of the experiment using a custom computing language program such as Matlab. Compute the duration of time one or both of the LEDs on each foot that activated the mobile were above the threshold. Since the mobile does not activate after an interval of 3 sec, subtract the duration of time in which one or both LEDs were above the threshold for greater than a 3 sec interval.
	<ol>
		<li>Measure performance of the group each day by statistically analyzing whether the %RLA during any one of the three, 2 min acquisition intervals significantly exceeds the 2 min baseline interval3,4,7,9,10.</li>
		<li>Categorize individual infants as having performed the task each day if the %RLA during any one 2 min acquisition interval is equal to or greater than 1.5 times the %RLA in the 2 min baseline interval3,4,6,9,10.</li>
		<li>Measure learning of the group across days by determining statistically whether the %RLA during the entire 6 min acquisition condition Day 2 exceeds the %RLA during the baseline condition Day 13,6.</li>
		<li>Categorize individual infants as Learners if the %RLA during the entire 6 min acquisition condition of Day 2 is equal to or greater than 1.5 times the baseline condition of Day 13,6,11.</li>
	</ol>
	</li>
	<li>Analyze arousal and attention by coding videotapes during each 2 min interval of the experiment. The arousal scale is defined as: drowsy = 1, alert and inactive = 2, alert and active = 3, fussy = 4, and crying = 53,8,11. The attention scale is defined as: 0 = not looking at the mobile, 1 = looking at the mobile3,8.</li>
	<li>Process position data and extract kicks using custom Matlab programs.
	<ol>
		<li>Load position data files outputted from the motion capture system into a custom Matlab program to interpolate missing position data (maximum of 20 consecutive frames) using a cubic spline.</li>
		<li>Load the interpolated files into a custom Matlab program to (a) filter position data using a fourth-order Butterworth with a cut-off frequency of 5 Hz as determined from power spectrum analysis, and (b) compute the following joint angles: hip flexion/extension, hip abduction/adduction, hip external/internal rotation, knee flexion/extension, ankle inversion/eversion, ankle dorsiflexion/plantarflexion as described in 12.</li>
		<li>Load the angle files into a custom Matlab program to extract kicks. Define the beginning of a kick as the onset of a continuous leg movement in which the hip or knee joint angle change exceeded 11.5&#176; (0.2 radians) into either flexion or extension3,9,13-15. Define the end of the kick as the frame of peak extension following a flexion movement or peak flexion following an extension movement3,9,14.</li>
	</ol>
	</li>
	<li>For all kicks, compute kinematic parameters using custom Matlab programs.
	<ol>
		<li>Compute position variance in the z-direction (vertical, task-specific direction) of the LED on each foot that activated the mobile3.</li>
		<li>Compute hip flexion/extension and knee flexion/extension joint correlations using Pearson correlation coefficients (r) at zero lag between hip and knee joint angle excursions. To compare correlations (r) among infants, convert hip-knee joint angle correlations to Fisher Z scores3,9,15.</li>
		<li>Time-normalize the joint angle data, then compute hip flexion/extension and knee flexion/extension continuous relative phase (CRP) from the angular position/velocity data16,17. Analyze the results of the CRP computation at the following five time points: (a) beginning of kick, (b) peak velocity of the first segment, (c) joint reversal, (d) peak velocity of the second segment, and (e) end of kick3.</li>
	</ol>
	</li>
	<li>For all kicks, identify non-contact kicks by viewing the synchronous video data. Compute kinetic parameters for non-contact kicks using custom Matlab programs.
	<ol>
		<li>Compute the segmental mass and center-of-mass from equations modified for infants from Hatze&#8217;s anthropometric model for adults18. Compute the 3D moments of inertia of the thigh, shank, and foot segments from equations modified for infants from Jensen&#8217;s anthropometric model for adults19.</li>
		<li>Calculate the terms in the following equation of motion using the screw theory of spatial manipulations20. 
		<img alt="equation 1" src="/files/ftp_upload/52841/52841equation1.jpg" /> 
		is derived using the Lagrangian approach, where M(&#952;) is the inertia matrix, <img alt="theta1" src="/files/ftp_upload/52841/theta1.jpg" />&#160;the Coriolis and centrifugal torque matrix, N(&#952;) the gravitational (GRA) torques and T the muscle (MUS) torques.</li>
		<li>Compute joint torques using the 3D kinematic data from the non-contact kicks, body-segment inertial parameters, and the biomechanic equation of motion.</li>
		<li>Partition the net (NET) torque at each joint into motion-dependent (MDT), GRA, and MUS torque contributions21. NET torque is directly proportional to the accelerations at each joint. MDT torque is related to the passive torques associated with mechanical interactions among the moving interconnected segments of the limb. GRA torque is related to the passive force of gravity acting downward on the limb. MUS torque includes forces from active muscle contractions and passive deformations of muscles, tendons, ligaments, and other periarticular tissues.</li>
		<li>For the hip and knee separately, compute torque impulse as the magnitude of the contribution of each partitioned torque (MUS, GRA, MDT) to NET torque. Compute the positive or negative torque impulse (torque * time) during intervals in which the knee MUS torque acted in the same or opposite direction compared with the knee NET torque. Perform this same computation with knee GRA and MDT torques and hip MUS, GRA, and MDT torques. For the hip and knee separately, sum all positive and negative impulses for each torque component to yield a measure of the magnitude of the contribution of each partitioned torque impulse (MUS, GRA, MDT) to NET torque impulse.</li>
	</ol>
	</li>
</ol>

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No conflicts of interest declared.

Design of discovery-learning tasks for young infants
Discovery-learning tasks for young infants must be thoughtfully designed to assure that infants are independently discovering the contingency. In several mobile paradigms at the beginning of the acquisition condition, infants are either shown that the mobile activates by a non-contingent activation of the mobile7,22 or the leg of each infant is passively moved by the investigator to introduce the infant to the contingency9</s...

Task-specific actions emerge from spontaneous movements during infancy. It has been proposed that task-specific actions emerge through a discovery-learning process1,2. Tasks are discovered by infants as they spontaneously move and explore actions which produce novel effects in the environment. Task-specific actions emerge as infants exploit the connections between their actions and their effects on the world around them. However, little is known about the precise processes that infants explore and exploit to learn to modify their spontaneous movements to perform task-specific actions. 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.
<img alt="Figure 1" src="/files/ftp_upload/52841/52841fig1.jpg" />
Figure 1: Infant kicking-activated mobile task. The center light-emitting diode (LED) attached to the rigid body of each foot (yellow circle) activates the mobile when it crosses the virtual threshold (red dashed line). Re-printed with permission from Sargent et al.3
This discovery-learning task uses an infant activated mobile that rotates and plays music based on the specified leg action of infants3. Infants placed supine under the mobile activate it by moving their feet vertically across a virtual threshold (Figure 1). 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.
The experimental protocol includes two days of data collection. Day 1 consists of a 2 min baseline condition in which an infant kicks spontaneously but his leg actions cannot activate the infant mobile, followed by a 6 min acquisition condition in which the infant&#8217;s leg actions activate the infant mobile if the infant moves his feet vertically to cross a virtual threshold. This protocol allows for the quantification of infants&#8217; spontaneous leg actions as well as the quantification of various aspects of the movements as infants explore the relation between their leg actions and activation of the infant mobile. On Day 2, in addition to the 2 min baseline condition and 6 min acquisition condition, a 2 min extinction condition is added in which the infant&#8217;s leg actions do not activate the infant mobile. This allows for the quantification of how infants change their leg actions when an already learned environmental response is discontinued.
In previous infant mobile paradigms, frequency of leg movement4-6, specific hip and knee angles7,8, or kicking a panel9 have been reinforced with mobile movement. Performance each day was defined as an increase in these leg actions during the acquisition or extinction condition as compared to the baseline condition4-9. Learning across days was defined as an increase in these leg actions during the baseline or acquisition condition of Days 2 or 3 and the baseline condition of Day 15,6. These previous mobile paradigms demonstrate that infants increase the frequency of leg actions that are reinforced with mobile activation, however, they do not provide information on the movement options infants have available to them when learning the task. For example, if kicking rate is reinforced, infants demonstrate performance and learning when their kicking rate increases either when interacting with the mobile or when the mobile no longer activates. This demonstrates that infants can refine their kicking rate, but it is unknown if infants can refine their leg coordination pattern or torque production to generate leg actions that are not within their preferred movement repertoire.
This mobile paradigm is unique in that infants are required to demonstrate more refined&#160;leg action to activate the mobile than in previous mobile paradigms. In this mobile paradigm, the height of each foot above the table is computed during the 2 min baseline condition using position data from a light emitting diode (LED) attached to each foot. A virtual threshold is then set parallel to the table at a height that is within the upper range of the height of both feet during the baseline condition. During acquisition, the mobile rotates and plays music if either foot crosses the threshold. After 3 sec, the mobile stops and reactivates only if the infant moves the foot below the threshold, and then moves the foot vertically and again crosses the threshold. To activate the mobile for the greatest amount of time, infants need to move a foot above the threshold and maintain it against gravity for 3 sec, then quickly move the foot below the threshold and again move it above the threshold and hold it there for 3 sec, etc. This requires more refined leg action&#160;than simply increasing kicking rate.
<img alt="Figure 2" src="/files/ftp_upload/52841/52841fig2.jpg" />
Figure 2: Unfiltered position data of end effectors (feet) from a representative infant. Unfiltered position data from Day 2 of a 3 month old infant who demonstrated learning based on the individual learning criteria. The red line is position data of the z-coordinate of the light-emitting diode (LED) placed on the right foot. The blue line is position data from the LED on the left foot. Thick black line is the table. Dotted line is the virtual threshold placed 14 cm above the table as individually determined for each infant based on the height of their kicking during baseline condition of Day 1. X-axis is time labeled by 2 min intervals. Note how the infant moves his feet during baseline when the mobile does not activate and during the first 30 sec of acquisition 1, then he consistently keeps both feet off the table and moves his feet right around the threshold for the next 5&#189; min until the mobile no longer activates during the extinction condition.
The second unique feature of this mobile paradigm is that each infant&#8217;s leg action is tracked using state-of-the-art motion capture techniques to quantify how infants use their movement options&#160;to learn the task. Unfiltered position data of the LED on each foot that activates the mobile from one representative infant is included in Figure 2. Note how the infant moves his feet at various heights above the table during baseline and the first part of acquisition, but then moves both feet right around the threshold during the rest of the acquisition condition until the mobile no longer activates during extinction. This is one of many potential movement strategies to accomplish the discovery-learning task. The strategies can be quantified by computing three-dimensional kinematics and kinetics using position data acquired from the motion capture system. Specifically, the learning process is quantified in terms of the percentage of reinforced leg action (%RLA), which is equal to the duration of mobile activation, position variance of the end effectors (feet) which activate the mobile, hip-knee coordination patterns, and hip and knee joint torques.

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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.

The learning process of young infants can be quantified in terms of the %RLA, position variance of the end effectors (feet), hip-knee angle correlation coefficients, and hip and knee joint torques. Each level of analysis provides unique information about how infants explore the relation between their leg actions and activation of the infant mobile during the discovery-learning process.
For the statistical analysis of %RLA and hip-knee angle correlation coefficients, mixed regression models wit...

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.

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

Research

JoVE Journal

Behavior

10.6K Views.  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.

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

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

The ability to recall the past allows us to report on details of previous experiences, from the everyday to the significant. Because recall memory is commonly assessed using verbal report paradigms in adults, studying the development of this ability in preverbal infants and children proved challenging. Over the past 30 years, researchers have developed a non-verbal means of assessing recall memory known as the elicited or deferred imitation paradigm. In one variant of the procedure, participants are presented with novel three-dimensional stimuli for a brief baseline period before a researcher demonstrates a series of actions that culminate in an end- or goal-state. The participant is allowed to imitate the demonstrated actions immediately, after a delay, or both. Recall performance is then compared to baseline or to performance on novel control sequences presented at the same session; memory can be assessed for the individual target actions and the order in which they were completed. This procedure is an accepted analogue to the verbal report techniques used with adults, and it has served to establish a solid foundation of the nature of recall memory in infancy and early childhood. In addition, the elicited or deferred imitation procedure has been modified and adapted to answer questions relevant to other aspects of cognitive functioning. The broad utility and application of imitation paradigms is discussed, along with limitations of the approach and directions for future research.

The elicited imitation procedure was established to examine the development of recall memory in infancy and early childhood. This procedure has been widely used to establish a solid foundation of the nature of recall memory in infancy and early childhood.

The ability to recall the past allows us to report on details of previous experiences, from the everyday to the significant. Because recall memory is ...

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.

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

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.

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

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.

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.

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

Neuroscience

Automated Assessment of Fine Motor Performance in Mice Using the Erasmus Ladder

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.

Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder 

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.

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.

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

Habituation: Studying Infants Before They Can Talk

Source: Laboratories of Nicholaus Noles, Judith Danovitch, and Cara Cashon&#8212;University of Louisville
Infants are one of the purest sources of information about human thinking and learning, because they&#8217;ve had very few life experiences. Thus, researchers are interested in gathering data from infants, but as participants in experimental research, they are a challenging group to study. Unlike older children and adults, young infants are unable to reliably speak, understand speech, or even move and control their own bodies. Eating, sleeping, and looking around are the only activities babies can perform reliably. Given these limitations, researchers have developed clever techniques for exploring infants&#8217; thoughts. One of the most popular methods makes use of a characteristic of attention called habituation.
Like adults, infants prefer to pay attention to new and interesting things. If they are left in the same environment, over time they become accustomed to their surroundings and pay less attention to them. This process is called habituation. However, the moment something new happens, infants are waiting and ready to pay attention again. This reengagement of attention following habituation is referred to as dishabituation. Scientists can use these characteristic changes in attention as a tool for studying the thinking and learning of young infants. This method involves initially presenting stimuli to infants until they are habituated, and then presenting them with different kinds of stimuli to see if they dishabituate, i.e., notice a change. By carefully choosing the stimuli shown to the infants, researchers can learn a great deal about how infants think and learn.
This experiment demonstrates how habituation can be used to study infant shape discrimination.

Source: Laboratories of Nicholaus Noles, Judith Danovitch, and Cara Cashon&#8212;University of Louisville
Infants are one of the purest sources of ...

Education

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Psychology

Developmental Psychology

Using Your Head: Measuring Infants' Rational Imitation of Actions

Source: Laboratories of Judith Danovitch and Nicholaus Noles&#8212;University of Louisville
One of the main challenges of infancy is to learn how to achieve one&#8217;s goals in the world, whether they are to pick up a toy or to express desires to another person, and one of the most powerful tools in this learning process is imitation. However, imitation is not always as simple as copying other people&#8217;s actions; it is also necessary to realize that goals and intentions guide behavior. The world is a complicated place, and the actions that babies imitate are not perfectly presented. For example, consider a baby watching their father drinking from a plastic water bottle. As he picks up the bottle, he accidentally drops it on the floor. He picks it up and dusts it off, before turning the cap and taking a drink. If the baby wants to drink from the bottle on their own, they have to decode this complex set of events and determine which actions are related to their goal. Do they have to drop it and dust it off, or can they simply turn the cap and take a drink?
One way to solve this problem is to view many examples of the same behavior, but some behaviors are rare or different each time they are performed. Thus, it is beneficial for infants to think more about the person they are imitating and less about the specific situation they are observing. If infants assume that others are rational and have goals they are efficiently pursuing, then they can observe a complex event and separate the goal-oriented actions from actions that are incidental or unrelated to their goals. Analyzing the reasons underlying a person&#8217;s behavior allows infants to decide whether it makes sense to imitate that person&#8217;s exact actions or to do things in a simpler or more effective way.
This experiment demonstrates how to measure infant&#8217;s rational imitation of an actor using the methods developed by Meltzoff1 and Gergely, Bekkering, and Kiraly.2

Using Your Head: Measuring Infants&#039; Rational Imitation of Actions

Source: Laboratories of Judith Danovitch and Nicholaus Noles&#8212;University of Louisville
One of the main challenges of infancy is to learn how to ...

Numerical Cognition: More or Less

Source: Laboratories of Nicholaus Noles and Judith Danovitch&#8212;University of Louisville
One of the goals of the modern education system is to teach children mathematical literacy. They are taught to add, subtract, multiply, and divide, and this base knowledge is used to support learning about geometry, algebra, calculus, physics, and statistics. School-aged children usually acquire these skills in formal educational settings, but the foundation of mathematical understanding is developed much earlier in life.
As infants, humans begin to form the rough representations that allow them to make judgments about number, and perhaps the first numerical concept that humans develop is the idea of less versus more. However, probing these concepts can be challenging, because even if babies have some understanding of number, they have very few ways of showing off what they know. What they can do is crawl, eat, cry, and sleep. Thus, researchers developed a task using this limited set of responses to investigate whether babies can mentally represent number.
This experiment demonstrates how researchers can creatively use food to study concepts of numerical cognition in infants using the method by Feigenson, Carey, and Hauser.1

Investigating Numerical Cognition More/Less in Infants

Source: Laboratories of Nicholaus Noles and Judith Danovitch&#8212;University of Louisville
One of the goals of the modern education system is to teach ...