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 vistas.  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

P50 Sensory Gating in Infants

Attentional deficits are common in a variety of neuropsychiatric disorders including attention deficit-hyperactivity disorder, autism, bipolar mood disorder, and schizophrenia. There has been increasing interest in the neurodevelopmental components of these attentional deficits; neurodevelopmental meaning that while the deficits become clinically prominent in childhood or adulthood, the deficits are the results of problems in brain development that begin in infancy or even prenatally. Despite this interest, there are few methods for assessing attention very early in infancy. This report focuses on one method, infant auditory P50 sensory gating.
Attention has several components. One of the earliest components of attention, termed sensory gating, allows the brain to tune out repetitive, noninformative sensory information. Auditory P50 sensory gating refers to one task designed to measure sensory gating using changes in EEG. When identical auditory stimuli are presented 500 ms apart, the evoked response (change in the EEG associated with the processing of the click) to the second stimulus is generally reduced relative to the response to the first stimulus (i.e. the response is &#34;gated&#34;). When response to the second stimulus is not reduced, this is considered a poor sensory gating, is reflective of impaired cerebral inhibition, and is correlated with attentional deficits.
Because the auditory P50 sensory gating task is passive, it is of potential utility in the study of young infants and may provide a window into the developmental time course of attentional deficits in a variety of neuropsychiatric disorders. The goal of this presentation is to describe the methodology for assessing infant auditory P50 sensory gating, a methodology adapted from those used in studies of adult populations.

Methods to record auditory P50 sensory gating, a physiological marker of cerebral inhibition which reflects early stages of attention, are described.

Attentional deficits are common in a variety of neuropsychiatric disorders including attention deficit-hyperactivity disorder, autism, bipolar mood ...

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Fear of certain threat and anxiety about uncertain threat are distinct emotions with unique behavioral, cognitive-attentional, and neuroanatomical components. Both anxiety and fear can be studied in the laboratory by measuring the potentiation of the startle reflex. The startle reflex is a defensive reflex that is potentiated when an organism is threatened and the need for defense is high. The startle reflex is assessed via electromyography (EMG) in the orbicularis oculi muscle elicited by brief, intense, bursts of acoustic white noise (i.e., &#8220;startle probes&#8221;). Startle potentiation is calculated as the increase in startle response magnitude during presentation of sets of visual threat cues that signal delivery of mild electric shock relative to sets of matched cues that signal the absence of shock (no-threat cues). In the Threat Probability Task, fear is measured via startle potentiation to high probability (100% cue-contingent shock; certain) threat cues whereas anxiety is measured via startle potentiation to low probability (20% cue-contingent shock; uncertain) threat cues. Measurement of startle potentiation during the Threat Probability Task provides an objective and easily implemented alternative to assessment of negative affect via self-report or other methods (e.g., neuroimaging) that may be inappropriate or impractical for some researchers. Startle potentiation has been studied rigorously in both animals (e.g., rodents, non-human primates) and humans which facilitates animal-to-human translational research. Startle potentiation during certain and uncertain threat provides an objective measure of negative affective and distinct emotional states (fear, anxiety) to use in research on psychopathology, substance use/abuse and broadly in affective science. As such, it has been used extensively by clinical scientists interested in psychopathology etiology and by affective scientists interested in individual differences in emotion.

Potentiation of the startle reflex is measured via electromyography of the orbicularis oculi muscle during low (uncertain) and high (certain) probability electric shock threat in the Threat Probability Task. This provides an objective measure of distinct negative emotional states (fear/anxiety) for research on psychopathology, substance use/abuse, and broad affective science.

Fear of certain threat and anxiety about uncertain threat are distinct emotions with unique behavioral, cognitive-attentional, and neuroanatomical ...

fMRI Validation of fNIRS Measurements During a Naturalistic Task

We present a method to compare brain activity recorded with near-infrared spectroscopy (fNIRS) in a dance video game task to that recorded in a reduced version of the task using fMRI (functional magnetic resonance imaging). Recently, it has been shown that fNIRS can accurately record functional brain activities equivalent to those concurrently recorded with functional magnetic resonance imaging for classic psychophysical tasks and simple finger tapping paradigms. However, an often quoted benefit of fNIRS is that the technique allows for studying neural mechanisms of complex, naturalistic behaviors that are not possible using the constrained environment of fMRI. Our goal was to extend the findings of previous studies that have shown high correlation between concurrently recorded fNIRS and fMRI signals to compare neural recordings obtained in fMRI procedures to those separately obtained in naturalistic fNIRS experiments. Specifically, we developed a modified version of the dance video game Dance Dance Revolution (DDR) to be compatible with both fMRI and fNIRS imaging procedures. In this methodology we explain the modifications to the software and hardware for compatibility with each technique as well as the scanning and calibration procedures used to obtain representative results. The results of the study show a task-related increase in oxyhemoglobin in both modalities and demonstrate that it is possible to replicate the findings of fMRI using fNIRS in a naturalistic task. This technique represents a methodology to compare fMRI imaging paradigms which utilize a reduced-world environment to fNIRS in closer approximation to naturalistic, full-body activities and behaviors. Further development of this technique may apply to neurodegenerative diseases, such as Parkinson&#8217;s disease, late states of dementia, or those with magnetic susceptibility which are contraindicated for fMRI scanning.

We present a method to compare functional brain activity recorded during a naturalistic task using fNIRS with activity recorded during fMRI.

We present a method to compare brain activity recorded with near-infrared spectroscopy (fNIRS) in a dance video game task to that recorded in a reduced ...

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

This protocol describes how to temporarily and remotely silence neuronal activity in discrete brain regions while animals are engaged in learning and memory tasks. The approach combines pharmacogenetics (Designer-Receptors-Exclusively-Activated-by-Designer-Drugs) with a behavioral paradigm (sensory preconditioning) that is designed to distinguish between different forms of learning. Specifically, viral-mediated delivery is used to express a genetically modified inhibitory G-protein coupled receptor (the Designer Receptor) into a discrete brain region in the rodent. Three weeks later, when designer receptor expression levels are high, a pharmacological agent (the Designer Drug) is administered systemically 30 min prior to a specific behavioral session. The drug has affinity for the designer receptor and thus results in inhibition of neurons that express the designer receptor, but is otherwise biologically inert. The brain region remains silenced for 2-5 hr (depending on the dose and route of administration). Upon completion of the behavioral paradigm, brain tissue is assessed for correct placement and receptor expression. This approach is particularly useful for determining the contribution of individual brain regions to specific components of behavior and can be used across any number of behavioral paradigms.

This protocol describes how to temporarily and remotely silence neuronal activity in discrete brain regions while rats are engaged in learning and memory tasks. The approach combines pharmacogenetics (Designer-Receptors-Exclusively-Activated-by-Designer-Drugs) with a behavioral paradigm (sensory preconditioning) that is designed to distinguish between different components of learning.

This protocol describes how to temporarily and remotely silence neuronal activity in discrete brain regions while animals are engaged in learning and ...

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.

The &quot;Motor&quot; 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 ...

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

The present work is a description and an assessment of a methodology designed to quantify different aspects of the interaction between language processing and the perception of the visual world. The recording of eye-gaze patterns has provided good evidence for the contribution of both the visual context and linguistic/world knowledge to language comprehension. Initial research assessed object-context effects to test&#160;theories of modularity in language processing. In the introduction, we describe how subsequent investigations have taken the role of the wider visual context in language processing as a research topic in its own right, asking questions such as how our visual perception of events and of speakers contributes to&#160;comprehension informed by&#160;comprehenders&#39; experience. Among the examined aspects of the visual context are actions, events, a speaker&#39;s gaze, and emotional facial expressions, as well as spatial object configurations. Following an overview of the eye-tracking method and its different applications, we list the key steps of the methodology in the protocol, illustrating how to successfully use it to study visually-situated language comprehension. A final section presents three sets of representative results and illustrates the benefits and limitations of eye tracking for investigating the interplay between the perception of the visual world and language comprehension.

The present article reviews an eye-tracking methodology for studies on language comprehension. To obtain reliable data, key steps of the protocol must be followed. Among these are the correct set-up of the eye tracker (e.g., ensuring good quality of the eye and head images) and accurate calibration.

The present work is a description and an assessment of a methodology designed to quantify different aspects of the interaction between language processing ...

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Regressive eye movements are eye movements that move backwards through the text and comprise approximately 10-25% of eye movements during reading. As such, understanding the causes and mechanisms of regressions plays an important role in understanding eye movement behavior. Inhibition of return (IOR) is an oculomotor effect that results in increased latency to return attention to a previously attended target versus a target that was not previously attended. Thus, IOR may affect regressions. This paper describes how to design materials to distinguish between regressions caused by comprehension-related and oculomotor processes; the latter is subject to IOR. The method allows researchers to identify IOR and control the causes of regressions. While the method requires tightly controlled materials and large numbers of participants and materials, it allows researchers to distinguish and control the types of regressions that occur in their reading studies.

The method was designed to investigate the role of inhibition of return (IOR) in regressive eye movements during reading. The focus is on differentiating between regressions triggered as a result of comprehension difficulty versus those triggered from oculomotor error, including the role of IOR in the two types of regressions.

Regressive eye movements are eye movements that move backwards through the text and comprise approximately 10-25% of eye movements during reading. As ...

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.

Defining the Role Of Language in Infants&#039; Object Categorization with Eye-tracking Paradigms

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 ...

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)

The Drosophila model has been invaluable for the study of neurological function and for understanding the molecular and cellular mechanisms that underlie neurodegeneration. While fly techniques for the manipulation and study of neuronal subsets have grown increasingly sophisticated, the richness of the resultant behavioral phenotypes has not been captured at a similar detail. To be able to study subtle fly leg movements for comparison amongst mutants requires the ability to automatically measure and quantify high-speed and rapid leg movements. Hence, we developed a machine-learning algorithm for automated leg claw tracking in freely walking flies, Feature Learning-based Limb segmentation and Tracking (FLLIT). Unlike most deep learning methods, FLLIT is fully automated and generates its own training sets without a need for user annotation, using morphological parameters built into the learning algorithm. This article describes an in depth protocol for carrying out gait analysis using FLLIT. It details the procedures for camera setup, arena construction, video recording, leg segmentation and leg claw tracking. It also gives an overview of the data produced by FLLIT, which includes raw tracked body and leg positions in every video frame, 20 gait parameters, 5 plots and a tracked video. To demonstrate the use of FLLIT, we quantify relevant diseased gait parameters in a fly model of Spinocerebellar ataxia 3.

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

We describe detailed protocols for using FLLIT, a fully automated machine learning method for leg claw movement tracking in freely moving Drosophila melanogaster and other insects. These protocols can be used to quantitatively measure subtle walking gait movements in wild type flies, mutant flies and fly models of neurodegeneration.

The Drosophila model has been invaluable for the study of neurological function and for understanding the molecular and cellular mechanisms that underlie ...