The authors would like to acknowledge the members of the Laboratory of Vision Research and the Sensory-Motor Integration Laboratory for helping run participants in this study, Polina Yanovich, Joshua Dobias, and Robert W. Isenhower for help in the initial design phase, and Tom Grace for his help in building the stimulus. This work was supported by the following sources: the NSF Graduate Research Fellowship Program: Award #DGE-0937373, the NSF CyberEnabled Discovery and Innovation Type I (Idea): Grant #094158, and the Rutgers-UMDNJ NIH Biotechnology Training Program: Grant # 5T32GM008339-22.

1. Building the Stimulus Apparatus
<ol>
	<li>Construct a moveable platform on a sliding track. Each stimulus will be placed on the moveable platform depending on the type of trial called for.</li>
	<li>Secure the track onto a table at an appropriate height that allows for the stimulus platform to be at eye-level with the participant to be seated in front of the table.</li>
	<li>Attach a retractable spring mechanism to the stimulus platform. Connect the input to the spring mechanism to a circuit board.</li>
	<li>Place a set of lamps behind the participant's seat, facing the stimulus platform. It is important to illuminate the stimulus platform evenly because uneven lighting may cast shadows that interfere with the illusory percept. Connect the set of lamps to a converter that links it to the circuit board.</li>
	<li>Attach a switch box to the edge of the table closest to the where the participant will be seated. Participants place their hand on the switch box at the beginning of each trial and activate the switch as soon as they lift their hand to execute the reach movement. Link the switch box input to the circuit board.</li>
	<li>Connect each output pin of the circuit board to a pin on the microcontroller to control the simultaneous activation of the retraction of the moving platform via the spring mechanism and the turning off of lights once the switch box is triggered. The stimulus must retract and the lights must turn off after the initiation of the reach movement in each trial to prevent any online visual corrections and haptic feedback from occurring. The switch box is employed so that the stimulus retraction and darkness onset are performed only after movement begins, making this an immediate reach task.</li>
	<li>Write a MATLAB program that controls the microcontroller signals. Use the MATLAB code to store a sequence of trials and instruct the experimenter what stimuli and viewing conditions to use for each trial.</li>
	<li>Construct training stimuli, the reverse-perspective stimulus, and the proper-perspective stimulus (Figures 1 and 2). Training stimuli consist of two rectangular panels representing the isolated right surface wall of the middle building embedded in the reverse-perspective stimulus and the proper-perspective stimulus. The purpose of the training stimuli will be discussed in the experimental procedure. Affix red planar disk targets to the right of the midline of the stimuli.</li>
</ol>
2. Participants
<ol>
	<li>Obtain written informed consent of the IRB approved protocol in compliance with the Declaration of Helsinki before beginning the experimental session.</li>
	<li>Test the participant for visual acuity in each eye, stereopsis (using a Randot-Stereo Test), and eye dominance.</li>
	<li>Set-up the motion capture system. Use fourteen electro-magnetic sensors at 240 Hz and motion-tracking software. The high-resolution recording system allows for the in-depth analysis of the unfolding of movement in three dimensions of fourteen sensors simultaneously, that past studies lack.
	<ol>
		<li>Place twelve of the fourteen sensors on the following body segments using sports bands designed to optimize unrestricted movement of the body: head, trunk, right and left shoulders, left upper arm, left forearm, left wrist, right upper arm, right forearm, right wrist, right hand index finger, and right hand thumb.</li>
		<li>Place the remaining two sensors on the backside of the stimuli directly behind the target location to attain an accurate position of the target in 3D space relative to the participant during the training and experimental blocks.</li>
	</ol>
	</li>
</ol>
3. Experimental Procedure
<ol>
	<li>Place all stimuli out of view from the participant at this time. Turn off all lights except for the lamps used to illuminate the stimulus platform. Dim any computer screens that are in use to run the experiment so that their lights do not interfere with the even lighting projected onto the apparatus.</li>
	<li>Before beginning any trials, inform the participant of the experiment flow. Notify them of the stimulus retraction and turning off of lights once they initiate movement by lifting their hand off the switch box. Remind them not to try to follow the retracting platform, but to only grab at where the target was last seen. Demonstrate how to grab at where they last remember seeing the target by approaching it normal to the perceived surface.</li>
	<li>Begin practice trials. These trials allow for the participant to become comfortable with the setup. There is no test stimulus on the platform - only a black board with a center pole protrusion used to attach stimuli. Instruct the participant to reach at the center pole and to bring the hand back to rest upon completing the reach, at his/her own pace; repeat for three trials. Note: It is important not to give instructions on how to retract the hand; this component should be automatic and below conscious control.</li>
	<li>Initiate training trials. Ask the participant to close his/her eyes after each trial for the remainder of the experiment. While the participant's eyes are closed, affix the training stimulus called for in the MATLAB program to the center pole; the order of training stimulus presentation is randomized by the MATLAB program for a total of eight trials, four for each stimulus. Training stimuli help demonstrate the curvature of the reach when asked to grab at targets on physical surfaces representative of the targets used in the experimental stimuli.</li>
	<li>Begin experimental trials. There are three stimulus conditions for the experimental trials: (1) reverspective under illusory percept, as in Figure 1f (REV-ILLU), (2) reverspective under veridical percept, as in Figure 1e (REV-VER), and (3) proper-perspective (PRO), as in Figure 1c. Recall that conditions (1) and (2) utilize the same physical reverspective stimulus.
	<ol>
		<li>First present the reverspective stimulus. Ask the participant if he/she can stabilize the illusory percept of the middle building "popping out" towards him/her. If the participant has trouble stabilizing the illusory percept, place a de-focusing lens on the nondominant eye to weaken stereopsis in order to preserve the illusory percept while maintaining reaching distance to the target18. If the participant requires the de-focusing lens, then make sure to instruct him/her to put them on before each REV-ILLU trial.</li>
		<li>After the first REV-ILLU trial, the MATLAB program will randomize the order of trials. For each trial, give the following instructions depending on the stimulus condition:
		 REV-ILLU: "View the middle building as popping out towards you." 
		REV-VER: "View the middle building as caving in away from you." 
		PRO: "View the middle building as popping out towards you." 
		Once the participant confirms a stable percept, ask them to grab at the target. Perform twelve trials for each condition for a total of 36 experimental trials.</li>
	</ol>
	</li>
</ol>
4. Data Analysis
<ol>
	<li>To analyze the movements in terms of the goal-directed reach and automatic retractions, first decompose the data into two movement classes by detecting the point at which the velocity of the movement, after its initiation, nears instantaneous zero velocity.</li>
	<li>To look for differences in the curvature of hand path trajectories for each stimulus condition, perform the Wilk's Lambda Test Statistic on the 3-dimensional dataset at each point in time during the trajectory. The Wilk's Lambda Test reduces the likelihood test statistic &#923; to a scalar value by way of determinants to help us deduce whether or not the mean trajectory vector for REV-ILLU is similar to REV-VER or PRO22.</li>
	<li>To study the orientation of the hand towards the target at the end of the goal-directed reach, compare the angle formed between the unit approach vector generated by the thumb, index, and wrist sensor positions relative to the target's unit vector normal to the surface (Figures 5a and 5b).</li>
</ol>

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The authors declare no competing financial interests.

Our methods provide a platform to test the validity of perception-action models by analyzing the entire unfolding of movement in relation to the experimental task. The paradigm can be modified to test other types of visual stimuli to broaden this area of research. For example, other 3D DIIs can be tested on the apparatus to see how interactions between top-down and bottom-up processes translate to various stimuli. The methods can also be tailored to test clinical populations that may have perturbations in perception and action processes. Moreover, the motion capture system utilized in our study can be replaced with other types of recording equipment to best suit the experimental task. The possible generalization of these methods for other applications therefore holds significant value in the advancement of human behavioral research.
However, as with any technique, the current paradigm has its limitations. Because of the removal of haptic feedback and online visual control by turning off the lights and retracting the stimulus, the present study does not allow for the simultaneous recording of eye movements in conjunction with the execution of movement. Eye movements can help identify whether or not participants use an allocentric or egocentric frame of reference to employ a top-down or bottom-up strategy25. Because the current design does not have the capability of implementing this additional measure, it is constrained to only capturing the body&#39;s kinematic function. Alternative strategies to remove haptic feedback and online visual control may be sought out to capture eye movement measures.
Besides this setback, the experimental design has several advantages over existing methods. Since past studies focused on the deliberate, goal-direction actions and end-point data, researchers overlooked any effects in the non-instructed, automatic retraction, and in the actual unfolding of the movement from initiation to rest. The protocol presented here takes into account both deliberate and automatic forms of movement to help build a better understanding of sensory-motor behavior under different perceptual states. Unlike other strategies, this paradigm focuses on both spatial and temporal effects to gain a full understanding of the visuomotor loop. Moreover, the strength of the reverspective stimulus used in this experiment trumps other DIIs used in the past (e.g. hollow-face illusion) as its configuration generates nearly 90&#176; differences in perceived surface orientation under veridical and illusory states while remaining close enough to the participant for he/she to interact with it. This maximal difference aids in the disambiguation of the role of top-down processes on sensory-motor behavior.
Since the study of top-down influences on sensory-motor processes is important not just in the normative system, but also in clinical populations, this paradigm may prove to be a useful tool to study them. Future applications of this protocol may include tailoring the study for pathologies such as Schizophrenia (SZ). It is known that a certain subpopulation of patients with SZ exhibit a decrease in top-down functioning and have known issues in perceptual organization26-28. Thus, understanding how this translates to the motor domain can advance our knowledge to develop better diagnostic tools and therapies for SZ.
This protocol was carefully designed to investigate the role of top-down processes on sensory-motor behavior, specifically when a participant is asked to reach at a target on a stimulus that produces multiple percepts. The critical steps within this protocol are in the selection of the stimuli and in the high resolution of motion capture from the initiation of movement back to resting state. Also, the powerful statistical analyses help elucidate whether or not the illusory percept influences motor strategies. Because this experimental design allows for the high resolution recording of natural intended and spontaneous motor behaviors, the analytical platform developed may help elucidate the existing issues in perception-action models that have long been debated. The preliminary results for Representative Subject VT illustrate this potential.

Vision-for-Perception vs. Vision-for-Action
In order to successfully navigate the environment, information from the visual system is utilized to help coordinate human movement. How visual information is selected and prioritized to influence motor actions remains unclear. Two major anatomical projections arise from the primary visual cortex to form the ventral ("what", or "vision for perception") pathway, extending to the temporal area, and the dorsal ("where", or "vision for action") pathway, to the parietal lobe1-2. The ventral stream is implicated in utilizing visual information for perceptual processes such as object recognition and identification, whereas the dorsal stream is thought to exclusively process signals for action guidance and spatial awareness. The question asked is whether or not top-down processes from the ventral stream shape the way in which movements are executed.
The famous case study of Patient DF, evaluated by Goodale and Milner in 1992, provided strong evidence and support for the visual two-streams hypothesis, which claims that ventral and dorsal stream processes are separable for perception and action3. In theory, bottom-up signals of motion parallax and binocular disparity can override top-down perceptual information such as prior knowledge and familiarity in order to accurately guide our actions, suggesting that motor planning is impervious to ventral stream control. DF, who suffered from visual form agnosia caused by bilateral ventral occipital lesions, retained accurate grasping ability towards objects that she had difficulty recognizing, supporting the premise of the visual two-streams hypothesis3-4. Because of case studies like DF, it was assumed that the functional ventral-dorsal stream dichotomy also existed in healthy, nonpathological individuals. However, whether or not these findings provide evidence for an absolute division of labor for perception and action in neurotypical populations has been hotly debated over the past twenty years5-10.
The Use of Illusions to Segregate Perception and Action
To test the visual two-streams hypothesis in neurotypical subjects, researchers employ visual illusions to investigate how skewed perceptual judgments of the environment affect our motor actions. The Ebbinghaus/Titchener Illusion, for example, uses a disk target surrounded by smaller disks that appears to be larger than another disk of the same size surrounded by larger circles; this is due to a size-contrast effect11. When participants reach to grasp the disk target, if the two-streams hypothesis holds true, then the grip aperture of the hand grabbing at the disk target would be unaffected by the illusion, causing the participant to act on the true geometry of the disk target rather than rely on incorrect perceptual size estimates. Aglioti et al. in fact report this behavior, reasoning that separate visual processes govern skilled actions and conscious perception11. Conversely, other groups have contested these results, finding no dissociation between perception and action processes when carefully controlling the matching of perceptual and grasping tasks, proposing an integration of visual stream information rather than a separation12. Despite several follow-up studies conducted to validate or refute the visual two-streams hypothesis using the Ebbinghaus Illusion, there are competing pieces of evidence to support both sides of the argument13.
To further explore the influence of visual perception on action processes, 3D depth inversion illusions (DII) have also been utilized. DIIs produce illusory motion and perceived depth reversal of scenes in which physically concave angles are perceived as convex and vice versa14. The Hollow Face Illusion is an example of a DII that generates the perception of a normal, convex face although the stimulus is physically concave, implicating the role of top-down influences such as prior knowledge and convexity bias to elicit the illusory percept15-16. Despite efforts to characterize motor behavior in reaching towards targets on the Hollow Face Illusion, evidence remains equivocal: one study reports an effect on motor output17 while another does not18. These studies rely on comparing perceptual depth estimates to endpoint distance calculations of the hand relative to targets located on the Hollow Face Illusion. Conflicting results on actions performed on this type of stimuli may be a result of the variations in methods used by researchers. Because the way in which ventral and dorsal stream information is utilized is still up to debate, this controversy sparks the need for a more robust stimulus with additional advanced measures of motor behavior.
This is precisely why a technique was developed using reverse-perspective stimuli, commonly referred to as "reverspectives", which form another class of DIIs14. Linear perspective cues that are painted on piecewise 3D planar surfaces produce competition between the physical geometry of the stimulus and the actual painted scene. Data-driven sensory signals, such as binocular disparity and motion parallax favor the veridical percept of the physical geometry, whereas experience-based familiarity with perspective favors the depth-inversion percept (Figure 1). The advantage of the reverspective is that it allows for the placement of a target on a stimulus surface whose perceived spatial orientation under the illusion differs by nearly 90 degrees from its physical orientation (Figures 1e and 1f). This huge difference greatly facilitates testing whether reach-for-grasping movements are or are not influenced by the illusion. This notion is key to exploring whether or not motor actions performed on the reverspective are affected by top-down influences from the ventral stream.
Movement Classes in Perception-Action Models
If different motor strategies are employed under illusory and veridical percepts when grabbing towards a target on a reverspective stimulus, then it can be easily tracked by studying the curvature of the hand&#39;s approach. Moreover, an analysis of the entire unfolding movement from initiation of the goal-directed movement to the spontaneous, automatic retraction of the hand back to its resting state may in fact bypass any shortcomings found in past methods of testing for perceptual influence on motor output. Recent studies highlight the significance of studying the balance between these two movement classes as well as the use of the spontaneous segments by the nervous systems for predictive and anticipatory control19-21,23-24. The newly statistically defined class of spontaneous-automatic movements provides new metrics and features that turn out to be as crucial as the goal-directed ones have been thus far to track sensory-motor changes and to quantify subtle aspects of natural behaviors.
To our knowledge, existing research on the visual two-streams hypothesis only focuses on goal-directed acts, thereby ignoring any effects on automatic transitional movements that are significant components to completing the visuomotor action loop. Emphasis therefore must be placed on the importance of automatic motions in order to fully capture both modes of motor behavior in the present paradigm to clarify issues concerning visual perception-action models. Here methods are developed to investigate the role of top-down signaling in the visual ventral stream on modulating motor behavior in the deliberate, goal-directed action domain in conjunction with spontaneous, transitional movements using a robust DII reverse-perspective stimulus.
Rationale
It is hypothesized that, if top-down visual processes influence the sensory-motor system, full movement trajectories toward the embedded target in the 3D reverse-perspective scene under the illusory percept will differ from the target approach elicited by the veridical percept (Figures 1e and 1f). Moreover, since the illusory percept of the reverspective stimulus is very similar to that obtained by a proper ("forced") perspective stimulus, reaches performed toward an embedded target on a reverspective should therefore be similar in characteristics to reaches conducted under the influence of the illusion on the reverspective stimulus (Figures 1c and 1f).
If top-down visual influences do not impact the movement trajectory, then it is hypothesized that reaches made under the illusory percept would exhibit the same characteristics as reaches made under the veridical percept on the reverspective stimulus (Figure 1e). In other words, both illusory and veridical percept reaches would be similar in nature, such that both forward trajectory paths would act on the true geometry of the stimulus. How effects observed in the forward reach translate in the automatic retraction of the hand is unknown. By employing a full motor analysis, we aim to advance our understanding of action and perception loops to clarify the existing issues at hand.

<table><tbody><tr><td>Laboratory bench</td><td></td><td></td><td></td></tr><tr><td>Slidable Track with Retractable Spring</td><td></td><td></td><td>built in-house</td></tr><tr><td>Retractable Spring</td><td></td><td></td><td></td></tr><tr><td>Adjustable Lamps</td><td></td><td></td><td></td></tr><tr><td>Switch Box</td><td></td><td></td><td></td></tr><tr><td>Circuit Board</td><td></td><td></td><td></td></tr><tr><td>Arduino</td><td>Smart Projects, Italy</td><td></td><td></td></tr><tr><td>MATLAB</td><td>The MathWorks Inc., Natick, MA, USA</td><td></td><td></td></tr><tr><td>Randot-dot Stereo Test</td><td></td><td></td><td></td></tr><tr><td>Reverse-Perspective Stimulus</td><td></td><td></td><td>built in-house</td></tr><tr><td>Proper-Perspective Stimulus</td><td></td><td></td><td>built in-house</td></tr><tr><td>Training Stimuli</td><td></td><td></td><td>built in-house</td></tr><tr><td>Polhemus Motion Capture System</td><td>Liberty, Colchester, VT, USA</td><td></td><td></td></tr><tr><td>The Motion Monitor Motion-Tracking Software</td><td>Innovative Sports Training, Inc., Chicago, IL</td><td></td><td></td></tr><tr><td>Sport Sweatbands</td><td></td><td></td><td></td></tr><tr><td>De-Focusing Lens</td><td></td><td></td><td></td></tr></tbody></table>

methods to explore the influence of top-down visual processes on motor behavior

Kinesthetic awareness is important to successfully navigate the environment. When we interact with our daily surroundings, some aspects of movement are deliberately planned, while others spontaneously occur below conscious awareness. The deliberate component of this dichotomy has been studied extensively in several contexts, while the spontaneous component remains largely under-explored. Moreover, how perceptual processes modulate these movement classes is still unclear. In particular, a currently debated issue is whether the visuomotor system is governed by the spatial percept produced by a visual illusion or whether it is not affected by the illusion and is governed instead by the veridical percept. Bistable percepts such as 3D depth inversion illusions (DIIs) provide an excellent context to study such interactions and balance, particularly when used in combination with reach-to-grasp movements. In this study, a methodology is developed that uses a DII to clarify the role of top-down processes on motor action, particularly exploring how reaches toward a target on a DII are affected in both deliberate and spontaneous movement domains.

1. Hand Path Trajectories
Results are shown for Representative Subject VT. The Wilk&#39;s Lambda Test Statistic allows for the reduction of our three-dimensional space data into a scalar value by the use of determinants. The Wilk&#39;s lambda statistic uses the likelihood ratio test <img alt="" fo:content-width=".8in" src="/files/ftp_upload/51422/51422eq1.jpg" width="90" />, in which the &#39;within&#39; sum of squares and products form matrix E, and the &#39;total&#39; sum of squares and products form matrix (E+H). The rule states that, when <img alt="" fo:content-width="1in" src="/files/ftp_upload/51422/51422eq2.jpg" width="110" />, the null hypothesis is rejected. In <img alt="" fo:content-width=".8in" src="/files/ftp_upload/51422/51422eq3.jpg" width="80" />, <img alt="" fo:content-width=".15in" src="/files/ftp_upload/51422/51422eq4.jpg" width="15" /> is the level of confidence, <img alt="" fo:content-width=".15in" src="/files/ftp_upload/51422/51422eq5.jpg" width="15" /> is the number of variables or dimensions, and <img alt="" fo:content-width=".6in" src="/files/ftp_upload/51422/51422eq6.jpg" width="60" /> and <img alt="" fo:content-width=".8in" src="/files/ftp_upload/51422/51422eq7.jpg" width="80" /> are the degrees of freedom for the hypothesis and error, respectively, in which <img alt="" fo:content-width=".15in" src="/files/ftp_upload/51422/51422eq8.jpg" width="15" /> is the number of conditions and <img alt="" fo:content-width=".15in" src="/files/ftp_upload/51422/51422eq9.jpg" width="15" /> is the number of trials. In our case, <img alt="" fo:content-width="1in" src="/files/ftp_upload/51422/51422eq10.jpg" width="120" /> and <img alt="" fo:content-width="1.2in" src="/files/ftp_upload/51422/51422eq11.jpg" width="120" />. Therefore, we obtain <img alt="" fo:content-width="1.5in" src="/files/ftp_upload/51422/51422eq12.jpg" width="150" /> from the look-up table found in Rencher&#39;s Methods for Multivariate Analysis22.
Hand path trajectory analysis using the Wilk&#39;s Lambda Test reveal a statistically significant difference between REV-ILLU and REV-VER conditions in the forward, goal-directed movement (Figure 3a), as <img alt="" fo:content-width="1.8in" src="/files/ftp_upload/51422/51422eq13.jpg" width="180" /> throughout the entire path&#39;s progression (Figure 3d). This behavior is also preserved in the noninstructed retraction as seen in the graph (Figures 4a and 4d). As expected, the comparison between the REV-VER and PRO conditions differs significantly in both the forward and retractory movements (Figures 3b, 3e, 4b, and 4e). Since the unfolding of movement is critical in determining differences in approach, Wilk&#39;s lambda values are plotted based on the percentage of hand path trajectory complete (Figures 3d-3f and 4d-4f). Averaging these values over the entire path would not fully recapitulate the kinematics of the entire action loop. Wilk&#39;s lambda values for forward hand path trajectories in the REV-VER vs. REV-ILLU comparison (Figure 3d) are similar to those found in the REV-VER vs. PRO comparison (Figure 3e). The same holds true for the retraction of the hand (Figures 4d and 4e). The REV-ILLU and PRO conditions do not differ significantly in either movement class, as <img alt="" fo:content-width="1.8in" src="/files/ftp_upload/51422/51422eq14.jpg" width="180" />for all lambda values based on the percentage of path complete in both the forward and retraction cases (Figures 3c, 3f, 4c, and 4f).
2. Hand Orientation
When examining the orientation of the hand as it approaches the target in each condition, hand-approach vectors in REV-VER cases differ from those in the REV-ILLU and PRO cases (Figure 5c). REV-ILLU and PRO conditions produce similar hand postures when orienting towards the perceived target for REV-ILLU and the physical target for PROPER conditions. The angle formed between the mean unit approach vector for REV-ILLU trials and the unit vector normal to the target surface produces a 97.5197&#176; &#177; 3.2228 difference (Figure 5d). Recall that the reverse-perspective stimulus generates nearly 90-degree maximal differences between illusory and veridical states. This therefore suggests that Representative Subject VT oriented her hand towards the perceived target and not the physical location of the target under the illusory percept.
<img alt="Figure 1" fo:content-width="5in" src="/files/ftp_upload/51422/51422fig1highres.jpg" width="500"/> 
Figure 1. The Proper- and Reverse- Perspective Stimuli. (a-c) The proper- or &#34;forced&#34; perspective. (a) Front view of the painted stimulus. (b) Orthographic views. (c) Top view: the veridical percept of a concave scene with an arrow illustrating a typical reach trajectory for the target. (a, d-f) The reverse-perspective gives rise to two percepts, shown in parts (e) and (f). (a) Front view of the painted stimulus. (d) Orthographic views. (e) Top view: the veridical percept of a convex scene with an arrow illustrating a typical reach trajectory. (f) Top view: the illusory percept of a concave scene &#8211; shown by dotted lines &#8211; with an arrow illustrating a typical reach trajectory. The dotted-line figure shows the perceived illusory 3D shape only. The position of the object is not accurate; in fact, the illusory object was deliberately offset toward the observer in order to clarify the reach trajectory. The curvature of all trajectories is exaggerated to illustrate the differences that might result, depending on the percept. The percept of (f) provides an excellent test for examining whether the reaching trajectory is governed by the illusion (trajectory of (f)) or by the physical surface (trajectory of (e)). Note that the proper- and reverse-perspectives share the same front view (a). <a href="https://www.jove.com/files/ftp_upload/51422/51422fig1highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" fo:content-width="5in" fo:src="/files/ftp_upload/51422/51422fig2highres.jpg" src="/files/ftp_upload/51422/51422fig2.jpg" /> 
Figure 2. Training Stimuli. (a-b) The rectangular panel has the same spatial orientation as the right wall of the middle building in the proper-perspective 3D stimulus. (c-d) The rectangular panel has the same orientation as the right wall of the middle building in the reverse-perspective 3D stimulus. (a, c) Schematic drawings of top views to illustrate the placement of the panels, with arrows indicating typical reach trajectories. The curvature of the trajectories is exaggerated to illustrate the difference. (b, d) Photographs of the stimuli as they appeared to participants. <a href="https://www.jove.com/files/ftp_upload/51422/51422fig2highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" fo:content-width="5in" fo:src="/files/ftp_upload/51422/51422fig3highres.jpg" src="/files/ftp_upload/51422/51422fig3.jpg" /> 
Figure 3. Forward Hand Path Trajectory Analysis. (a-c) Mean trajectories plotted in white with confidence intervals (colored tubes) for each point in the trajectory for reverse-perspective veridical (REV-VER in green), reverse-perspective illusory (REV-ILLU in blue), and proper-perspective (PROPER in red) conditions for the goal-directed, intended forward movement. (d-f) Lambda values for pairwise comparisons of conditions based on percentage of path complete. Using the Wilk&#39;s Lambda Test, when <img alt="" fo:content-width="2in" src="/files/ftp_upload/51422/51422eq15.jpg" width="180" />, the null hypothesis is rejected. <img alt="" fo:content-width=".3in" src="/files/ftp_upload/51422/51422eq16.jpg" width="30" />is given by the dotted line. In (d) REV-VER vs. REV-ILLU and (e) REV-VER vs. PROPER comparisons, <img alt="" fo:content-width=".5in" src="/files/ftp_upload/51422/51422eq17.jpg" width="50" />, indicating a significant difference between hand path trajectories. For the (f) REV-ILLU vs. PROPER .5comparison, <img alt="" fo:content-width=".5in" src="/files/ftp_upload/51422/51422eq18.jpg" width="50" />, therefore hand path trajectories between conditions do not differ significantly. <a href="https://www.jove.com/files/ftp_upload/51422/51422fig3highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" fo:content-width="5in" fo:src="/files/ftp_upload/51422/51422fig4highres.jpg" src="/files/ftp_upload/51422/51422fig4.jpg" /> 
Figure 4. Retraction Hand Path Trajectory Analysis. (a-c) Mean trajectories plotted in white with confidence intervals (colored tubes) for each point in the trajectory for reverse-perspective veridical (REV-VER in green), reverse-perspective illusory (REV-ILLU in blue), and proper-perspective (PROPER in red) conditions for the spontaneous, automatic retraction of the hand. (d-f) Lambda values for pairwise comparisons of conditions based on percentage of path complete. Using the Wilk&#39;s Lambda Test, when <img alt="" fo:content-width="2in" src="/files/ftp_upload/51422/51422eq19.jpg" width="180" />, the null hypothesis is rejected. <img alt="" fo:content-width=".3in" src="/files/ftp_upload/51422/51422eq20.jpg" width="30" /> is given by the dotted line. In (d) REV-VER vs. REV-ILLU and (e) REV-VER vs. PROPER comparisons, <img alt="" fo:content-width=".5in" src="/files/ftp_upload/51422/51422eq21.jpg" width="50" />, indicating a significant difference between hand path trajectories. For the (f) REV-ILLU vs. PROPER comparison, <img alt="" fo:content-width=".5in" src="/files/ftp_upload/51422/51422eq22.jpg" width="50" />, therefore hand path trajectories between conditions do not differ significantly. <a href="https://www.jove.com/files/ftp_upload/51422/51422fig4highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" fo:content-width="5in" fo:src="/files/ftp_upload/51422/51422fig5highres.jpg" src="/files/ftp_upload/51422/51422fig5.jpg" /> 
Figure 5. Hand Orientation. (a) The unit approach vector of the hand is defined by sensors located on the thumb, index, and wrist positions. (b) Unit (dashed) vectors normal to the target surface for the proper-perspective (top) and reverse-perspective (middle and bottom) stimuli. Under the illusory percept (bottom panel, dotted lines) this vector is perceived as nearly perpendicular to the physical unit vector (middle). (c) Hand approach vectors plotted for REV-VER (green), REV-ILLU (blue), and PROPER (red) trials, (d) Mean angle values formed between the unit approach vector and the physical unit vector normal to the target surface corresponding to the stimulus used. <a href="https://www.jove.com/files/ftp_upload/51422/51422fig5highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior at JoVE.com

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

It is unclear how top-down signals from the ventral visual stream affect movement. We developed a paradigm to test motor behavior towards a target on a 3D depth inversion illusion. Significant differences are reported in both deliberate, goal-directed movements and automatic actions under illusory and veridical viewing conditions.

Kinesthetic awareness is important to successfully navigate the environment. When we interact with our daily surroundings, some aspects of movement are ...

methods-to-explore-influence-top-down-visual-processes-on-motor

Nanyang Technological University

Research

JoVE Journal

Behavior

24.3K Views.  Rutgers University. It is unclear how top-down signals from the ventral visual stream affect movement. We developed a paradigm to test motor behavior towards a target on a 3D depth inversion illusion. Significant differences are reported in both deliberate, goal-directed movements and automatic actions under illusory and veridical viewing conditions.

Video: Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

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

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

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

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

Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another

As far as acquiring motor skills is concerned, training by voluntary physical movement is superior to all other forms of training (e.g. training by observation or passive movement of trainee&#39;s hands by a robotic device). This obviously presents a major challenge in the rehabilitation of a paretic limb since voluntary control of physical movement is limited. Here, we describe a novel training scheme we have developed that has the potential to circumvent this major challenge. We exploited the voluntary control of one hand and provided real-time movement-based manipulated sensory feedback as if the other hand is moving. Visual manipulation through virtual reality (VR) was combined with a device that yokes left-hand fingers to passively follow right-hand voluntary finger movements. In healthy subjects, we demonstrate enhanced within-session performance gains of a limb in the absence of voluntary physical training. Results in healthy subjects suggest that training with the unique VR setup might also be beneficial for patients with upper limb hemiparesis by exploiting the voluntary control of their healthy hand to improve rehabilitation of their affected hand.

We describe a novel virtual-reality based setup which exploits voluntary control of one hand to improve motor-skill performance in the other (non-trained) hand. This is achieved by providing real-time movement-based sensory feedback as if the non-trained hand is moving. This new approach may be used to enhance rehabilitation of patients with unilateral hemiparesis.

As far as acquiring motor skills is concerned, training by voluntary physical movement is superior to all other forms of training (e.g. training by ...

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 ...

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions

Our knowledge of human object vision is based almost exclusively on studies in which the stimuli are presented in the form of computerized two-dimensional (2-D) images. In everyday life, however, humans interact predominantly with real-world solid objects, not images. Currently, we know very little about whether images of objects trigger similar behavioral or neural processes as do real-world exemplars. Here, we present methods for bringing the real-world into the laboratory. We detail methods for presenting rich, ecologically-valid real-world stimuli under tightly-controlled viewing conditions. We describe how to match closely the visual appearance of real objects and their images, as well as novel apparatus and protocols that can be used to present real objects and computerized images on successively interleaved trials. We use a decision-making paradigm as a case example in which we compare willingness-to-pay (WTP) for real snack foods versus 2-D images of the same items. We show that WTP increases by 6.6% for food items displayed as real objects versus high-resolution 2-D colored&#160;images of the same foods -suggesting that real foods are perceived as being more valuable than their images. Although presenting real object stimuli under controlled conditions presents several practical challenges for the experimenter, this approach will fundamentally expand our understanding of the cognitive and neural processes that underlie naturalistic vision.

We describe methods for presenting real-world objects and matched images of the same objects under tightly-controlled experimental conditions. The methods are described in the context of a decision-making task, but the same real-world approach can be extended to other cognitive domains such as perception, attention, and memory.

Our knowledge of human object vision is based almost exclusively on studies in which the stimuli are presented in the form of computerized two-dimensional ...

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Previous studies have shown that neurons in parieto-frontal areas of the macaque brain can be highly selective for real-world objects, disparity-defined curved surfaces, and images of real-world objects (with and without disparity) in a similar manner as described in the ventral visual stream. In addition, parieto-frontal areas are believed to convert visual object information into appropriate motor outputs, such as the pre-shaping of the hand during grasping. To better characterize object selectivity in the cortical network involved in visuomotor transformations, we provide a battery of tests intended to analyze the visual object selectivity of neurons in parieto-frontal regions.

A detailed protocol to analyze object selectivity of parieto-frontal neurons involved in visuomotor transformations is presented.

Previous studies have shown that neurons in parieto-frontal areas of the macaque brain can be highly selective for real-world objects, disparity-defined ...

Neuroscience

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

To reach towards a seen object, visual information has to be transformed into motor commands. Visual information such as the object&#8217;s color, shape, and size are processed and integrated within numerous brain areas, then ultimately relayed to the motor periphery. In some instances, a reaction is needed as fast as possible. These fast visuomotor transformations, and their underlying neurological substrates, are poorly understood in humans as they have lacked a reliable biomarker. Stimulus-locked responses (SLRs) are short latency (&#60;100 ms) bursts of electromyographic (EMG) activity representing the first wave of muscle recruitment influenced by visual stimulus presentation. SLRs provide a quantifiable output of rapid visuomotor transformations, but SLRs have not been consistently observed in all subjects in past studies. Here we describe a new, behavioral paradigm featuring the sudden emergence of a moving target below an obstacle that consistently evokes robust SLRs. Human participants generated visually guided reaches toward or away from the emerging target using a robotic manipulandum while surface electrodes recorded EMG activity from the pectoralis major muscle. In comparison to previous studies that investigated SLRs using static stimuli, the SLRs evoked with this emerging target paradigm were larger, evolved earlier, and were present in all participants. Reach reaction times (RTs) were also expedited in the emerging target paradigm. This paradigm affords numerous opportunities for modification that could permit systematic study of the impact of various sensory, cognitive, and motor manipulations on fast visuomotor responses. Overall, our results demonstrate that an emerging target paradigm is capable of consistently and robustly evoking activity within a fast visuomotor system.

Presented here is a behavioral paradigm that elicits robust fast visuomotor responses on human upper limb muscles during visually guided reaches.

To reach towards a seen object, visual information has to be transformed into motor commands. Visual information such as the object&#8217;s color, shape, ...

In Vivo Wireless Optogenetic Control of Skilled Motor Behavior

Fine motor skills are essential in everyday life and can be compromised in several nervous system disorders. The acquisition and performance of these tasks require sensory-motor integration and involve precise control of bilateral brain circuits. Implementing unimanual behavioral paradigms in animal models will improve the understanding of the contribution of brain structures, like the striatum, to complex motor behavior as it allows manipulation and recording of neural activity of specific nuclei in control conditions and disease during the performance of the task. 
Since its creation, optogenetics has been a dominant tool for interrogating the brain by enabling selective and targeted activation or inhibition of neuronal populations. The combination of optogenetics with behavioral assays sheds light on the underlying mechanisms of specific brain functions. Wireless head-mounted systems with miniaturized light-emitting diodes (LEDs) allow remote optogenetic control in an entirely free-moving animal. This avoids the limitations of a wired system being less restrictive for animals' behavior without compromising light emission efficiency. The current protocol combines a wireless optogenetics approach with high-speed videography in a unimanual dexterity task to dissect the contribution of specific neuronal populations to fine motor behavior.

In Vivo Wireless Optogenetic Control of Skilled Motor Behavior

The present protocol describes how to use wireless optogenetics combined with high-speed videography in a single pellet reach-to-grasp task to characterize the neural circuits involved in the performance of skilled motor behavior in freely moving mice.

Fine motor skills are essential in everyday life and can be compromised in several nervous system disorders. The acquisition and performance of these ...

Assessing Corticospinal Excitability During Goal-Directed Reaching Behavior

Reaching is a widely studied behavior in motor physiology and neuroscience research. While reaching has been examined using a variety of behavioral manipulations, there remain significant gaps in the understanding of the neural processes involved in reach planning, execution, and control. The novel approach described here combines a two-dimensional reaching task with transcranial magnetic stimulation (TMS) and concurrent electromyography (EMG) recording from multiple muscles. This method allows for the noninvasive detection of corticospinal activity at precise time points during the unfolding of reaching movements. The example task code includes a delayed response reaching task with two possible targets displayed &#177; 45&#176; off the midline. Single pulse TMS is delivered on the majority of task trials, either at the onset of the preparatory cue (baseline) or 100 ms prior to the imperative cue (delay). This sample design is suitable for investigating changes in corticospinal excitability during reach preparation. The sample code also includes a visuomotor perturbation (i.e., cursor rotation of &#177; 20&#176;) to investigate the effects of adaptation on corticospinal excitability during reach preparation. The task parameters and TMS delivery can be adjusted to address specific hypotheses about the state of the motor system during reaching behavior. In the initial implementation, motor evoked potentials (MEPs) were successfully elicited on 83% of TMS trials, and reach trajectories were recorded on all trials.

Reaching is a fundamental skill that allows humans to interact with the environment. Several studies have aimed to characterize reaching behavior using a variety of methodologies. This paper offers an open-source application of transcranial magnetic stimulation to assess the state of corticospinal excitability in humans during reaching task performance.

Reaching is a widely studied behavior in motor physiology and neuroscience research. While reaching has been examined using a variety of behavioral ...

Evaluating Optokinetic Reflex Responses in Mice: A New High-Precision Protocol

Quantification of Visual Feature Selectivity of the Optokinetic Reflex in Mice

The optokinetic reflex (OKR) is an essential innate eye movement that is triggered by the global motion of the visual environment and serves to stabilize retinal images. Due to its importance and robustness, the OKR has been used to study visual-motor learning and to evaluate the visual functions of mice with different genetic backgrounds, ages, and drug treatments. Here, we introduce a procedure for evaluating OKR responses of head-fixed mice with high accuracy. Head fixation can rule out the contribution of vestibular stimulation on eye movements, making it possible to measure eye movements triggered only by visual motion. The OKR is elicited by a virtual drum system, in which a vertical grating presented on three computer monitors drifts horizontally in an oscillatory manner or unidirectionally at a constant velocity. With this virtual reality system, we can systematically change visual parameters like spatial frequency, temporal/oscillation frequency, contrast, luminance, and the direction of gratings, and quantify tuning curves of visual feature selectivity. High-speed infrared video-oculography ensures accurate measurement of the trajectory of eye movements. The eyes of individual mice are calibrated to provide opportunities to compare the OKRs between animals of different ages, genders, and genetic backgrounds. The quantitative power of this technique allows it to detect changes in the OKR when this behavior plastically adapts due to aging, sensory experience, or motor learning; thus, it makes this technique a valuable addition to the repertoire of tools used to investigate the plasticity of ocular behaviors.

Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice 

Here, we describe a standard protocol for quantifying the optokinetic reflex. It combines virtual drum stimulation and video-oculography, and thus allows precise evaluation of the feature selectivity of the behavior and its adaptive plasticity.

The optokinetic reflex (OKR) is an essential innate eye movement that is triggered by the global motion of the visual environment and serves to stabilize ...

An Introduction to Motor Control

Motor control involves integration and processing of sensory information by our nervous system, followed by a response through our skeletal system to perform a voluntary or involuntary action. It is vital to understand how our neuroskeletal system controls motor behavior in order to evaluate injuries pertaining to general movement, reflexes, and coordination. An improved understanding of motor control will help behavioral neuroscientists in developing useful tools to treat motor disorders, such as Parkinson's or Huntington's disease.
This video briefly reviews the neuroanatomical structures and connections that play a major role in controlling motion. Fundamental questions currently being asked in the field of motor control are introduced, followed by some of the methods being employed to answer those questions. Lastly, the application sections reviews a few specific experiments conducted in neuroscience labs interested in studying this phenomenon.

Motor Control: Neural Basis; Techniques to Study Motor Behavior

Motor control involves integration and processing of sensory information by our nervous system, followed by a response through our skeletal system to ...

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

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

Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another

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

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

In Vivo Wireless Optogenetic Control of Skilled Motor Behavior

Assessing Corticospinal Excitability During Goal-Directed Reaching Behavior

Quantification of Visual Feature Selectivity of the Optokinetic Reflex in Mice

An Introduction to Motor Control