The authors are thankful to the Coordination for the Improvement of Higher Education Personnel (CAPES) - Finance Code 001 and the Federal University of ABC (UFABC). Jo&#227;o R. Sato received financial support from the S&#227;o Paulo Research Foundation (FAPESP, Grants Nos. 2018/21934-5, 2018/04654-9, and 2023/02538-0).

Real-Time Recording and Mapping of Human Interaction with 3D Virtual Objects

The authors have no conflicts of interest to disclose.

As previously stated, this paper aims to present a detailed procedure of real-time mapping of fixation and saccade motion data on interactive 3D objects, which is easily customizable and only uses software available for free, providing step-by-step instructions to make everything work.
While this experimental setup involved a highly interactive task, such as moving a 3D object to match another object&#39;s orientation with PR in two of the three possible axes, we ensured thorough documentation of our scripts through proper commenting to facilitate any customization. Various other types of experiments can be designed, with the eye-tracking device being only one of many other possible devices used for temporal data acquisition.
The headers in the copied file from step 1.1.3.3 define the content and location the data will be collected online. Supplemental Table S1 lists the variable names (all case-sensitive) and their meaning. These variables mirror those found in the JavaScript files within the GitHub repository. The type and variety of data and variable names, both from this sheet and the JavaScript files, should be changed according to the scope and requirements of the research.
The recording of rotation data in quaternions allows the researcher to reproduce the same movements made by the participants during the tasks, facilitating an analysis of the process and using storage space much more efficiently if compared to a screen capture. More detailed analysis, such as 3D rotation trajectory, shown in Figure 7 using the ball of rotations, is only possible through the internal quaternion data of the interactive objects. Expanding from the AD plot over time by Gardony22 and Adams23, this new type of graph provides more detailed information, with the actual 3D rotation coordinates in time.
Another advantage comes from using a standard time measure to synchronize all data sources. Merging different layers of time-dependent information with this becomes much easier, such as superimposing graphs with multiple data sources, as in Figure 5B with the pupil dilation measurement, or in Figure 5A with colored vertical bands, denoting possible patterns in the participants&#39; solving process, even while there was almost no rotation happening in iObj. The 3D fixation heatmap shown in Figure 3 is only possible from both quaternion data and data synchronization.
It is crucial to use synchronization through a standard time measure to ensure any integration of temporal data. The time standard chosen for our project was the UNIX Epoch, which is used in JavaScript and most other programming languages. Some type of known time standard must be in use for each data set, even if a different standard, which can later, be converted to UNIX Epoch. Temporal data that do not use any standards will most certainly be incapable of synchronization and lose their usefulness.
Another limitation is the relatively low frequency of 10 Hz used in the iRT tests in relation to the eye tracker frequency of 60 Hz. This happens partly due to data processing and transfer limitations within the browser, as any higher frequency used would proportionally reduce the maximum time limit of each task, currently at 327 s. Additionally, smoothly rendering complex animations in Jmol at this framerate already presented challenges. Supplemental Video S1 is a video recording of Jmol rendering a replay with the change of opacity in time, mapping the amount of focus each vertex received. While the video duration is almost 2 min, the actual task was completed in 63 s. Future software developments catered specifically to such functionalities instead of adapting existing ones, could address these limitations and enhance data collection and analysis capabilities.

Mental rotation (MR) is a cognitive ability that enables individuals to mentally manipulate and rotate objects, facilitating a better understanding of their features and spatial relationships. It is one of the visuospatial abilities, a fundamental cognitive group that was studied as early as 18901. Visuospatial abilities are an important component of an individual&#39;s cognitive repertoire that is influenced by both inherited and environmental factors2,3,4,5. Interest in visuospatial abilities has grown throughout the twentieth century due to mounting evidence of their importance in key subjects such as aging6 and development7, performance in science, technology, engineering, and mathematics (STEM)8,9, creativity10, and evolutionary traits11.
The contemporary idea of MR derives from the pioneering work published by Shepard and Metzler (SM) in 197112. They devised a chronometric method using a series of &#34;same or different&#34; tasks, presenting two projections of abstract 3D objects displayed side by side. Participants had to mentally rotate the objects on some axis and decide if those projections portrayed the same object rotated differently or distinct objects. The study revealed a positive linear correlation between response time (RT) and the angular disparity (AD) between representations of the same object. This correlation is known as the angle disparity effect (ADE). ADE is regarded as a behavioral manifestation of MR and became ubiquitous in several influential subsequent studies in the field13,14,15,16,17,18,19,20,21,22,23,24,25. The 3D objects employed in the SM study were composed of 10 contiguous cubes generated by the computer graph pioneer Michael Noll at Bell Laboratories26. They are referred to as SM figures and are widely employed in MR studies.
Two advancements were of great importance in Shepard and Metzler&#39;s seminal work; first, considering the contributions in the field of MR assessments. In 1978, Vanderberg and Kuze27 developed a psychometric 20-item pencil-and-paper test based on SM &#34;same or different&#34; figures that became known as the mental rotation test (VKMRT). Each test item presents a target stimulus. Participants must select among four stimuli, which ones represent the same object depicted in the target stimulus, and which do not. VKMRT has been used to investigate the correlation between MR ability and various other factors, such as sex-related differences6,21,24,28,29,30, aging and development6,31,32, academic performance8,33, and skills in music and sports34. In 1995, Peters et al. published a study with redrawn figures for the VKMRT35,36. Similarly, following the &#34;same or different&#34; task design, a variety of other libraries of computer-generated stimuli have been employed to investigate MR processes and to assess MR abilities (3D versions of the original SM stimuli19,22,23,37,38, human body mimicking SM figures25,39,40, flat polygons for 2D rotation41,42, anatomy, and organs43, organic shapes44, molecules45,46, among others21). The Purdue Spatial Visualization Test (PSVT) proposed by Guay in 197647 is also relevant. It entails a battery of tests, including MR (PSVT:R). Employing different stimuli from those in VKMRT, PSVT:R requires participants to identify a rotation operation in a model stimulus and mentally apply it to a different one. PSVT:R is also widely used, particularly in studies investigating the role of MR in STEM achievement48,49,50.
The second advancement of great importance in Shepard and Metzler&#39;s seminal work comprises the contributions to the understanding of the MR process, in particular, with the use of eye-tracking devices. In 1976, Just and Carpenter14 used analog video-based eye-tracking equipment to conduct a study based on Shepard and Metzler&#39;s ADE experiment. From their results on saccadic eye movements and RTs, they proposed a model of MR processes consisting of three phases: 1) the search phase, in which similar parts of the figures are recognized; 2) the transformation and comparison phase, in which one of the identified parts is mentally rotated; 3) the confirmation phase, in which it is decided whether the figures are the same or not. The phases are repeated recursively until a decision can be made. Each step corresponds to specific saccadic and fixational eye movement patterns in close relation to observed ADEs. Thus, by correlating eye activity to chronometric data, Just and Carpenter provided a cognitive signature for the study of MR processes. To date, this model, albeit with adaptations, has been adopted in several studies15,42,46,51,52,53.
Following this track, several ensuing studies monitoring behavioral18,19,22,23,25,34,40,54,55 and brain activity20,22,56,57 functions during stimuli rotation were conducted. Their findings point to a cooperative role between MR and motor processes. Moreover, there is a growing interest in investigating problem-solving strategies involving MR in relation to individual differences 15,41,46,51,58.
Overall, it can be considered that the design of studies aiming at understanding MR processes is based on presenting a task with visual stimuli that requests participants to perform an MR operation that, in turn, entails a motor reaction. If this reaction allows rotation of the stimuli, it is often called physical rotation (PR). Depending on the specific objectives of each study, different strategies and devices have been employed for data acquisition and analysis of MR and PR. In the task stimulus presentation step, it is possible to change the types of stimuli (i.e., previously cited examples); the projection (computer-generated images in traditional displays22,23,25,29,40,41,59, as well as in stereoscopes19 and virtual60 and mixed43 reality environments); and the interactivity of the stimuli (static images12,27,36, animations61, and interactive virtual objects19,22,23,43,53,59).
MR is usually inferred from measures of RTs (ADE), as well as ocular and brain activity25,46,62. Ocular activity is measured using eye-tracking data consisting of saccadic movements and fixations14,15,42,51,52,54,58,60, as well as pupillometry40. RT data typically arise from motor response data recorded while operating various devices such as levers13, buttons and switches14,53, pedals53, rotary knobs19, joysticks37, keyboard61 and mouse29,58,60, drive wheels53, inertial sensors22,23, touch screens52,59, and microphones22. To measure PR, in addition to the RTs, the study design will also include recording manual rotations of interactive stimuli while participants perform the MR task22,23,52,53.
In 1998, Wohlschl&#228;ger and Wohlschl&#228;ger19 used &#34;same or different&#34; tasks with interactive virtual SM stimuli manipulated with a knob, with rotations limited to one axis per task. They measured RT and the cumulative record of physical rotations performed during the tasks. Comparing situations with and without actual rotation of the interactive stimuli, they concluded that MR and PR share a common process for both imagined and actually performed rotations.
In 2014, two studies were conducted employing the same type of tasks with virtual interactive stimuli22,23. However, the objects were manipulated with inertial sensors that captured motion in 3D space. In both cases, in addition to RTs, rotation trajectories were recorded - the evolution of rotation differences between reference and interactive stimuli during the tasks. From these trajectories, it was possible to extract both cumulative information (i.e., the total number of rotations, in quaternionic units) and detailed information about solution strategies. Adams et al.23 studied the cooperative effect between MR and PR. In addition to RTs, they used the integral of the rotation trajectories as a parameter of accuracy and objectivity of resolution. Curve profiles were interpreted according to a three-step model63 (planning, major rotation, fine adjustment). The results indicate that MR and PR do not necessarily have a single, common factor. Gardony et al.22 collected data on RT, accuracy, and real-time rotation. In addition to confirming the relationship between MR and PR, the analysis of rotation trajectories revealed that participants manipulated the figures until they could identify whether they were different or not. If they were the same, participants would rotate them until they looked the same.
Continuing this strategy, in 2018, Wetzel and Bertel52 also used interactive SM figures in &#34;same or different&#34; tasks using touchscreen tablets as the interface. In addition, they used an eye-tracking device to obtain cumulative data on fixation time and saccadic amplitude as parameters of the cognitive load involved in solving MR tasks. The authors confirmed the previous studies discussed above regarding the relationships between MR and PR and the task-solving processes. However, in this study, they did not use fixation mapping and saccades data for the stimuli.
Methodological approaches for mapping eye-tracking data over virtual 3D objects have been proposed and constantly improved, commonly by researchers interested in studying the factors related to visual attention in virtual environments64. Although affordable and using similar eye-tracking devices, apparently, these methods have not been effectively integrated into the experimental repertoire employed in mental rotation studies with interactive 3D objects such as those previously mentioned. Conversely, we did not find any studies in the literature reporting real-time mapping of fixation and saccade motion data on interactive 3D objects. There seems to be no convenient method to integrate eye-activity data with rotation trajectories easily. In this research, we aim to contribute to filling this gap. The procedure is presented in detail, from data acquisition to graphical output generation.
In this paper, we describe in detail a method for studying mental rotation processes with virtual interactive 3D objects. The following advances are highlighted. First, it integrates quantitative behavioral motor (hand-driven object rotation via a computer interface) and ocular (eye-tracking) data collection during interaction sessions with 3D virtual models. Second, it requires only conventional computer equipment and eye-tracking devices for visual task design, data acquisition, recording, and processing. Third, it easily generates graphical output to facilitate data analysis - angular disparity, physical rotation, quaternionic rotation trajectories, and hit mapping of eye-tracking data over 3D virtual objects. Finally, the method requires only free software. All developed code and scripts are available free of charge (https://github.com/rodrigocnstest/rodrigocnstest.github.io).

<table><tbody><tr><td>Firefox</td><td>Mozilla Foundation (Open Source)</td><td></td><td>Any updated modern browser that is compatible with WebGL (https://caniuse.com/webgl), and in turn with Jmol, can be used</td></tr><tr><td>GNU Octave</td><td>Open Source</td><td></td><td>https://octave.org/</td></tr><tr><td>Google Apps Script</td><td>Google LLC</td><td></td><td>script.google.com</td></tr><tr><td>Google Sheets</td><td>Google LLC</td><td></td><td>https://www.google.com/sheets/about/</td></tr><tr><td>Laptop</td><td></td><td></td><td>Any computer that can run the eye tracking system software.</td></tr><tr><td>Mangold Software Suite</td><td>Mangold&amp;nbsp;</td><td></td><td>Software interface used for the Eye tracking device. Any Software that outputs the data with system time values can be used.</td></tr><tr><td>Mouse</td><td></td><td></td><td>Any mouse capable of clicking and dragging with simple movements should be compatible. Human interfaces analogous to a mouse with the same capabilities, such as a touchscreen or pointer, should be compatible, but may behave differently.</td></tr><tr><td>Vt3mini</td><td>EyeTech Digital Systems</td><td></td><td>60 Hz. Any working Eye Tracking device should be compatible.</td></tr></tbody></table>

three-dimensional mapping of the rotation of interactive virtual objects with eye-tracking data

We present a method for real-time recording of human interaction with three-dimensional (3D) virtual objects. The approach consists of associating rotation data of the manipulated object with behavioral measures, such as eye tracking, to make better inferences about the underlying cognitive processes.
The task consists of displaying two identical models of the same 3D object (a molecule), presented on a computer screen: a rotating, interactive object (iObj) and a static, target object (tObj). Participants must rotate iObj using the mouse until they consider its orientation to be identical to that of tObj. The computer tracks all interaction data in real time. The participant&#39;s gaze data are also recorded using an eye tracker. The measurement frequency is 10 Hz on the computer and 60 Hz on the eye tracker.
The orientation data of iObj with respect to tObj are recorded in rotation quaternions. The gaze data are synchronized to the orientation of iObj and referenced using this same system. This method enables us to obtain the following visualizations of the human interaction process with iObj and tObj: (1) angular disparity synchronized with other time-dependent data; (2) 3D rotation trajectory inside what we decided to call a &#34;ball of rotations&#34;; (3) 3D fixation heatmap. All steps of the protocol have used free software, such as GNU Octave and Jmol, and all scripts are available as supplementary material.
With this approach, we can conduct detailed quantitative studies of the task-solving process involving mental or physical rotations, rather than only the outcome reached. It is possible to measure precisely how important each part of the 3D models is for the participant in solving tasks, and thus relate the models to relevant variables such as the characteristics of the objects, cognitive abilities of the individuals, and the characteristics of human-machine interface.

Evolution of angular disparity and other variables 
As depicted in step 3.3.1 in Supplemental File 2, two canvases are presented to the participant on the video monitor screen, displaying copies of the same 3D virtual object in different orientations. On the left canvas, the target object (tObj) remains static and serves as the target position or tObj position. On the right canvas, the interactive object (iObj) is shown in a different position and allows the participant to move it over time around a fixed rotation center using a mouse (only rotations; translations are disabled). The task at hand involves adjusting iObj to closely match tObj based on the participant&#39;s judgment. The three 3D objects used&#160;can be seen in Figure 1. The solving process, although complex, can be meticulously recorded for subsequent analysis. This recording goes beyond mere video footage as each position over time is captured at fixed 0.1 s intervals as a quaternion, forming a time series that enables a complete reconstruction of the entire process. At any position, there exists a unique rotation around a specific axis, ranging from 0&#176; up to 180&#176;, that directly transforms the tObj position into the iObj position. While this rotation is abstract and unrelated to the participant&#39;s PR during the task, it accurately indicates the precise iObj position relative to tObj. AD is the angle of this rotation and may be calculated from the respective quaternion. As the iObj position approaches the tObj position, this value approaches zero.
After step 3.1.6 of the Data processing and analysis section, two files were created: output merge X Y.xlsx and output jmol console X Y.xlsx, where X is the sessionID value, and Y is the taskID value. If using the default values by leaving the input fields blank, the files should be named output merge 1682707472090 bolaBastao_c.xlsx and output jmol console 1682707472090 bolaBastao_c.xlsx. The output merge X Y.xlsx files contain the selected eye tracker data merged into the iRT data, aligned by the UNIX Epoch time, similar to Figure 2A if everything proceeded correctly, or Figure 2B if some problem occurred.
The output jmol console X Y.xlsx files contain up to five tabs filled with Jmol console commands that, when pasted to the Jmol console, will reproduce the participant&#39;s movements when solving the task: rotation replay reproduces the iObj rotations made by the participant; gaze replay int reproduces the iObj rotations with added fixation heatmap on the object in time by using a transparency/opaque scale; gaze replay tgt shows only the 3D fixation heatmap of tObj during the task; gaze frame int and gaze frame tgt show the overall fixation mapping of the whole process for both iObj and tObj. All of them are illustrated in Figure 3A-F. Jmol and JSmol are essentially identical, Jmol being the plugin based in Java programming language, and JSmol in the JavaScript programming language, both having the same functionalities and being used interchangeably.
Figure 4 illustrates the evolution of angular disparity as a function of time for six different scenarios involving two participants and three objects. The duration of the process can vary significantly depending on the participant&#39;s performance with the interactive task object. In any task completed correctly by the participant, AD tends to zero at the end. If the same graph does not exhibit this behavior, either the participant was unable to complete the task because they gave up or reached the time limit per task (approximately 5 min), or an error occurred in the data processing.
The combined results of the iObj PR records and the data obtained from the eye-tracking measurements are shown in Figure 5. The variation in the angular disparity between the target and the inertial objects as a function of time indicates three distinct stages in the process of solving the task given: initial observation of the models; ballistic rotation of the interactive model; fine-tuning of the rotation of the interactive model. Figure 5A shows the gaze alternating between the models in the initial phase and, more specifically, in the fine-tuning phase. Figure 5B shows that the pupil remains more dilated in the initial and fine-tuning phases. In the fine-tuning phase, the long fixation period on the interactive model (40-47s in Figure 5A) corresponds to a plateau in pupil diameter (40-47s, Figure 5B).
These results suggest that the data obtained with the method proposed here are consistent with the model of mental rotation problem solving proposed on the basis of gaze fixation data for static models14,66 and for interactive models23. Such a model would encompass three stages: search, transformation and comparison, and confirmation of the match or mismatch between the models. In addition, the alternation of fixations between the target and interactive models in the comparison stages observed in Figure 5A is consistent with the results obtained in Sheppard and Metzler-type tests that use static images42,66. However, in the case of interactive models, it is likely that these stages of search, transformation, comparison, and confirmation occur successively through interaction and repositioning of the interactive model.
3D rotation trajectories 
Each rotation in a 3D space from 0&#176; to 180&#176; can be translated to a point inside a ball (which is understood as the volume inside a sphere) with a radius equal to 180&#176;. Figure 6 demonstrates this correspondence with three example rotations. The distance of the point to the center of the ball is the iObj angle disparity from the tObj position, and the vector pointing from the center of the ball to the point is the rotation direction, the rotation being made in the clockwise sense viewing from the center. This translation of rotations into points in a ball allows someone to directly visualize, in a single 3D drawing, the entire trajectory of rotations made by the participant in a task. We call this drawing the 3D rotation trajectory.
Analogously to the AD measure, for any tasks correctly completed by the participant, the trajectory should approach, in the end, the center of the ball. If the trajectory reaches the sphere&#39;s boundary at a rotation of 180&#176;, it will wrap around to the antipodal point on the sphere. Figure 7 illustrates the rotation trajectory taken by the two previously mentioned participants performing the third task (C1 and C2 in Figure 4), viewed both in perspective and in projections on the three coordinate planes. It is clear from the figure that, despite the relatively small starting AD close to 45&#176;, participant 1 initially deviated from the target position before finding a definitive path to the solution, unlike participant 2, who completed the task faster.
3D Fixation heatmap 
During the problem-solving process, the participant alternates their gaze between tObj and iObj while interacting with iObj. With the eye-tracking data, we can extract the participant&#39;s gaze position and create a heatmap of the screen regions that captured the most and least attention from the participant in any given interval. Going further, with both the eye-tracking and iRT quaternion data synchronized, we can simultaneously map in 3D space and in time, how much attention each of the object vertices is receiving, even for objects being rotated in time.
In Figure 3, the attention given to the object is represented by the opacity level of each vertex. The closer it is to the participant&#39;s gaze and the longer it remains in proximity, the more attention it receives, resulting in higher opacity in that region of the object. The spatial decrease in attention is modeled using a bivariate homogeneous Gaussian function for the gaze position and a simple homogeneous Gaussian function applied for the elapsed time. The standard deviation of these Gaussians was chosen assuming a visual angle of 2 degrees67 and a visual short-term memory of 10 s68. To prevent any visual artifacts with this method, the gaze proximity data is set to zero while the gaze is outside the object canvas (iObj receives no residual attention when the gaze is inside the tObj canvas or outside both). Figure 3 shows a single frame from each object of an entire replay animation and the same frames with the 3D fixation heatmap. A possible comparison between tObj and iObj by the participant during the solving process can be seen (Figure 3C,D) as the task is nearing its conclusion (time = 6.3 s). The whole process can be seen as a video in Supplemental Video S1.&#160;We report the results of the computer-mediated rotation of 3D models presented to participants as a task taken under ordinary conditions.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65977/65977fig01.jpg" /> 
Figure 1: Target objects used. Image of the 3D models used in the webpage tasks. (A) A molecule with ball and stick representation; (B) The same molecule with filled polygons, no hydrogens, and represented by sticks only; (C) a polycube similar to one of the Shepard and Metzler figures13, derived from the stimuli library of Peters and Battista36. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65977/65977fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65977/65977fig02.jpg" /> 
Figure 2: Sheets comparison. (A,B) Images are taken from the spreadsheet output merge 1682707472090 bolaBastao_c.xlsx. Columns A to G contain iRT data values, whereas columns H to N contain eye tracker data values. In (A), everything is correct, while in (B), in the eye tracker columns, all values are constant and do not match the iRT system time values. If any problem occurs with the data synchronization process, this error will likely happen. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65977/65977fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65977/65977fig03.jpg" /> 
Figure 3: 3D fixation heatmap. Fixation heatmap over the 3D object using a scale of opacity, where more opaque correlates to more time spent near the participant gaze. (A,B) tObj and iObj images of the task being solved by the participant at the 6.3 s mark. (C,D) Same images as (A,B) at the same instant with the added opacity scale of the heatmap. (E,F) Fixation heatmap images considering the entire period for which the participant could see the objects. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65977/65977fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65977/65977fig04.jpg" /> 
Figure 4: AD grid. Plot grid of angular disparity across two participants and three tasks. Columns represent participants 1 and 2, and rows represent the tasks solved by the participants using the three objects illustrated in Figure 3. Note that while AD varies between 0&#176; and 180&#176;, the time range is not fixed and varies with the participant&#39;s performance and their own decision to stop the process. As the participant rotates iObj, the AD between tObj and iObj varies as time progresses, and eventually the participant chooses the current iObj orientation as the closest to tObj. In the 1st and 2nd tasks, both participants seemed to have progressed in a similar manner, but participant 1 took half as much time as participant 2. And in the 3rd task, although participant 2 took less time to complete the task, participant 1 had already solved the task before the 20 s mark and kept making small adjustments to better match iObj to tObj. Abbreviation: AD = angular disparity. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65977/65977fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/65977/65977fig05.jpg" /> 
Figure 5: AD with eye-tracking data. Evolution of angular disparity combined with eye-tracker data. (A)&#160;Angular disparity and gaze position, theevolution of angular disparity between tObj and iObj, coupled with regional fixation data for each model. The graph shows what region the participant&#39;s gaze is located in: red when inside iObj canvas, blue when inside tObj canvas, and gray when outside both of them, looking at another element on the screen or looking away from it. (B)&#160;Angular disparity and pupil diameter. Angular disparity, in blue, coupled with pupil diameter data, in orange. Pupil diameter is the mean value of left and right pupils at each point in time. Abbreviation: AD = angular disparity. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65977/65977fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/65977/65977fig06.jpg" /> 
Figure 6: Ball of rotations. This figure illustrates how each possible rotation position of an object from a reference position can be represented as a point in a ball of radius 180&#176;, allowing a complete representation of the object&#39;s rotational position in all three axes. Here, a ball is understood as the volume bounded by a sphere. (A)&#160;The object used as an example is an asymmetric union of seven cubes, depicted at the top, to the left. Three simple rotations numbered I, II, and III are applied to this object, as shown to the right. They are, respectively, +90&#176; on the x-axis, -60&#176; on the z-axis, and 180&#176; on an axis between +x and -y, at 45&#176; from both axes. (B) The ball of rotation is shown with the points corresponding to the rotations I, II, and III. The distance to the center of the ball is the angle disparity. As III achieves the maximum rotation angle (180&#176;), it is also represented at its antipodal point, as they are essentially the same. Rotation II, being anticlockwise with respect to the positive direction of axis z, appears on the negative side. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65977/65977fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/65977/65977fig07.jpg" /> 
Figure 7: 3D Rotation trajectory. The rotation trajectory inside the ball of rotations taken by the two participants in the third task, viewed both in perspective (A) and in projections on the coordinate planes (B-D). Line thickness decreases over time. Each column corresponds to a participant (v1 and v2). As the trajectories approach the center of the ball, the participants are closer to solving the task. &#39;0&#39; indicates the initial position of the task. Subsequent numbers indicate points where the trajectory reaches the edge of the ball and continues through the antipodal point at the opposite side (1 to 2, 2 to 3, 3 to 4, etc). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65977/65977fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental Table S1: Sheet headers. List of headers in the cloned sheet file. Each header corresponds to a variable name and will receive data from this variable forming a column of values used in the processing and analysis of our data. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65977/ST1 - sheet headers.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 1: Protocol step 1 guide. A list of screenshots guiding through the steps of the protocol method &#34;1. Preparation of data collection tools&#34;. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65977/SF1 - Protocol step 1 guide.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 2: Protocol step 3 guide. A list of screenshots guiding through the steps of the protocol method &#34;3. Data processing and analysis&#34;. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65977/SF2 - Protocol step 3 guide.zip" target="_blank">Please click here to download this File.</a>
Supplemental Video 1: Fixation mapping replay. An example of animated replays of the temporal attention mapping in 3D from iObj and tObj simultaneously. Recorded using OBS Studios and rendered using OpenShot Video Editor. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65977/SV1 - Fixation Mapping Replay.zip" target="_blank">Please click here to download this File.</a>

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

We have developed a simple, customizable, and efficient method for recording quantitative processual data from interactive spatial tasks and mapping these rotation data with eye-tracking data.

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data

We present a method for real-time recording of human interaction with three-dimensional (3D) virtual objects. The approach consists of associating ...

three-dimensional-mapping-rotation-interactive-virtual-objects-with

Nanyang Technological University

Without Section

Research

JoVE Journal

Neuroscience

831 Views.  University of São Paulo, Institute of Chemistry. We have developed a simple, customizable, and efficient method for recording quantitative processual data from interactive spatial tasks and mapping these rotation data with eye-tracking data.

Video: Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Functional Mapping with Simultaneous MEG and EEG

We use magnetoencephalography (MEG) and electroencephalography (EEG) to locate and determine the temporal evolution in brain areas involved in the processing of simple sensory stimuli. We will use somatosensory stimuli to locate the hand somatosensory areas, auditory stimuli to locate the auditory cortices, visual stimuli in four quadrants of the visual field to locate the early visual areas. These type of experiments are used for functional mapping in epileptic and brain tumor patients to locate eloquent cortices. In basic neuroscience similar experimental protocols are used to study the orchestration of cortical activity. The acquisition protocol includes quality assurance procedures, subject preparation for the combined MEG/EEG study, and acquisition of evoked-response data with somatosensory, auditory, and visual stimuli. We also demonstrate analysis of the data using the equivalent current dipole model and cortically-constrained minimum-norm estimates. Anatomical MRI data are employed in the analysis for visualization and for deriving boundaries of tissue boundaries for forward modeling and cortical location and orientation constraints for the minimum-norm estimates.

We use magnetoencephalography (MEG) and electroencephalography (EEG) to map brain areas involved in the processing of simple sensory stimuli.

We use magnetoencephalography (MEG) and electroencephalography (EEG) to locate and determine the temporal evolution in brain areas involved in the ...

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions

The ability to gauge social interactions is crucial in the assessment of others&#x2019; intentions. Factors such as facial expressions and body language affect our decisions in personal and professional life alike 1. These "friend or foe" judgements are often based on first impressions, which in turn may affect our decisions to "approach or avoid". Previous studies investigating the neural correlates of social cognition tended to use static facial stimuli 2. Here, we illustrate an experimental design in which whole-body animated characters were used in conjunction with functional magnetic resonance imaging (fMRI) recordings. Fifteen participants were presented with short movie-clips of guest-host interactions in a business setting, while fMRI data were recorded; at the end of each movie, participants also provided ratings of the host behaviour. This design mimics more closely real-life situations, and hence may contribute to better understanding of the neural mechanisms of social interactions in healthy behaviour, and to gaining insight into possible causes of deficits in social behaviour in such clinical conditions as social anxiety and autism 3.

This article demonstrates an experimental design in which whole-body animated characters are used in conjunction with functional magnetic resonance imaging (fMRI) to investigate the neural correlates of observing virtual social interactions.

The ability to gauge social interactions is crucial in the assessment of others&#x2019; intentions. Factors such as facial expressions and body language ...

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Studies investigating how people represent themselves and their own body often use variants of &#34;ownership illusions&#34;, such as the traditional rubber-hand illusion or the more recently discovered enfacement illusion. However, these examples require rather artificial experimental setups, in which the artificial effector needs to be stroked in synchrony with the participants&#39; real hand or face&#8212;a situation in which participants have no control over the stroking or the movements of their real or artificial effector. Here, we describe a technique to establish ownership illusions in a setup that is more realistic, more intuitive, and of presumably higher ecological validity. It allows creating the virtual-hand illusion by having participants control the movements of a virtual hand presented on a screen or in virtual space in front of them. If the virtual hand moves in synchrony with the participants&#39; own real hand, they tend to perceive the virtual hand as part of their own body. The technique also creates the virtual-face illusion by having participants control the movements of a virtual face in front of them, again with the effect that they tend to perceive the face as their own if it moves in synchrony with their real face. Studying the circumstances that illusions of this sort can be created, increased, or reduced provides important information about how people create and maintain representations of themselves.

Here, we describe virtual-hand and virtual-face illusion paradigms that can be used to study body-related self-perception/-representation. They have already been used in various studies to demonstrate that, under specific conditions, a virtual hand or face can be incorporated into one&#39;s body representation, suggesting that body representations are rather flexible.

Studies investigating how people represent themselves and their own body often use variants of &#34;ownership illusions&#34;, such as the traditional ...

Behavior

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

In a typical eye tracking study using the visual world paradigm, participants' eye movements to objects or pictures in the visual workspace are recorded via an eye tracker as the participant produces or comprehends a spoken language describing the concurrent visual world. This paradigm has high versatility, as it can be used in a wide range of populations, including those who cannot read and/or who cannot overtly give their behavioral responses, such as preliterate children, elderly adults, and patients. More importantly, the paradigm is extremely sensitive to fine grained manipulations of the speech signal, and it can be used to study the online processing of most topics in language comprehension at multiple levels, such as the fine grained acoustic phonetic features, the properties of words, and the linguistic structures. The protocol described in this article illustrates how a typical visual world eye tracking study is conducted, with an example showing how the online processing of some semantically complex statements can be explored with the visual world paradigm.

The visual world paradigm monitors participants' eye movements in the visual workspace as they are listening to or speaking a spoken language. This paradigm can be used to investigate the online processing of a wide range of psycholinguistic questions, including semantically complex statements, such as disjunctive statements.

In a typical eye tracking study using the visual world paradigm, participants' eye movements to objects or pictures in the visual workspace are recorded ...

Three Dimensional Vestibular Ocular Reflex Testing Using a Six Degrees of Freedom Motion Platform

The vestibular organ is a sensor that measures angular and linear accelerations with six degrees of freedom (6DF). Complete or partial defects in the vestibular organ results in mild to severe equilibrium problems, such as vertigo, dizziness, oscillopsia, gait unsteadiness nausea and/or vomiting. A good and frequently used measure to quantify gaze stabilization is the gain, which is defined as the magnitude of compensatory eye movements with respect to imposed head movements. To test vestibular function more fully one has to realize that 3D VOR ideally generates compensatory ocular rotations not only with a magnitude (gain) equal and opposite to the head rotation but also about an axis that is co-linear with the head rotation axis (alignment). Abnormal vestibular function thus results in changes in gain and changes in alignment of the 3D VOR response.
Here we describe a method to measure 3D VOR using whole body rotation on a 6DF motion platform. Although the method also allows testing translation VOR responses 1, we limit ourselves to a discussion of the method to measure 3D angular VOR. In addition, we restrict ourselves here to description of data collected in healthy subjects in response to angular sinusoidal and impulse stimulation.
Subjects are sitting upright and receive whole-body small amplitude sinusoidal and constant acceleration impulses. Sinusoidal stimuli (f = 1 Hz, A = 4&deg;) were delivered about the vertical axis and about axes in the horizontal plane varying between roll and pitch at increments of 22.5&deg; in azimuth. Impulses were delivered in yaw, roll and pitch and in the vertical canal planes. Eye movements were measured using the scleral search coil technique 2. Search coil signals were sampled at a frequency of 1 kHz.
The input-output ratio (gain) and misalignment (co-linearity) of the 3D VOR were calculated from the eye coil signals 3.
Gain and co-linearity of 3D VOR depended on the orientation of the stimulus axis. Systematic deviations were found in particular during horizontal axis stimulation. In the light the eye rotation axis was properly aligned with the stimulus axis at orientations 0&deg; and 90&deg; azimuth, but gradually deviated more and more towards 45&deg; azimuth.
The systematic deviations in misalignment for intermediate axes can be explained by a low gain for torsion (X-axis or roll-axis rotation) and a high gain for vertical eye movements (Y-axis or pitch-axis rotation (see Figure 2). Because intermediate axis stimulation leads a compensatory response based on vector summation of the individual eye rotation components, the net response axis will deviate because the gain for X- and Y-axis are different.
In darkness the gain of all eye rotation components had lower values. The result was that the misalignment in darkness and for impulses had different peaks and troughs than in the light: its minimum value was reached for pitch axis stimulation and its maximum for roll axis stimulation.
Case Presentation
Nine subjects participated in the experiment. All subjects gave their informed consent. The experimental procedure was approved by the Medical Ethics Committee of Erasmus University Medical Center and adhered to the Declaration of Helsinki for research involving human subjects.
Six subjects served as controls. Three subjects had a unilateral vestibular impairment due to a vestibular schwannoma. The age of control subjects (six males and three females) ranged from 22 to 55 years. None of the controls had visual or vestibular complaints due to neurological, cardio vascular and ophthalmic disorders.
The age of the patients with schwannoma varied between 44 and 64 years (two males and one female). All schwannoma subjects were under medical surveillance and/or had received treatment by a multidisciplinary team consisting of an othorhinolaryngologist and a neurosurgeon of the Erasmus University Medical Center. Tested patients all had a right side vestibular schwannoma and underwent a wait and watch policy (Table 1; subjects N1-N3) after being diagnosed with vestibular schwannoma. Their tumors had been stabile for over 8-10 years on magnetic resonance imaging.

A method is described to measure three-dimensional vestibulo ocular reflexes (3D VOR) in humans using a six degrees of freedom (6DF) motion simulator. The gain and misalignment of the 3D angular VOR provide a direct measure of the quality of vestibular function. Representative data on healthy subjects are provided

The vestibular organ is a sensor that measures angular and linear accelerations with six degrees of freedom (6DF). Complete or partial defects in the ...

Three-dimensional Confocal Analysis of Microglia/macrophage Markers of Polarization in Experimental Brain Injury

After brain stroke microglia/macrophages (M/M) undergo rapid activation with dramatic morphological and phenotypic changes that include expression of novel surface antigens and production of mediators that build up and maintain the inflammatory response. Emerging evidence indicates that M/M are highly plastic cells that can assume classic pro-inflammatory (M1) or alternative anti-inflammatory (M2) activation after acute brain injury. However a complete characterization of M/M phenotype marker expression, their colocalization and temporal evolution in the injured brain is still missing.	
	Immunofluorescence protocols specifically staining relevant markers of M/M activation can be performed in the ischemic brain. Here we present immunofluorescence-based protocols followed by three-dimensional confocal analysis as a powerful approach to investigate the pattern of localization and co-expression of M/M phenotype markers such as CD11b, CD68, Ym1, in mouse model of focal ischemia induced by permanent occlusion of the middle cerebral artery (pMCAO). Two-dimensional analysis of the stained area reveals that each marker is associated to a defined M/M morphology and has a given localization in the ischemic lesion. Patterns of M/M phenotype marker co-expression can be assessed by three-dimensional confocal imaging in the ischemic area. Images can be acquired over a defined volume (10 &mu;m z-axis and a 0.23 &mu;m step size, corresponding to a 180 x 135 x 10 &mu;m volume) with a sequential scanning mode to minimize bleed-through effects and avoid wavelength overlapping. Images are then processed to obtain three-dimensional renderings by means of Imaris software. Solid view of three dimensional renderings allows the definition of marker expression in clusters of cells. We show that M/M have the ability to differentiate towards a multitude of phenotypes, depending on the location in the lesion site and time after injury.

A way to gain new insights into the complexity of the brain inflammatory response is presented. We describe immunofluorescence-based protocols followed by three-dimensional confocal analysis to investigate the pattern of co-expression of microglia/macrophage phenotype markers in a mouse model of focal ischemia.

After brain stroke microglia/macrophages (M/M) undergo  rapid activation with dramatic morphological and phenotypic changes that  include expression of ...

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency sensors, such as eye-trackers, electrodermal activity wristbands, EEG bands, and motion sensors provide observations at the millisecond level. This level of precision allows researchers to collect large datasets on social interactions. In this paper, I discuss how multiple eye-trackers can capture a fundamental construct in social interactions, joint visual attention (JVA). JVA has been studied by developmental psychologists to understand how children acquire language, learning scientists to understand how small groups of learners work together, and social scientists to understand interactions in small teams. This paper describes a methodology for capturing JVA in colocated settings using mobile eye-trackers. It presents some empirical results and discusses implications of capturing microobservations to understand social interactions.

Using multimodal sensors is a promising way to understand the role of social interactions in educational settings. This paper describes a methodology for capturing joint visual attention from colocated dyads using mobile eye-trackers.

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency ...

Controlled Rotation of Human Observers in a Virtual Reality Environment

The low cost and availability of Virtual Reality (VR) systems have supported a recent acceleration of research into perception and behavior under more naturalistic, multisensory, and immersive conditions. One area of research that has particularly benefited from the use of VR systems is multisensory integration, for example, the integration of visual and vestibular cues to give rise to a sense of self-motion. For this reason, an accessible method for the controlled physical rotation of an observer in a virtual environment represents a useful innovation. This paper presents a method for automating the rotation of an office swivel chair along with a method for integrating that motion into a VR experience. Using an example experiment, it is demonstrated that the physical motion, thus produced, is integrated with the visual experience of an observer in a way consistent with expectations; high integration when the motion is congruent with the visual stimulus and low integration when the motion is incongruent.

The controlled physical rotation of a human observer is desirable for certain experimental, recreational, and educational applications. This paper outlines a method for converting an office swivel chair into a medium for controlled physical rotation in a virtual reality environment.

The low cost and availability of Virtual Reality (VR) systems have supported a recent acceleration of research into perception and behavior under more ...

Protocol for Three-dimensional Confocal Morphometric Analysis of Astrocytes

As glial cells in the brain, astrocytes have diverse functional roles in the central nervous system. In the presence of harmful stimuli, astrocytes modify their functional and structural properties, a condition called reactive astrogliosis. Here, a protocol for assessment of the morphological properties of astrocytes is presented. This protocol includes quantification of 12 different parameters i.e. the surface area and volume of the tissue covered by an astrocyte (astrocyte territory), the entire astrocyte including branches, cell body, and nucleus, as well as total length and number of branches, the intensity of fluorescence immunoreactivity of antibodies used for astrocyte detection, and astrocyte density (number/1,000 &#181;m2). For this purpose three-dimensional (3D) confocal microscopic images were created, and 3D image analysis software such as Volocity 6.3 was used for measurements. Rat brain tissue exposed to amyloid beta1-40 (A&#946;1-40) with or without a therapeutic&#160;intervention was used to present the method. This protocol can also be used for 3D morphometric analysis of other cells from either in vivo or in vitro conditions.

Astrocytes in the CNS change their functional and structural properties in response to harmful stimuli. This report presents a protocol for assessment of three-dimensional astrocyte morphology in diseased conditions or after therapeutic interventions.

As glial cells in the brain, astrocytes have diverse functional roles in the central nervous system. In the presence of harmful stimuli, astrocytes modify ...

Three-dimensional Imaging and Analysis of Mitochondria within Human Intraepidermal Nerve Fibers

The goal of this protocol is to study mitochondria within intraepidermal nerve fibers. Therefore, 3D imaging and analysis techniques were developed to isolate nerve-specific mitochondria and evaluate disease-induced alterations of mitochondria in the distal tip of sensory nerves. The protocol combines fluorescence immunohistochemistry, confocal microscopy and 3D image analysis techniques to visualize and quantify nerve-specific mitochondria. Detailed parameters are defined throughout the procedures in order to provide a concrete example of how to use these techniques to isolate nerve-specific mitochondria. Antibodies were used to label nerve and mitochondrial signals within tissue sections of skin punch biopsies, which was followed by indirect immunofluorescence to visualize nerves and mitochondria with a green and red fluorescent signal respectively. Z-series images were acquired with confocal microscopy and 3D analysis software was used to process and analyze the signals. It is not necessary to follow the exact parameters described within, but it is important to be consistent with the ones chosen throughout the staining, acquisition and analysis steps. The strength of this protocol is that it is applicable to a wide variety of circumstances where one fluorescent signal is used to isolate other signals that would otherwise be impossible to study alone.

This protocol uses three-dimensional (3D) imaging and analysis techniques to visualize and quantify nerve-specific mitochondria. The techniques are applicable to other situations where one fluorescent signal is used to isolate a subset of data from another fluorescent signal.

The goal of this protocol is to study mitochondria within intraepidermal nerve fibers. Therefore, 3D imaging and analysis techniques were developed to ...