tracking the interaction of a human eye with virtual objects in real time

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

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).

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

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

We developed a method for recording human interactions with 3D virtual objects in real time. Our approach involves linking rotation data for the manipulated objects with behavior measures like eye tracking. This lets us gain deeper insights on the cognitive process behind the task, rather than just the final outcome.The following topics are considered:the relationship between material rotation and eye movements, individual differences, applications and practical implications, methodological advancements, cognitive models and theories, developmental and clinical perspectives, and neurocognitive mechanisms. This work contributes on the methodological front. It appears that there is no straightforward approach to integrating eye activity data with rotation trajectories.In this research, we aim to contribute to fill in this gap. A straightforward and cost-effective experimental setup was devised to map conventional eye tracking data over 3D models with rotational tests. Our future research will focus on the advancement of our method for the study of individual differences in the interpretation and interaction with virtual and physical three dimensional objects.For example, we will examine the case for students and experts in chemistry.

Tracking the Interaction of a Human Eye with Virtual Objects in Real Time

To begin, launch an eye tracking software on a computer. Open the iRT webpage, fill in a name and email and press next. Seat the participant in a comfortable position before the eye tracking system.Move the chair to ensure an optimal distance between the participant and the camera. Now adjust the camera's height to correctly capture the participant's pupils. Press calibrate to begin calibration.Ask the participant to look at a series of dots on the screen and follow the dots'movement without moving their head. After the calibration process, a realtime display of the captured screen with gaze data can be seen in the primary display window. Click on the gaze video icon in the main menu to display the user's face captured by the eye tracker.Then click on start record to begin the experiment. Open the previously opened iRT's window and ask the participants to click on the go button, following which they will have to rotate the object on the right until it closely matches the one on the left. Click done to conclude the rotation and to repeat this process until all tasks are concluded.Once the eye tracker data collection has been completed, click on analyze data. Then export a CSV file with all the data recorded for the user. If the online interactive rotation tasks page is being used, open the Google Sheets file used to receive the online data.Then click on file, followed by download and xlsx. Next, download and uncompress the repository that was being used. Ensure that the scripts 1.unpacking_sheets.m, 2.data_merge_and_process. m, 3.3D rotational trajectory. m, and the folder models are inside the downloaded repository in the folder Octave.Move the data files into the Octave folder. Open the script 1.unpacking_sheets. m with the GNU Octave launcher.In the editor tab, click on the green save file and run icon to run the script. When two prompts appear in succession, input the name of the downloaded file in the first prompt and the name of the unpackaged file inside the second field. A pop-up informing the user of the process completion will show up in a few minutes.Open and run the script 2.data_merge_and_process. m to merge the data from both the eye tracker and iRT. Now input the session ID value, task ID value from the save data sheet.Then input the unpackaged iRT data file name and eye tracker data file name. Launch and run the 3.3D rotation trajectory. m script.When three prompts appear, input the session ID value, task ID value, and unpackaged iRT data file name, or leave them blank. To replay the participant's task interaction, first, go to the interactive task webpage. Start the test, then move the mouse pointer.Next, click on the invisible debug text to enable the debug mode. Press the timer stop icon to interrupt the task and click on the console to open the JSmol console of the model. Now open the file output jmol console XLSX and copy the entire page of Jmol commands.Paste the list of commands copied inside the JSmol console and click the run button to execute it. To generate a gif animation, write the command capture file name script output inside the JSmol console where file name is the name of the gif file to be created and output is the entire list of commands copied. The pupil of the participant remained more dilated in the initial and fine tuning phases.The long fixation period in the fine tuning corresponded to a plateau in the pupil diameter. The rotation trajectory analysis showed that participant one initially deviated from the target position before finding a definitive path to the solution.

tracking-interaction-human-eye-with-virtual-objects-real

Research

JoVE Journal

Neuroscience

97 Views. To begin, launch an eye tracking software on a computer. Open the iRT webpage, fill in a name and email and press next. Seat the participant in a comfortable position before the eye tracking system.Move the chair to ensure an optimal distance between the participant and the camera. Now adjust the camera's height to correctly capture the participant's pupils. Press calibrate to begin calibration.Ask the participant to look at a series of dots on the screen and follow the dots'mov...