dataesr/scientific-paragraphs-categorization huggingface dataset:
Sound Another important aspect of VR software development is the quality of the sound. The addition of spatialized sounds in the VE (e. g. , ambient and feedback sounds) augments the level of immersion and enjoyment  (Vorländer & Shinn-Cunningham, 2014) , and significantly reduces the frequency of VRISE  (Viirre et al. , 2014). Spatialised sounds in VR assist the user to orient and navigate  (Ferrand et al. , 2017) , and enhance the geometry of the VE without reducing the software's performance  (Kobayashi et al. , 2015). In Unity, a developer may use tools like SteamAudio, Oculus Audio Spatializer, or Microsoft Audio Spatializer for good quality and spatialization of the audio aspects. In VR-EAL's development, Steam Audio was used. SteamAudio spatializes the sound to the location of the audio source's location and improves the reverberance of sounds (i.
3D characters Furthermore, VR research software like VR-EAL, which includes social interactions with virtual characters, should also consider the quality of the 3D characters in terms of realistic appearance and behavior. For example, Morph 3D and Mixamo both offer free and low-cost realistic 3D characters that may be used in VR software development. For VR-EAL, Morph 3D was preferred, though, other virtual humans from Unity's asset store were used to populate the scenes (e. g. , individuals waiting for the bus at the bus stop). The 3D characters provided by Morph 3D have modifiable features, which may be used by the developer to customize the character's appearance (e. g. , body size) and expressions (e. g. , facial expressions which signify emotions such as happiness and sadness). Morph 3D provides two free 3D characters (i.
The addition of 3D characters was important because they deliver an interaction metaphor resembling human-to-human interactions  (Korre, 2019). Even though adding a 3D character in the scene can introduce biases, the illusion of humanness -which is defined as the user's notion that the system (in this case the 3D NPC) possesses human attributes and/or cognitive functions -has been found to increase usability  (Korre, 2019). Realistic voices may be established by employing voice actors to produce the script. However, the employment of temporary staff increases development costs. For VR-EAL, text-to-speech technologies were used as an alternative solution to deliver realistic voices. Balabolka software was used in conjunction with Ivona3D Voices (n. b. , Ivona3D has been replaced by Amazon Polly).
Balabolka is an IDE for text-to-speech which allows further manipulation of voices (i. e. , pitch, rate, and volume), while Ivona3D provides realistic voices. The developer types or pastes the text into Balabolka, Balabolka modifies it with respect to the desired outcome (e. g. , high-pitched or lowpitched voice) and exports the file in a. wav format. Additionally, free software like Audacity may be used, which offers greater variety in sound modifications. The second crucial part is to synchronize the eyes and lip movements with the voice clips and body animations. There are assets on Unity's asset store that may be used to achieve this desired outcome. In VR-EAL, Salsa3D and RandomEyes3D were used to attain good quality facial animations and lip synchronization. Salsa3D synchronizes the lips with the voice clip, while RandomEyes3D allows the developer to control the proportion of eye contact with the user for each voice clip.
e. , deceiving the user into thinking that the virtual time is real-time) only relies on changes in environmental cues (e. g. , the movement of the sun, and changes in lighting). Therefore, a VR digital watch was developed (freely distributed on GitHub) and used in an attempt to increase the strength of the temporal illusion. To conclude, the development of VR research software is feasible mainly using free or low-cost assets from GitHub, Unity Asset's store, and other webpages. However, the suitability and quality of the VR software should be evaluated before its implementation in research settings.
e. , gamer or non-gamer) based on the participants' response to a question asking whether they played games on a weekly basis. The current study has been approved by the Philosophy, Psychology and Language Sciences Research Ethics Committee of the University of Edinburgh. All participants were informed about the procedures, possible adverse effects (e. g. , VRISE), data utilization, and the general aims of the study both orally and in writing; subsequently, every participant gave written informed consent.
Hardware and software An HTC Vive HMD, two lighthouse-stations for motion tracking, and two 6DoF controllers were used. The HMD was connected to a laptop with a 2. 80GHz Intel Core i7 7700HQ processor, 16 GB RAM, a 4095MB NVIDIA GeForce GTX 1070 graphics card, a 931 GB TOSHIBA MQ01ABD100 (SATA) hard disk, and Realtek High Definition Audio. The size of the VR play area was 4. 4 m2. The software was the alpha version of VR-EAL for session 1, the beta version of VR-EAL for session 2, and the final version of VR-EAL for session 3.
VRNQ The VRNQ is a paper-and-pencil questionnaire containing 20 questions, where each question refers to one of the criteria necessary to assess VR research/clinical software in neuroscience  (Kourtesis et al. , 2019b). The 20 questions assess four domains: user experience, game mechanics, in-game assistance and VRISE. The VRNQ has a maximum total score of 140, and 35 for each domain. VRNQ responses are indicated on a 7-point Likert style scale ranging from 1= extremely low to 7 = extremely high. Higher scores indicate a more positive outcome; this also applies to the evaluation of VRISE intensity. Hence, higher VRISE scores indicate lower intensities of VRISE (i. e. , 1 = extremely intense feeling, 2 = very intense feeling, 3 = intense feeling, 4 = moderate feeling, 5 = mild feeling, 6 = very mild feeling, 7 = absent).
The VR-EAL versions The present study attempted to develop a cost-effective VR research/clinical software (i. e. , VR-EAL) of a high enough quality for implementation in cognitive studies and that does not induce VRISE. The development included three versions of VR-EAL (i. e. , alpha, beta and final) until the attainment of these desired outcomes. The alpha version of VR-EAL revealed several limitations. It had low frames per second (fps), which increased the frequency and the intensity of VRISE. Also, the alpha version did not include haptics during the interactions, and the in-game assistance props were low in number. Lastly, the shaders of the 3D models were not converted to VR shaders (i. e. , the function of the Lab renderer) and numerous game objects were defined as non-static. As a result, the quality of the graphics was below average, and the fps were substantially below 90 (i.
e. , 70 -80) which is the lowest threshold for VR software targeting high-end HMDs such as HTC Vive and Oculus Rift. However, the feedback also confirmed that several game mechanics and approaches (e. g. , tutorials) were in the right direction, which was encouraging for further VR-EAL development. The principal improvements in the beta version of VR-EAL were pertinent to the alpha version's shortcomings. The shaders for all the game objects were converted to VR shaders, and several game objects, with which the user does not interact, were defined as static. The fps for the beta version were above 90, though, there were various points where the fps dropped for a couple of seconds. Although these fps drops were brief, their existence negatively affected the users who reported moderate to intense VRISE.
e. , the features of object-oriented programming languages like C# that have substantially lower costs towards the performance of the software). Furthermore, the lightmapping of the 3D environments of scenes was upgraded by calculating highresolution lightmaps instead of the medium resolution used in previous versions of VR-EAL. Redundant shadows were also deactivated to improve the performance of VR-EAL without degrading the quality of the graphics. Moreover, major parts of the 3D environments were baked together (i. e. , merged) through the implementation of MeshBaker's predominant functions to significantly reduce the draw-calls of VR-EAL. Interestingly, the result was a stable number of fps during gameplay. Specifically, the final version of VR-EAL has 120-140 fps during gameplay. Lastly, there was an improvement and enrichment of in-game assistance.
This substantial decrease of VRISE also highlights the importance of fps in VR. A developer should use the Unity profiler to check whether the VR software has a steady number of fps during gameplay, which the HMD requires. Also, the final version of VR-EAL appeared to have better in-game assistance and game mechanics. However, there was not any upgrade pertinent to the game mechanics. The increase in the evaluation of the game mechanics probably resulted due to the addition and improvement of in-game aids in both tutorial and storyline scenes. This finding also supports that in-game assistance has a paramount role in VR software. This is especially the case when the software is developed for clinical or research purposes, where the users could be either gamers or non-gamers. The quality of the tutorials and in-game aids should be cautiously designed to ensure the usability of the VR research software.
VR software development in cognitive sciences The current study demonstrated the procedure for the development of immersive VR research/clinical software (i. e. , VR-EAL) with strong placement and plausibility illusions, which are necessary for collecting genuine responses (i. e. , ecological valid) from users  (Slater, 2009; Slater et al. , 2010; Maister et al. , 2015; Pan & Hamilton, 2018). The implementation of good quality 3D models (e. g. , objects, buildings, and artificial humans) in conjunction with optimization tools (e. g. , Lab Renderer and MeshBaker) facilitated an analogous placement illusion. Also, VR-EAL incorporates naturalistic and ergonomic interactions with the VE facilitated by the VR hardware (e. g. , HTC Vive and 6DoF controllers), SDKs (e. g. , SteamVR and VRTK), and Unity assets pertinent to spatialized audio (e.
g. , Steam Audio) and artificial characters' animations (e. g. , Salsa3D). These naturalistic and ergonomic interactions with the VE are capable of inducing a robust plausibility illusion. Furthermore, a predominant concern for the implementation of VR in cognitive sciences is the presence of VRISE  (Bohil et al. , 2011; de Franca & Soares, 2017; Palmisano et al. , 2017) , which may compromise health and safety standards  (Parsons et al. , 2018) , as well as the reliability of cognitive  (Nalivaiko et al. , 2015) , physiological  (Nalivaiko et al. , 2015) , and neuroimaging data  (Arafat et al. , 2018; Gavgani et al, 2018). Equally, the high cost of VR software development may additionally deter the adoption of VR as a research tool in cognitive sciences  (Slater, 2018). However, the development of VR-EAL provides evidence that the obstacles above can be surpassed to implement VR software in cognitive sciences effectively.
The users of the final version of VR-EAL reported mild to no VRISE, with the average value in the VRISE sub-score being very mild to no VRISE. Importantly, these reports were offered by the users after spending around 60 minutes uninterrupted in VR. Typically, VRISE are intensified in longer VR sessions  (Sharples et al. , 2008). However, the utilization of the parsimonious cut-offs from the VRNQ guaranteed the significant alleviation of VRISE, which was also supported by the users' reports. Notably, the results of this study are in line with our previous work  (Kourtesis et al. , 2019b) , where the gaming experience (i. e. , gamer or non-gamer) did not affect the responses on the VRNQ. Also, the results support that the gaming experience does not affect the presence or intensity of VRISE in software of adequate quality.
Therefore, VR software with technical features similar to VR-EAL would be suitable for implementation in cognitive sciences. Cognitive scientists already implement computational approaches to investigate cognitive functions at the neuronal and cellular level  (Sejnowski, 1988; Farrell & Lewandowsky, 2010; Kriegeskorte & Douglas, 2018) , develop computerized neuropsychological tasks compatible with neuroimaging techniques  (Mathôt et al. , 2011; Peirce, 2009; Peirce et al, 2011) , as well as conducting flexible statistical analyses and creating high-quality graphics and simulations  (Culpepper & Aguinis, 2011; Revelle, 2011; Stevens, 2017). The development of VR-EAL was achieved by using C# and Unity packages (i. e. , SteamVR SDK, VRTK, Lab renderer, MeshBaker, Salsa3D, RandomEyes3D, 3D models, 3D environments, and 3D characters) on the Unity game engine, which is a user-friendly IDE equivalent to OpenSesame, PsychoPy, and MATLAB.
The majority of these Unity packages are cost-free, while the remainder are relatively low-cost, and could be used in future VR software development. Also, the acquisition of VR development skills by cognitive scientists with a background in either psychology or computers science can be realized in a moderately short period. Although, collaboration with a psychologist who has the required knowledge and clinical experience is crucial for a computer scientist with VR skills. Likewise, psychologists should either collaborate with a computer scientist with VR expertise or acquire VR development skills themselves. For the acquisition of VR skills by a computer scientist or a psychologist, there are online and on-campus interdisciplinary modules (e. g. , Unity tutorials and documentation, game development courses, programming workshops, and specializations in VR) which further support the feasibility of acquiring the necessary skills.
Conclusion This study provided guidelines for the development of immersive VR research software that can be implemented in cognitive sciences to improve the ecological validity of the cognitive tasks and automate the administration and scoring of the neuropsychological assessment. The results substantially support the feasibility of the development of low-cost and effective immersive VR software without the presence of VRISE during a 60 minutes VR session by cognitive scientists who have skills in VR software development. Technologically competent cognitive scientists are able to develop cost-effective immersive VR research software that guarantees the safety of the users and the reliability of the collected data (i. e. , neuropsychological, physiological, and neuroimaging data).
The first participant was also left out from further analysis because her trial served as a pilot test to finetune the study setup. Therefore, data from 58 participants (57%, n = 33 female; 43. 1%, n = 25 male) between 17 and 30 years old (M = 21. 26, SD = 2. 99) were used for analysis. Most participants were Dutch (83%, n = 48); 17% (n = 10) were German. Most of them (80%, n = 48) were bachelor students of Behavioral Sciences (Psychology and Communication Science) and Engineering Technology (Creative Technology). The participants were approached through convenience sampling and could earn credit points or a small monetary compensation for their participation. About one-third of the participants (10 male, 10 female) indicated that they had been victimized at least once in their life, and the majority of the participants (67%; 19 male, 20 female) knew someone in their direct social network who had been victimized at least once in their lives.
Design The participants were asked to take the role of a person who was victimized and -later on -attended a face-to-face mediation with the (fictitious) offender. A correlational design was adopted to test the associations between pre-measures (the attitude toward resocialization programs and the expectations toward the offender's sincerity) eye movement data and post-measures, namely, the emotional inferences and the perceived sincerity of the apology. To determine the participants' visual attention behavior on the offender (eye movement data), fixations and duration of eye movements were measured and analyzed in terms of their associations with the proposed variables. All procedures in this study were approved by the ethics committee of the Faculty of Behavioral, Management and Social Sciences of the University of Twente.
Overview of the Study The participants were welcomed and they first read and signed an informed consent form that covered all aspects about the voluntary, confidential, and anonymous nature of the study. They were also informed that eye tracking technology would be used to trace and record their eye movement. The study was designed and conducted with the online software tool Qualtrics. After the participants gave their informed consent, they put on the head unit of the eye tracking device that was adjusted to the participant's nose and head by the researcher. Subsequently, the glasses were calibrated to generate measurement accuracy by looking at a target mark (Ø 2. 5 cm) that was placed at a distance of approximately 50 cm to the participant's face. Then, measuring of the participant's gaze behavior started and lasted through the whole trial in order for the participants to get used to wearing the glasses and not to disrupt their gaze behavior during the mediation scenario presented at a later stage of the study.
Next, the participants' expectations toward the offender's sincerity and the participants' attitude toward resocialization programs were assessed. They were then asked to contact the researcher who showed them the video clip in which the offender of the burglary scenario offered a remorseful apology to the victim. This stimulus material was not embedded in Qualtrics to facilitate showing of the video clip in a higher resolution. Subsequently, the participants indicated the degree of suffering, responsibility, and empathy that they perceived in the offender as well as the perceived degree of sincerity of the apology. At the end of the study, demographic data including gender, age, nationality, and current educational status were gathered. Also, own experiences with crime were recorded. Finally, text entry was given for personal remarks or questions concerning the study.
Visual fixation and areas of interest With Tobii Analyzer, a list of gaze data was obtained for each participant. Gaze behavior consists of (a) fixations that were defined as the amount of continuous time that was spent looking at a 20 × 20 pixels region and (b) eye movement that is necessary to inspect the whole visual scene in detail  (Noton and Stark, 1971; Boraston and Blakemore, 2007). In the Tobii Software Analyzer, a minimal fixation time of 50 ms was set as a standard; this standard was also used in this study. The number of eye fixations on the whole visual area of the stimulus material was calculated for each participant, representing the locations and the sequences (saccades) of the eye fixations. Also, the duration of eye fixations (in seconds) was examined. In order to compare the fixation distributions, the offender's face was categorized into look zones, each called AOI.
Mapping Eye Fixations and Data Processing We obtained a video showing the visual spectrum of each participant, including fixation points representing his or her gaze behavior. To compare and visualize the fixation distributions of all participants, we created a fixed snapshot that was most appropriate to represent an average frame of the position of the offender in the video. For each participant, every sequence with a value of 30 ms as minimum fixation duration was compared to the fixed snapshot to which a fixation point was added automatically by Tobii Analyzer. When the eyes rested on a visual area for at least 30 ms, the analysis tool added this as a fixation point to the output table. Every fixation point on the snapshot was compared to the corresponding video sequence. If the software did not map it accurately, the first author corrected it manually.
Fixation distributions on all parts of the visual area including AOIs Figures 3, 4 provide a visualization of the (a) fixation duration and (b) fixation count that was acquired from the 58 participants, represented on the snapshot that was used to map eye tracking metrics. Fixations that were outside the computer screen were left out for further analysis as they did not relate to the visual area of the apology-mediation scenario. Figure  3  illustrates the distribution of the visual attention (gaze behavior) of all participants, summarized in a heat map. It provides a heuristic overview of all data points; the order of fixations, the individual scan paths, and the minor fixations are not visualized. The center of the heat map, indicated by warm colors such as red and orange, shows that the fixation focus of all participants predominantly lies on the left eye of the offender and the space between both eyes, including the upper part of the nose.
Colored in green, the visual areas around the fixation focus, mainly consisting of the right eye, the forehead, and the mouth, also gained the attention of the participants but to a smaller degree. Figure  4  provides a visualization of all gaze data in detail. This gaze plot was created to show the viewing pattern including location, gaze sequence, and time of attention distribution for every participant apart -each color represents a different participant. Each circle in Figure  4  represents the time a participant was looking at a fixation point (fixation duration). To the degree that a participant looked longer at a fixation point, the larger was the diameter of (and thus) the circle. A divergent distribution of fixation points on all areas of the stimulus material can be seen; notably, the density of fixation points on the offender's face is higher than on the remaining areas.
For instance, before the stimulus material was presented, they were instructed to judge the apology afterward. Keeping this in mind, the participants might have been encouraged to remain in eye contact regardless of the verbal and the nonverbal information of the offender, expressing that they were carefully watching the apology and also knowing that their gaze data were recorded. We further examined how distinct a priori and perceptual factors particularly determine the assessment of the apology. This study demonstrates that the general standpoint the participants had toward programs applied to facilitate an offender's resocialization (VOM being a typical example of such a program) substantially accounted for the perceived sincerity. In other words, for participants who were more in favor of these programs (i.
Limitations of the Study The current study is not without limitations. First and foremost, these are related to the setup of this study, which took place in an artificial experimental environment. We presented an apology given by a confederate and were interested in the participants' gaze pattern. Data were gathered from college students who were instructed to imagine that they were victimized and that they were approached to participate in VOM by a mediator. In a natural VOM setting, it would not have been possible to do this in a standardized way except if all victims would have experienced the same crime and commonly received the offender's apology. However, the participants' perceptions may differ from the behavior and the negative emotions and feelings the target population (victims) might experience after a crime and during mediation (although a fair share of the participants indicated that they had been a victim of crime in their lives; 34.
ACKNOWLEDGMENTS We would like to thank the participants in this study. Thanks are also extended to the  BMS laboratory of the University of Twente  for providing the technical equipment necessary to conduct this study. This article elaborates on a previous work by FB (2018; thesis to obtain a Master's Degree at the  University of Twente, Enschede ) and uses data collected for this research. The concepts included in the theoretical framework, the analyses, and the methods are based on this earlier work.