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1. Eye-tracking as a proxy for coherence and complexity of texts

   https://doi.org/10.1371/journal.pone.0260236  

   Torres, Débora ; Sena, Wagner R. ; Carmona, Humberto A. ; Moreira, André A. ; Makse, Hernán A. ; Andrade, José S. ( December 2021 , PLOS ONE)

   Ribeiro, Haroldo V. (Ed.)

   Reading is a complex cognitive process that involves primary oculomotor function and high-level activities like attention focus and language processing. When we read, our eyes move by primary physiological functions while responding to language-processing demands. In fact, the eyes perform discontinuous twofold movements, namely, successive long jumps (saccades) interposed by small steps (fixations) in which the gaze “scans” confined locations. It is only through the fixations that information is effectively captured for brain processing. Since individuals can express similar as well as entirely different opinions about a given text, it is therefore expected that the form, content and style of a text could induce different eye-movement patterns among people. A question that naturally arises is whether these individuals’ behaviours are correlated, so that eye-tracking while reading can be used as a proxy for text subjective properties. Here we perform a set of eye-tracking experiments with a group of individuals reading different types of texts, including children stories, random word generated texts and excerpts from literature work. In parallel, an extensive Internet survey was conducted for categorizing these texts in terms of their complexity and coherence, considering a large number of individuals selected according to different ages, gender and levels of education. The computational analysis of the fixation maps obtained from the gaze trajectories of the subjects for a given text reveals that the average “magnetization” of the fixation configurations correlates strongly with their complexity observed in the survey. Moreover, we perform a thermodynamic analysis using the Maximum-Entropy Model and find that coherent texts were closer to their corresponding “critical points” than non-coherent ones, as computed from the Pairwise Maximum-Entropy method, suggesting that different texts may induce distinct cohesive reading activities. 

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2. Exploring Eye Gaze Visualization Techniques for Identifying Distracted Students in Educational VR

   https://doi.org/10.1109/VR46266.2020.00009  

   Rahman, Yitoshee ; Asish, Sarker M. ; Fisher, Nicholas P. ; Bruce, Ethan C. ; Kulshreshth, Arun K. ; Borst, Christoph W. ( March 2020 , 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR))

   Virtual Reality (VR) headsets with embedded eye trackers are appearing as consumer devices (e.g. HTC Vive Eye, FOVE). These devices could be used in VR-based education (e.g., a virtual lab, a virtual field trip) in which a live teacher guides a group of students. The eye tracking could enable better insights into students’ activities and behavior patterns. For real-time insight, a teacher’s VR environment can display student eye gaze. These visualizations would help identify students who are confused/distracted, and the teacher could better guide them to focus on important objects. We present six gaze visualization techniques for a VR-embedded teacher’s view, and we present a user study to compare these techniques. The results suggest that a short particle trail representing eye trajectory is promising. In contrast, 3D heatmaps (an adaptation of traditional 2D heatmaps) for visualizing gaze over a short time span are problematic. 

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3. What reading aloud reveals about speaking: Regressive saccades implicate a failure to monitor, not inattention, in the prevalence of intrusion errors on function words

   https://doi.org/10.1177/1747021818819480  

   Schotter, Elizabeth R. ; Li, Chuchu ; Gollan, Tamar H. ( January 2019 , Quarterly Journal of Experimental Psychology)

   Bilinguals occasionally produce language intrusion errors (inadvertent translations of the intended word), especially when attempting to produce function word targets, and often when reading aloud mixed-language paragraphs. We investigate whether these errors are due to a failure of attention during speech planning, or failure of monitoring speech output by classifying errors based on whether and when they were corrected, and investigating eye movement behaviour surrounding them. Prior research on this topic has primarily tested alphabetic languages (e.g., Spanish–English bilinguals) in which part of speech is confounded with word length, which is related to word skipping (i.e., decreased attention). Therefore, we tested 29 Chinese–English bilinguals whose languages differ in orthography, visually cueing language membership, and for whom part of speech (in Chinese) is less confounded with word length. Despite the strong orthographic cue, Chinese–English bilinguals produced intrusion errors with similar effects as previously reported (e.g., especially with function word targets written in the dominant language). Gaze durations did differ by whether errors were made and corrected or not, but these patterns were similar for function and content words and therefore cannot explain part of speech effects. However, bilinguals regressed to words produced as errors more often than to correctly produced words, but regressions facilitated correction of errors only for content, not for function words. These data suggest that the vulnerability of function words to language intrusion errors primarily reflects automatic retrieval and failures of speech monitoring mechanisms from stopping function versus content word errors after they are planned for production.

    

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4. Distraction “Hangover”: Characterization of the Delayed Return to Baseline Driving Risk After Distracting Behaviors

   https://doi.org/10.1177/00187208211012218  

   Snider, Joseph ; Spence, Ryan J. ; Engler, Anne-Marie ; Moran, Ryan ; Hacker, Sarah ; Chukoskie, Leanne ; Townsend, Jeanne ; Hill, Linda ( April 2021 , Human Factors: The Journal of the Human Factors and Ergonomics Society)

   null (Ed.)

   Objective We measured how long distraction by a smartphone affects simulated driving behaviors after the tasks are completed (i.e., the distraction hangover). Background Most drivers know that smartphones distract. Trying to limit distraction, drivers can use hands-free devices, where they only briefly glance at the smartphone. However, the cognitive cost of switching tasks from driving to communicating and back to driving adds an underappreciated, potentially long period to the total distraction time. Method Ninety-seven 21- to 78-year-old individuals who self-identified as active drivers and smartphone users engaged in a simulated driving scenario that included smartphone distractions. Peripheral-cue and car-following tasks were used to assess driving behavior, along with synchronized eye tracking. Results The participants’ lateral speed was larger than baseline for 15 s after the end of a voice distraction and for up to 25 s after a text distraction. Correct identification of peripheral cues dropped about 5% per decade of age, and participants from the 71+ age group missed seeing about 50% of peripheral cues within 4 s of the distraction. During distraction, coherence with the lead car in a following task dropped from 0.54 to 0.045, and seven participants rear-ended the lead car. Breadth of scanning contracted by 50% after distraction. Conclusion Simulated driving performance drops dramatically after smartphone distraction for all ages and for both voice and texting. Application Public education should include the dangers of any smartphone use during driving, including hands-free. 

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5. PoseASL: An RGBD Dataset of American Sign Language

   https://doi.org/10.17910/b7.1279  

   Huenerfauth, Matt ( January 2021 , Databrary)

   The PoseASL dataset consists of color and depth videos collected from ASL signers at the Linguistic and Assistive Technologies Laboratory under the direction of Matt Huenerfauth, as part of a collaborative research project with researchers at the Rochester Institute of Technology, Boston University, and the University of Pennsylvania. Access: After becoming an authorized user of Databrary, please contact Matt Huenerfauth if you have difficulty accessing this volume. We have collected a new dataset consisting of color and depth videos of fluent American Sign Language signers performing sequences ASL signs and sentences. Given interest among sign-recognition and other computer-vision researchers in red-green-blue-depth (RBGD) video, we release this dataset for use by the research community. In addition to the video files, we share depth data files from a Kinect v2 sensor, as well as additional motion-tracking files produced through post-processing of this data. Organization of the Dataset: The dataset is organized into sub-folders, with codenames such as "P01" or "P16" etc. These codenames refer to specific human signers who were recorded in this dataset. Please note that there was no participant P11 nor P14; those numbers were accidentally skipped during the process of making appointments to collect video stimuli. Task: During the recording session, the participant was met by a member of our research team who was a native ASL signer. No other individuals were present during the data collection session. After signing the informed consent and video release document, participants responded to a demographic questionnaire. Next, the data-collection session consisted of English word stimuli and cartoon videos. The recording session began with showing participants stimuli consisting of slides that displayed English word and photos of items, and participants were asked to produce the sign for each (PDF included in materials subfolder). Next, participants viewed three videos of short animated cartoons, which they were asked to recount in ASL: - Canary Row, Warner Brothers Merrie Melodies 1950 (the 7-minute video divided into seven parts) - Mr. Koumal Flies Like a Bird, Studio Animovaneho Filmu 1969 - Mr. Koumal Battles his Conscience, Studio Animovaneho Filmu 1971 The word list and cartoons were selected as they are identical to the stimuli used in the collection of the Nicaraguan Sign Language video corpora - see: Senghas, A. (1995). Children’s Contribution to the Birth of Nicaraguan Sign Language. Doctoral dissertation, Department of Brain and Cognitive Sciences, MIT. Demographics: All 14 of our participants were fluent ASL signers. As screening, we asked our participants: Did you use ASL at home growing up, or did you attend a school as a very young child where you used ASL? All the participants responded affirmatively to this question. A total of 14 DHH participants were recruited on the Rochester Institute of Technology campus. Participants included 7 men and 7 women, aged 21 to 35 (median = 23.5). All of our participants reported that they began using ASL when they were 5 years old or younger, with 8 reporting ASL use since birth, and 3 others reporting ASL use since age 18 months. Filetypes: *.avi, *_dep.bin: The PoseASL dataset has been captured by using a Kinect 2.0 RGBD camera. The output of this camera system includes multiple channels which include RGB, depth, skeleton joints (25 joints for every video frame), and HD face (1,347 points). The video resolution produced in 1920 x 1080 pixels for the RGB channel and 512 x 424 pixels for the depth channels respectively. Due to limitations in the acceptable filetypes for sharing on Databrary, it was not permitted to share binary *_dep.bin files directly produced by the Kinect v2 camera system on the Databrary platform. If your research requires the original binary *_dep.bin files, then please contact Matt Huenerfauth. *_face.txt, *_HDface.txt, *_skl.txt: To make it easier for future researchers to make use of this dataset, we have also performed some post-processing of the Kinect data. To extract the skeleton coordinates of the RGB videos, we used the Openpose system, which is capable of detecting body, hand, facial, and foot keypoints of multiple people on single images in real time. The output of Openpose includes estimation of 70 keypoints for the face including eyes, eyebrows, nose, mouth and face contour. The software also estimates 21 keypoints for each of the hands (Simon et al, 2017), including 3 keypoints for each finger, as shown in Figure 2. Additionally, there are 25 keypoints estimated for the body pose (and feet) (Cao et al, 2017; Wei et al, 2016). Reporting Bugs or Errors: Please contact Matt Huenerfauth to report any bugs or errors that you identify in the corpus. We appreciate your help in improving the quality of the corpus over time by identifying any errors. Acknowledgement: This material is based upon work supported by the National Science Foundation under award 1749376: "Collaborative Research: Multimethod Investigation of Articulatory and Perceptual Constraints on Natural Language Evolution." 

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