scholarly journals ECSCW 2011 Conference Supplement: European Conference on Computer-Supported Cooperative Work Aarhus, 24.-28. September 2011

2011 ◽  
Vol 40 (594) ◽  
Author(s):  
Susanne Bødker
Keyword(s):  
Eye Tracking ◽  
Collaborative Work ◽  
Cooperative Work ◽  
Computer Supported Cooperative Work ◽  
Tracking Data ◽  
Font Size ◽  
European Conference ◽  
Task Specificity ◽  
The Gaze ◽  
Common Task

<span style="font-family: Arial; font-size: x-small;"><span style="font-family: Arial; font-size: x-small;"><p>Dual eye-tracking (DUET) is a promising methodology to study and support</p> <p>collaborative work. The method consists of simultaneously recording the gaze of two</p> <p>collaborators working on a common task. The main themes addressed in the workshop</p> <p>are eye-tracking methodology (how to translate gaze measures into descriptions of joint</p> <p>action, how to measure and model gaze alignment between collaborators, how to address</p> <p>task specificity inherent to eye-tracking data) and more generally future applications of</p> <p>dual eye-tracking in CSCW. The DUET workshop will bring together scholars who</p> <p>currently develop the approach as well as a larger audience interested in applications of</p> <p>eye-tracking in collaborative situations. The workshop format will combine paper</p> <p>presentations and discussions. The papers are available online as PDF documents at</p> <p>http://www.dualeyetracking.org/DUET2011/.</p></span></span>

The Analysis on the Construction of CSCW System and Group Collaborative Mode

2013 ◽  
Vol 756-759 ◽  
pp. 2966-2970
Author(s):  
Zhong Wei Wang ◽  
Yan An
Keyword(s):  
Computer Technology ◽  
Collaborative Design ◽  
Manufacturing Industry ◽  
Collaborative Work ◽  
Cooperative Work ◽  
Computer Support ◽  
Collaborative Model ◽  
Computer Support Cooperative Work ◽  
Collaborative Manufacturing ◽  
Common Task

With the development and popularization of intranet/Extranet and Internet, it provides vast development space for manufacturing industry. The computer support cooperative work such as collaborative design and collaborative manufacturing based on web is presented under the background. Computer Support Cooperative Work (CSCW) is defined that in the environment supported by computer technology, a groupware cooperatively work to accomplish a common task. The paper is focus on the construction of CSCW system and group collaborative work mode in CSCW. The group collaborative model of CSCW should be intensified further to abstract the features of group collaboration and direct the research on collaborative work technology.

Survey on Collaborative Awareness Model for CSCW and Trends of the New Age

2012 ◽  
Vol 155-156 ◽  
pp. 357-362
Author(s):  
Jing Wang ◽  
Da Wei Chu
Keyword(s):  
Computer Science ◽  
Collaborative Work ◽  
New Age ◽  
Development Trend ◽  
Cooperative Work ◽  
Computer Supported Cooperative Work ◽  
Critical Theories ◽  
Key Issues ◽  
Object Of Study ◽  
The Development Trend

Currently Computer Supported Cooperative Work (CSCW) has become an important area of research in computer science. The main purpose of CSCW system is to support people interact cooperation, and collaborative awareness between users must be the basis for multi-user collaborative work, and each user's behavior or the operation of the shared object should be immediately perceived by other users. Therefore, collaborative awareness is one of the key issues to be resolved in CSCW system. In this paper, collaborative awareness models are the object of study, and we discussed the collaborative awareness models respectively based on space, role and event, and a lot of critical theories and techniques about them. The analysis of existing theory helped us to further understand various collaborative awareness models, to study the development trend of them, and establish the appropriate framework for collaborative awareness.

scholarly journals Theory of mind affects the interpretation of another person's focus of attention

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jessica Dawson ◽  
Alan Kingstone ◽  
Tom Foulsham
Keyword(s):  
Theory Of Mind ◽  
Eye Tracking ◽  
Computer System ◽  
Target Object ◽  
Focus Of Attention ◽  
Gaze Following ◽  
Tracking Data ◽  
The Gaze

AbstractPeople are drawn to social, animate things more than inanimate objects. Previous research has also shown gaze following in humans, a process that has been linked to theory of mind (ToM). In three experiments, we investigated whether animacy and ToM are involved when making judgements about the location of a cursor in a scene. In Experiment 1, participants were told that this cursor represented the gaze of an observer and were asked to decide whether the observer was looking at a target object. This task is similar to that carried out by researchers manually coding eye-tracking data. The results showed that participants were biased to perceive the gaze cursor as directed towards animate objects (faces) compared to inanimate objects. In Experiments 2 and 3 we tested the role of ToM, by presenting the same scenes to new participants but now with the statement that the cursor was generated by a ‘random’ computer system or by a computer system designed to seek targets. The bias to report that the cursor was directed toward faces was abolished in Experiment 2, and minimised in Experiment 3. Together, the results indicate that people attach minds to the mere representation of an individual's gaze, and this attribution of mind influences what people believe an individual is looking at.

scholarly journals Collaborative Use of a Shared System Interface: The Role of User Gaze—Gaze Convergence Index Based on Synchronous Dual-Eyetracking

2020 ◽  
Vol 10 (13) ◽  
pp. 4508 ◽  
Author(s):  
Armel Quentin Tchanou ◽  
Pierre-Majorique Léger ◽  
Jared Boasen ◽  
Sylvain Senecal ◽  
Jad Adam Taher ◽  
...  
Keyword(s):  
Eye Movements ◽  
Eye Tracking ◽  
Task Performance ◽  
Tracking Data ◽  
Nomological Network ◽  
Practical Applications ◽  
The Gaze ◽  
Research Perspectives ◽  
System Interfaces

Gaze convergence of multiuser eye movements during simultaneous collaborative use of a shared system interface has been proposed as an important albeit sparsely explored construct in human-computer interaction literature. Here, we propose a novel index for measuring the gaze convergence of user dyads and address its validity through two consecutive eye-tracking studies. Eye-tracking data of user dyads were synchronously recorded while they simultaneously performed tasks on shared system interfaces. Results indicate the validity of the proposed gaze convergence index for measuring the gaze convergence of dyads. Moreover, as expected, our gaze convergence index was positively associated with dyad task performance and negatively associated with dyad cognitive load. These results suggest the utility of (theoretical or practical) applications such as synchronized gaze convergence displays in diverse settings. Further research perspectives, particularly into the construct’s nomological network, are warranted.

scholarly journals Mobile Eye-Tracking Data Analysis Using Object Detection via YOLO v4

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7668
Author(s):  
Niharika Kumari ◽  
Verena Ruf ◽  
Sergey Mukhametov ◽  
Albrecht Schmidt ◽  
Jochen Kuhn ◽  
...  
Keyword(s):  
Eye Tracking ◽  
Object Detection ◽  
Tracking Data ◽  
Time System ◽  
Real Time System ◽  
Flow Estimation ◽  
The Gaze ◽  
Manual Analysis ◽  
Real Objects ◽  
Mobile Eye Tracking

Remote eye tracking has become an important tool for the online analysis of learning processes. Mobile eye trackers can even extend the range of opportunities (in comparison to stationary eye trackers) to real settings, such as classrooms or experimental lab courses. However, the complex and sometimes manual analysis of mobile eye-tracking data often hinders the realization of extensive studies, as this is a very time-consuming process and usually not feasible for real-world situations in which participants move or manipulate objects. In this work, we explore the opportunities to use object recognition models to assign mobile eye-tracking data for real objects during an authentic students’ lab course. In a comparison of three different Convolutional Neural Networks (CNN), a Faster Region-Based-CNN, you only look once (YOLO) v3, and YOLO v4, we found that YOLO v4, together with an optical flow estimation, provides the fastest results with the highest accuracy for object detection in this setting. The automatic assignment of the gaze data to real objects simplifies the time-consuming analysis of mobile eye-tracking data and offers an opportunity for real-time system responses to the user’s gaze. Additionally, we identify and discuss several problems in using object detection for mobile eye-tracking data that need to be considered.

scholarly journals Characterizing missed identifications and errors in latent fingerprint comparisons using eye-tracking data

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251674
Author(s):  
Thomas A. Busey ◽  
Nicholas Heise ◽  
R. Austin Hicklin ◽  
Bradford T. Ulery ◽  
JoAnn Buscaglia
Keyword(s):  
Decision Making ◽  
Eye Tracking ◽  
No Value ◽  
Eye Gaze ◽  
Tracking Data ◽  
Gaze Behavior ◽  
Cognitive Capacities ◽  
Latent Fingerprint ◽  
The Gaze ◽  
Decision Making Factors

Latent fingerprint examiners sometimes come to different conclusions when comparing fingerprints, and eye-gaze behavior may help explain these outcomes. missed identifications (missed IDs) are inconclusive, exclusion, or No Value determinations reached when the consensus of other examiners is an identification. To determine the relation between examiner behavior and missed IDs, we collected eye-gaze data from 121 latent print examiners as they completed a total 1444 difficult (latent-exemplar) comparisons. We extracted metrics from the gaze data that serve as proxies for underlying perceptual and cognitive capacities. We used these metrics to characterize potential mechanisms of missed IDs: Cursory Comparison and Mislocalization. We find that missed IDs are associated with shorter comparison times, fewer regions visited, and fewer attempted correspondences between the compared images. Latent print comparisons resulting in erroneous exclusions (a subset of missed IDs) are also more likely to have fixations in different regions and less accurate correspondence attempts than those comparisons resulting in identifications. We also use our derived metrics to describe one atypical examiner who made six erroneous identifications, four of which were on comparisons intended to be straightforward exclusions. The present work helps identify the degree to which missed IDs can be explained using eye-gaze behavior, and the extent to which missed IDs depend on cognitive and decision-making factors outside the domain of eye-tracking methodologies.

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