Joint attention difficulties in autistic adults: An interactive eye-tracking study

Joint attention – the ability to coordinate attention with a social partner – is critical for social communication, learning and the regulation of interpersonal relationships. Infants and young children with autism demonstrate impairments in both initiating and responding to joint attention bids in naturalistic settings. However, little is known about joint attention abilities in adults with autism. Here, we tested 17 autistic adults and 17 age- and nonverbal intelligence quotient–matched controls using an interactive eye-tracking paradigm in which participants initiated and responded to joint attention bids with an on-screen avatar. Compared to control participants, autistic adults completed fewer trials successfully. They were also slower to respond to joint attention bids in the first block of testing but performed as well as controls in the second block. There were no group differences in responding to spatial cues on a non-social task with similar attention and oculomotor demands. These experimental results were mirrored in the subjective reports given by participants, with some commenting that they initially found it challenging to communicate using eye gaze, but were able to develop strategies that allowed them to achieve joint attention. Our study indicates that for many autistic individuals, subtle difficulties using eye-gaze information persist well into adulthood.

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Techniques Used for Eye Gaze Interfaces and Survey

International Journal of Advances in Scientific Research ◽

10.7439/ijasr.v1i6.2125 ◽

2015 ◽

Vol 1 (6) ◽

pp. 276

Author(s):

Maria Rashid ◽

Wardah Mehmood ◽

Aliya Ashraf

Keyword(s):

Eye Tracking ◽

Eye Gaze ◽

3D Models ◽

Image Process ◽

Usability Problems ◽

Software Algorithms ◽

Pupil Detection ◽

Eye Movement Tracking ◽

2D And 3D ◽

Movement Tracking

Eye movement tracking is a method that is now-a-days used for checking the usability problems in the contexts of Human Computer Interaction (HCI). Firstly we present eye tracking technology and key elements.We tend to evaluate the behavior of the use when they are using the interace of eye gaze. Used different techniques i.e. electro-oculography, infrared oculography, video oculography, image process techniques, scrolling techniques, different models, probable approaches i.e. shape based approach, appearance based methods, 2D and 3D models based approach and different software algorithms for pupil detection etc. We have tried to compare the surveys based on their geometric properties and reportable accuracies and eventually we conclude this study by giving some prediction regarding future eye-gaze. We point out some techniques by using various eyes properties comprising nature, appearance and gesture or some combination for eye tracking and detection. Result displays eye-gaze technique is faster and better approach for selection than a mouse selection. Rate of error for all the matters determines that there have been no errors once choosing from main menus with eye mark and with mouse. But there have been a chance of errors when once choosing from sub menus in case of eye mark. So, maintain head constantly in front of eye gaze monitor.

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Tracking eye-gaze in smart home systems (SHS): first insights from eye-tracking and self-report measures

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-021-03134-8 ◽

2021 ◽

Author(s):

Federico Cassioli ◽

Laura Angioletti ◽

Michela Balconi

Keyword(s):

Eye Tracking ◽

Personality Traits ◽

Smart Home ◽

Near Infrared ◽

Tracking System ◽

Eye Gaze ◽

Self Report ◽

Visual Exploration ◽

Living Room ◽

Psychological Traits

AbstractHuman–computer interaction (HCI) is particularly interesting because full-immersive technology may be approached differently by users, depending on the complexity of the interaction, users’ personality traits, and their motivational systems inclination. Therefore, this study investigated the relationship between psychological factors and attention towards specific tech-interactions in a smart home system (SHS). The relation between personal psychological traits and eye-tracking metrics is investigated through self-report measures [locus of control (LoC), user experience (UX), behavioral inhibition system (BIS) and behavioral activation system (BAS)] and a wearable and wireless near-infrared illumination based eye-tracking system applied to an Italian sample (n = 19). Participants were asked to activate and interact with five different tech-interaction areas with different levels of complexity (entrance, kitchen, living room, bathroom, and bedroom) in a smart home system (SHS), while their eye-gaze behavior was recorded. Data showed significant differences between a simpler interaction (entrance) and a more complex one (living room), in terms of number of fixation. Moreover, slower time to first fixation in a multifaceted interaction (bathroom), compared to simpler ones (kitchen and living room) was found. Additionally, in two interaction conditions (living room and bathroom), negative correlations were found between external LoC and fixation count, and between BAS reward responsiveness scores and fixation duration. Findings led to the identification of a two-way process, where both the complexity of the tech-interaction and subjects’ personality traits are important impacting factors on the user’s visual exploration behavior. This research contributes to understand the user responsiveness adding first insights that may help to create more human-centered technology.

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Black and White Lies: Race-Based Biases in Deception Judgments

Psychological Science ◽

10.1177/0956797617705399 ◽

2017 ◽

Vol 28 (8) ◽

pp. 1125-1136 ◽

Cited By ~ 8

Author(s):

E. Paige Lloyd ◽

Kurt Hugenberg ◽

Allen R. McConnell ◽

Jonathan W. Kunstman ◽

Jason C. Deska

Keyword(s):

Law Enforcement ◽

Eye Tracking ◽

Interpersonal Relationships ◽

Late Stage ◽

Racial Bias ◽

Lie Detection ◽

Intergroup Communication ◽

Black And White ◽

Truth Bias ◽

Practical Implications

In six studies ( N = 605), participants made deception judgments about videos of Black and White targets who told truths and lies about interpersonal relationships. In Studies 1a, 1b, 1c, and 2, White participants judged that Black targets were telling the truth more often than they judged that White targets were telling the truth. This truth bias was predicted by Whites’ motivation to respond without prejudice. For Black participants, however, motives to respond without prejudice did not moderate responses (Study 2). In Study 3, we found similar effects with a manipulation of the targets’ apparent race. Finally, in Study 4, we used eye-tracking techniques to demonstrate that Whites’ truth bias for Black targets is likely the result of late-stage correction processes: Despite ultimately judging that Black targets were telling the truth more often than White targets, Whites were faster to fixate on the on-screen “lie” response box when targets were Black than when targets were White. These systematic race-based biases have important theoretical implications (e.g., for lie detection and improving intergroup communication and relations) and practical implications (e.g., for reducing racial bias in law enforcement).

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A Study on the Development of the Psychological Assessment a Using Eye-Tracking: Focused on Eye Gaze Processing of Literacy Text

Augmented Cognition - Lecture Notes in Computer Science ◽

10.1007/978-3-030-22419-6_6 ◽

2019 ◽

pp. 74-83

Author(s):

Joon Hyun Jeon ◽

Gyoung Kim ◽

Jeong Ae Kim

Keyword(s):

Eye Tracking ◽

Psychological Assessment ◽

Eye Gaze

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Eye-gaze disengagement from emotional faces predicts depressive symptoms via ruminative brooding: A five-month longitudinal study with two eye-tracking assessments

International Journal of Psychophysiology ◽

10.1016/j.ijpsycho.2018.07.136 ◽

2018 ◽

Vol 131 ◽

pp. S44-S45

Author(s):

A.S.L. Sanchez-Lopez ◽

E.K. Koster ◽

R.D.R. de Raedt

Keyword(s):

Depressive Symptoms ◽

Longitudinal Study ◽

Eye Tracking ◽

Eye Gaze ◽

Emotional Faces

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Automated Filtering of Eye Movements Using Dynamic AOI in Multiple Granularity Levels

International Journal of Multimedia Data Engineering and Management ◽

10.4018/ijmdem.2021010104 ◽

2021 ◽

Vol 12 (1) ◽

pp. 49-64

Author(s):

Gavindya Jayawardena ◽

Sampath Jayarathna

Keyword(s):

Eye Movements ◽

Eye Tracking ◽

Eye Movement ◽

Eye Gaze ◽

Movement Analysis ◽

Movement Data ◽

Bounding Boxes ◽

Multiple Granularity ◽

Instance Segmentation ◽

Segmentation Models

Eye-tracking experiments involve areas of interest (AOIs) for the analysis of eye gaze data. While there are tools to delineate AOIs to extract eye movement data, they may require users to manually draw boundaries of AOIs on eye tracking stimuli or use markers to define AOIs. This paper introduces two novel techniques to dynamically filter eye movement data from AOIs for the analysis of eye metrics from multiple levels of granularity. The authors incorporate pre-trained object detectors and object instance segmentation models for offline detection of dynamic AOIs in video streams. This research presents the implementation and evaluation of object detectors and object instance segmentation models to find the best model to be integrated in a real-time eye movement analysis pipeline. The authors filter gaze data that falls within the polygonal boundaries of detected dynamic AOIs and apply object detector to find bounding-boxes in a public dataset. The results indicate that the dynamic AOIs generated by object detectors capture 60% of eye movements & object instance segmentation models capture 30% of eye movements.

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Is it avoidance or hypoarousal? A systematic review of emotion recognition, eye-tracking, and psychophysiological studies in young adults with autism spectrum conditions

Research in Autism Spectrum Disorders ◽

10.1016/j.rasd.2018.07.002 ◽

2018 ◽

Vol 55 ◽

pp. 1-13 ◽

Cited By ~ 5

Author(s):

Hélio Clemente Cuve ◽

Yu Gao ◽

Akiko Fuse

Keyword(s):

Systematic Review ◽

Young Adults ◽

Eye Tracking ◽

Emotion Recognition ◽

Autism Spectrum ◽

Autism Spectrum Conditions ◽

Adults With Autism ◽

Young Adults With Autism

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Eye-Tracking Technology

Eye-Tracking Technology Applications in Educational Research - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-5225-1005-5.ch014 ◽

2017 ◽

pp. 283-302

Author(s):

Chandni Parikh

Keyword(s):

Eye Movements ◽

Eye Tracking ◽

Eye Gaze ◽

Autism Spectrum ◽

Gaze Direction ◽

Red Flags ◽

High Risk Infants ◽

Behavioral Markers ◽

Tracking Technology ◽

Insight Into

Eye movements and gaze direction have been utilized to make inferences about perception and cognition since the 1800s. The driving factor behind recording overt eye movements stem from the fundamental idea that one's gaze provides tremendous insight into the information processing that takes place early on during development. One of the key deficits seen in individuals diagnosed with Autism Spectrum Disorders (ASD) involves eye gaze and social attention processing. The current chapter focuses on the use of eye-tracking technology with high-risk infants who are siblings of children diagnosed with ASD in order to highlight potential bio-behavioral markers that can inform the ascertainment of red flags and atypical behaviors associated with ASD within the first few years of development.

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