Comparing Automatic Eye Tracking and Manual Gaze Coding Methods in Young Children with Autism Spectrum Disorder

Venker, Courtney E.; Pomper, Ron; Mahr, Tristan; Edwards, Jan; Saffran, Jenny R.; Weismer, Susan Ellis

doi:10.1002/aur.2225

Cited by 29 publications

(31 citation statements)

References 44 publications

(85 reference statements)

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“…We attribute this loss of data directly to the network's decreased performance on the "away" class, since it plays a vital role in the criterion set to select valid data. This increase in data loss is comparable to the 15% difference in data loss between manually-coded data and data obtained using an automatic eye tracker reported obtained by Venker et al (2020).…”

Section: Figuresupporting

confidence: 82%

“…precision, studies have used the looking-while-listening (LWL) procedure, also called the preferential looking procedure, to understand the emergence of infants' language comprehension, developmental changes in processing efficiency, and young children's sensitivity to various types of linguistic information (Bergelson & Swingley, 2012;Fernald et al, 1998;Trueswell & Gleitman, 2004). Because of the simplicity of the task, the LWL procedure can be used with a wide range of participants, including individuals of different ages and abilities, and can even be ported outside of the lab setting (Naigles & Tovar, 2012;Venker et al, 2020). Measures of looking to a stimulus on one side of a screen vs. the other have also been used to study diverse topics including infants' object recognition (Fagan III, 1974), visual working memory (Ross-Sheehy et al, 2003), and even AUTOMATED CODING OF YOUNG CHILDREN'S EYE MOVEMENTS 4 social attention (Kinzler et al, 2007).…”

mentioning

confidence: 99%

“…However, the coding process is labor-intensive: a five-minute session usually takes AUTOMATED CODING OF YOUNG CHILDREN'S EYE MOVEMENTS 5 45-75 minutes to code, and achieving high inter-coder reliability requires great vigilance. Venker et al (2020) provide detailed discussion of many benefits and disadvantages of automated and hand-coded methods.…”

mentioning

confidence: 99%

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iCatcher: A Neural Network Approach for Automated Coding of Young Children’s Eye Movements

Erel¹,

Potter

Jaffe-Dax³

et al. 2021

Preprint

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Infants’ looking behaviors are often used for measuring attention, real-time processing, and learning – often using low-resolution videos. Despite the ubiquity of gaze-related methods in developmental science, current techniques usually involve laborious post hoc coding, imprecise real-time coding, or expensive eye trackers that may increase data loss and require a calibration phase. As a solution, we used computer-vision methods to perform automatic gaze estimation from low-resolution videos. At the core of this approach is an artificial neural network that classifies gaze directions in real time. We tested our method, called iCatcher, on data collected using the looking-while-listening procedure, where infants look at one of two locations on a screen. Using a large dataset of manually-annotated videos from prior research, we demonstrate that the accuracy of iCatcher approximates that of human annotators and replicates the results. Our method is publicly available as an open-source repository at https://github.com/yoterel/iCatcher.

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Section: Figuresupporting

confidence: 82%

mentioning

confidence: 99%

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iCatcher: A Neural Network Approach for Automated Coding of Young Children’s Eye Movements

Erel¹,

Potter

Jaffe-Dax³

et al. 2021

Preprint

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“…Participants were part of a broader study investigating lexical processing [Ellis Weismer et al, 2016; Mahr, McMillan, Saffran, Ellis Weismer, & Edwards, 2015; Pomper, Ellis Weismer, Saffran, & Edwards, 2019; Venker et al, 2020; Venker, Edwards, Saffran, & Ellis Weismer, 2019]. The study was prospectively approved by the university Institutional Review Board, and parents or legal guardians provided written informed consent for their child's participation.…”

Section: Methodsmentioning

confidence: 99%

Competing Perceptual Salience in a Visual Word Recognition Task Differentially Affects Children With and Without Autism Spectrum Disorder

et al. 2020

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Differences in visual attention have long been recognized as a central characteristic of autism spectrum disorder (ASD). Regardless of social content, children with ASD show a strong preference for perceptual salience—how interesting (i.e., striking) certain stimuli are, based on their visual properties (e.g., color, geometric patterning). However, we do not know the extent to which attentional allocation preferences for perceptual salience persist when they compete with top‐down, linguistic information. This study examined the impact of competing perceptual salience on visual word recognition in 17 children with ASD (mean age 31 months) and 17 children with typical development (mean age 20 months) matched on receptive language skills. A word recognition task presented two images on a screen, one of which was named (e.g., Find the bowl!). On Neutral trials, both images had high salience (i.e., were colorful and had geometric patterning). On Competing trials, the distracter image had high salience but the target image had low salience, creating competition between bottom‐up (i.e., salience‐driven) and top‐down (i.e., language‐driven) processes. Though both groups of children showed word recognition in an absolute sense, competing perceptual salience significantly decreased attention to the target only in the children with ASD. These findings indicate that perceptual properties of objects can disrupt attention to relevant information in children with ASD, which has implications for supporting their language development. Findings also demonstrate that perceptual salience affects attentional allocation preferences in children with ASD, even in the absence of social stimuli. Lay Summary This study found that visually striking objects distract young children with autism spectrum disorder (ASD) from looking at relevant (but less striking) objects named by an adult. Language‐matched, younger children with typical development were not significantly affected by this visual distraction. Though visual distraction could have cascading negative effects on language development in children with ASD, learning opportunities that build on children's focus of attention are likely to support positive outcomes.

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“…Given that the scene is constantly changing in real-world settings, automatic processing of eye movements (as used for computer-based tasks) is usually not possible. Instead, videos are extracted and analysed on a frame-by-frame basis, which can be time-consuming and is somewhat subjective (Venker, Pomper, Mahr, Edwards, Saffran, & Weismer, 2019). Consequently, assessments of inter-rater reliability are considered good practice.…”

Section: Practical Issues In Data Collectionmentioning

confidence: 99%

Eye tracking and cardiovascular measurement to assess and improve sporting performance

Crowe¹,

Wilson²,

Harris³

et al. 2020

Advancements in Mental Skills Training

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Eye tracking and cardiovascular measurement techniques to improve sporting performance Why are Eye Tracking and Cardiovascular Measures Useful in Sport? One of the difficulties of working in applied sports psychology is the lack of objective data to help understand the psychological components of an athlete's performance (Watson & Coker-Cranney, 2018). This may be one of the primary explanations for the disparity between how important coaches and athletes report psychological elements of elite performance and how much they invest in tracking and supporting such elements (Pain and Harwood, 2004). In this chapter, we discuss two objective psychophysiological measures which provide valuable insight into the psychology of performing under pressure in elite sports, and can address the need for objective data to support athlete performance. Both measureseye tracking and cardiovascular reactivityhave considerable evidence underpinning their efficacy in research environments (e.g. Kredel, Vater, Klostermann, & Hossner, 2017; Moore, Vine, Wilson, & Freeman, 2012). We discuss some practical issues that require consideration when using such measures in applied sports settings and report a case study. Combining these measures enables an in-depth and objective investigation into the complex interplay between visuomotor and physiological factors in the sporting environment. Eye Tracking In dynamic and unpredictable sporting arenas, athletes must be able to identify and process only the most important visual information to support successful decision-making and motor actions (Mann, Williams, Ward, & Janelle, 2007). It is therefore unsurprising that mobile eye-trackers have frequently been applied in sporting environments to understand the perceptual and attentional mechanisms that underpin sporting expertise (see Moran, Campbell, & Ranieri, 2018 for a recent review). Identifying the optimal gaze behaviour for a

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Comparing Automatic Eye Tracking and Manual Gaze Coding Methods in Young Children with Autism Spectrum Disorder

Cited by 29 publications

References 44 publications

iCatcher: A Neural Network Approach for Automated Coding of Young Children’s Eye Movements

iCatcher: A Neural Network Approach for Automated Coding of Young Children’s Eye Movements

Competing Perceptual Salience in a Visual Word Recognition Task Differentially Affects Children With and Without Autism Spectrum Disorder

Eye tracking and cardiovascular measurement to assess and improve sporting performance

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