Gaze Following and Attention to Objects in Infants at Familial Risk for ASD

Abstract

Reduced gaze following has been associated previously with lower language scores in children with autism spectrum disorder (ASD). Here, we use eye-tracking in a controlled experimental setting to investigate whether gaze following and attention distribution during a word learning task associate with later developmental and clinical outcomes in a population of infants at familial risk for ASD. Fifteen-month-old infants ( n = 124; n = 101 with familial risk) watched an actress repeatedly gaze toward and label one of two objects present in front of her. We show that infants who later developed ASD followed gaze as frequently as typically developing peers but spent less time engaged with either object. Moreover, more time spent on faces and less on objects was associated with lower concurrent or later verbal abilities, but not with later symptom severity. No outcome group showed evidence for word learning. Thus, atypical distribution of attention rather than poor gaze following is a limiting factor for language development in infants at familial risk for ASD.

Efficiency of scanning and attention to faces in infancy independently predict language development in a multiethnic and bilingual sample of 2-year-olds

“…Please do not copy or cite without authors' permission. The final article will be available, upon publication, via its DOI: 10.1177/0142723720966815 lead to longer looking at faces, thus likely disrupting the flow of social interactions and learning sequences (Elsabbagh et al, 2013;Hendry et al, 2018;Jones, Dawson & Webb, 2018;Wagner et al, 2016, Parsons et al, 2019. These findings suggest that looking to faces is important for language development, but that it is also important to know when to disengage.…”

“…Frank et al, 2011; Yu & Smith, 2017). Studies of atypical development also suggest that inefficient attention shifting or disengagement may lead to longer looking at faces, thus likely disrupting the flow of social interactions and learning sequences (Elsabbagh et al, 2013; Hendry et al, 2018; Jones et al, 2018; Parsons et al, 2019; Wagner et al, 2016). These findings suggest that looking to faces is important for language development, but that it is also important to know when to disengage.…”

Investigation of Eye-Tracking Scan Path as a Biomarker for Autism Screening Using Machine Learning Algorithms

“…Furthermore, unlike contemporary questionnaire-based ASD screening tools such as QCHAT and M-CHAT, which are prone to subjectivity, screening by ET data provides an objective assessment because it does not require any manual input. According to research studies [ 47 , 48 , 49 , 50 ], ASD symptoms are likely to be reflected on ET data in children as young as 6 to 24 months of age, well before ASD-related behavioral traits manifest. So, if we can record ET data accurately from toddlers, we can discover early indicators of ASD in them using the proposed DNN model.…”

Investigating the Mechanisms Driving Referent Selection and Retention in Toddlers at Typical and Elevated Likelihood for Autism Spectrum Disorder

“…Many of the studies showing success in referent mapping have placed participants in conditions optimal for attentionfor example, minimizing distractionwhich may overestimate learning abilities and may not reflect learning outside the lab. We know from previous work that participants at elevated likelihood for ASD have more difficulty distributing attention away from salient features to the referents of communication (Parsons et al, 2019) and the proportion of time spent on distractors during a word learning task predicted subsequent word learning measured with a looking while listening paradigm (Gliga, Elsabbagh, Hudry, Charman, Johnson & BASIS team. 2012).…”