What you see is what you get: contextual modulation of face scanning in typical and atypical development

Abstract

Infants’ visual scanning of social scenes is influenced by both exogenously and endogenously driven shifts of attention. We manipulate these factors by contrasting individual infants’ distribution of visual attention to the eyes relative to the mouth when viewing complex dynamic scenes with multiple communicative signals (e.g. peek-a-boo), relative to the same infant viewing simpler scenes where only single features move (moving eyes, mouth and hands). We explore the relationship between context-dependent scanning patterns and later social and communication outcomes in two groups of infants, with and without familial risk for autism. Our findings suggest that in complex scenes requiring more endogenous control of attention, increased scanning of the mouth region relative to the eyes at 7 months is associated with superior expressive language (EL) at 36 months. This relationship holds even after controlling for outcome group. In contrast, in simple scenes where only the mouth is moving, those infants, irrespective of their group membership, who direct their attention to the repetitive moving feature, i.e. the mouth, have poorer EL at 36 months. Taken together, our findings suggest that scanning of complex social scenes does not begin as strikingly different in those infants later diagnosed with autism.

Face engagement during infancy predicts later face recognition ability in younger siblings of children with autism

“…We consider this measure a prerequisite to configural processing as it has been suggested that eye movements between facial features are functional in obtaining information about the configuration of these features (Henderson, Williams & Falk, 2005). As there was no eye-tracking data recorded during the face recognition task we used the gaze data recorded when the children observed the still image of a face during a different task that was administered during the same testing session to investigate differences in face-scanning patterns between the high-risk siblings and low-risk controls and its relationship with face recognition performance (for more information about the eyetracking task see Elsabbagh et al, 2013b).…”

“…a focus on irrelevant features of the face such as the hairline and ears) might reduce exposure to internal face features (Golarai et al, 2006) while also increasing the total amount of looking. As the images used in the pop-out task were too small to define separate AOIs around the facial features we investigated the face-scanning patterns of the same group of infants using gaze data that was recorded when they observed the still image of a face during a different task administered during the same testing session (for more information about the task see Elsabbagh et al, 2013b).…”

Latest findings in autism research: how do they support the importance of early diagnosis and immediate intervention?

“…Two other groups used similar prospective designs to identify early signs of autism. Elsabaggh and colleagues [78,79] performed two relatively standardised experiments in a cohort of children at risk for autism, for which eye-gaze data was recorded at 7 and 14 months old, and formal ASD diagnosis established at 2 and 3 years old. In their first paper [78], they examined how infants reorient their attention to peripheral, nonsocial stimuli.…”

“…Elsabaggh and colleagues [78,79] performed two relatively standardised experiments in a cohort of children at risk for autism, for which eye-gaze data was recorded at 7 and 14 months old, and formal ASD diagnosis established at 2 and 3 years old. In their first paper [78], they examined how infants reorient their attention to peripheral, nonsocial stimuli. Their results showed that reduced flexibility in the control of visual attention starts to emerge only after 14 months old, which indeed suggests that the visual processing of nonsocial stimuli is not primarily affected in very young children that will develop autism later on.…”

Classificador para auxílio ao diagnóstico de TEA baseado em um modelo computacional de atenção visual

“…The main method used for this purpose was the ANOVA, used in 57% of the works. Some works also used Pearson correlation [50], likelihood stimator [51], and nonparametric tests [52], among others.…”