Attention capture by trains and faces in children with and without autism spectrum disorder

Abstract

This study examined involuntary capture of attention, overt attention, and stimulus valence and arousal ratings, all factors that can contribute to potential attentional biases to face and train objects in children with and without autism spectrum disorder (ASD). In the visual domain, faces are particularly captivating, and are thought to have a ‘special status’ in the attentional system. Research suggests that similar attentional biases may exist for other objects of expertise (e.g. birds for bird experts), providing support for the role of exposure in attention prioritization. Autistic individuals often have circumscribed interests around certain classes of objects, such as trains, that are related to vehicles and mechanical systems. This research aimed to determine whether this propensity in autistic individuals leads to stronger attention capture by trains, and perhaps weaker attention capture by faces, than what would be expected in non-autistic children. In Experiment 1, autistic children (6–14 years old) and age- and IQ-matched non-autistic children performed a visual search task where they manually indicated whether a target butterfly appeared amongst an array of face, train, and neutral distractors while their eye-movements were tracked. Autistic children were no less susceptible to attention capture by faces than non-autistic children. Overall, for both groups, trains captured attention more strongly than face stimuli and, trains had a larger effect on overt attention to the target stimuli, relative to face distractors. In Experiment 2, a new group of children (autistic and non-autistic) rated train stimuli as more interesting and exciting than the face stimuli, with no differences between groups. These results suggest that: (1) other objects (trains) can capture attention in a similar manner as faces, in both autistic and non-autistic children (2) attention capture is driven partly by voluntary attentional processes related to personal interest or affective responses to the stimuli.

How Do Children and Adolescents with ASD Look at Animals? A Scoping Review

“…Three of the four publications based on tests and tasks on computer used performance measures based on response time and accuracy [89,97,104]. New et al [89] added an index of automatic attentional prioritization.…”

“…The authors define systematization as the level of organization of the image (e.g., a high level of systematization means that the elements in the scene are highly organized). Lastly, Scheerer et al [104] presented stimuli consisting of arrays of six items (1 AOI per item) on a screen. The number of AOIs varied according to the level of accuracy on the faces; Yamashiro et al [101] used only one AOI encompassing the entire face.…”

“…The articles addressing this specific topic were published between 2010 and 2022, with at least one publication each year since 2010. The majority of these publications (12 papers out of 21 [82][83][84][97][98][99][100][101][102][103][104][105]) were published during the last 5 years, with an acceleration in publication speed during the last years (i.e., from 2 papers per year in 2018 [97,98], to 4 papers in 2019 [83,[99][100][101], and 3 papers in 2020 [84,102,103]); then down to 2 papers in 2021, and finally one in 2022 (deceleration probably due to the COVID-19 health crisis in 2020 and 2021).…”

Visual search and childhood vision impairment: A GAMLSS-oriented multiverse analysis approach

“…Visual search studies may involve complex populations where multiple underlying factors such as visual acuity, oculomotor control, or cognitive ability may impact their search ability and contribute to the overall results. For example, in studies involving neurodevelopmental disorders such as autism spectrum disorder (ASD) where attention (Scheerer et al, 2021 ), oculomotor control (Pruett Jr et al, 2013 ), sex (Harrop et al, 2019 ), crowding (Lindor et al, 2018 ), diagnostic procedure (Almeida et al, 2010 ), age of the population (Constable et al, 2010 ), or search strategy interpretation (Keehn & Joseph, 2016 ) may all impact on overall performance and interpretation of the results. Similarly, in studies involving patient groups with an acquired loss of function due to neurodegeneration such as dementia or vision impairment through disease such as glaucoma or age-related macular degeneration, similar group characteristics such as the duration of the vision impairment, cortical evoked potentials, contrast, cognitive ability, or extent of visual field may be additional factors that impact on search performance (Lee et al, 2020 ; Sklar et al, 2020 ; Vullings et al, 2022 ; Xue et al, 2022 ) but are not readily analyzed using standard statistical analyses.…”

“…These findings may be applicable to other studies in which multiple factors may affect search efficiency owing to physical or psychological differences between study populations. Such variables may include, but not limited to, attention (Scheerer et al, 2021 ), saccade time (Vullings et al, 2022 ), age (Borges et al, 2020 ; Xue et al, 2022 ), sex (Harrop et al, 2019 ), visual field size (Wiecek et al, 2012 ), visual acuity (Kuyk et al, 2005 ), oculomotor control (Chen et al, 2018 ; Huurneman et al, 2014 ), clinical diagnosis severity in the case of neurological disorder (Almeida et al, 2010 ), or crowding (Levi, 2008 ). Thus, GAMLSS-based decision trees may provide a useful method to identify factors that may influence search performance depending on the task.…”