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The primary goal of this project is to investigate early patterns of sensory responsiveness (SR) over time, assessed via parent report, in a large sample of young children with an elevated likelihood (EL) of autism spectrum disorder (ASD) and children with a typical likelihood (TL) of ASD. In the current proposal, SR was operationalized by the Infant/Toddler Sensory Profile (ITSP | Dunn, 2002), based on the model of Dunn. This model is one of the most recognized models and assumes an interaction between neurological thresholds and behavioral responses (Dunn, 1997). Both thresholds and behavioral responses may differ based on the different sensory modalities (i.e. visual, auditory, vestibular, tactile, and oral processing). Individuals with low threshold patterns are often described as hyperresponsive, whereas individuals with high threshold patterns are often called hyporesponsive. During the last decades a growing body of literature has focused on the relation between SR and ASD at a very young age (e.g. Damiano-Goodwin et al., 2018; Jones, Dawson, & Webb, 2018; Niedzwiecka, Domasiewicz, Kawa, Tomalski, & Pisula, 2019). These studies indicate that children with ASD, as compared to typically developing groups, show atypical sensory profiles. These atypical profiles already emerge early in life (Wolff et al., 2019), possibly before social-communicative symptoms of ASD can be measured. Although evidence is growing, we know relatively little about how SR changes over time, especially across the first years of life, and how patterns may differ within the group of young children (subsequently) diagnosed with ASD. Most research on sensory patterns in ASD has focused on between-group comparisons of sensory symptom severity in individuals with ASD to those with typical development or other developmental delays, and did not focus on sensory patterns within the ASD population. Given that SR patterns may differ within the EL and TL groups, we are interested in studying who follows which sensory pattern by identifying subgroups within the EL and TL group together, investigating how these subgroups compare to each other, and what the characteristics are of children with specific patterns.

Data request 15

The aim of this study is to assess whether infants’ sensitivity to faces and their visual exploration of items at 5 months of age predicts their vocabulary scores at 3 years of age. We will additionally assess whether the age of the child (both during the Face pop-out in R0_5months and during the Peabody language task in R3) has a moderating effect on the strength and direction of relationship that is observed between the Face pop-out and the Peabody Vocabulary task.

The aim of this paper is to assess whether the Face pop-out phenomenon is observable prior to 5 months of age. More specifically, do younger infants perform above chance level for the measurements of infants’ first looks, frequency of looks and the length of looks.

The assessment of visual field sensitivities in young children continues to be a challenge. Children often do not sit still, fail to fixate stimuli for longer durations, and have limited verbal capacity to report visibility. We investigated the use of a head-mounted VR display, gaze-contingent flicker pupil perimetry (gcFPP), and three fixation stimulus conditions to determine best practices for optimal fixation and pupil response quality. A total of twenty children (3-11y) passively fixated a dot, counted the repeated appearance of an animated character, and watched an animated movie in separate trials of 80s each. We presented large flickering patches at different eccentricities and angles in the periphery to evoke pupillary oscillations (20 locations, 4s per location). The results showed that gaze precision and accuracy did not differ significantly across the fixation conditions but pupil amplitudes were strongest for the dot and count task. We recommend the use of the fixation counting task for pupil perimetry because children enjoyed it the most and it achieved strongest pupil responses. The VR set-up appears to be an ideal apparatus for children to allow free range of movement, an engaging visual task, and reliable eye measurements.

Funder: H2020 European Research Council; doi: http://dx.doi.org/10.13039/100010663, Funder: Research Foundation Flanders, Funder: Universiteit Gent; doi: http://dx.doi.org/10.13039/501100004385, Funder: Marguerite-Marie Delacroix, Funder: Autistica; doi: http://dx.doi.org/10.13039/100011706, Funder: Riksbankens Jubileumsfond; doi: http://dx.doi.org/10.13039/501100004472; Grant(s): NHS14-1802:1, Funder: K.F. Hein Fonds, Funder: Scott Family Junior Research Fellowship, Autism is frequently associated with difficulties with top-down attentional control, which impact on individuals’ mental health and quality of life. The developmental processes involved in these attentional difficulties are not well understood. Using a data-driven approach, 2 samples (N = 294 and 412) of infants at elevated and typical likelihood of autism were grouped according to profiles of parent report of attention at 10, 15 and 25 months. In contrast to the normative profile of increases in attentional control scores between infancy and toddlerhood, a minority (7–9%) showed plateauing attentional control scores between 10 and 25 months. Consistent with pre-registered hypotheses, plateaued growth of attentional control was associated with elevated autism and ADHD traits, and lower adaptive functioning at age 3 years.

Emotional facial expressions are important visual communication signals that indicate a sender’s intent and emotional state to an observer. As such, it is not surprising that reactions to different expressions are thought to be automatic and independent of awareness. What is surprising, is that studies show inconsistent results concerning such automatic reactions, particularly when using different face stimuli. We argue that automatic reactions to facial expressions can be better explained, and better understood, in terms of quantitative descriptions of their low-level image features rather than in terms of the emotional content (e.g. angry) of the expressions. Here, we focused on overall spatial frequency (SF) and localized Histograms of Oriented Gradients (HOG) features. We used machine learning classification to reveal the SF and HOG features that are sufficient for classification of the initial eye movement towards one out of two simultaneously presented faces. Interestingly, the identified features serve as better predictors than the emotional content of the expressions. We therefore propose that our modelling approach can further specify which visual features drive these and other behavioural effects related to emotional expressions, which can help solve the inconsistencies found in this line of research.

Emotional facial expressions are important visual communication signals that indicate a sender’s intent and emotional state to an observer. As such, it is not surprising that reactions to different expressions are thought to be automatic and independent of awareness. What is surprising, is that studies show inconsistent results concerning such automatic reactions, particularly when using different face stimuli. We argue that automatic reactions to facial expressions can be better explained, and better understood, in terms of quantitative descriptions of their low-level image features rather than in terms of the emotional content (e.g. angry) of the expressions. Here, we focused on overall spatial frequency (SF) and localized Histograms of Oriented Gradients (HOG) features. We used machine learning classification to reveal the SF and HOG features that are sufficient for classification of the initial eye movement towards one out of two simultaneously presented faces. Interestingly, the identified features serve as better predictors than the emotional content of the expressions. We therefore propose that our modelling approach can further specify which visual features drive these and other behavioural effects related to emotional expressions, which can help solve the inconsistencies found in this line of research.

Highlights • We report longitudinal ERP data of 80 infants in a face-discrimination task. • P1, N290, Nc are all sensitive to faces in five-month-olds. • P1, N290, Nc show equal face-categorization in infants tested longitudinally. • N290 shows less variation in face-categorization trajectories than P1 or Nc. • Visual ERPs increase in amplitude over infancy, but this is not face-specific., From infancy onwards, EEG is widely used to measure face-categorization, i.e. differential brain activity to faces versus non-face stimuli. Four ERP components likely signal infants’ face-sensitivity but reflect different underlying mechanisms: the P1, N290, P400, Nc. We test whether these components reveal similar developmental patterns from early to late infancy, using a longitudinal dataset of 80 infants tested at 5 and 10 months. The P1, N290, and the Nc show face-categorization already in 5-months-olds, a pattern which did not change over time. Development is visible as increased amplitudes in all components, but similar for face and non-face stimuli. By using Markov models, we illustrate that there are differences in the distribution of individual trajectories of face-categorization components from 5 to 10 months. Whereas individual trajectories appear more varied for the Nc and the P1, the N290 reveals a more consistent pattern: a larger proportion of 5-month-olds shows the dominant group response; a larger proportion of 10-month-olds remains in this group, and larger proportions of the alternative trajectories from 5- to 10-month-olds move towards the dominant group. This is vital information when one wants to examine individual differences in infant ERPs related to face-categorization.

Perception of visual stimuli improves with training, but improvements are specific for trained stimuli rendering the development of generic training programs challenging. It remains unknown to which extent training of low-level visual features transfers to high-level visual perception, and whether this is accompanied by neuroplastic changes. The current event-related potential (ERP) study showed that training-induced increased sensitivity to a low-level feature, namely low spatial frequency (LSF), alters neural processing of this feature in high-level visual stimuli. Specifically, neural activity related to face processing (N170), was decreased for low (trained) but not high (untrained) SF content in faces following LSF training. These novel results suggest that: (1) SF discrimination learning transfers from simple stimuli to complex objects; and that (2) training the use of specific SF information affects neural processing of facial information. These findings may open up a new avenue to improve face recognition skills in individuals with atypical SF processing, such as in cataract or Autism Spectrum Disorder (ASD).

Social interaction starts with perception of the world around you. This study investigated two fundamental issues regarding the development of discrimination of higher spatial frequencies, which are important building blocks of perception. Firstly, it mapped the typical developmental trajectory of higher spatial frequency discrimination. Secondly, it developed and validated a novel design that could be applied to improve atypically developed vision. Specifically, this study examined the effect of age and reward on task performance, practice effects, and motivation (i.e., number of trials completed) in a higher spatial frequency (reference frequency: 6 cycles per degree) discrimination task. We measured discrimination thresholds in children aged between 7 to 12 years and adults (N = 135). Reward was manipulated by presenting either positive reinforcement or punishment. Results showed a decrease in discrimination thresholds with age, thus revealing that higher spatial frequency discrimination continues to develop after 12 years of age. This development continues longer than previously shown for discrimination of lower spatial frequencies. Moreover, thresholds decreased during the run, indicating that discrimination abilities improved. Reward did not affect performance or improvement. However, in an additional group of 5-6 year-olds (N = 28) punishments resulted in the completion of fewer trials compared to reinforcements. In both reward conditions children aged 5-6 years completed only a fourth or half of the run (64 to 128 out of 254 trials) and were not motivated to continue. The design thus needs further adaptation before it can be applied to this age group. Children aged 7-12 years and adults completed the run, suggesting that the design is successful and motivating for children aged 7-12 years. This study thus presents developmental differences in higher spatial frequency discrimination thresholds. Furthermore, it presents a design that can be used in future developmental studies that require multiple stimulus presentations such as visual perceptual learning.