Visual processing in genetic conditions linked to autism: A behavioral study of binocular rivalry in individuals with 16p11.2 deletions and age-matched controls.

Close phenotypic characterization of individuals with genetic conditions linked to autism provides a promising approach to navigating the heterogeneity of autism spectrum conditions. The current study investigated sensory processing in individuals with a rare genetic event that is highly penetrant for autism, 16p11.2 deletions, using a well-characterized visual paradigm, binocular rivalry, which is thought to be a non-invasive index of excitatory/inhibitory balance in the visual cortex. We characterized rivalry dynamics in 45 adolescent and adult individuals (19 individuals with 16p11.2 deletions, 26 age-matched neurotypical controls). We found that binocular rivalry perceptual transition rates were significantly slower for individuals with 16p11.2 deletions, relative to controls. Importantly, these results could not be accounted for by differences in motor response latencies or perceptual decision criteria, which were matched between groups. Results should be interpreted with caution given the unmatched psychometric features between groups, such as IQ. Future studies should study visual processing in other genetic groups linked to autism beyond 16p to understand the specificity of these findings. These results highlight the importance of characterizing sensory functions in individuals with genetic alterations associated with autism.

Brief Report: Differences in Naturalistic Attention to Real-World Scenes in Adolescents with 16p.11.2 Deletion.

Sensory differences are nearly universal in autism, but their genetic origins are poorly understood. Here, we tested how individuals with an autism-linked genotype, 16p.11.2 deletion ("16p"), attend to visual information in immersive, real-world photospheres. We monitored participants' (N = 44) gaze while they actively explored 360° scenes via headmounted virtual reality. We modeled the visually salient and semantically meaningful information in scenes and quantified the relative bottom-up vs. top-down influences on attentional deployment. We found, when compared to typically developed control (TD) participants, 16p participants' attention was less dominantly predicted by semantically meaningful scene regions, relative to visually salient regions. These results suggest that a reduction in top-down relative to bottom-up attention characterizes how individuals with 16p.11.2 deletions engage with naturalistic visual environments.

THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images.

In recent years, the use of a large number of object concepts and naturalistic object images has been growing strongly in cognitive neuroscience research. Classical databases of object concepts are based mostly on a manually curated set of concepts. Further, databases of naturalistic object images typically consist of single images of objects cropped from their background, or a large number of naturalistic images of varying quality, requiring elaborate manual image curation. Here we provide a set of 1,854 diverse object concepts sampled systematically from concrete picturable and nameable nouns in the American English language. Using these object concepts, we conducted a large-scale web image search to compile a database of 26,107 high-quality naturalistic images of those objects, with 12 or more object images per concept and all images cropped to square size. Using crowdsourcing, we provide higher-level category membership for the 27 most common categories and validate them by relating them to representations in a semantic embedding derived from large text corpora. Finally, by feeding images through a deep convolutional neural network, we demonstrate that they exhibit high selectivity for different object concepts, while at the same time preserving variability of different object images within each concept. Together, the THINGS database provides a rich resource of object concepts and object images and offers a tool for both systematic and large-scale naturalistic research in the fields of psychology, neuroscience, and computer science.