Project Summary Visual attention differences are a promising diagnostic marker for autism spectrum conditions (ASC). Yet, despite mounting evidence for group-level differences in visual attention, particularly for visual attention directed toward socially relevant information (i.e., “social gaze”) between autistic and non-autistic individuals, the source of gaze differences in autism remains unclear. Prominent theories of social gaze differences focus heavily on a particular category of visual stimuli, namely: faces. What these theories leave unanswered is whether reduced social attention is, in fact, best explained by atypical attention to a specific stimulus class or whether it reflects an underlying reduction in attention to distributed (face and non-face) sources of important social information in complex environments. In other words, social information may not be limited to a single visual category, and it may not be categorical in nature at all. Yet, by focusing on object categories, eyetracking analyses have failed to capture the richness and complexity of real-world environments in which visual attention supports an individual’s behavior. In order to leverage visual attention as a clinically actionable tool, a critical knowledge gap must be addressed: are social gaze differences in autism driven by information at the level of visual categories, or instead, by higher-order conceptual information beyond the visual domain? The objective of this project is to examine the impact of both categorical and conceptual levels of information on individual and autistic group differences in visual attention. The central hypothesis is that visual attention differences in autism stem from conceptual-level, rather than categorical-level, differences in mental processing. To test this hypothesis, I have developed a novel approach that uses tools from computer vision (computational neural networks; CNNs) and natural language processing (NLP) to characterize individually unique patterns of visual attention. First, Specific Aim 1a will test whether gaze patterns reflect high-dimensional conceptual priorities that are unique to individual participants (N = 62 non-autistic adults). Specific Aim 1b will test whether conceptual priorities reliably guide autistic individuals’ (N = 28) gaze and can be used to classify individuals by diagnostic status (autistic vs. non-autistic). Specific Aim 2, the postdoctoral research direction, will extend the focus of my dissertation research, on conceptual priorities that drive visual attention, to conceptual priorities outside the visual domain, such as language. These aims have been articulated as part of a structured training plan designed to facilitate the transition to a postdoctoral position and independent research career. This training plan emphasizes skill development in multivariate statistical analysis, experimental design, and scientific communication. This training plan is sponsored by Dr. Caroline Robertson, whose expertise in autism, visual processing, and novel experimental techniques (e.g., virtual reality) is ideally complemented by the technical and computational strengths in the Psychological and Brain Sciences Department at Dartmouth.

Project Narrative Visual attention – “gaze” – is a promising biomarker for autism: it can be passively measured from non- and pre-verbal individuals, thus facilitating clinical diagnosis before hallmark behavioral traits (e.g., language delay) have fully emerged in development. A key step in leveraging gaze as a clinical tool will be to understand the mental processes underlying autistic group differences in visual attention. The project proposed here directly relates to the NIH BRAIN priority to understand foundational mental processes using new theoretical and data analysis tools.