Computational Approaches to Measurement of Visual Attention: Modeling Overselectivity in Intellectual and Developmental Disabilities

Computational Approaches to Measurement of Visual Attention: Modeling Overselectivity in Intellectual and Developmental Disabilities

Nurit Haspel (University of Massachusetts Boston, USA), Alison Shell (University of Massachusetts Medical School, USA) and Curtis K. Deutsch (University of Massachusetts Medical School, USA)
DOI: 10.4018/978-1-4666-2539-6.ch002


Alterations in gazing patterns and visual attention have often been noted among patients with intellectual and developmental disabilities (IDD) relative to neurotypical individuals. Here, the authors discuss visual attention with a particular focus on attention overselectivity. Overselectivity is observed when a subject focuses on a limited subset of available stimuli, or attends to a limited spatial field of vision. It is a widely-observed problem among individuals with IDD, notably, children with autism spectrum disorders (ASD). In this chapter, the authors survey computational and experimental approaches to analyze selective visual attention patterns, including overselectivity. These may provide useful computational frameworks for modeling visual attention in ASD patients and quantifying how it differs from neurotypical patterns. Computer-automated routines would be a boon for the field, distilling key dependent measures for aberrant attentional processes (a) for group studies of pathological processes and (b) on a single-subject basis for clinical description and possible remediation of attentional deficits.
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There are limits to the ability to process information, and what is commonly referred to as “attention” provides one means of winnowing down available data for more efficient management. Perhaps no one has captured the essence of selective attention as succinctly as William James, over 120 years ago:

Everyone knows what attention is. It is the taking possession by the mind, in clear and vivid form, of one out of what seem several simultaneously possible objects or trains of thought.

W James (1890). The Principles of Psychology

Over recent decades, psychologists have subdivided attention itself into a variety of forms. One category, that of visual selective attention, can be parsed into several sub-categories, including attention to regions of space, stimulus features, and whole objects (Freiwald & Kanwisher, 2004).

Computational and experimental approaches have been widely used to investigate patterns of human visual attention. In this chapter we review computational approaches designed to characterize selective attention, with a worked example on attention to facial patterns. We also discuss the potential application of computational methods to quantitatively analyze the gazing patterns of patients with IDD.

Selective attention has been classified in psychology using different organizational principles. In this discussion, we will refer to visual selective attention to specific stimuli and areas of a visual array. Posner (1987) also discusses selective attention, focusing on the process of attentional engagement and subsequent disengagement from visual stimuli. Specifically, the inability to disengage from a stimulus, which could be seen as overselectivity, has been observed in some IDD including ADHD and Schizophrenia (see for example (Butler and Javitt, 2005, Elahipanath et al., 2007, Laprevote et al., 2010, Le et al., 2003, Nestor et al., 2009, Tseng et al., 2010)).

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