Abstract
Visual neglect is a debilitating neuropsychological phenomenon that has many clinical implications and—in cognitive neuroscience—offers an important lesion deficit model. In this article, we describe a computational model of visual neglect based upon active inference. Our objective is to establish a computational and neurophysiological process theory that can be used to disambiguate among the various causes of this important syndrome; namely, a computational neuropsychology of visual neglect. We introduce a Bayes optimal model based upon Markov decision processes that reproduces the visual searches induced by the line cancellation task (used to characterize visual neglect at the bedside). We then consider 3 distinct ways in which the model could be lesioned to reproduce neuropsychological (visual search) deficits. Crucially, these 3 levels of pathology map nicely onto the neuroanatomy of saccadic eye movements and the systems implicated in visual neglect.
Highlights
Visual neglect is a common syndrome in which patients neglect one side of space (Halligan and Marshall 1998)
We describe a computational model of visual neglect based upon active inference
We introduce a Bayes optimal model based upon Markov decision processes that reproduces the visual searches induced by the line cancellation task
Summary
Visual neglect is a common syndrome in which patients neglect one side (typically the left) of space (Halligan and Marshall 1998) It is often caused by right middle cerebral artery strokes, but has been reported as a consequence of inflammatory (Gilad et al 2006), metabolic (Auclair et al 2008), and degenerative (Ho et al 2003; Andrade et al 2010) diseases. In addition to the wide range of pathological processes which can cause the syndrome, visual neglect can be caused by a range of anatomical lesions. These include both cortical (Corbetta and Shulman 2002) and subcortical (Karnath et al 2002) insults. There is some evidence that the heterogeneity of the causes of visual neglect map on to distinct behavioral phenotypes (Hillis et al 2005; Grimsen et al 2008; Medina et al 2009; Verdon et al 2009), and this has the potential to be exploited clinically and scientifically
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