The Computational Anatomy of Visual Neglect

Parr, Thomas; Friston, Karl J.

doi:10.1093/cercor/bhx316

Cited by 46 publications

(48 citation statements)

References 127 publications

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“…This neatly unifies theories that relate disorders of spatial processing to interhemispheric ( Kinsbourne, 1970 ) or intrahemispheric disruptions ( Bartolomeo et al, 2007 ; Bartolomeo, 2014 ). Any intrahemispheric lesion that induces a bias toward one side of space necessarily alters the interhemispheric balance of activity ( Parr and Friston, 2017b ).…”

Section: Sensory Streams and Disconnection Syndromesmentioning

confidence: 99%

Computational Neuropsychology and Bayesian Inference

Parr

Rees

Friston

2018

Front. Hum. Neurosci.

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141

105

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Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine ‘prior’ beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology – optimal inference with suboptimal priors – and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient’s behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.

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Section: Sensory Streams and Disconnection Syndromesmentioning

confidence: 99%

Computational Neuropsychology and Bayesian Inference

Parr

Rees

Friston

2018

Front. Hum. Neurosci.

Self Cite

141

105

View full text Add to dashboard Cite

show abstract

“…This implies that minimising variational free energy maximises the expected likelihood of observations under the approximate posterior ('accuracy') whilst minimising the divergence between the approximate and true distribution over hidden causes ('complexity'). Having defined the objective function, the sufficient statistics encoding posterior beliefs can be updated by minimising variational free energy, as discussed in detail in ; see also appendix of Parr & Friston, 2018 for the derivation of these updates). Given the focus of this paper, we will discuss inference on valuable policies in detail below.…”

Section: Variational Free Energy and Inferencementioning

confidence: 99%

“…Using the same definition of free energy as in equation 1), but now with respect to the approximate posterior under a given policy, we obtain (Parr & Friston, 2018):…”

Section: Variational Free Energy and Inferencementioning

confidence: 99%

Computational mechanisms of curiosity and goal-directed exploration

Schwartenbeck

Passecker

Hauser

et al. 2018

Preprint

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Successful behaviour depends on the right balance between maximising reward and soliciting information about the world. Here, we show how different types of information-gain emerge when casting behaviour as surprise minimisation. We present two distinct mechanisms for goal-directed exploration that express separable profiles of active sampling to reduce uncertainty. 'Hidden state' exploration motivates agents to sample unambiguous observations to accurately infer the (hidden) state of the world. Conversely, 'model parameter' exploration, compels agents to sample outcomes associated with high uncertainty, if they are informative for their representation of the task structure.We illustrate the emergence of these types of information-gain, termed active inference and active learning, and show how these forms of exploration induce distinct patterns of 'Bayes-optimal' 2 behaviour. Our findings provide a computational framework to understand how distinct levels of uncertainty induce different modes of information-gain in decision-making.

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“…The above raises an important question: What is the source of the abnormal prior beliefs in conditions such as Lewy body dementia? We have previously argued that pathological prior beliefs might arise through anatomically defined vascular lesions (Parr and Friston, 2017b ). Here, too, we can appeal to the anatomical distribution of the lesions to try to understand the relationship between tissue pathology and computational (network level) dysfunction.…”

Section: Simulationsmentioning

confidence: 99%

“…A third way in which certain stimuli may fail to enter into perceptual awareness is the failure to attend to certain kinds of stimuli, as in visual neglect (Halligan and Marshall, 1998 ). We have previously argued that this syndrome, in which stimuli on the left of space are ignored, depends upon a failure to actively engage with stimuli on the left (Parr and Friston, 2017b ).…”

Section: Computational Neuropathologymentioning

confidence: 99%

Precision and False Perceptual Inference

Parr

Benrimoh

Vincent

et al. 2018

Front. Integr. Neurosci.

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Accurate perceptual inference fundamentally depends upon accurate beliefs about the reliability of sensory data. In this paper, we describe a Bayes optimal and biologically plausible scheme that refines these beliefs through a gradient descent on variational free energy. To illustrate this, we simulate belief updating during visual foraging and show that changes in estimated sensory precision (i.e., confidence in visual data) are highly sensitive to prior beliefs about the contents of a visual scene. In brief, confident prior beliefs induce an increase in estimated precision when consistent with sensory evidence, but a decrease when they conflict. Prior beliefs held with low confidence are rapidly updated to posterior beliefs, determined by sensory data. These induce much smaller changes in beliefs about sensory precision. We argue that pathologies of scene construction may be due to abnormal priors, and show that these can induce a reduction in estimated sensory precision. Having previously associated this precision with cholinergic signaling, we note that several neurodegenerative conditions are associated with visual disturbances and cholinergic deficits; notably, the synucleinopathies. On relating the message passing in our model to the functional anatomy of the ventral visual stream, we find that simulated neuronal loss in temporal lobe regions induces confident, inaccurate, empirical prior beliefs at lower levels in the visual hierarchy. This provides a plausible, if speculative, computational mechanism for the loss of cholinergic signaling and the visual disturbances associated with temporal lobe Lewy body pathology. This may be seen as an illustration of the sorts of hypotheses that may be expressed within this computational framework.

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The Computational Anatomy of Visual Neglect

Cited by 46 publications

References 127 publications

Computational Neuropsychology and Bayesian Inference

Computational Neuropsychology and Bayesian Inference

Computational mechanisms of curiosity and goal-directed exploration

Precision and False Perceptual Inference

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