Is the perception of illusions abnormal in schizophrenia?

Grzeczkowski, Lukasz; Roinishvili, Maya; Chkonia, Eka; Brand, Andreas; Mast, Fred W.; Herzog, Michael H.; Shaqiri, Albulena

doi:10.1016/j.psychres.2018.10.063

Cited by 44 publications

(61 citation statements)

References 57 publications

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“…Paradoxically, however, the predictive processing framework has also been used to derive the opposite conclusion about the nature of the imbalance in psychosis -that is, that it is underpinned by an underreliance on top-down processing -both clinically 107 and as a part of the psychedelic drug experience 129 . This idea has been partly inspired by the relative resistance of people with psychosis to illusions [105][106][107][108] (but see REF 133 ), which are generally conceptualised as arising from top-down processing. To resolve this apparent inconsistency, it has recently been speculated that people with hallucinations exhibit under-weighting of top-down processing early in the hierarchy (conferring illusion-resistance) and over-weighting of top-down processing higher up in the hierarchy (conferring hallucination-proneness) 105,106 .…”

Section: [H2] Identifying Mechanisms To Inform Clinically-relevant Qumentioning

confidence: 99%

Forms of prediction in the nervous system

2020

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The idea that predictions shape how we perceive and comprehend the world has become increasingly influential in the field of systems neuroscience. It also forms an important framework for understanding neuropsychiatric disorders, which are proposed to be the result of disturbances in the mechanisms through which prior information influences perception and belief, leading to the production of sub-optimal models of the world. There is a widespread tendency to conceptualize the influence of predictions exclusively in terms of 'top-down' processes, whereby predictions generated in higher-level areas exert their influence on lower-level areas within an information-processing hierarchy. However, this excludes from consideration the predictive information embedded in the 'bottom-up' stream of information processing. We describe evidence for the importance of this distinction and argue that it is critical for the development of the predictive processing framework and, ultimately, for an understanding of the perturbations that drive the emergence of neuropsychiatric symptoms and experiences.

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Section: [H2] Identifying Mechanisms To Inform Clinically-relevant Qumentioning

confidence: 99%

Forms of prediction in the nervous system

2020

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“…Interestingly, we also found very weak correlations-except for one-between the magnitudes of visual illusions (Grzeczkowski, Clarke, Francis, Mast, & Herzog, 2017; see also Axelrod, Schwarzkopf, Gilaie-Dotan, & Rees, 2017). Patients with schizophrenia similarly showed only weak correlations between different illusion magnitudes (Grzeczkowski et al, 2018; see also Kaliuzhna et al, 2018). Improvements in perceptual learning are very specific.…”

Section: Introductionmentioning

confidence: 54%

“…Previous research did not find evidence for a common factor for visual illusions Grzeczkowski et al, 2018). Here, we systematically examined hyper-specificity of factors for visual illusions.…”

Section: Discussionmentioning

confidence: 94%

Factors underlying visual illusions are illusion-specific but not feature-specific

et al. 2019

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Common factors are ubiquitous. For example, there is a common factor, g, for intelligence. In vision, there is much weaker evidence for such common factors. For example, visual illusion magnitudes correlate only weakly with each other. Here, we investigated whether illusions are hyper-specific as in perceptual learning. First, we tested 19 variants of the Ebbinghaus illusion that differed in color, shape, or texture. Correlations between the illusion magnitudes of the different variants were mostly significant. Second, we reanalyzed a dataset from a previous experiment where 10 illusions were tested under four conditions of luminance and found significant correlations between the different luminance conditions of each illusion. However, there were only very weak correlations between the 10 different illusions. Third, five visual illusions were tested with four orientations. Again, there were significant correlations between the four orientations of each illusion, but not across different illusions. The weak inter-illusion correlations suggest that there is no unique common mechanism for the tested illusions. We suggest that most illusions make up their own factor.

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“…Low-level priors were found to be both reduced in delusion-prone patients (Schmack et al 2015) and enhanced in hallucinationprone healthy individuals (Powers et al 2017). Even more perplexingly, recent studies showed that the derivation of perceptual priors from natural scene statistics (Kaliuzhna et al 2019) or the susceptibility to a wider range of visual illusions (Grzeczkowski et al 2018) is actually unaffected in schizophrenic individuals.…”

Section: Prospection Does Not Imply Predictive Processingmentioning

confidence: 99%

Above and beyond the concrete: The diverse representational substrates of the predictive brain

Gilead¹,

Trope²,

Liberman³

2019

Behav Brain Sci

104

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Abstract In recent years, scientists have increasingly taken to investigate the predictive nature of cognition. We argue that prediction relies on abstraction, and thus theories of predictive cognition need an explicit theory of abstract representation. We propose such a theory of the abstract representational capacities that allow humans to transcend the “here-and-now.” Consistent with the predictive cognition literature, we suggest that the representational substrates of the mind are built as a hierarchy, ranging from the concrete to the abstract; however, we argue that there are qualitative differences between elements along this hierarchy, generating meaningful, often unacknowledged, diversity. Echoing views from philosophy, we suggest that the representational hierarchy can be parsed into: modality-specific representations, instantiated on perceptual similarity; multimodal representations, instantiated primarily on the discovery of spatiotemporal contiguity; and categorical representations, instantiated primarily on social interaction. These elements serve as the building blocks of complex structures discussed in cognitive psychology (e.g., episodes, scripts) and are the inputs for mental representations that behave like functions, typically discussed in linguistics (i.e., predicators). We support our argument for representational diversity by explaining how the elements in our ontology are all required to account for humans’ predictive cognition (e.g., in subserving logic-based prediction; in optimizing the trade-off between accurate and detailed predictions) and by examining how the neuroscientific evidence coheres with our account. In doing so, we provide a testable model of the neural bases of conceptual cognition and highlight several important implications to research on self-projection, reinforcement learning, and predictive-processing models of psychopathology.

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Is the perception of illusions abnormal in schizophrenia?

Cited by 44 publications

References 57 publications

Forms of prediction in the nervous system

Forms of prediction in the nervous system

Factors underlying visual illusions are illusion-specific but not feature-specific

Above and beyond the concrete: The diverse representational substrates of the predictive brain

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