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Understanding visual hallucinations: A new synthesis.
- Source :
-
Neuroscience & Biobehavioral Reviews . Jul2023, Vol. 150, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- Despite decades of research, we do not definitively know how people sometimes see things that are not there. Eight models of complex visual hallucinations have been published since 2000, including Deafferentation, Reality Monitoring, Perception and Attention Deficit, Activation, Input, and Modulation, Hodological, Attentional Networks, Active Inference, and Thalamocortical Dysrhythmia Default Mode Network Decoupling. Each was derived from different understandings of brain organisation. To reduce this variability, representatives from each research group agreed an integrated Visual Hallucination Framework that is consistent with current theories of veridical and hallucinatory vision. The Framework delineates cognitive systems relevant to hallucinations. It allows a systematic, consistent, investigation of relationships between the phenomenology of visual hallucinations and changes in underpinning cognitive structures. The episodic nature of hallucinations highlights separate factors associated with the onset, persistence, and end of specific hallucinations suggesting a complex relationship between state and trait markers of hallucination risk. In addition to a harmonised interpretation of existing evidence, the Framework highlights new avenues of research, and potentially, new approaches to treating distressing hallucinations. • We provide a new Visual Hallucination Framework which integrates eight current models. • All models suggest a key role for visual input. • Variations in factors such as attention or memory may relate to differences in the phenomenology of specific hallucinations. • That hallucinations are episodic suggests different traits are relevant in the onset, persistence and end of hallucinations. [ABSTRACT FROM AUTHOR]
- Subjects :
- *HALLUCINATIONS
*DEFAULT mode network
*COGNITIVE structures
Subjects
Details
- Language :
- English
- ISSN :
- 01497634
- Volume :
- 150
- Database :
- Academic Search Index
- Journal :
- Neuroscience & Biobehavioral Reviews
- Publication Type :
- Academic Journal
- Accession number :
- 163996123
- Full Text :
- https://doi.org/10.1016/j.neubiorev.2023.105208