ArticleFrontiers in human neuroscience2023
Modelling phenomenological differences in aetiologically distinct visual hallucinations using deep neural networks.
Article in Frontiers in human neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A Rosetta Stone hypothesis for neurophenomenology: mathematical predictions from predictive processing.Neuroscience of consciousness · 2026Article
- Perceptual distortions in PredNet and quantification of top-down/bottom-up flow.Frontiers in computational neuroscience · 2026Article
- Beyond the reducing valve: towards a computational neurophenomenology of altered states via deep neural networks.Frontiers in psychology · 2026Article
- Cyberdelics: Virtual reality hallucinations modulate cognitive-affective processes.Dialogues in clinical neuroscience · 2025Article
- Visual Hallucinations in Serotonergic Psychedelics and Lewy Body Diseases.Schizophrenia bulletin · 2025Review
- When perception meets grief: how the brain reconstructs person networks in response to absence.Social cognitive and affective neuroscience · 2025Article
- A Computational Account of the Development and Evolution of Psychotic Symptoms.Biological psychiatry · 2025Review
- Stroboscopically induced visual hallucinations: historical, phenomenological, and neurobiological perspectives.Neuroscience of consciousness · 2025Review
- Diagnostic and therapeutic challenges in PD-associated non-motor symptoms: the roles of neurologists and consultant physicians.Journal of neural transmission (Vienna, Austria : 1996) · 2024Review
- Article
- Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward Alignment.Advances in neural information processing systems · 2023Article
- Article
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3 authors.
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Abstract
Visual hallucinations (VHs) are perceptions of objects or events in the absence of the sensory stimulation that would normally support such perceptions. Although all VHs share this core characteristic, there are substantial phenomenological differences between VHs that have different aetiologies, such as those arising from Neurodegenerative conditions, visual loss, or psychedelic compounds. Here, we examine the potential mechanistic basis of these differences by leveraging recent advances in visualising the learned representations of a coupled classifier and generative deep neural network-an approach we call 'computational (neuro)phenomenology'. Examining three aetiologically distinct populations in which VHs occur-Neurodegenerative conditions (Parkinson's Disease and Lewy Body Dementia), visual loss (Charles Bonnet Syndrome, CBS), and psychedelics-we identified three dimensions relevant to distinguishing these classes of VHs: realism (veridicality), dependence on sensory input (spontaneity), and complexity. By selectively tuning the parameters of the visualisation algorithm to reflect influence along each of these phenomenological dimensions we were able to generate 'synthetic VHs' that were characteristic of the VHs experienced by each aetiology. We verified the validity of this approach experimentally in two studies that examined the phenomenology of VHs in Neurodegenerative and CBS patients, and in people with recent psychedelic experience. These studies confirmed the existence of phenomenological differences across these three dimensions between groups, and crucially, found that the appropriate synthetic VHs were rated as being representative of each group's hallucinatory phenomenology. Together, our findings highlight the phenomenological diversity of VHs associated with distinct causal factors and demonstrate how a neural network model of visual phenomenology can successfully capture the distinctive visual characteristics of hallucinatory experience.
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