University of Sussex
Browse
s41598-017-16316-2.pdf (2.44 MB)

A deep-dream virtual reality platform for studying altered perceptual phenomenology

Download (2.44 MB)
journal contribution
posted on 2023-06-09, 08:05 authored by Keisuke Suzuki, Warrick RoseboomWarrick Roseboom, David SchwartzmanDavid Schwartzman, Anil SethAnil Seth
Altered states of consciousness, such as psychotic or pharmacologically-induced hallucinations, provide a unique opportunity to examine the mechanisms underlying conscious perception. However, the phenomenological properties of these states are difficult to isolate experimentally from other, more general physiological and cognitive 36 effects of psychoactive substances or psychopathological conditions. Thus, simulating phenomenological aspects of altered states in the absence of these other more general effects provides an important experimental tool for consciousness science and psychiatry. Here we describe such a tool, which we call the Hallucination Machine. It comprises a novel combination of two powerful technologies: deep convolutional neural networks (DCNNs) and panoramic videos of natural scenes, viewed immersively through a head-mounted display (panoramic VR). By doing this, we are able to simulate visual hallucinatory experiences in a biologically plausible and ecologically valid way. Two experiments illustrate potential applications of the Hallucination Machine. First, we show that the system induces visual phenomenology qualitatively similar to classical psychedelics. In a second experiment, we find that simulated hallucinations do not evoke the temporal distortion commonly associated with altered states. Overall, the Hallucination Machine offers a valuable new technique for simulating altered phenomenology without directly altering the underlying neurophysiology.

History

Publication status

  • Published

File Version

  • Published version

Journal

Scientific Reports

ISSN

2045-2322

Publisher

Nature Publishing Group

Volume

7

Page range

15982 1-11

Department affiliated with

  • Informatics Publications

Research groups affiliated with

  • Sackler Centre for Consciousness Science Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2017-11-20

First Open Access (FOA) Date

2017-11-22

First Compliant Deposit (FCD) Date

2017-11-20

Usage metrics

    University of Sussex (Publications)

    Categories

    No categories selected

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC