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The Hallucination Machine: A Deep-Dream VR platform for Studying the Phenomenology of Visual Hallucinations

Keisuke Suzuki, Warrick Roseboom, David J. Schwartzman, Anil K. Seth

bioRxiv Preprint Server November 3, 2017 preprint DOI: 10.1101/213751 via bioRxiv

Summary

AI-generated from the abstract

A tool called the Hallucination Machine simulates visual hallucinatory experiences using deep convolutional neural networks and panoramic virtual reality videos of natural scenes. It induces visual phenomenology qualitatively similar to classical psychedelics, but does not evoke the temporal distortion commonly associated with altered states. This technique allows researchers to study altered consciousness without the confounding physiological and cognitive effects of psychoactive substances or psychopathological conditions, offering a valuable method for consciousness science and psychiatry.

Study at a glance

Characteristics Experimental study
Citations 2
Key finding The Hallucination Machine induces visual phenomenology similar to classical psychedelics but does not produce temporal distortion.

Abstract

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 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, 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.

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