The Hallucination Machine combines deep convolutional neural networks with panoramic virtual reality to simulate visual hallucinatory experiences without drugs or psychosis. In one experiment, the system induced visual phenomenology similar to classical psychedelics. In a second experiment, the simulated hallucinations did not produce the temporal distortion typically linked to altered states. This tool allows researchers to study altered consciousness without the confounding physiological and cognitive effects of psychoactive substances or psychopathological conditions.
Hypnotizability reflects a broader trait called phenomenological control, which allows people to create subjective experiences in nonhypnotic contexts to fulfill goals. This control operates as a metacognitive process where intentional actions occur without awareness of specific intentions, known as cold control. Laboratory phenomena such as vicarious pain, mirror-touch synesthesia, and the rubber hand illusion may partly result from phenomenological control. A new theory of intentional binding measures the absence of conscious intentions in hypnosis. No evidence suggests cold control confers abilities beyond changes in metacognitive monitoring, and a negative correlation exists between mindfulness and cold control, viewed as a lack of awareness of intentions.
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.