![]() These findings demonstrate that even though DCNNs were not explicitly designed to model the visual system, after training for challenging object recognition tasks they show marked similarities to the functional and hierarchical structure of human visual cortices. Features selectively detected by lower layers of the same DCNN bear striking similarities to the low-level features processed by the early visual cortices such as V1 and V4. For example, the neural responses induced by a visual stimulus in the human inferior temporal (IT) cortex, widely implicated in object recognition, have been shown to be similar to the activity pattern of higher (deeper) layers of the DCNN 22, 23. Studies comparing the internal representational structure of trained DCNNs with primate and human brains performing similar object recognition tasks, have revealed surprising similarities in the representational spaces between these two distinct systems 19, 20, 21. For example, deep convolutional neural networks (DCNNs) have been particularly successful in the difficult task of object recognition in photographs of natural scenes 17, 18. In machine learning, deep neural networks (DNNs) developed for machine vision have now improved to a level comparable to that achieved by humans 15, 16. Here, we address this challenge by combining virtual reality and machine learning to isolate and simulate one specific aspect of psychedelic phenomenology: visual hallucinations. Understanding the specific nature of altered phenomenology in the psychedelic state therefore stands as an important experimental challenge. ![]() It is difficult, using pharmacological manipulations alone, to distinguish the primary causes of altered phenomenology from the secondary effects of other more general aspects of neurophysiology and basic sensory processing. ![]() However, psychedelic compounds have many systemic physiological effects, not all of which are likely relevant to the generation of altered perceptual phenomenology. These studies attempt to understand the neural underpinnings that cause altered conscious experience 11, 12, 13 as well as investigating the potential psychotherapeutic applications of these drugs 4, 12, 14. In recent years, there has been a resurgence in research investigating altered states induced by psychedelic drugs. Causes of ASC include psychedelic drugs (e.g., LSD, psilocybin) as well as pathological or psychiatric conditions such as epilepsy or psychosis 8, 9, 10. ASC are not defined by any particular content of consciousness, but cover a wide range of qualitative properties including temporal distortion, disruptions of the self, ego-dissolution, visual distortions and hallucinations, among others 4, 5, 6, 7. Altered states are defined as a qualitative alteration in the overall pattern of mental functioning, such that the experiencer feels their consciousness is radically different from “normal” 1, 2, 3, and are typically considered distinct from common global alterations of consciousness such as dreaming. There is a long history of studying altered states of consciousness (ASC) in order to better understand phenomenological properties of conscious perception 1, 2. Overall, the Hallucination Machine offers a valuable new technique for simulating altered phenomenology without directly altering the underlying neurophysiology. In a second experiment, we find that simulated hallucinations do not evoke the temporal distortion commonly associated with altered states. First, we show that the system induces visual phenomenology qualitatively similar to classical psychedelics. Two experiments illustrate potential applications of the Hallucination Machine. By doing this, we are able to simulate visual hallucinatory experiences in a biologically plausible and ecologically valid way. ![]() 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). Here we describe such a tool, which we call the Hallucination Machine. 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. 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. Altered states of consciousness, such as psychotic or pharmacologically-induced hallucinations, provide a unique opportunity to examine the mechanisms underlying conscious perception.
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