November 30, 2020
Adam Safron, Arthur Juliani, Nicco Reggente et al.
44 citations
preprint
Psychedelics may both relax and strengthen beliefs depending on the dose and brain system involved. The REBUS model holds that 5-HT2a receptor activation relaxes prior expectations, enabling new perspectives. This paper proposes that at very high levels of 5-HT2a agonism, opposite effects can occur—termed SEBUS—where synchronous neural activity strengthens beliefs, enhancing meaning-making, hallucinations, and even delusional thinking. The ALBUS framework integrates these opposing effects across the dose-response curve, suggesting psychedelic experiences resemble waking dream states with varying lucidity. The authors provide neurophenomenological models of perceptual synthesis, dreaming, and episodic memory to support this view.
Neuroscience of Consciousness
January 1, 2025
Adam Safron, Arthur Juliani, Nicco Reggente et al.
13 citations
Psychedelics profoundly impact brain and mind by altering belief systems. The REBUS model proposes that 5-HT2a receptor agonism relaxes prior expectations, enabling new perspectives. An alternative but compatible view, ALBUS (Altered Beliefs Under Psychedelics), suggests that at very high levels of 5-HT2a agonism, opposite effects may occur—synchronous neural activity becomes more powerful, leading to strengthened beliefs (SEBUS). These strengthened beliefs align with enhanced meaning-making in psychedelic therapy, hallucinations, and delusional thinking. ALBUS proposes that the balance between REBUS and SEBUS effects varies across the dose-response curve. Psychedelic experiences are described as waking dream states with varying lucidity, involving mechanisms of conscious perceptual synthesis, dreaming, and episodic memory.
May 18, 2023
Arthur Juliani, Adam Safron, Ryota Kanai
9 citations
preprint
Psychedelic therapy shows promise for treating various mental disorders. The REBUS model proposes that psychedelics help by relaxing overly rigid, maladaptive beliefs. The CANAL model extends this by suggesting that canalization—the development of excessively rigid belief structures—may underlie psychopathology. This paper refines the CANAL model by drawing on learning theory from deep neural networks, distinguishing two separate optimization landscapes for belief representation. Each landscape can develop pathologies from either too much or too little canalization, indicating a non-linear relationship with psychopathology. The refined model generates novel predictions about which psychopathologies might respond to psychedelic therapy and which forms of therapy may benefit specific individuals.
Neuroscience of Consciousness
January 1, 2024
Arthur Juliani, Adam Safron, Ryota Kanai
8 citations
Psychedelic therapy shows promise for treating mental disorders, and the "RElaxed Beliefs Under pSychedelics" (REBUS) model explains this by suggesting psychedelics loosen maladaptive high-level beliefs. The newer "CANAL" model proposes that overly rigid belief landscapes (canalization) contribute to psychopathology. This work uses deep neural network learning theory to refine the CANAL model, distinguishing two separate optimization landscapes for belief representation in the brain. Each can develop unique pathologies from either too much or too little canalization, indicating that canalization's link to psychopathology is not simply linear. The refined model makes novel predictions about which aspects of psychopathology psychedelic therapy may treat and which therapy forms might benefit a given individual.
bioRxiv (Cold Spring Harbor Laboratory)
April 15, 2024
Arthur Juliani, Veronica Chelu, Laura Graesser et al.
4 citations
preprint
Serotonergic psychedelics show promise for treating mood and anxiety disorders, but their therapeutic mechanism remains debated. A popular theory holds that strong 5-HT2a receptor activation disrupts cortical dynamics, loosening rigid, maladaptive beliefs and making them open to revision. This work extends that perspective by developing a simple energy-based model of cortical dynamics rooted in predictive processing and neuromodulation. The model simulates hypothetical computational mechanisms for both 5-HT2a and 5-HT1a agonism, and its results account for several existing empirical observations about psychedelics' effects on cognition and affect. The model provides a theoretically grounded hypothesis for the clinical success of LSD, psilocybin, and DMT, and identifies biased 5-HT1a agonist psychedelics like 5-MeO-DMT as potentially fruitful for developing more effective and tolerable psychotherapeutic agents.
arXiv Preprint Archive
February 11, 2026
Arthur Juliani
The paper uses the mathematics of Variational Autoencoders to formalize religious epistemology. It models religious traditions as distinct generative mappings from a shared latent space to observable cultural forms. The authors define four configurations—exclusivism, universalism, perennialism, and syncretism—and argue through abductive comparison that perennialism best explains cross-traditional contemplative convergence. Exclusivism cannot account for such convergence, syncretism produces incoherent artifacts, and universalism suffers from posterior collapse, discarding structural information. The paper contends that strict orthodoxy is a structural necessity: contemplative practices must match the specific tradition whose forms they interpret. The unity of religions, if real, requires depth rather than breadth.
Frontiers in Computational Neuroscience
January 1, 2024
Rousslan Fernand Julien Dossa, Kai Arulkumaran, Arthur Juliani et al.
An embodied agent with a structure based on global workspace theory, trained on realistic audiovisual inputs to navigate 3D environments, performs better and more robustly at smaller working memory sizes compared to a standard recurrent architecture. Task complexity and regularization are essential for feature learning and the development of meaningful attentional patterns within the workspace.
arXiv Preprint Archive
March 24, 2022
Arthur Juliani, Kai Arulkumaran, Shuntaro Sasai et al.
The authors examine three contemporary theories of conscious function—Global Workspace Theory, Information Generation Theory, and Attention Schema Theory—and find that each relates conscious function to some aspect of domain-general intelligence in humans. They then observe that state-of-the-art deep learning methods have begun incorporating key aspects of these theories, though they remain far from demonstrating general intelligence. Using mental time travel in humans as a motivating example, the authors propose combining insights from all three theories into a single unified model. Such artificial agents would possess greater general intelligence and align more closely with current understanding of consciousness's functional role, making this a promising near-term AI research goal.