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.
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.