Deep CANALs: a deep learning approach to refining the canalization theory of psychopathology

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.

Study at a glance

Abstract

Abstract Psychedelic therapy has seen a resurgence of interest in the last decade, with promising clinical outcomes for the treatment of a variety of psychopathologies. In response to this success, several theoretical models have been proposed to account for the positive therapeutic effects of psychedelics. One of the more prominent models is “RElaxed Beliefs Under pSychedelics,” which proposes that psychedelics act therapeutically by relaxing the strength of maladaptive high-level beliefs encoded in the brain. The more recent “CANAL” model of psychopathology builds on the explanatory framework of RElaxed Beliefs Under pSychedelics by proposing that canalization (the development of overly rigid belief landscapes) may be a primary factor in psychopathology. Here, we make use of learning theory in deep neural networks to develop a series of refinements to the original CANAL model. Our primary theoretical contribution is to disambiguate two separate optimization landscapes underlying belief representation in the brain and describe the unique pathologies which can arise from the canalization of each. Along each dimension, we identify pathologies of either too much or too little canalization, implying that the construct of canalization does not have a simple linear correlation with the presentation of psychopathology. In this expanded paradigm, we demonstrate the ability to make novel predictions regarding what aspects of psychopathology may be amenable to psychedelic therapy, as well as what forms of psychedelic therapy may ultimately be most beneficial for a given individual.