Deep CANALs: A Deep Learning Approach to Refining the Canalization Theory of Psychopathology

Psychedelic therapy has seen a resurgence of interest in the past decade, with promising clinical outcomes for treatment of a variety of psychopathologies. In response to this success, a number of theoretical models have been proposed to account for psychedelic's positive therapeutic effects. One of the more prominent models is `RElaxed Beliefs Under pSychedelics,' (REBUS) which suggests 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 REBUS by proposing that canalization (the development of overly rigid belief landscapes) may be a primary factor in psychopathology. Here we take inspiration from 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, 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, suggesting that at the very least canalization does not have a linear relationship with psychopathology. In this expanded paradigm, we demonstrate the ability to make novel predictions regarding which psychopathologies may be amenable to psychedelic therapy, as well as what forms of psychedelic therapy may ultimately be most beneficial for a given individual.