Our results highlight the phenomenological diversity of VHs associated with distinct causal factors and demonstrate how a neural network model of visual phenomenology can successfully capture the distinctive visual characteristics of hallucinatory experience. (9/n)

The novel combination of deep neural network architectures and a computational neurophenomenological approach provides a powerful approach toward closing the loop between hallucinatory experiences and their underlying neurocomputational mechanisms (10/10)