Artificial perturbation of local neural activity in the high-level visual cortex alters visual perception. Quantitative characterization of these perceptual alterations holds the key to the development of a mechanistic theory of visual perception1. Historically, though, the complexity of these perceptual alterations, as well as their subjective nature, has rendered them difficult to quantify. Here, we trained macaque monkeys to detect and report brief optogenetic impulses delivered to their inferior temporal cortex, the high-level visual area associated with object recognition, via an implanted LED array2. We assumed that the animals perform this task by detecting the stimulation-induced alterations of the contents of their vision. We required the animals to fixate on a set of images during the task and utilized a machine-learning structure aiming at physically perturbing the viewed images in order to trick the animals into thinking they were being stimulated. In a high-throughput iterative process of behavioral data collection, we developed highly specific perturbed images,perceptograms, looking at which would trick the animals into feeling cortically stimulated. Perceptograms provide parametric and pictorial evidence of the visual hallucinations induced by cortical stimulation. Objective characterization of stimulation-induced perceptual events, besides its theoretical value, opens the door to making better visual prosthetic devices as well as a deeper understanding of visual hallucinations in mental disorders.

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