Human visual system depends on imagery representations in addition to the visual input to build a concrete holistic picture of the object of interest. These representations are correlated and shared in the human brain and help provide a crisp understanding of the environment. In this work, we study if a similar correlation exists in Artificial Neural Networks (ANNs) and if the correlation is strong enough to use the corresponding fMRI data of both perceptual and quasi-perceptual experiences interchangeably for downstream tasks such as object category prediction.