An essential task in Arti cial Intelligence (AI) is the prediction of future inputs from a given sequence, such as predicting the next frame of a video, with prominent applications such as self-driving cars. The human brain is very good at this task, without this ability we would be too slow to catch a ball or jump out of the way.
A theory called "predictive coding" in neuro- science introduced by Ballard and Rao in 1999, explains the phenomena. A recently developed predictive network from Massachusetts Institute of Technology (MIT) called 'PredNet' leverages the ideas of predictive coding for next-frame video prediction. Interestingly, PredNet has shown a very brain-like ability to be fooled by illusions of motion (when a static image appears to be moving). This leads us to pose the question of whether PredNet can be used to study perceptual disruptions in the brain associated with mental disorders, particularly schizophrenia.
In this study, we show how several types of modifications to PredNet designed to simulate different models of perception disruption in schizophre- nia from neuroscience literature, affects its ability to be fooled by illusions of motion, and the overall effect these disruptions have on the predictive ability.
For more information, check the project web link: https://ufshaik.github.io/PIS/