TY  - GEN
A1  - Ofner, Andre
A1  - Stober, Sebastian
T1  - Hybrid variational predictive coding as a bridge between human and artificial cognition
T2  - ALIFE 2019: The 2019 Conference on Artificial Life
N2  - Predictive coding and its generalization to active inference offer a unified theory of brain function. The underlying predictive processing paradigmhas gained significant attention in artificial intelligence research for its representation learning and predictive capacity. Here, we suggest that it is possible to integrate human and artificial generative models with a predictive coding network that processes sensations simultaneously with the signature of predictive coding found in human neuroimaging data. We propose a recurrent hierarchical predictive coding model that predicts low-dimensional representations of stimuli, electroencephalogram and physiological signals with variational inference. We suggest that in a shared environment, such hybrid predictive coding networks learn to incorporate the human predictive model in order to reduce prediction error. We evaluate the model on a publicly available EEG dataset of subjects watching one-minute long video excerpts. Our initial results indicate that the model can be trained to predict visual properties such as the amount, distance and motion of human subjects in videos.
Y1  - 2019
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/50553
SP  - 68
EP  - 69
PB  - MIT Press
CY  - Cambridge
ER  -