Despite several decades of functional neuroimaging research the relationship between brain networks and cognition remains elusive. This is because the taxonomy of cognitive processes was developed largely blind to the functional organization of the human brain. In this work, we leverage recent advances in artificial neural networks to gain insights into shared cognitive processes among six different cognitive tasks. We trained a single recurrent neural networks (RNN) to perform cognitive tasks. In this manner, we were able to evaluate shared representations between multiple cognitive tasks without relying on predefined cognitive processes. Next, we tested if the learned representations provide a good explanation for human brain activation patterns associated with these tasks. While we found little similarity between the RNN’s learned representation and real brain data, our approach offers a roadmap to gain more mechanistic insights into how cognitive processes map to brain networks with potential important implications for studying cognitive dysfunction in disease.