Distributed Representations for Cognitive Control in Frontal Medial Cortex.

Abstract

In natural and artificial neural networks, modularity and distributed structure afford complementary but competing benefits. The former allows for hierarchical representations that can flexibly recombine modules to address novel problems, whereas the latter can benefit from less constrained training, potentially uncovering fruitful statistical regularities. Here, we investigate these competing demands in the context of human sequential behavior. First, we explore this setting by comparing the properties of several recurrent neural network models. We find that explicit hierarchical structure by itself fails to provide a critical performance advantage when compared with a "flat" model that does not incorporate hierarchical structure. However, hierarchy appears to facilitate cognitive control processes that support nonroutine behaviors and behaviors that are carried out under computational stress. Second, we compare these models against fMRI data using representational similarity analysis. We find that a model that incorporates so-called wiring costs in the cost function, which produces a hierarchically organized gradient of representational structure across the hidden layer of the neural network, best accounts for fMRI data collected from human participants in a previous study [Holroyd, C. B., Ribas-Fernandes, J. J. F., Shahnazian, D., Silvetti, M., & Verguts, T., Human midcingulate cortex encodes distributed representations of task progress. Proceedings of the National Academy of Sciences, U.S.A., 115, 6398-6403, 2018]. The results reveal that the ACC encodes distributed representations of sequential task context along a rostro-caudal gradient of abstraction: Rostral ACC encodes relatively abstract and temporally extended patterns of activity compared with those encoded by caudal ACC. These results provide insight into the role of ACC in motivation and cognitive control.