UC Davis

Compositionality, a natural property of symbolic systems, is thought to be a key principle underlying human intelligence: known concepts can be combined in novel ways according to systematic rules, allowing for the potentially infinite expressivity of human thought and language. Neural network models of cognition have long been criticized for failing to capture this important property. Despite their massive success in cognitive domains such as natural language processing, modern deep neural networks still struggle to generalize on compositional problems in the same ways that humans do, leading some to conclude that these networks must be augmented with symbolic or rule-like operations to fully account for key aspects of human cognition. Others have attempted to discover the inductive biases that would encourage compositionality to emerge in neural networks, without the need for explicit symbols or rules.The work presented in this dissertation takes the latter approach, exploring in particular the possibility that the mechanisms in the human brain responsible for cognitive control and top-down attentional modulation may constitute just such an inductive bias. Deep neural networks are used to study compositionality, cognitive control, and their relationship in both machine learning and cognitive neuroscience settings. Methods for measuring the extent to which compositional processing has emerged in neural networks are developed, and cognition-inspired attentional mechanisms are tested on compositional generalization problems. Additionally, neural networks are used to model phenomena observed with fMRI regarding control processes in the context of cognitive map formation.