Subjective confidence reveals the hierarchical nature of learning under uncertainty

Abstract

Hierarchical processing is pervasive in the brain, but its computational significance for learning in real-world conditions, with uncertainty and changes, is disputed. We show that previously proposed qualitative signatures which relied on reports of learned quantities or choices in simple experiments are insufficient to categorically distinguish hierarchical from non-hierarchical models of learning under uncertainty. Instead, we present a novel test which leverages a more complex task, whose hierarchical structure allows generalization between different statistics tracked in parallel. We use reports of confidence to quantitatively and qualitatively arbitrate between the two accounts of learning. Our results indicate that human subjects can track multiple, interdependent levels of uncertainty, and provide clear evidence for hierarchical processing, thereby challenging some influential neurocomputational accounts of learning.