SummaryPlanning allows actions to be structured in pursuit of a future goal. However, in natural environments, planning over multiple possible future states incurs prohibitive computational costs. To represent plans efficiently, states can be clustered hierarchically into “contexts”. For example, representing a journey through a subway network as a succession of individual states (stations) is more costly than encoding a sequence of contexts (lines) and context switches (line changes). Here, using functional brain imaging, we asked humans to perform a planning task in a virtual subway network. Behavioral analyses revealed that humans executed a hierarchically organized plan. Brain activity in the dorsomedial prefrontal cortex and premotor cortex scaled with the cost of hierarchical plan representation and unique neural signals in these regions signaled contexts and context switches. These results suggest that humans represent hierarchical plans using a network of caudal prefrontal structures.Video Abstract

SignificanceHumans learn to perform many different tasks over the lifespan, such as speaking both French and Spanish. The brain has to represent task information without mutual interference. In machine learning, this “continual learning” is a major unsolved challenge. Here, we studied the patterns of errors made by humans and state-of-the-art neural networks while they learned new tasks from scratch and without instruction. Humans, but not machines, seem to benefit from training regimes that blocked one task at a time, especially when they had a prior bias to represent stimuli in a way that encouraged task separation. Machines trained to exhibit the same prior bias suffered less interference between tasks, suggesting new avenues for solving continual learning in artificial systems.

Humans and other animals accumulate resources, or wealth, by making successive risky decisions. If and how risk attitudes vary with wealth remains an open question. Here humans accumulated reward by accepting or rejecting successive monetary gambles within arbitrarily defined temporal contexts. Risk preferences changed substantially toward risk aversion as reward accumulated within a context, and blood oxygen level dependent (BOLD) signals in the ventromedial prefrontal cortex (PFC) tracked the latent growth of cumulative economic outcomes. Risky behavior was captured by a computational model in which reward prompts an adaptive update to the function that links utilities to choices. These findings can be understood if humans have evolved economic decision policies that fail to maximize overall expected value but reduce variance in cumulative outcomes, thereby ensuring that resources remain above a critical survival threshold.