Peril, Prudence and Planning as Risk, Avoidance and Worry

Risk occupies a central role in both the theory and practice of decision-making. Although it is deeply implicated in many conditions involving dysfunctional behavior and thought, modern theoretical approaches to understanding and mitigating risk in either one-shot or sequential settings, which are derived largely from finance and economics, have yet to permeate fully the fields of neural reinforcement learning and computational psychiatry. I will discuss the use of dynamic and static versions of one prominent approach, namely conditional value-at-risk, to examine both the nature of risk avoidant choices, encompassing such things as justified gambler's fallacies, and the optimal planning that can lead to consideration of such choices, with implications for offline, ruminative, thinking.

Abstraction and Inference in the Prefrontal Hippocampal Circuitry

The cellular representations and computations that allow rodents to navigate in space have been described with beautiful precision. In this talk, I will show that some of these same computations can be found in humans doing tasks that appear very different from spatial navigation. I will describe some theory that allows us to think about spatial and non-spatial problems in the same framework, and I will try to use this theory to give a new perspective on the beautiful spatial computations that inspired it. The overall goal of this work is to find a framework where we can talk about complicated non-spatial inference problems with the same precision that is only currently available in space.

Thinking the Right Thoughts

In many learning and decision scenarios, especially sequential settings like mazes or games, it is easy to state an objective function but difficult to compute it, for instance because this can require enumerating many possible future trajectories. This, in turn, motivates a variety of more tractable approximations which then raise resource-rationality questions about whether and when an efficient agent should invest time or resources in computing decision variables more accurately. Previous work has used a simple all-or-nothing version of this reasoning as a framework to explain many phenomena of automaticity, habits, and compulsion in humans and animals. Here, I present a more finegrained theoretical analysis of deliberation, which attempts to address not just whether to deliberate vs. act, but which of many possible actions and trajectories to consider. Empirically, I first motivate and compare this account to nonlocal representations of spatial trajectories in the rodent place cell system, which are thought to be involved in planning. I also consider its implications, in humans, for variation over time and situations in subjective feelings of mental effort, boredom, and cognitive fatigue. Finally, I present results from a new study using magnetoencephalography in humans to measure subjective consideration of possible trajectories during a sequential learning task, and study its relationship to rational prioritization and to choice behavior.

Values Encoded in Orbitofrontal Cortex Are Causally Linked to Economic Choices

Classic economists proposed that economic choices rely on the computation and comparison of subjective values. This hypothesis continues to inform economic theory and experimental research, but behavioral measures are ultimately not sufficient to prove the proposal. Consistent with the hypothesis, when agents make choices, neurons in the orbitofrontal cortex (OFC) encode the subjective value of offered and chosen goods. Moreover, neuronal activity in this area suggests the formation of a decision. However, it is unclear whether these neural processes are causally related to choices. More generally, the evidence linking choices to value signals in the brain remains correlational. In my talk, I will present recent results showing that neuronal activity in OFC are causal to economic choices.

Individual differences in decision-making under uncertainty: a neuroeconomic approach