Optimal utility and probability functions for agents with finite computational precision.

When making economic choices, such as those between goods or gambles, humans act as if their internal representation of the value and probability of a prospect is distorted away from its true value. These distortions give rise to decisions which apparently fail to maximize reward, and preferences that reverse without reason. Why would humans have evolved to encode value and probability in a distorted fashion, in the face of selective pressure for reward-maximizing choices? Here, we show that under the simple assumption that humans make decisions with finite computational precision--in other words, that decisions are irreducibly corrupted by noise--the distortions of value and probability displayed by humans are approximately optimal in that they maximize reward and minimize uncertainty. In two empirical studies, we manipulate factors that change the reward-maximizing form of distortion, and find that in each case, humans adapt optimally to the manipulation. This work suggests an answer to the longstanding question of why humans make "irrational" economic choices.

Comparing continual task learning in minds and machines.

Humans can learn to perform multiple tasks in succession over the lifespan ("continual" learning), whereas current machine learning systems fail. Here, we investigated the cognitive mechanisms that permit successful continual learning in humans and harnessed our behavioral findings for neural network design. Humans categorized naturalistic images of trees according to one of two orthogonal task rules that were learned by trial and error. Training regimes that focused on individual rules for prolonged periods (blocked training) improved human performance on a later test involving randomly interleaved rules, compared with control regimes that trained in an interleaved fashion. Analysis of human error patterns suggested that blocked training encouraged humans to form "factorized" representation that optimally segregated the tasks, especially for those individuals with a strong prior bias to represent the stimulus space in a well-structured way. By contrast, standard supervised deep neural networks trained on the same tasks suffered catastrophic forgetting under blocked training, due to representational interference in the deeper layers. However, augmenting deep networks with an unsupervised generative model that allowed it to first learn a good embedding of the stimulus space (similar to that observed in humans) reduced catastrophic forgetting under blocked training. Building artificial agents that first learn a model of the world may be one promising route to solving continual task performance in artificial intelligence research.

Gain control explains the effect of distraction in human perceptual, cognitive, and economic decision making.

When making decisions, humans are often distracted by irrelevant information. Distraction has a different impact on perceptual, cognitive, and value-guided choices, giving rise to well-described behavioral phenomena such as the tilt illusion, conflict adaptation, or economic decoy effects. However, a single, unified model that can account for all these phenomena has yet to emerge. Here, we offer one such account, based on adaptive gain control, and additionally show that it successfully predicts a range of counterintuitive new behavioral phenomena on variants of a classic cognitive paradigm, the Eriksen flanker task. We also report that blood oxygen level-dependent signals in a dorsal network prominently including the anterior cingulate cortex index a gain-modulated decision variable predicted by the model. This work unifies the study of distraction across perceptual, cognitive, and economic domains.

Robust sampling of decision information during perceptual choice.

Humans move their eyes to gather information about the visual world. However, saccadic sampling has largely been explored in paradigms that involve searching for a lone target in a cluttered array or natural scene. Here, we investigated the policy that humans use to overtly sample information in a perceptual decision task that required information from across multiple spatial locations to be combined. Participants viewed a spatial array of numbers and judged whether the average was greater or smaller than a reference value. Participants preferentially sampled items that were less diagnostic of the correct answer ("inlying" elements; that is, elements closer to the reference value). This preference to sample inlying items was linked to decisions, enhancing the tendency to give more weight to inlying elements in the final choice ("robust averaging"). These findings contrast with a large body of evidence indicating that gaze is directed preferentially to deviant information during natural scene viewing and visual search, and suggest that humans may sample information "robustly" with their eyes during perceptual decision-making.

Scaffolding cooperation in human groups with deep reinforcement learning.

Effective approaches to encouraging group cooperation are still an open challenge. Here we apply recent advances in deep learning to structure networks of human participants playing a group cooperation game. We leverage deep reinforcement learning and simulation methods to train a 'social planner' capable of making recommendations to create or break connections between group members. The strategy that it develops succeeds at encouraging pro-sociality in networks of human participants (N = 208 participants in 13 groups) playing for real monetary stakes. Under the social planner, groups finished the game with an average cooperation rate of 77.7%, compared with 42.8% in static networks (N = 176 in 11 groups). In contrast to prior strategies that separate defectors from cooperators (tested here with N = 384 in 24 groups), the social planner learns to take a conciliatory approach to defectors, encouraging them to act pro-socially by moving them to small highly cooperative neighbourhoods.

Human-centred mechanism design with Democratic AI.

Building artificial intelligence (AI) that aligns with human values is an unsolved problem. Here we developed a human-in-the-loop research pipeline called Democratic AI, in which reinforcement learning is used to design a social mechanism that humans prefer by majority. A large group of humans played an online investment game that involved deciding whether to keep a monetary endowment or to share it with others for collective benefit. Shared revenue was returned to players under two different redistribution mechanisms, one designed by the AI and the other by humans. The AI discovered a mechanism that redressed initial wealth imbalance, sanctioned free riders and successfully won the majority vote. By optimizing for human preferences, Democratic AI offers a proof of concept for value-aligned policy innovation.

A Network for Computing Value Equilibrium in the Human Medial Prefrontal Cortex.

Humans and other animals make decisions in order to satisfy their goals. However, it remains unknown how neural circuits compute which of multiple possible goals should be pursued (e.g., when balancing hunger and thirst) and how to combine these signals with estimates of available reward alternatives. Here, humans undergoing fMRI accumulated two distinct assets over a sequence of trials. Financial outcomes depended on the minimum cumulate of either asset, creating a need to maintain "value equilibrium" by redressing any imbalance among the assets. Blood-oxygen-level-dependent (BOLD) signals in the rostral anterior cingulate cortex (rACC) tracked the level of imbalance among goals, whereas the ventromedial prefrontal cortex (vmPFC) signaled the level of redress incurred by a choice rather than the overall amount received. These results suggest that a network of medial frontal brain regions compute a value signal that maintains value equilibrium among internal goals.

Ventromedial Prefrontal Cortex Encodes a Latent Estimate of Cumulative Reward.

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.

Neural Mechanisms of Hierarchical Planning in a Virtual Subway Network.

Planning 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.