Estimating cognitive biases with attention-aware inverse planning

TL;DR

This paper introduces an attention-aware inverse planning method to infer cognitive biases from human behavior, combining deep reinforcement learning with cognitive modeling, demonstrated on real-world driving data.

Contribution

It formalizes the attention-aware inverse planning problem and presents a scalable approach to estimate cognitive biases from observed actions.

Findings

Successfully inferred attentional strategies in driving scenarios

Demonstrated differences from standard inverse reinforcement learning

Scalable estimation of cognitive biases from real-world data

Abstract

People's goal-directed behaviors are influenced by their cognitive biases, and autonomous systems that interact with people should be aware of this. For example, people's attention to objects in their environment will be biased in a way that systematically affects how they perform everyday tasks such as driving to work. Here, building on recent work in computational cognitive science, we formally articulate the attention-aware inverse planning problem, in which the goal is to estimate a person's attentional biases from their actions. We demonstrate how attention-aware inverse planning systematically differs from standard inverse reinforcement learning and how cognitive biases can be inferred from behavior. Finally, we present an approach to attention-aware inverse planning that combines deep reinforcement learning with computational cognitive modeling. We use this approach to infer the attentional strategies of RL agents in real-life driving scenarios selected from the Waymo Open Dataset, demonstrating the scalability of estimating cognitive biases with attention-aware inverse planning.