Estimating cognitive biases with attention-aware inverse planning
Sounak Banerjee, Daphne Cornelisse, Deepak Gopinath, Emily Sumner, Jonathan DeCastro, Guy Rosman, Eugene Vinitsky, Mark Ho
People's goal-directed actions are influenced by their cognitive biases, and autonomous systems that interact with people should be aware of this. For example, because attention is always limited, people's attention to objects in their environment will be biased in a way that systematically affects their actions when performing everyday tasks (e.g., driving to work, making breakfast). 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, from a person's behavior, what attentional biases influence their behavior on a task. We then 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 a scalable algorithmic 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 agents in real-life driving scenarios selected from the Waymo Open Dataset, demonstrating the scalability of estimating cognitive biases with attention-aware inverse planning.