Estimating cognitive biases with attention-aware inverse planning

Sounak Banerjee, Daphne Cornelisse, Deepak Edakkattil Gopinath, Emily Sumner, Jonathan DeCastro, Guy Rosman, Eugene Vinitsky, Mark K Ho

Keywords: Inverse Planning, Bounded Rationality, Computational Cognitive Modeling, Inverse Reinforcement Learning, Human Interaction

TL;DR: We introduce the problem of attention-aware inverse planning, in which the goal is to estimate a resource-limited decision-maker’s attentional strategies from their behavior, and propose algorithms for solving this problem.

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 \textit{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.

Primary Area: Neuroscience and cognitive science (e.g., neural coding, brain-computer interfaces)

Submission Number: 14926