Inferring Cognitive Profiles with Cognition-Conditioned Deep Inverse Planning

Bounded rationality proposes that humans think and act as best they can given limited cognitive resources. For example, to solve complex problems, people routinely engage in *subtasking*, in which they chain together solutions to simpler subtasks. But the boundedly rational solution to a task depends on a person's *cognitive profile*, the individual factors that determine what they find rewarding or cognitively effortful. This makes it hard to use bounded rationality to analyze human behavior in naturalistic tasks, as it requires computing optimal solutions under many different candidate profiles. Our method, *cognition-conditioned deep inverse planning* (CCDIP), addresses this by training a single hierarchical policy that takes cognitive parameters as input and yields tractable action likelihoods. By inverting this forward model, we can then recover people's subtasking strategies and cognitive profiles from their behavior. In simulation, CCDIP recovers parameters of agents with known profiles. On a new dataset of participants performing a driving task ($N=39$, $\sim 345,000$ timesteps), CCDIP shows that attentional factors are needed to explain behavior and reveals stable individual differences in cognitive parameters. CCDIP thus offers a route to theory-driven, interpretable cognitive modeling in naturalistic tasks where traditional approaches do not scale.