Humans form rich causal models of the world that support prediction, explanation, planning and control. While Bayesian methods help formalize how such representations can be learned from data, they are only tractable in the simplest cases. Thus, a key question is how bounded human learners succeed in the face of the world’s formidable complexity. Bramley will discuss three projects aimed at unravelling this mystery.

The first project investigates how people learn about probabilistic causal systems by performing interventions (actions that perturb a system of interest, like pushing a button, taking a medicine, or implementing a policy). Across a line of studies and extensive model comparison, Bramley shows that people adjust their causal representations in a piecemeal fashion, making small local changes rather than more extensive “Kuhnian” revisions. Bramley formalizes this with a model inspired by algorithms for approximating Bayesian inference, and use this model to explain how bounded learners can find high probability hypotheses even in complex learning domains. In a second project, Bramley asks how event timing influences causal inference. Bramley shows that people combine event timing patterns with expectations to infer causal relationships in dynamic causal systems, and also readily use real-time interventions to structure and simplify this learning process. In a third project, Bramley asks how people learn interactively about the physical world. Participants interact with microworlds governed by simulated Newtonian physics with the goal of inferring masses and local forces. Using a simulation-based inference model, Bramley shows that they perform sequences of actions that have the structure of informal experiments which reveal target properties while minimizing noise. Across these projects Bramley’s algorithmic account suggests that humans succeed at learning complex representations by striking a mutually supportive balance between exploring in the world – actively interacting with their surroundings – and in the mind – actively adapting their theories and generating new hypotheses.