# How does this thing work? Evaluating computational models of intervention-based causal learning

- Anna Coenen,
*New York University*
- Bob Rehder,
*New York University*
- Todd Gureckis,
*New York University*

## Abstract

The present study explores how people learn about a causal system
by interacting with it. Participants were given the task to identify the
operation of virtual '"computer chips" by setting the value of various components
and observing how those interventions influenced the setting of other components.
Across conditions we manipulate the complexity of the causal system (i.e., number
of nodes and connections), the number of alternative hypotheses (i.e. possible
causal graphs) on each trial, and aspects of the "temporal stability" of the
learning environment (if repeated interventions were made on a single, stationary
system or if the system reset to different starting states following each
intervention). Interventions were modeled by comparing them to an optimal
Bayesian learner who chooses interventions to quickly reduce uncertainty about
the structure. Our results suggest that naive Internet-recruited subjects choose
highly informative interventions, but also deviate from the predictions of the
optimal model in certain ways.

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