When do people use containment heuristics for physical predictions?
- Erik Brockbank, Computational Cognition Lab, University of California San Diego, San Diego, California, United States
- Ed Vul, Department of Psychology, University of California, San Diego, La Jolla, California, United States
- Kevin Smith, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
AbstractAccounts of human physical reasoning based on simulation from a noisy physics engine have enjoyed considerable success in recent years. However, simulating complex physical dynamics can be a computationally expensive process, and it is possible that people use faster, cheaper shortcuts to make predictions and inferences in complicated physical scenarios. Here we asked people to predict the eventual destination of a ball on a 2D bumper table (in the style of Smith, de Peres, Vul, and Tenenbaum (2017)). We designed scenarios that we expected would modulate the use of heuristics and simulation: the bumper table provided varying degrees of containment to constrain future outcomes and to make a containment heuristic more useful, and could have more or less internal structure to vary the reliability of noisy simulation. As the containment heuristic becomes more useful, and as simulation becomes more expensive, we expected that people would switch from using simulation to rely more on rapid heuristic-based predictions and therefore respond faster. Instead, we found that even when containment was very predictive, people were progressively slower and less accurate as simulation complexity increased, indicating that they persisted in using simulation rather than containment heuristics.
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