@article{123516,
  author = {Thomas Blanchard and Tania Lombrozo and Shaun Nichols},
  title = {Bayesian Occam{\textquoteright}s razor is a razor of the people},
  abstract = {<p>Occam{\textquoteright}s razor-the idea that all else being equal, we should pick the simpler hypothesis-plays a prominent role in ordinary and scientific inference. But why are simpler hypotheses better? One attractive hypothesis known as Bayesian Occam{\textquoteright}s razor (BOR) is that more complex hypotheses tend to be more flexible-they can accommodate a wider range of possible data-and that flexibility is automatically penalized by Bayesian inference. In two experiments, we provide evidence that people{\textquoteright}s intuitive probabilistic and explanatory judgments follow the prescriptions of BOR. In particular, people{\textquoteright}s judgments are consistent with the two most distinctive characteristics of BOR: They penalize hypotheses as a function not only of their numbers of free parameters but also as a function of the size of the parameter space, and they penalize those hypotheses even when their parameters can be "tuned" to fit the data better than comparatively simpler hypotheses.</p>
},
  year = {2018},
  journal = {Cognitive Science},
  volume = {42},
  pages = {1345-1359},
  issn = {1551-6709},
  url = {https://doi.org/10.1111/cogs.12573},
  language = {eng},
}