@article{204551,
  author = {Kerem Oktar and Tania Lombrozo and Thomas Griffiths},
  title = {Learning From Aggregated Opinion},
  abstract = {<p>The capacity to leverage information from others{\textquoteright} opinions is a hallmark of human cognition. Consequently, past research has investigated how we learn from others{\textquoteright} testimony. Yet a distinct form of social information{\textemdash}aggregated opinion{\textemdash}increasingly guides our judgments and decisions. We investigated how people learn from such information by conducting three experiments with participants recruited online within the United States (N = 886) comparing the predictions of three computational models: a Bayesian solution to this problem that can be implemented by a simple strategy for combining proportions with prior beliefs, and two alternatives from epistemology and economics. Across all studies, we found the strongest concordance between participants{\textquoteright} judgments and the predictions of the Bayesian model, though some participants{\textquoteright} judgments were better captured by alternative strategies. These findings lay the groundwork for future research and show that people draw systematic inferences from aggregated opinion, often in line with a Bayesian solution.</p>},
  year = {2024},
  journal = {Psychological Science},
  volume = {35},
  pages = {1010{\textendash}1024},
}