Learning From Aggregated Opinion

Abstract

The capacity to leverage information from others’ opinions is a hallmark of human cognition. Consequently, past research has investigated how we learn from others’ testimony. Yet a distinct form of social information— aggregated opinion —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’ judgments and the predictions of the Bayesian model, though some participants’ 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.

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