Do I know that you know what you know? Modeling testimony in causal inference

Abstract

We rely on both our own observations and on others' testimony when making causal inferences. To integrate these sources of information we must consider an informant's statements about the world, her expressed level of certainty, her previous accuracy, and perhaps her apparent self-knowledge -- how accurately she conveys her own certainty. It can be difficult to tease apart the contributions of all these variables simply by observing people's causal judgments. We present a computational account of how these different cues contribute to a rational causal inference, and two experiments looking at adults' inferences from causal demonstrations and informant testimony, focusing on cases where these sources conflict. We find that adults are able to combine social information with their own observations, and are sensitive to the reliability of each. Adults are also sensitive to the accuracy, certainty, and self-knowledge of the informant, a result confirmed by comparing predictions from models with and without these variables.


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