Knowledge updating in real-world estimation: Connecting hindsight bias and seeding effects

What is it about?

After learning a previously unknown fact, how well are people able to assess their initial, naïve state of knowledge? Previous research has found that people seem to be subject to a so-called hindsight bias. For instance, when people try to recall their previous judgment on some issue (e.g., “What is the population of Sweden?”) and had in the meantime learned the true value (“Sweden’s population is about 10.4 million”), they seem to misremember their previous judgment to be closer to the true value than it had actually been. Hindsight bias has traditionally been interpreted as reflecting a deficiency of the mind. Here, by contrast, we demonstrate that hindsight bias likely represents a side effect of adaptive learning processes. Specifically, we show in the context of real-world estimation that hindsight bias emerges because people use the information about the true numerical values of objects to recalibrate the numerical ballpark ("metric knowledge") of their estimates of objects in the domain.

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Photo by AbsolutVision on Unsplash

Photo by AbsolutVision on Unsplash

Why is it important?

Historically, it has been argued that hindsight bias—reflecting a lack of awareness of one’s limited prior knowledge—restricts people’s ability to learn. Our research shows that, on the contrary, people learn and adaptively adjust their mental model of a domain when presented with factual information, even if their assessment of their prior knowledge is distorted. We establish this conclusion by integrating theories on hindsight bias with research on quantitative real-world estimation. Biased retrospection and beneficial learning thus co-occur, suggesting that they are two sides of the same coin.