When a helpful bias is unhelpful: Limitations in reasoning about random and deliberately misleading evidence

Social information aids learning: by making assumptions about other people's knowledge and intentions, people can draw strong and accurate inferences from limited data. In this study, we systematically tested people's ability to reason from information providers with different intentions. The task was an adaptation of Shafto et al (2014)'s rectangle game, where learners guessed a rectangle’s size and location based on provided clues. We examined reasoning based on information from four types of providers: a helpful provider, a provider who sampled randomly, and two misleading providers (who could mislead but not lie). We also varied whether people were given a cover story describing the provider in advance, or whether they could infer how helpful a provider was based on what the provider shared. Participants learned efficiently from helpful providers, aligning closely with the predictions of a normative Bayesian model, even without a cover story. However, while people usually recognized unhelpful providers, they struggled to identify and respond appropriately to misleading strategies. Overall, our results suggest a helpful bias: in our task, participants assumed helpful intent unless given explicit feedback, and even then, they did not fully adjust in line with Bayesian predictions. People also struggled to overcome this bias when learning from randomly sampled information, especially when they had experience being an information provider themselves (Experiment 3).