People routinely make decisions based on predictions made by others (e.g., political pundits, market analysts), so it is in their best interest to identify high-quality forecasts. Experts characterize good forecasting as minimization of continuous error (i.e., predictions close to the eventual outcome). By contrast, the present work reveals that laypeople typically see good forecasts as those that correctly predict an event’s categorical outcome (e.g., the winning team). Using within-subjects, between-subjects, and incentive-compatible designs, fifteen studies demonstrate this “pick-the-winner-picker heuristic” as well as its psychological mechanism: People evaluate forecasts by assigning separate weights to (a) categorical correctness and (b) continuous error minimization, depending on the overall importance of the categorical and continuous dimensions for that situation. Thus, in the common case when the categorical dimension matters most (e.g., sports contests), people prize forecasts that accurately predicted the categorical outcome (e.g., the winner, not the margin of victory). However, when the categorical dimension’s stakes are experimentally reduced, an attenuation is observed. While this describes how people typically evaluate forecasts, crucially, a dimension’s importance is not necessarily related to its diagnosticity of forecaster skill or reliability. Accordingly, the pick-the-winner-picker heuristic may constitute a normative mistake, while framing manipulations help debias judgments.
People predominantly interact with similar others, a tendency most often explained by structural, social, or emotional factors. We provide evidence that this behavior is also driven by a lack of appreciation that disagreement can be informative. Across eight studies, we show that people disproportionately seek advice from sources that agree with them, even when a disagreeing advisor provides objectively more valuable information. We replicate this bias across a variety of tasks: for choices and ratings of advisors, in joint and separate evaluations, and with naturalistic and controlled stimuli. We show that this bias persists in nonsocial contexts, which indicates that it operates independently of the social and emotional factors discussed by past research; and that it can be attenuated through deliberation and framing, which suggests that it reflects an intuitive disregard for negative information.
People often group continuous data into crude categorical bins (Fisher & Keil, 2018). We show that this broad tendency disrupts probability learning: People appear to evaluate a continuous cue’s validity by assessing the correspondence between a categorical encoding of the cue (e.g., election candidate who raised more money) and the criterion (e.g., candidate who won). As a result of relying on this sign-match heuristic, people fail to learn the underlying cue-criterion relationship with fidelity. We first present an analytical model of how categorical encoding may disrupt learning. We then test the model’s predictions across several probability learning experiments. For instance, participants in one experiment were tasked with learning the relationship between candidates’ fundraising amounts and final vote shares. Participants were randomly assigned to a High-Divergence (vs. Low-Divergence) condition, in which the candidate who raised more money usually (vs. almost always) won the election. Across conditions, the money-votes relationship was equally strong so, on a standard Brunswik lens model (Brunswik, 1952; Hammond, 1955), participants should learn it equally well. To the contrary, but consistent with our proposed sign-match model of learning, “High-Divergence” participants made less accurate out-of-sample predictions, were less confident in these predictions, and gave greater weight to spurious cues.
We find that gift givers expect recipients to like gifts more when they had more (vs. less) positive purchase experiences. Drawing on transaction utility, affective spillover, and social projection, we argue that the positive affect from securing a deal transfers to inferences about the recipient’s enjoyment. This effect appears across products, price levels, and contexts. Findings reveal an interpersonal consequence of transaction utility.