The desirability bias refers to when people’s expectations about an uncertain event are biased by outcome preferences. Prior work has provided limited evidence that the magnitude of this motivated bias depends on (is moderated by) how expectations are solicited—as discrete outcome predictions or as likelihood judgments expressed on more continuous scales. The present studies extended the generalizability and understanding of the moderating process. The authors proposed that solicitations of predictions and likelihood judgments have different connotations that ultimately affect how much bias is expressed; this varies from a prior account that attributed the moderation effect to response scale differences (dichotomous vs. continuous). Study 1 confirmed the connotation difference, with predictions being viewed as more affording of hunches. Studies 2-4 directly tested the moderation effect, and unlike prior work focusing on expectations for purely stochastic events, the present studies involved more naturalistic events for which likelihood information was not supplied or directly knowable. Before viewing scenes from a basketball game (Study 2) or an endurance race (Studies 3-4), participants were led to prefer one contestant over another. After viewing most of the closely-fought contest, they made either a prediction or likelihood judgment about the outcome. Participants’ tendency to forecast their preferred contestant to win was significantly stronger among those making predictions rather than likelihood judgments. In support of the proposed account, this effect persisted even when both types of solicitations offered only dichotomous response options. Broader implications for measuring and understanding people’s expectations/forecasts are discussed.
Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
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