Poster E105, Monday, March 26, 2:30-4:30 pm, Exhibit Hall C
Attentional Differences and Estimation Frame Incongruence Predict Bias in Economic Judgments
Kylie Fernandez1, Joseph Schmidt1, Camelia Kuhnen2, Nichole Lighthall1; 1University of Central Florida, 2UNC Kenan-Flagler Business School
Previous research has found that when probabilistic outcome likelihoods are estimated from experience, estimations in the gain domain are relatively optimistic while predictions in the loss domain are relatively pessimistic (Kuhnen, 2015). The current study examines two potential mechanisms of this bias: a) high-magnitude gains and losses receive greater attention and are subsequently overweighted in outcome estimations, b) situations with greater incongruence between outcome-estimation framing and outcome valence enhance domain-specific errors through increased cognitive demand (e.g., difficulty of assigning positive evaluation to loss-minimizing vs. gain-maximizing options). Contributions of these effects were examined using an economic task that required estimating outcome likelihoods of probabilistic options relative to sure-thing options through experience. Outcome domain and stock-payout distributions varied by block. Study 1 measured probability evaluations under a positive estimation frame (“How good is the stock?”) and examined attentional predictors of estimation bias via reaction time. Study 2 examined these variables under a negative estimation frame (“How bad is the stock?”). Study 3 replicated the design of Study 1, but examined the role of visual attention at choice and stock payout using eye tracking. Linear mixed models were used to examine the relationship between trial-level attention to gain and loss stimuli and subsequent estimation errors. Our results suggest that attention to high-magnitude outcomes and situational incongruence between estimation framing and outcome valence interact to drive judgment bias. Together our findings indicate that controlling attention to high-magnitude outcomes and minimizing conflict during cognitive estimations can help to reduce bias in judgments of future outcomes.
Topic Area: THINKING: Decision making