aging, well-being, affective forecasting,

Objective We address a lingering concern in research on hedonic adaptation to adverse circumstances. This research typically relies on self-report measures of well-being, which are subjective and depend on the standards that people use in making judgments. We employed a novel method to test for, and rule out, such scale recalibration in self-reports of well-being. Design We asked patients with chronic illness (either lung disease or diabetes) and non-patients to evaluate quality of life (QoL) for the patients’ disease. In addition, we also asked them to rank and rate the aversiveness of a diverse set of adverse circumstances, allowing us to examine both the numerical ratings and ordering among items. Main Outcome Measures We compared patients’ and non-patients’ ratings and rankings for the patients’ disease and other conditions. Results and Conclusion We found that patients not only assigned higher numerical quality QoL ratings to their own disease than did non-patients, but also ranked it higher among the broad set of conditions. These results suggest that scale recalibration cannot account for discrepant QoL ratings between patients and non-patients. More generally, this study presents a new approach for measuring well-being that is not subject to the problem of scale recalibration.

No abstract

Current theories of risk perception point to the powerful role of emotion and the neglect of probabilistic information in the face of risk, but these tendencies differ across individuals. We propose a method for measuring individuals' emotional sensitivity to probability to assess how feelings about probabilities, rather than the probabilities themselves, influence decisions. Participants gave affective ratings (worry or excitement) to 14 risky events, each with a specified probability ranging from 1 in 10 to 1 in 10,000,000. For each participant, we regressed these emotional responses against item probabilities, estimating a slope (the degree to which emotional responses change with probability) and an intercept (the emotional reaction to an event with a fixed probability). These two parameters were treated as individual difference scores and included in models predicting reactions to several health risk scenarios. Both emotional sensitivity to probability (slope) and emotional reactivity to possibility (intercept) significantly predicted responses to these scenarios, above and beyond the predictive power of other well-established individual difference measures.