Abstract
One of the most noted phenomena in social and political decision-making is the occurrence of a framing effect. For example, on problems involving risky choices, individuals tend to act risk-averse when the problem is framed in terms of gains (e.g., saving lives, making money) and risk-seeking when the same problem is instead framed in terms of losses (e.g., deaths, losing money). Scholars have begun to identify the processes underlying framing effects as well as the conditions under which framing effects occur. Yet, extant work focuses nearly exclusively on cognitive processes, despite growing recognition of the importance of emotion in general decision-making tasks. In this paper, we explore the impact of emotional states on risk attitudes and framing. We find that emotions significantly influence both individuals’ tendencies to take risks and the impact of a frame on risky choices (e.g., emotions amplify or depress a frame’s impact). The precise role of emotions depends on the problem domain (e.g., a life-death or a financial decision), and the specific type of emotion under study. Moreover, in contrast to much work in political science, we show that emotions need to be distinguished beyond their positive or negative valence, as different negative emotions exert opposite effects. Our results accentuate the importance of integrating emotions into research areas traditionally dominated by more cognitive perspectives.
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Notes
Druckman (2004, p. 672) explains that these types of equivalency framing effects “differ from value or issue framing effects, as studied in the political communication literature… Issue framing effects refer to situations where, by emphasizing a subset of potentially relevant considerations, a speaker leads individuals to focus on these considerations when constructing their opinions. For example, describing a hate group in terms of free speech as opposed to public safety causes people to base their rally opinions on free speech instead of public safety considerations.” For studies on issue framing and emotion—with particular focus on how different frames lead to alternative emotional reactions—see Brewer (2001), Gross and D’Ambrosio (2004), Marcus et al. (2005), Gross (2006), Gross and Brewer (2006). Also see Lerner and Keltner (2001), who explore emotions and equivalency framing effects. Some of their results are similar to ours although we look across domains, at preference confidence, and at different specific emotions.
In later work, DeSteno et al. (2004) apply their model to persuasive communication, arguing that specific emotions can alter the effectiveness of certain appeals based on their emotional framing. They note that the “influence of affective states on judgments of likelihood may not occur as a function of simple valence.” In other words, the extent to which an emotional state will influence a particular judgment depends on the event and the context, as well as the specific emotion elicited. Based on their earlier findings, they suggest that persuasive attempts will be more successful when they are framed to match the emotional state of the observer. Mayer et al. (1992) also investigated mood congruent judgments in a series of experiments designed to show the generalizability of the phenomenon. They showed that individuals judge attributes to be more characteristic, and judge outcomes to be more likely, when mood is congruent. In addition, they note that negative affect deflates the perceived risk of a positive event (also see, e.g., Berkowitz and Harmon-Jones 2004).
This view mirrors Slovic’s (1999) argument that emotions serve an orienting function. In this view (Slovic et al. 2004, 2005), risk is understood and acted upon in two fundamental, and profoundly different, ways. The first approach considers risk as feelings; the second understands risk as analysis. The first represents a rapid, intuitive, often unconscious experiential response or reaction to perceived danger or threat; this interpretation constitutes the “affect heuristic” (Finucane et al. 2000). Because this system evolved to aid humans in survival, it remains the most common and automatic way for individuals to assimilate risk. The second approach considers risk as an analytic problem requiring normative solutions involving rationality and logic. This process is relatively slower, and requires much greater conscious effort. While many advocates of this approach consider emotional responses to risk as irrational, more recent work in neuroscience demonstrates that these systems work largely in parallel and take direction from each other (Damasio 1994).
We thank an anonymous reviewer for guidance on this point.
This is consistent with Kowert and Hermann (1997) who find that experimental participants who are low on measures of the negative emotion anxiety appear less susceptible to framing effects.
We thank an anonymous reviewer for this insight.
This prediction finds a counterpart in the decisional paralysis so characteristic of depression. Anxiety, distress, and depression often intertwine. And, indeed, there may be some good evolutionary reasons why paralysis in feelings of distress promotes survival. Freezing in fear or distress, for example, often prevents predators from attacking.
The average age was about 26. Other sample statistics include: 57% females, 88% Caucasian, 52% self-identified Democrats, and 27% self-identified Republicans.
Respondents actually responded to four randomly ordered problems, however, two of these problems are not relevant to this study. There also were randomly assigned experimental conditions that are not relevant to this study, and thus, we do not discuss them (see Druckman 2004 for discussion). Like other studies that include multiple problems (e.g., Fagley and Miller 1997; Jou et al. 1996), we instructed participants to treat each problem independently and imagine that they were being confronted with each scenario (see Frisch 1993, p. 419; Levin et al. 2002 on using multiple problems).
We use a variation of the Tversky and Kahneman’s (1987, pp. 74–75) investment problem.
Consistency in the frame used follows prior work (Fagley and Miller 1997).
These questions clearly tapped into the current emotional state of the subject, which could have reflected either “state”-based (i.e., the subject is transiently enthusiastic at the moment) or “trait”-based tendencies (the subject is endemically anxious in most situations). We remain agnostic as to the source of the emotion in this context, although future research might explore potential differences in the impact of state versus trait based emotion on risky decision-making. Obviously, this test examines the impact of background emotion on current choice. Experiment 2, reported below, experimentally manipulates background emotion to induce equivalency in emotional states in order to examine their impact on choice.
As we will shortly discuss, the background questionnaire included various expertise measures such as need for cognition items and questions about statistics background. We worried that asking these items before having the participants complete the problems could prompt them to view the problems in a more statistical sense and exert more thought, which could substantially change the impact of the problems (see Bless et al. 1998 for evidence of such effects). For this reason, participants filled out the background questionnaire after all participants in a given session finished the problems (the typical session had ten participants). We did, however, include another condition that randomly assigned participants to complete the background questionnaire without taking part in the framing problems. Forty participants were assigned to this condition (these participants were not included in the aforementioned 214 who were in the “main part” of the experiment). The average emotion scores for these participants were nearly identical to the scores of participants who read the problems first. Specifically, the respective averages for the participants in the main experiment and the forty participants in the non-treatment condition are as follows. For anger: 1.78 (SD: 1.13; n: 213) and 1.79 (1.06; 40); for distress: 1.87 (1.17; 212) and 1.85 (1.04; 40); and for enthusiasm: 3.75 (2.76; 213) and 3.77 (.82; 40). This confirms that, as our hypotheses assume, the problems themselves did not affect the participants’ emotional states, and thus, any emotion effects stem from emotion influencing reactions to the problems.
The results are the same if we instead use a continuous measure of age. We opt for the dichotomous measure since it is mostly meant to test for differences between student and non-student participants.
The results are the same when using alternative models, such as ANOVA.
We used Clarify to compute these probabilities (as well as ones subsequently reported). Standard errors are available from the authors.
While a score of 1 is not sufficient evidence that the frame had a causal impact, it is a necessary condition and is the most direct measure available.
We also ran regressions with dummy variables indicating the order in which the individual received the problem (e.g., the disease problem first or second). These variables do not affect the substantive results we present here.
In Table 4, we also note significance when p ≤ .13 for a two-tailed test, since on both problems this allows us to note that distress is very close to significant (and would be if we used a one-tailed test).
Given the nature of experiment 2, we were unable to measure some of the relevant controls used in experiment 1 including risk neutrality and expertise.
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Acknowledgments
We acknowledge support from the Russell Sage Foundation’s Small Grants Program in Behavioral Economics, the University of Minnesota McKnight Land-Grant Professorship, and Northwestern University’s AT&T Research Scholar Fund. We also thank the participants at the 2005 “Workshop on new Methods for Studying Social Behavior in Political Science” at Rice University for stimulating conversations.
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Druckman, J.N., McDermott, R. Emotion and the Framing of Risky Choice. Polit Behav 30, 297–321 (2008). https://doi.org/10.1007/s11109-008-9056-y
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DOI: https://doi.org/10.1007/s11109-008-9056-y