The framing effect and risky decisions: Examining cognitive functions with fMRI

https://doi.org/10.1016/j.joep.2004.08.004Get rights and content

Abstract

The “framing effect” is observed when the description of options in terms of gains (positive frame) rather than losses (negative frame) elicits systematically different choices. Few theories explain the framing effect by using cognitive information-processing principles. In this paper we present an explanatory theory based on the cost–benefit tradeoffs described in contingent behavior. This theory proposes that individuals examining various alternatives try to determine how to make a good decision while expending minimal cognitive effort. For this study, we used brain activation functional magnetic resonance imaging (fMRI) to evaluate individuals that we asked to choose between one certain alternative and one risky alternative in response to problems framed as gains or losses. Our results indicate that the cognitive effort required to select a sure gain was considerably lower than the cognitive effort required to choose a risky gain. Conversely, the cognitive effort expended in choosing a sure loss was equal to the cognitive effort expended in choosing a risky loss. fMRI revealed that the cognitive functions used by the decision makers in this study were localized in the prefrontal and parietal cortices of the brain, a finding that suggests the involvement of working memory and imagery in the selection process.

Introduction

The “framing effect” is observed when a decision maker’s risk tolerance (as implied by their choices) is dependent upon how a set of options is described. Specifically, people’s choices when faced with consequentially identical decision problems framed positively (in terms of gains) versus negatively (in terms of losses) are often contradictory. The “Asian disease problem” described by Tversky and Kahneman (1981) is a classic example of the framing effect. Decision makers were asked to choose between a certain (i.e., sure) or a probabilistic (i.e., risky) option to save lives (positive frame) or minimize deaths (negative frame) from an unusual disease:

Imagine that the United States is preparing for an outbreak of an unusual Asian disease that is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Scientific estimates of the consequences of the programs are as follows:

Positive frame:

  • If Program A is adopted, exactly 200 people will be saved.

  • If Program B is adopted, there is a 1 in 3 probability that all 600 people will be saved and a 2 in 3 probability that no people will be saved.

Negative frame:

  • If Program C is adopted, exactly 400 people will die.

  • If Program D is adopted, there is a 1 in 3 probability that nobody will die and a 2 in 3 probability that all 600 will die.

Researchers who examine responses to problems of this sort generally find that negatively framed problems primarily elicit risky responses while positively framed problems primarily elicit more sure (i.e., less risky) responses. After consideration of the above example, most people chose options A and D, despite the fact that in terms of consequences, these choices are contradictory (A is equivalent to C, as B is to D). People appear to exhibit a general tendency to be risk seeking when confronted with negatively framed problems and risk averse when presented with positively framed problems.

In the past 30 years, hundreds of empirical studies1 have been conducted to demonstrate and investigate the framing effect in many different contexts (Kuhberger, 1998). Similarly, many theories have been developed to explain human behavior based on assessments of gains and losses (Kuhberger, 1997). Despite all this research, cognitive theories designed to evaluate the processing demands and the kind of cognitive functions involved in the framing effect are very scarce. In this paper we propose a cognitive model based on cognitive cost–benefit tradeoff theory (Payne, Bettman, & Johnson, 1993). In the proposed model, costs and benefits interplay with cognitive and affective processes. In addition, we test this model by using functional magnetic resonance imaging (fMRI), a technique that helps us measure the cognitive effort involved in making a choice.

Multiple theories have been devised to explain the framing effect (Kuhberger, 1998). These are broadly divided into formal, cognitive and motivational theories.

Prospect Theory, the most well-known formal theory, explains the framing effect in terms of the value function for goods perceived as gains and losses from a reference point (Kahneman and Miller, 1986b, Kahneman and Tversky, 1979). Whether an outcome is perceived as a gain or a loss depends upon the individual’s reference point, which is usually taken to the “status quo” asset level at the time of the choice. The value function yields the preference value assigned to outcomes, and is concave for gains, convex for losses, and steeper for losses than for gains. This functional form implies that decision makers are more sensitive to losses than to gains and exhibit diminishing marginal sensitivity to both. Therefore, people will tend to opt for a sure alternative perceived as a gain rather than for a risky alternative of equal expected value, while the converse will hold true for perceived losses.

Cognitive theories are designed to determine the cognitive processing involved in weighting gains and losses. For example, the fuzzy-trace theory proposes that the framing effect is the result of superficial and simplified processing of information (Reyna & Brainerd, 1991). To evaluate this theory, researchers suggested and tested mechanisms by which decision makers might simplify framing problems by reasoning in qualitative patterns rather than in probabilistic and numerical patterns. The findings suggest that participants follow the path of greatest simplicity by using simplification mechanisms to reduce cognitive demands.

More comprehensively, cognitive cost–benefit tradeoff theory defines choice as a result of a compromise between the desire to make a correct decision and the desire to minimize effort (Payne et al., 1993). This theory holds that individuals initially peruse the available alternatives to determine if they can make a good decision and expend minimal cognitive effort. They only commit to a more complicated cognitive effort if they cannot fulfill their desire to arrive at a good decision by embracing a simpler alternative. Although this is an appealing explanation of the framing effect, this model ignores affective processes that should play an important role in determining what constitutes a good decision.

Motivational theories explain the framing effect as a consequence of hedonic forces, such as the fears and wishes of an individual (Lopes, 1987, Maule, 1995). According to these models, decision makers assign stronger value to feelings of displeasure than to feelings of pleasure, and this disparity increases proportionately with the amount of gain or loss involved in a decision (Mellers, Schwartz, & Ritov, 1999). In other words, like Prospect Theory’s assumption that losses loom larger than equivalent gains, motivational models are based on the claim that the emotions evoked by the losses generally are greater than those evoked by gains.

We will present a theory which brings cognitive and affective theories together. This new model proposes an interplay of the cognitive cost–benefit tradeoff and the motivational models to explain the choice process that leads to the framing effect and perhaps to other decisions under risk and uncertainty. The model is additionally motivated by recent findings from neuroscience that may prove relevant to economics and decision making.

We propose that the framing effect occurs due to a tradeoff between the cognitive effort required to calculate expected values of an alternative (if processing is costly, people are less likely to choose the stimulus) and the affective value of the alternative (if the outcome produces a feeling of displeasure, people are less likely to choose the stimulus).

In a positive frame, the compromise between arriving at a good decision and minimizing cognitive effort is easy to achieve; for example, selecting the option in which “200 people will be saved” feels “correct” in an emotional sense and is effortless (i.e., no calculations are necessary). If the decision maker expends the cognitive effort required to analyze the more risky option, this alternative also will feel emotionally correct and thus appear viable. In contrast, such compromises are more difficult to attain in the negative frame. Although the option in which “400 people will die” is easy to analyze the relatively bad outcome makes it a less than ideal choice (i.e., strong feeling of displeasure). Thus when selecting among options presented in a negative frame, individuals are more willing to undertake the cognitive effort demanded to assess the more risky option because they are more focused on improving the outcome.

Payne et al. (1993) have published findings showing that individuals take longer to make decisions when the options are framed as losses rather than gains. But does that mean that cognitive effort is greater in the negative than in the positive frame or does that mean that the affective cost is larger? And how would this cost vary for different risk levels? We propose that the costs and benefits involved in this kind of choices are of two types—cognitive and affective—and that both play a role in the framing effect. On the one hand, the cognitive effort involved in calculating an expected value is larger in risky than in certain choices and on the other hand, the affective cost is higher for losses than gains.

Neuroscience can help disentangle these issues, as it is possible to measure the amount and strength of processing involved in making choices. A better understanding of the physical mechanisms by which human decisions are made is of growing interest for both economists and neuroscientists (Glimcher, 2003). fMRI studies suggest that cognition and emotion integrate in the prefrontal cortex (PFC) of the brain when making simple choices (Gray, Braver, & Raichle, 2002).2 Independently, the PFC has been associated with both affective processes and with the processing of risk and uncertainty.

Damasio and colleagues have documented the role of the PFC in decision making (Bechara, Damasio, & Damasio, 2000). The most general conclusion from these studies is that emotional defects produce impaired decision making and that a section of the PFC known as the ventromedial prefrontal cortex (BA 11, 12, 13, and 25) is particularly important to decision making. Their methodology often involves patients with lesions in the PFC as well as healthy participants (Bechara, Damasio, Tranel, & Damasio, 1997).

The task used in their studies involves two decks of cards that produce negative expected values in the long run but have extreme gains and losses and two other decks of cards that produce positive expected values with less extreme outcomes. The main finding is that PFC patients return rapidly to the less advantageous decks after suffering a loss, although the immediate emotional reaction (measured by skin conductance) to losses is the same as in normal subjects. They explain the results with the somatic-marker hypothesis which poses that decision making is dependent on emotional processes. As suggested by their results, damage in the ventromedial prefrontal cortex precludes the use of somatic signals necessary to guide decision making in an advantageous direction (Bechara et al., 2000).

In addition to affective processes, the PFC has been associated with processing risk and uncertainty in decision making. Different versions of a guessing task have been used to examine risky decisions (Elliott et al., 1999, Paulus et al., 2001, Rogers et al., 1999). For example, activity in the PFC increases during individuals’ consideration of uncertain rather than certain conditions in two-choice prediction tasks that have no “correct” response (Elliott et al., 1999, Paulus et al., 2001, Rogers et al., 1999). Conditions with uncertain outcomes elicit more activity in the prefrontal and parietal cortices (BA 10, 7, and 40) than do those with assured outcomes (Paulus et al., 2001). Furthermore, the PFC has also been associated with differential activation in alternatives involving monetary rewards and penalties (Delgado et al., 2000, Elliott et al., 2000, Knutson et al., 2001, O’Doherty et al., 2001). PFC activity continues for a longer period of time after a reward feedback than after a punishment feedback (Delgado et al., 2000). An fMRI study of the Prospect Theory also addresses the anticipation and receipt of monetary rewards and penalties (Breiter, Aharon, Kahneman, Dale, & Shizgal, 2001). When expectations of and responses to monetary gains and losses were mapped to brain activity, higher PFC (BA 10) activity was found in response to the size of the rewards or penalties than to whether they were gains or losses.

In summary, this research proposes a two-pronged explanation to describe the posited connection between the PFC and the formulation of responses to positively and negatively framed problems. First, the desire to arrive at a good decision can be heavily charged with emotions in people attempting to do well and avoid bad outcomes. Second, the desire to minimize cognitive effort can lead to activation in the PFC as individuals determine the expected value of various alternatives. Thus we expect to observe an interaction between the activations associated with frame and risk of the selected alternative. If cognitive and affective processes interplay to produce different choices in positive and negative frames, we should be able to see differential brain activity due to both the frame and the risk of the outcome. The negative frame would produce more feelings of displeasure than the positive frame resulting in more brain activity in the PFC; at the same time risky alternatives would be more cognitively difficult due to the calculation of an expected value, producing higher PFC activation than the certain choices.

Section snippets

Participants

Fifteen healthy, right-handed college student volunteers (5 females, 10 males) gave signed, informed consent to participate on this study, which was approved by the University of Pittsburgh and the Carnegie Mellon Institutional Review Boards. They were paid a standard amount of 30 dollars including training time in the lab and time in the MRI scanner.

Participants were familiarized with the scanner, the fMRI procedure, and the risk task by responding (while in the scanner) to four problems in the

Behavioral results

The distribution of choices over the positively and negatively framed problems was consistent with framing results generated in previous studies. Participants chose the certain option more often when responding to positively framed problems and the risky option more often in response to negatively framed problems (Fig. 2, top panel). The percentage of risk-seeking choices was 33% under positive framing and 59% under negative framing (χ2(1) = 13.6, p < 0.01). These results were obtained despite the

Discussion

The behavioral results generated in this study show that participants preferred sure gains to risky ones and risky losses to sure ones, a common empirical result. Individuals also took more time to make decisions framed in terms of losses rather than in terms of gains. The fMRI results demonstrate that the cognitive effort involved in choosing a guaranteed gain is considerably lower than the cognitive effort involved in selecting a risky gain. In contrast, the cognitive effort expended in

Conclusion

Hundreds of empirical studies have demonstrated the framing effect in many different contexts (Kuhberger, 1997, Kuhberger, 1998). Researchers performing these studies often have treated cognition as a black box by focusing on the outcomes rather than on the process by which decisions are made in these contexts. As a result, the subject of how people fall prey to apparently irrational processes such as the framing effect has gone largely unaddressed. Our findings offer an explanation based on

Acknowledgments

This research was supported by the Multidisciplinary University Research Initiative Program (MURI), Grant number N00014-01-1-0677. Also, we would like to thank the researchers at the Center for Cognitive Brain Imaging at Carnegie Mellon University for their support.

References (35)

  • A. Bechara et al.

    Deciding advantageously before knowing the advantageous strategy

    Science

    (1997)
  • V.S. Caviness et al.

    The developing human: A morphometric profile

  • M.R. Delgado et al.

    Tracking the hemodynamic responses to reward and punishment in the striatum

    The American Physiological Society

    (2000)
  • Eddy, W.F., Fitzgerald, M., Genovese, C.R., Mockus, A., & Noll, D.C. (1996). Functional imaging analysis...
  • R. Elliott et al.

    Dissociable neural responses in human reward systems

    The Journal of Neuroscience

    (2000)
  • P.W. Glimcher

    Decisions, uncertainty and the brain: the science of neuroeconomics

    (2003)
  • J.R. Gray

    Emotional modulation of cognitive control: Approach-withdrawal states double-dissociate spatial from verbal two-back task performance

    Journal of Experimental Psychology: General

    (2001)
  • Cited by (162)

    • Investigating the impact of offer frame manipulations on responders playing the ultimatum game

      2022, International Journal of Psychophysiology
      Citation Excerpt :

      The results of Experiments 1, 2 and 3 suggest that the offer framing effect results from complex balance between affective and cognitive processes (in line with the cognitive–affective tradeoff model; Gonzalez et al., 2005; Kahneman and Frederick, 2007) and that the offer frame manipulations may not always lead to a framing effect due to the relatively low emotional resonance of the ultimatum game (Costa et al., 2014).

    • COVID-19 in Singapore and New Zealand: Newspaper portrayal, crisis management

      2021, Tourism Management Perspectives
      Citation Excerpt :

      Media framing theories are divided into three categories of formal, cognitive, and motivational theories (Gonzalez, Dana, Koshino, & Just, 2005). These theories are used when there are different responses to the gains (positive frames) and losses (negative frames) (Gonzalez et al., 2005). A news frame is an ‘emphasis in salience of different aspects of a topic’ (De Vreese, 2005, p. 53).

    • Framing zero: Why losing nothing is better than gaining nothing

      2021, Journal of Behavioral and Experimental Economics
      Citation Excerpt :

      These mechanisms may provide an intriguing line of future research. For example, future research could examine the effects of memory and information processing and its effects on similar framing problems (Gonzalez, Dana, Koshino, & Just, 2005). How does a person's thought processes affect how they think through each part of the bet and how does memory effect these judgments?

    View all citing articles on Scopus
    View full text