Framing effect (psychology)

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The framing effect is an example of cognitive bias, in which people react to a particular choice in different ways depending on whether it is presented as a loss or as a gain.[1] People tend to avoid risk when a positive frame is presented but seek risks when a negative frame is presented.[2] Gain and loss are defined in the scenario as descriptions of outcomes (e.g. lives lost or saved, disease patients treated and not treated, lives saved and lost during accidents, etc.).

Prospect theory shows that a loss is more significant than the equivalent gain,[2] that a sure gain (certainty effect and pseudocertainty effect) is favored over a probabilistic gain,[3] and that a probabilistic loss is preferred to a definite loss.[2] One of the dangers of framing effects is that people are often provided with options within the context of only one of the two frames.[4]

The concept helps to develop an understanding of frame analysis within social movements, and also in the formation of political opinion where spin plays a large role in political opinion polls that are framed to encourage a response beneficial to the organization that has commissioned the poll. It has been suggested that the use of the technique is discrediting political polls themselves.[5] The effect reduces, or even eliminates, if ample, credible information is provided to people.[5]

Research[edit]

Amos Tversky and Daniel Kahneman explored how different phrasing affected participants' responses to a choice in a hypothetical life and death situation in 1981.[2]

Participants were asked to choose between two treatments for 600 people affected by a deadly disease. Treatment A was predicted to result in 400 deaths, whereas treatment B had a 33% chance that no one would die but a 66% chance that everyone would die. This choice was then presented to participants either with positive framing, i.e. how many people would live, or with negative framing, i.e. how many people would die.

Framing Treatment A Treatment B
Positive "Saves 200 lives" "A 33% chance of saving all 600 people, 66% possibility of saving no one."
Negative "400 people will die" "A 33% chance that no people will die, 66% probability that all 600 will die."

Treatment A was chosen by 72% of participants when it was presented with positive framing ("saves 200 lives") dropping to only 22% when the same choice was presented with negative framing ("400 people will die").

This effect has been shown in other contexts:

  • 93% of PhD students registered early when a penalty fee for late registration was emphasized, with only 67% doing so when this was presented as a discount for earlier registration.[6]
  • 62% of people disagreed with allowing "public condemnation of democracy", but only 46% of people agreed that it was right to "forbid public condemnation of democracy".[7]
  • More people will support an economic policy if the employment rate is emphasised than when the associated unemployment rates is highlighted.[5]
  • It has been argued that pretrial detention may increase a defendant's willingness to accept a plea bargain, since imprisonment, rather than freedom, will be his baseline, and pleading guilty will be viewed as an event that will cause his earlier release rather than as an event that will put him in prison.[8]

Tversky's and Kahneman's research has later been criticized for a subtle difference between the phrasings of the options in the two scenarios. Since the description of treatment A in the positive framing does not explicitly state that 400 people will die, nor does the description of treatment A in the negative framing state that 200 people will live, the probability of these consequences being 100% is only implied. This implication is, however, obvious only if one can familiarize themselves with both framings, as opposed to the respondents who participated in the experiment and saw only one of the framings, either the positive or the negative. In their case, it would be reasonable to assume, with the positive framing, that by choosing the option A, the probability of more than 200 people being saved is greater than zero. Respectively, if introduced only to the negative framing, it is reasonable to assume that by choosing option A, the probability of more than 400 people dying is greater than zero. According to these assumptions, it would indeed be rational for the respondents to choose A over B when introduced to the positive framing, and B over A when introduced to the negative framing.

Developmental factors[edit]

The framing effect has consistently proven to be one of the strongest biases in decision making.[9] In general, susceptibility to framing effects increases with age. Age difference factors are particularly important when considering health care[10][11][12] and financial decisions.[13] However, the framing effect seems to disappear when encountering it in a second language.[14]

Childhood and adolescence[edit]

Framing effects in decision-making become stronger as children age.[15][16][17] This is partially because qualitative reasoning increases with age.[15] While preschoolers are more likely to make decisions based on quantitative properties, such as probability of an outcome, elementary schoolers and adolescents become progressively more likely to reason qualitatively, opting for a sure option in a gain frame and a risky option in a loss frame regardless of probabilities.[15] The increase in qualitative thinking is related to an increase in “gist based” thinking that occurs over a lifetime.[18]

However, qualitative reasoning, and thus susceptibility to framing effects, is still not as strong in adolescents as in adults,[15][17] and adolescents are more likely than adults to choose the risky option under both the gain and loss frames of a given scenario.[16] One explanation for adolescent tendencies toward risky choices is that they lack real-world experience with negative consequences, and thus over-rely on conscious evaluation of risks and benefits,[16] focusing on specific information and details or quantitative analysis.[19] This reduces influence of framing effects and leads to greater consistency across frames of a given scenario.[19] Children between the ages of 10-12 are more likely to take risks and show framing effects, while younger children only considered the quantitative differences between the two options presented.[20]

Young adulthood[edit]

Younger adults are more likely than older adults to be enticed by risk-taking when presented with loss frame trials.[9]

In multiple studies of undergraduate students, researchers have found that students are more likely to prefer options framed positively.[21] For example, they are more likely to enjoy meat labeled 75% lean meat as opposed to 25% fat, or use condoms advertised as being 95% effective as opposed to having a 5% risk of failure.[21]

Young adults are especially susceptible to framing effects when presented with an ill-defined problem in which there is no correct answer and individuals must arbitrarily determine what information they consider relevant.[21] For example, undergraduate students are more willing to purchase an item such as a movie ticket after losing an amount equivalent to the item’s cost than after losing the item itself.[21]

Older adulthood[edit]

The framing effect is greater in older adults than in younger adults or adolescents.[10][11] This may be a result of enhanced negativity bias,[11] though some sources claim that the negativity bias actually decreases with age.[13] Another possible cause is that older adults have fewer cognitive resources available to them and are more likely to default to less cognitively demanding strategies when faced with a decision.[9] They tend to rely on easily accessible information, or frames, regardless of whether that information is relevant to making the decision in question.[9] Several studies have shown that younger adults will make less biased decisions than older adults because they base their choices on interpretations of patterns of events and can better employ decision making strategies that require cognitive resources like working-memory skills. Older adults, on the other hand, make choices based on immediate reactions to gains and losses.[9]

Older adults' lack of cognitive resources, such as flexibility in decision making strategies, may cause older adults to be influenced by emotional frames more so than younger adults or adolescents.[22] In addition, as individuals age, they make decisions more quickly than their younger counterparts.[9] It is significant that, when prompted to do so, older adults will often make a less biased decision with reevaluation of their original choice.[9][10]

The increase in framing effects among older adults has important implications, especially in medical contexts.[10][11][12] Older adults are influenced heavily by the inclusion or exclusion of extraneous details, meaning they are likely to make serious medical decisions based on how doctors frame the two options rather than the qualitative differences between the options, causing older adults to inappropriately form their choices.[9]

When considering cancer treatments, framing can shift older adults’ focus from short- to long-term survival under a negative and positive frame, respectively.[10] When presented with treatment descriptions described in positive, negative, or neutral terms, older adults are significantly more likely to agree to a treatment when it is positively described than they are to agree to the same treatment when it is described neutrally or negatively.[11] Additionally, framing often leads to inconsistency in choice: a change in description qualities after an initial choice is made can cause older adults to revoke their initial decision in favor of an alternative option.[11] Older adults also remember positively framed statements more accurately than negatively framed statements.[10][23] This has been demonstrated by evaluating older adults’ recall of statements in pamphlets about health care issues.[10][23]

See also[edit]

References[edit]

  1. ^ Plous, 1993
  2. ^ a b c d Tversky & Kahneman, 1981
  3. ^ Clark, 2009
  4. ^ Druckman, 2001a
  5. ^ a b c Druckman, 2001b
  6. ^ Gätcher, Orzen, Renner, & Stamer, (2009)
  7. ^ Rugg, as cited in Plous, 1993
  8. ^ Stephanos Bibas (June 2004). Plea Bargaining outside the Shadow of Trial 117 (8). Harvard Law Review. pp. 2463–2547 
  9. ^ a b c d e f g h Thomas, A. K.; Millar, P. R. (2011). "Reducing the Framing Effect in Older and Younger Adults by Encouraging Analytic Processing". The Journals of Gerontology Series B: Psychological Sciences and Social Sciences 67B (2): 139. doi:10.1093/geronb/gbr076.  edit
  10. ^ a b c d e f g Erber, Joan (2013). Aging and Older Adulthood (3 ed.). John Wiley & Sons. p. 218. 
  11. ^ a b c d e f Peters, Ellen; Finucane, Melissa; MacGregor, Donald; Slovic, Paul (2000). "The Bearable Lightness of Aging: Judgment and Decision Processes in Older Adults". In Paul C. Stern and Laura L. Carstensen. The aging mind: opportunities in cognitive research. Washington, D.C.: National Academy Press. ISBN 0-309-06940-8. 
  12. ^ a b Hanoch, Yaniv; Thomas Rice (2006). "Can Limiting Choice Increase Social Welfare? The Elderly and Health Insurance". The Milbank Quarterly 84 (1): 37–73. doi:10.1111/j.1468-0009.2006.00438.x. JSTOR 25098107. 
  13. ^ a b Carpenter, S. M.; Yoon, C. (2011). "Aging and consumer decision making". Annals of the New York Academy of Sciences 1235 (1): E1–E12. doi:10.1111/j.1749-6632.2011.06390.x. PMID 22360794.  edit
  14. ^ Keysar, Boaz, et al. (2012). The Foreign-Language Effect, Thinking in a Foreign Tongue Reduces Decision Biases. http://pss.sagepub.com/content/early/2012/04/18/0956797611432178
  15. ^ a b c d Reyna, V. F.; Farley, F. (2006). "Risk and Rationality in Adolescent Decision Making: Implications for Theory, Practice, and Public Policy". Psychological Science in the Public Interest 7: 1. doi:10.1111/j.1529-1006.2006.00026.x.  edit
  16. ^ a b c Albert, D.; Steinberg, L. (2011). "Judgment and Decision Making in Adolescence". Journal of Research on Adolescence 21: 211. doi:10.1111/j.1532-7795.2010.00724.x.  edit
  17. ^ a b Strough, J.; Karns, T. E.; Schlosnagle, L. (2011). "Decision-making heuristics and biases across the life span". Annals of the New York Academy of Sciences 1235: 57–74. doi:10.1111/j.1749-6632.2011.06208.x. PMID 22023568.  edit
  18. ^ Reyna, V. F. (2008). "A Theory of Medical Decision Making and Health: Fuzzy Trace Theory". Medical Decision Making 28 (6): 850–865. doi:10.1177/0272989X08327066. PMC 2617718. PMID 19015287.  edit
  19. ^ a b Schlottmann, A.; Tring, J. (2005). "How Children Reason about Gains and Losses: Framing Effects in Judgement and Choice". Swiss Journal of Psychology 64 (3): 153. doi:10.1024/1421-0185.64.3.153.  edit
  20. ^ Boyer, T. (2006). "The development of risk-taking: A multi-perspective review". Developmental Review 26 (3): 291–345. doi:10.1016/j.dr.2006.05.002.  edit
  21. ^ a b c d Revlin, Russell (2013). "Chapter 11: Solving Problems". Cognition Theory and Practice. New York, New York: Worth Publishers. 
  22. ^ Watanabe, S.; Shibutani, H. (2010). "Aging and decision making: Differences in susceptibility to the risky-choice framing effect between older and younger adults in Japan". Japanese Psychological Research 52 (3): 163. doi:10.1111/j.1468-5884.2010.00432.x.  edit
  23. ^ a b Löckenhoff, C. E. (2011). "Age, time, and decision making: From processing speed to global time horizons". Annals of the New York Academy of Sciences 1235: 44–56. doi:10.1111/j.1749-6632.2011.06209.x. PMID 22023567.  edit

Sources[edit]

Further reading[edit]

  • Kühberger, Anton; Tanner, Carmen (2010). "Risky choice framing: Task versions and a comparison of prospect theory and fuzzy-trace theory". Journal of Behavioral Decision Making 23 (3): 314–329. doi:10.1002/bdm.656.