The framing effect is a cognitive bias: people react differently to the same decision depending on how it’s presented, or “framed.” Identical information can produce different responses depending on whether it stresses gains or losses.
Take-home Messages
- Definition: the framing effect is a cognitive bias in which a choice is shaped more by how information is worded than by the information itself.
- Direction: a positive (gain-oriented) or negative (loss-oriented) framing of the same issue significantly changes how people decide.
- Mechanism: prospect theory, a model of how people actually choose under risk, explains why: people weigh losses more heavily than equivalent gains.
- Origins: Daniel Kahneman and Amos Tversky introduced both the framing effect and prospect theory.
- Age effect: susceptibility grows from childhood into adulthood, and older adults lean on the frame more as cognitive resources decline.
- Avoidance: critical, thorough investigation of diverse opinions helps guard against the framing effect.
The framing effect is a cognitive bias: a choice depends more on how options are presented than on the substance behind them (Plous, 1993).
Positive or negative connotations in the wording carry the most weight (Tversky & Kahneman, 1981).
People tend to seek risks when information is framed negatively, but avoid risks when it is framed positively.
Types and Examples of the Framing Effect
The same information can lead people to choose differently when its wording changes. Levin, Schneider and Gaeth (1998) identified three distinct types of framing.
- Risky-choice framing: changes which option people pick, as in the Asian disease problem below. Gain frames promote caution; loss frames promote risk-seeking.
- Attribute framing: changes how one feature is judged, as in the disinfectant and chocolate examples. It is the most consistent type, because a positive label links the item to positive associations in memory.
- Goal framing: changes how persuasive a message is, as in the energy-saving example. Loss messages are often, though not always, more persuasive. It is the smallest and least reliable effect.
Each example below pairs a positive and a negative frame of the same facts.
- Disinfectant: choosing one that “kills 95% of germs” (positive frame) over one where “5% of germs survive” (negative frame).
- Chocolate: picking a bar labelled “90% sugar-free” (positive frame) over one labelled “10% sugar” (negative frame). The sugar content is identical.
- Elective: choosing the course where “20% of students earn an A” (positive frame) over one where “80% fail to get an A” (negative frame), though the odds are identical.
- Medical procedure: choosing one described as a “90% success rate” (positive frame) over the same procedure described as a “10% failure rate” (negative frame).
- Energy saving: acting on “save $100 a year with energy-efficient appliances” (gain frame) more readily than on “lose $100 a year without them” (loss frame).
Prospect Theory
Essential to understanding the framing effect is prospect theory, developed by Amos Tversky and Daniel Kahneman (1979) as a psychological theory of choice.
Unlike expected utility theory, which assumes perfectly rational agents, prospect theory describes how people actually decide. It rests on three ideas.
- Reference point: outcomes are judged as gains or losses relative to a baseline, usually the status quo, not as final states of wealth.
- Diminishing sensitivity: the difference between $100 and $200 feels bigger than the difference between $1,100 and $1,200, for both gains and losses.
- Loss aversion: losses hurt more than equivalent gains feel good, so a fair coin-toss gamble needs a possible win of roughly 1.5 to 2.5 times the possible loss.
Diminishing sensitivity and loss aversion together shape the value function, the curve linking outcomes to how good or bad they feel. The curve is S-shaped: concave for gains, which favours the sure thing, and convex for losses, which favours the gamble.
Changing the frame changes the reference point. That single shift makes framing possible. A purely verbal change in wording can reverse a preference without altering a single underlying number (Kahneman & Tversky, 1979).
Origins of the Framing Effect
The framing effect’s classic demonstration is Tversky and Kahneman’s (1981) “Asian disease problem,” which tested whether wording alone could change a life-or-death choice.
- Aim: to test whether phrasing changes people’s choices even when the underlying probabilities and outcomes stay identical.
- Method: participants imagined an outbreak expected to kill 600 people and chose between a certain programme and a gamble. One group saw the options framed as lives saved, the other as deaths.
- Results: in the gain frame, 72% chose the certain option. In the loss frame, only 22% chose the equivalent certain option, and most gambled instead.
- Conclusion: the same policy attracted a clear majority under one wording and was rejected under the other. Choices follow how a problem is described, not just its content.
In the lives-saved version, Programme A would save 200 people for certain. Programme B carried a one-third chance of saving everyone and a two-thirds chance of saving no one.
In the deaths version, the certain programme meant 400 deaths. The risky one carried a one-third chance nobody dies against a two-thirds chance everybody dies.
Prospect theory explains the flip. The lives-saved wording invites people to take the loss of all 600 as the baseline. Every survivor then counts as a gain, so people protect the sure gain.
The deaths wording moves the baseline. People now start from the intact population, so every death counts as a loss and they gamble to escape it.
Framing Effect Experiments
Plea Bargaining
An analysis of plea-bargaining literature has yielded results unveiling the impact of framing on the criminal justice system (Bibas, 2004). Conventional wisdom holds that parties may strike a plea bargain in light of expected trial outcomes.
Following a forecast of the sentence, parties deduct the chance of exoneration. They then offer a proportional discount.
This conventional model, however, notably ignores the heuristics and the psychological biases which may warp the decision-making process. Among these biases are loss aversion, risk preferences, and framing, which can significantly shape the bargaining outcomes.
While skillful lawyering may ameliorate some biases, evidence suggests that the impact of framing remains a crucial component in the process.
Economists
A natural field experiment tested the framing effect using real conference fees (Gächter, Orzen, Renner & Starmer, 2009).
The pool was experimental economists. This is a group we might expect to resist framing, given their training.
The experiment ran during the run-up to a conference the participants were attending. Researchers wanted to know if framing would sway the decision to pay. They split participants into two treatment groups.
The first group presented information concerning the difference between early and late payment fees in a positive frame as a discount. The second group was given the same information in a negative frame as a late penalty.
The results indicated that while the framing effect influenced the junior experimental economists, the more senior economists were not.
Can Advice Overcome the Framing Effect?
James Druckman (2001) tested whether credible advice can reduce the framing effect. He ran two experiments.
The first repeated Kahneman and Tversky’s disease scenario, but added a party endorsement: one option was backed by Republicans, the other by Democrats.
The second offered a choice between surgery and radiation for lung cancer. Specialists from two medical organizations recommended one option.
In both experiments, endorsement from a credible source sharply reduced or removed the framing effect.
Once a Republican or Democrat saw their own party’s endorsement, the frame stopped mattering as much. A specialist’s recommendation had the same effect.
Physicians and the Power of Framing
Even experts are not immune. As Kahneman (2011) explains in Thinking, Fast and Slow, physicians given identical survival statistics chose differently depending on the wording. Told a treatment had a “90% survival rate,” 84% of doctors recommended surgery. Told the same treatment as “10% mortality,” only 50% did.
Kahneman argues that medical training was “no defense against the power of framing.” The emotional pull of a word like “mortality” reaches judgement before reasoning can intervene.
As Kahneman (2011) explains, each doctor saw only one wording and had no reason to suspect the other would change the answer. Translating between frames takes effort. Most people accept a problem as it is framed.
Kahneman draws a deeper conclusion. Framing “should not be viewed as an intervention that masks or distorts an underlying preference.” In many cases, he argues, there is no underlying preference to distort. Our preferences attach to framed descriptions, not to substance.
The Framing Effect in a Foreign Language
A notable study analyzed the framing effect in a foreign language, with interesting results (Keysar, Hayakawa & An, 2012).
Intuitively, framing might seem language-proof. A harder case: a foreign tongue could even amplify framing, if comprehension difficulty makes decisions less systematic.
The opposite is true. A foreign language actually reduces decision-making bias. Across four experiments, native-language speakers were risk-seeking for losses and risk-averse for gains, the classic framing pattern.
In a foreign language, that pattern disappeared entirely. The framing effect vanished once the choice was posed in a language other than the speaker’s own.
Two further experiments found the same thing: a foreign language increased acceptance of positive-value bets by reducing loss aversion. Emotional and cognitive distance from a foreign tongue seems to explain the effect.
The Neural Basis of Framing
- Aim: to locate the framing effect in the brain. The hypothesis was that susceptibility reflects a fast emotional system, while resisting a frame recruits cognitive-control regions (De Martino, Kumaran, Seymour & Dolan, 2006).
- Method: participants chose between a sure option and a gamble of equal value during a brain scan. The sure option was framed as keeping money (gain frame) or losing money (loss frame), with identical amounts.
- Results: participants were more risk-averse in the gain frame and more risk-seeking in the loss frame. Choices going with the frame activated the amygdala, a fast emotional region; choices going against it activated the anterior cingulate cortex, which detects conflict.
- Conclusion: framing has a specific neural signature. An emotional system biases choice toward the frame, while control-related prefrontal regions support resisting it.
Individual differences mattered too. The participants least swayed by the frame engaged orbitofrontal and ventromedial prefrontal cortex more. These regions integrate emotional signals with the demands of the task.
The study has limits. One brain region serves many functions, so amygdala activity alone does not prove an emotional cause. A scanner also shows correlation, not that the region is necessary for the effect.
Age and the Framing Effect
Childhood
The impact of framing on the decision-making processes of children seems to increase as they grow (Reyna & Farley, 2006).
For instance, preschoolers reason quantitatively. They weigh the literal probability of a result. Elementary schoolers instead rely on qualitative reasoning: they choose surer options for gains and riskier options for losses, whatever the probability.
This increase in qualitative reasoning is associated with a rise in “gist-based” thinking, which is correlated with age (Reyna, 2008).
Adolescence
Adolescents are more likely than children to be influenced by the framing effect. Their susceptibility is still weaker than that of adults (Strough, Karns & Schlosnagle, 2011).
Adolescents tend to opt for riskier choices under both loss and gain framing situations (Albert & Steinberg, 2011).
One explanation: adolescents lack real-life experience of negative outcomes. They lean too heavily on conscious risk-benefit analysis, weighing quantitative details (Schlottmann & Tring, 2005).
This diminishes the influence of the framing effect and induces more consistency between positive and negative frames.
Adulthood
Adults are more susceptible to framing than children or adolescents.
Consumers rated identical ground beef more favourably when it was labelled “75% lean” than when it was labelled “25% fat” (Levin & Gaeth, 1988).
They are also more willing to buy an item after losing an equivalent sum of money than after losing the item itself.
Older adults are susceptible (Peters, Finucane, MacGregor & Slovic, 2000). Ageing reduces cognitive resources, so older adults lean on easier, less demanding cues when deciding, including the frame itself (Thomas & Millar, 2012).
This shows up in real decisions.
Doctors’ framing shapes older patients’ medical choices more than the actual difference between the options. Choosing cancer treatment, framing can shift attention from short-term to long-term survival (Erber, 2013).
Positive wording works better than negative or neutral wording for treatment choices (Peters, Finucane, MacGregor & Slovic, 2000). Memory works the same way. Older adults recall positive health details more accurately than negative ones (Löckenhoff, 2011).
Applications of the Framing Effect
Framing works wherever risks, prices or policies must be put into words. Three areas show it most clearly.
Medicine and Informed Consent
McNeil, Pauker, Sox and Tversky (1982) gave physicians and patients identical statistics for lung-cancer surgery and radiation therapy. Some read a survival frame (“68% living for more than one year”). Others read a mortality frame (“32% dying within one year”).
Both groups shifted toward surgery when it was framed by survival, although the odds never changed. Medical training gave no protection.
This matters for informed consent. Describing a procedure’s risk as a “10% complication rate” instead of a “90% success rate” adds no information, yet it can change how a patient decides.
Health campaigns use the same mechanism on purpose. Rothman and Salovey (1997) reviewed the message-framing literature. Gain-framed messages tended to work better for low-risk preventive behaviours. Loss-framed messages tended to work better for behaviours that involve detecting an uncertain risk.
The lesson: match the frame to the risk structure of the behaviour, not simply to the more positive-sounding wording.
Consumer Choice and Nudges
Attribute framing changes real product judgements. In Levin and Gaeth’s (1988) study, identical ground beef labelled “75% lean” was rated more favourably than beef labelled “25% fat”. After eating it, people also judged the “lean” beef leaner and less greasy.
Pricing follows the same logic. A cash surcharge for paying by card reads as a loss, so customers resist it. An economically identical cash discount reads as a forgone gain, so they accept it far more readily.
Losses loom larger than equivalent gains (Kahneman & Tversky, 1979). From a purely persuasive standpoint, fees and penalties therefore sell better when presented as a forgone bonus.
Framing is also one tool in “nudging”, a behavioural-economics approach that steers choices without removing any option (Thaler & Sunstein, 2008).
Johnson and Goldstein (2003) documented a closely related default effect. Organ-donation rates were far higher in countries where donation is the default and people must opt out. They were far lower where non-donation is the default and people must opt in. The same choice was available in both.
Policymakers now frame the status-quo option deliberately in pension enrolment, tax forms and public-health directives. Most people accept whatever is presented as the default.
Politics and Law
The same policy can be sold or sunk by the baseline it is described against.
Kahneman (2011) recounts Thomas Schelling’s child-tax-exemption example. Students rejected both “a larger exemption for the rich” and “a larger surcharge on the childless poor”, although the two describe the same policy against different defaults.
A credible endorsement, such as one from a party the voter identifies with or a relevant specialist, can suppress this wording effect (Druckman, 2001).
Trusted, transparent communication is therefore a practical counter to manipulative framing.
In law, plea bargaining shows loss aversion at work. A defendant weighing a certain, smaller penalty against an uncertain, larger one behaves much like a participant in a loss-framed gamble (Bibas, 2004).
Decisions to accept or reject a bargain can turn on how the alternatives are posed as much as on the odds of conviction.
How to Avoid the Framing Effect
Knowing how framing works also shows how to resist it. These strategies help you judge the substance rather than the wording:
- Check the source: separate the raw details in an advertisement from the persuasive embellishments designed to entice you.
- Reverse the frame: mentally rephrase a positively or negatively framed fact in its opposite terms before deciding.
- Seek multiple sources: gather both critical and favourable evaluations of each option before choosing.
- Question media narratives: if a candidate is demonized by one outlet, seek out views from their supporters too.
- Slow down: deliberate, analytic thinking weakens the effect in both younger and older adults (Thomas & Millar, 2012).
- Seek credible advice: a trusted, relevant recommendation can shrink or remove the effect (Druckman, 2001).
Critical Evaluation
The framing effect is one of psychology’s best-supported findings, but it is not unlimited.
Strengths
- Replicable demonstration: the Asian disease problem is easy to test and has been reproduced many times since 1981, always in the same direction.
- Theoretical grounding: prospect theory predicts the direction of effects in advance, not just after the fact: gain frames encourage caution and loss frames encourage risk-taking.
- Neural evidence: brain imaging links frame-consistent choices to the amygdala and frame-resistant choices to the anterior cingulate and prefrontal control regions.
- Broad application: the effect shapes real decisions in medicine, marketing, politics, law, and everyday pricing.
- Cross-cultural evidence: large replications spanning many countries and languages show the basic pattern generalises, even when its exact size varies.
Limitations
- Not universal: credible advice, relevant expertise, analytic thinking, and even a foreign language can all shrink or remove the effect.
- Mixed mechanisms: risky-choice, attribute, and goal framing differ in size, process and reliability, so they form a family of related effects rather than one.
- Hypothetical scenarios: much foundational evidence rests on hypothetical choices and student samples, so real stakes and incentives may change the picture.
- Reference-point ambiguity: what counts as the baseline is not always specified in advance, so it can be criticised as read back from the choices it explains.
- Goal framing is weakest: of the three framing types, goal framing is the smallest and least reliable effect.
Is the Framing Effect Irrational?
Whether framing counts as an error depends on the standard of rationality. Two rival accounts disagree.
Expected utility theory (von Neumann & Morgenstern, 1944) says a rational agent weighs each outcome by its objective probability. Such an agent is indifferent to how equivalent outcomes are described.
It therefore predicts no framing effects. Prospect theory predicts systematic ones, and the data side with it.
Gerd Gigerenzer offers a different challenge. He argues that many so-called biases are fast and frugal heuristics: simple, minimal-information rules suited to the environments where real decisions are made (Gigerenzer & Goldstein, 1996).
On this ecological-rationality view, wording can be a useful cue. In everyday talk, how a speaker frames information signals their intent. As Gigerenzer (2008) argues, a listener who updates on that cue is using real information efficiently.
This does not deny the behavioural finding. It asks whether sensitivity to wording is a flaw or a feature outside a one-off hypothetical vignette.
The accounts are partly compatible. Prospect theory explains the mechanism, and the ecological view questions whether that mechanism is a design flaw. They draw sharply different conclusions from the same data, and the debate remains open.
Contemporary Research
Recent research asks a sharper question. Is the framing effect real, or an artefact of selective reporting?
Steiger and Kühberger (2018) re-analysed a large meta-analysis of risky-choice framing studies using p-curve analysis. This method checks the distribution of significant p-values to see whether findings reflect real evidence or selective reporting.
The effect is real. Their corrected estimate was a medium effect (d = 0.52), with no sign of intense p-hacking, the selective analysis that inflates significant results.
Ruggeri and colleagues (2020) went further, replicating the same risk-preference reversal in almost 4,000 people across 19 countries and 13 languages. The pattern held everywhere. Gains still prompted caution and losses still prompted risk-taking, though the exact size varied by country.
The strongest test of the classic result came from Many Labs 2, a coordinated set of preregistered replications (Klein et al., 2018).
- Aim: to test how much classic effects vary across samples and settings, using Tversky and Kahneman’s (1981) Asian disease problem as one of 28 targets.
- Method: peer-reviewed, preregistered protocols, including a close replication of the disease scenario, ran on about half of 125 samples. Together they covered more than 15,000 participants in 36 countries and territories.
- Results: choices shifted in the predicted direction between gain and loss frames, with little variation by country, language, or online versus lab testing. The effect was smaller than the 1981 original, roughly half as strong.
- Conclusion: the framing effect survived this demanding test, so it is genuine and broadly generalisable. The original 72%-versus-22% split, however, is not a stable estimate of how strongly wording moves real choices.
Because the project fixed one wording of the scenario, it shows that the effect reproduces, not why. It also says nothing about attribute or goal framing.
Together, these studies show the framing effect is genuine and generalisable, not a statistical fluke. Its size, however, varies by sample and is smaller than the classic 72%-versus-22% split suggests.
References
Albert, D., & Steinberg, L. (2011). Judgment and decision making in adolescence. Journal of Research on Adolescence, 21 (1), 211-224.
Bibas, S. (2004). Plea bargaining outside the shadow of trial. Harvard Law Review, 2463-2547.
De Martino, B., Kumaran, D., Seymour, B., & Dolan, R. J. (2006). Frames, biases, and rational decision-making in the human brain. Science, 313(5787), 684-687. https://doi.org/10.1126/science.1128356
Druckman, J. N. (2001). Evaluating framing effects. Journal of economic psychology, 22 (1), 91-101.
Druckman, J. N. (2001). Using credible advice to overcome framing effects. Journal of Law, Economics, and Organization, 17 (1), 62-82.
Erber, J. T. (2012). Aging and older adulthood . John Wiley & Sons.
Gächter, S., Orzen, H., Renner, E., & Starmer, C. (2009). Are experimental economists prone to framing effects? A natural field experiment. Journal of Economic Behavior & Organization, 70 (3), 443-446.
Gigerenzer, G. (2008). Why heuristics work. Perspectives on Psychological Science, 3(1), 20-29. https://doi.org/10.1111/j.1745-6916.2008.00058.x
Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103(4), 650-669. https://doi.org/10.1037/0033-295X.103.4.650
Johnson, E. J., & Goldstein, D. (2003). Do defaults save lives? Science, 302(5649), 1338-1339. https://doi.org/10.1126/science.1091721
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
Keysar, B., Hayakawa, S. L., & An, S. G. (2012). The foreign-language effect: Thinking in a foreign tongue reduces decision biases. Psychological science, 23 (6), 661-668.
Klein, R. A., Vianello, M., Hasselman, F., Adams, B. G., Adams, R. B., Jr., Alper, S., Aveyard, M., Axt, J. R., Babalola, M. T., Bahník, Š., Batra, R., Berkics, M., Bernstein, M. J., Berry, D. R., Bialobrzeska, O., Binan, E. D., Bocian, K., Brandt, M. J., Busching, R., … Nosek, B. A. (2018). Many Labs 2: Investigating variation in replicability across samples and settings. Advances in Methods and Practices in Psychological Science, 1(4), 443-490. https://doi.org/10.1177/2515245918810225
Levin, I. P., & Gaeth, G. J. (1988). How consumers are affected by the framing of attribute information before and after consuming the product. Journal of Consumer Research, 15(3), 374-378. https://doi.org/10.1086/209174
Levin, I. P., Schneider, S. L., & Gaeth, G. J. (1998). All frames are not created equal: A typology and critical analysis of framing effects. Organizational Behavior and Human Decision Processes, 76(2), 149–188. https://doi.org/10.1006/obhd.1998.2804
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.
McNeil, B. J., Pauker, S. G., Sox, H. C., Jr., & Tversky, A. (1982). On the elicitation of preferences for alternative therapies. New England Journal of Medicine, 306(21), 1259-1262. https://doi.org/10.1056/NEJM198205273062103
Peters, E., Finucane, M. L., MacGregor, D. G., & Slovic, P. (2000). The bearable lightness of aging: Judgment and decision processes in older adults. The aging mind: Opportunities in cognitive research, 144-165.
Plous, S. (1993). The psychology of judgment and decision making. Mcgraw-Hill Book Company.
Reyna, V. F. (2008). A theory of medical decision making and health: fuzzy trace theory. Medical decision making, 28 (6), 850-865.
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), 1-44.
Rothman, A. J., & Salovey, P. (1997). Shaping perceptions to motivate healthy behavior: The role of message framing. Psychological Bulletin, 121(1), 3-19. https://doi.org/10.1037/0033-2909.121.1.3
Ruggeri, K., Alí, S., Berge, M. L., Bertoldo, G., Bjørndal, L. D., Cortijos-Bernabeu, A., Davison, C., Demić, E., Esteban-Serna, C., Friedemann, M., Gibson, S. P., Jarke, H., Karakasheva, R., Khorrami, P. R., Kveder, J., Lind Andersen, T., Lofthus, I. S., McGill, L., Nieto, A. E., … Folke, T. (2020). Replicating patterns of prospect theory for decision under risk. Nature Human Behaviour, 4(6), 622-633. https://doi.org/10.1038/s41562-020-0886-x
Schlottmann, A., & Tring, J. (2005). How children reason about gains and losses: Framing effects in judgement and choice. Swiss Journal of Psychology, 64 (3), 153-171.
Steiger, A., & Kühberger, A. (2018). A meta-analytic re-appraisal of the framing effect. Zeitschrift für Psychologie, 226(1), 45-55. https://doi.org/10.1027/2151-2604/a000321
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.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.
Thomas, A. K., & Millar, P. R. (2012). Reducing the framing effect in older and younger adults by encouraging analytic processing. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 67 (2), 139-149.
Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211 (4481), 453-458.
von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton University Press.
