Affective forecasting (also known as hedonic forecasting or the hedonic forecasting mechanism) is the prediction of one's affect (emotional state) in the future. As a process that influences preferences, decisions, and behavior, affective forecasting is studied by both psychologists and economists, with broad applications.
Kahneman and Snell began research on hedonic forecasts in the early 1990s, examining its impact on decision making. The term "affective forecasting" was later coined by psychologists Timothy Wilson and Daniel Gilbert. While early research tended to focus solely on measuring emotional forecasts, subsequent studies also began to examine the accuracy of forecasts, revealing that people are surprisingly poor judges of their future emotional states. For example, in predicting how events like winning the lottery might affect their happiness, people are likely to overestimate future positive feelings, ignoring the numerous other factors that might contribute to their emotional state outside of the single lottery event. Some of the cognitive biases related to systematic errors in affective forecasts are focalism, empathy gap, and impact bias.
While affective forecasting has traditionally drawn the most attention from economists and psychologists, their findings have in turn generated interest from a variety of other fields, including happiness research, law, and health care. Its effect on decision making and well-being is of particular concern to policy-makers and analysts in these fields, although it also has applications in ethics. For example, the tendency to underestimate our ability to adapt to life-changing events has led to legal theorists questioning the assumptions behind tort damage compensation. Behavioral economists have incorporated discrepancies between forecasts and actual emotional outcomes into their models of different types of utility and welfare. This discrepancy also concerns healthcare analysts, in that many important health decisions depend upon patients' perceptions of their future quality of life.
- 1 Overview
- 2 Major sources of errors
- 3 In psychology
- 4 In economics
- 5 In law
- 6 In health
- 7 See also
- 8 References
- 9 Further reading
- 10 External links
Affective forecasting can be divided into four components: predictions about emotional valence (i.e. positive or negative), the specific emotions experienced, their duration, and their intensity. While errors may occur in all four components, research overwhelmingly indicates that the two areas most prone to bias, usually in the form of overestimation, are duration and intensity. Immune neglect is a form of impact bias in response to negative events whereby people fail to predict how much their psychological immune system will hasten their recovery. On average, people are fairly accurate about predicting which emotions they will feel in response to future events. However, some studies indicate that predicting specific emotions in response to more complex social events leads to greater inaccuracy. For example, one study found that while many women who imagine encountering gender harassment predict feelings of anger, in reality, a much higher proportion report feelings of fear. Other research suggests that accuracy in affective forecasting is greater for positive affect than negative affect, suggesting an overall tendency to overreact to perceived negative events. Gilbert and Wilson posit that this is a result of our psychological immune system.
While affective forecasts take place in the present moment, researchers also investigate its future outcomes. That is, they analyze forecasting as a two-step process, encompassing a current prediction as well as a future event. Breaking down the present and future stages allow researchers to measure accuracy, as well as tease out how errors occur. Gilbert and Wilson, for example, categorize errors based on which component they affect and when they enter the forecasting process. In the present phase of affective forecasting, forecasters bring to mind a mental representation of the future event and predict how they will respond emotionally to it. The future phase includes the initial emotional response to the onset of the event, as well as subsequent emotional outcomes, for example, the fading of the initial feeling.
When errors occur throughout the forecasting process, people are vulnerable to biases. These biases disable people from accurately predicting their future emotions. Errors may arise due to extrinsic factors, such as framing effects, or intrinsic ones, such as cognitive biases or expectation effects. Because accuracy is often measured as the discrepancy between a forecaster's present prediction and the eventual outcome, researchers also study how time affects affective forecasting. For example, the tendency for people to represent distant events differently from close events is captured in construal level theory.
The finding that people are generally inaccurate affective forecasters has been most obviously incorporated into conceptualizations of happiness and its successful pursuit, as well as decision making across disciplines. Findings in affective forecasts have stimulated philosophical and ethical debates, for example, on how to define welfare. On an applied level, findings have informed various approaches to healthcare policy, tort law, consumer decision making, and measuring utility (see below sections on psychology, economics, law, and health).
Newer and conflicting evidence suggests that intensity bias in affective forecasting may not be as strong as previous research indicates. Five studies, including a meta-analysis recovers evidence that overestimation in affective forecasting is partly due to the methodology of past research. Their results indicate that some participants misinterpreted specific questions in affective forecasting testing. For example, one study found that undergraduate students tended to overestimate experienced happiness levels when participants were asked how they were feeling in general with and without reference to the election, compared to when participants were asked how they were feeling specifically in reference to the election. Findings indicated that 75%-81% of participants asked general questions misinterpreted them. After clarification of tasks, participants were able to more accurately predict the intensity of their emotions
Major sources of errors
Because forecasting errors commonly arise from literature on cognitive processes, many affective forecasting errors derive from and are often framed as cognitive biases, some of which are closely related or overlapping constructs (e.g. projection bias and empathy gap). Below is a list of commonly cited cognitive processes that contribute to forecasting errors.
One of the most common sources of error in affective forecasting across various populations and situations is the impact bias, which is the tendency to overestimate the emotional impact of a future event, whether in terms of intensity or duration. Impact bias, including impact and durability bias findings are both robust and reliable errors found in affective forecasting. Studies have found that college students overestimated how happy or unhappy they would be after being assigned to a desirable or undesirable dormitory. Impact bias has also been found in retroactive assessments of the past events.
Some studies specifically address "durability bias," the tendency to overestimate the length of time future emotional responses will last. Even if people accurately estimate the intensity of their future emotions, they may not be able to estimate the duration of them. Durability bias is generally stronger in reaction to negative events. This is important because people tend to work toward events they believe will cause lasting happiness, and according to durability bias, people might be working toward the wrong things.
Proposed causes of impact bias include mechanisms like immune neglect and focalism, as well as misconstruals.The pervasiveness of impact bias in affective forecasts is of particular concern to healthcare specialists, in that it affects both patients' expectations of future medical events as well as patient-provider relationships. (See health.)
Focalism (or the "focusing illusion") occurs when people focus too much on certain details of an event, ignoring other factors. Research suggests that people have a tendency to exaggerate aspects of life when focusing their attention on it. A well-known example originates from a paper by Kahneman and Schkade, who coined the term "focusing illusion" in 1998. They found that although people tended to believe that someone from the Midwest would be more satisfied if they lived in California, results showed equal levels of life satisfaction in residents of both regions. In this case, concentrating on the easily observed difference in weather bore more weight in predicting satisfaction than other factors. Various studies have attempted to "defocus" participants, with mixed results depending on methods used. One successful study asked people to imagine how happy a winner of the lottery and a recently diagnosed HIV patient would be. The researchers were able to attenuate focalism by exposing participants to detailed and mundane descriptions of each person's life. These participants subsequently estimated similar levels of happiness for the HIV patient as well as the lottery-winner, as opposed to control participants, who made unrealistically disparate predictions of happiness.
Gilbert et al. originally coined the term "immune neglect" (or "immune bias") to describe a function of the psychological immune system. Immune neglect refers to forecasters' unawareness of their tendency to adapt to and cope with negative events. Bolger & Zuckerman found that coping strategies vary between individuals and are influenced by their personalities They assumed that since people generally do not take their coping strategies into account when they predict future events, that people with better coping strategies should have a bigger impact bias, or a greater difference between their predicted and actual outcome. Hoerger et al. examined this further by studying college students’ emotions for football games. They found that students who generally coped with their emotions instead of avoiding them would have a greater impact bias when predicting how they’d feel if their team lost the game. They found that those with better coping strategies recovered more quickly. Since the participants did not think about their coping strategies when making predictions, those who actually coped had a greater impact bias. Those who avoided their emotions, felt very closely to what they predicted they would. Hoerger ran another study on immune neglect after this, which studied both daters' and non-daters' forecasts about Valentine’s Day, and how they would feel in the days that followed. Hoerger found that different coping strategies would cause people to have different emotions in the days following Valentine’s Day, but participants’ predicted emotions would all be similar. This shows that most people do not realize the impact that coping can have on their feelings following an emotional event. He also found that, not only did immune neglect create a bias for negative events, but also for positive ones. This shows that people continually make inaccurate forecasts because they do not take into account their ability to cope & overcome emotional events.
A variant of immune neglect also proposed by Gilbert and Wilson is the region-beta paradox, where recovery from more intense suffering is faster than recovery from less intense experiences because of the engagement of coping systems. This complicates forecasting, leading to errors.
Projection bias is the tendency to falsely project current preferences onto a future event. Within economics, projection bias relates to utility, habit formation, and consumptive behavior. For example, when deciding whether or not to smoke cigarettes, people may predict how their current consumption will affect their future preferences. Projection bias, then, could help to enable habit-forming behavior like smoking by leading people to systematically underestimate future drawbacks.
Projection bias can arise from empathy gaps (or hot/cold empathy gaps), which occur when the present and future phases of affective forecasting are characterized by different states of physiological arousal, which the forecaster fails to take into account. For example, a forecaster in a state of hunger is likely to overestimate how much they will want to eat later, overlooking the effect of satiation on future preferences. As with projection bias, economists use the visceral motivations that produce empathy gaps to help explain impulsive or self-destructive behaviors, such as smoking.
"Construal level theory" theorizes that distant events are conceptualized more abstractly than immediate ones. Thus, psychologists suggest that a lack of concrete details prompts forecasters to rely on more general or idealized representations of events, which subsequently leads to simplistic and inaccurate predictions. For example, when asked to imagine what a 'good day' would be like for them in the near future, people often describe both positive and negative events. When asked to imagine what a 'good day' would be like for them in a year, however, people resort to more uniformly positive descriptions. Gilbert and Wilson call bringing to mind a flawed representation of a forecasted event the misconstrual problem. Framing effects, environmental context, and heuristics (such as schemas) can all affect how a forecaster conceptualizes a future event. For example, the way options are framed affects how they are represented: when asked to forecast future levels of happiness based on pictures of dorms they may be assigned to, college students use physical features of the actual buildings to predict their emotions. In this case, the framing of options highlighted visual aspects of future outcomes, which overshadowed more relevant factors to happiness, such as having a friendly roommate.
Time discounting (or time preference) is the tendency to weigh present events over future ones. Immediate benefits are normally preferred over delayed ones, especially in longer time periods and with younger children or adolescents. The longer the duration of time, the stronger people discount the effect of the future. Because of this, people expect their affective reactions to an event to be less intense in the future than in the present. This pattern is sometimes referred to as hyperbolic discounting or “present bias” because people’s judgements are bias to present events.
Future anhedonia is an affective forecasting error which occurs when a person believes that they will experience less intense affects of an event that will occur in the future, than if the same event occurred in the present. Forecasts of the duration of feelings often capture the tendency for emotions to fade over time, but underestimate the speed in which this happens. For example, in a study participants were asked to predict their happiness after receiving $20. They rated how happy they would feel if they received $20 tomorrow or if they received it in a year. They found that participants predicted that they would be happier receiving $20 sooner, compared to receiving it in the future. Applied to affective forecasting, this helps explain why people underestimate the intensity of future events. Economists often cite time discounting as a source of mispredictions of future utility.
Previously formed expectations can alter emotional responses to the event itself, motivating forecasters to confirm or debunk their initial forecasts. In this way, self-fulfilling prophecy can lead to the perception that forecasters have made accurate predictions. Inaccurate forecasts can also become amplified by expectation effects. For example, a forecaster who expects a movie to be enjoyable will, upon finding it dull, like it significantly less than a forecaster who had no expectations.
Affective forecasters often rely on memories of past events. However, predictions hinge on the accuracy of remembering past experiences. For example, using highly available, but unrepresentative past memories, increases the impact bias. Various studies indicate that retroactive assessments of past experiences are prone to various errors, such as duration neglect or decay bias. People tend to overemphasize the peaks and ends of their experiences when assessing them (peak/end bias), instead of analyzing the event as a whole. For example, in recalling painful experiences, people place greater emphasis on the most discomforting moments as well as the end of the event, as opposed to taking into account overall duration. Retroactive reports often conflict with present-moment reports of events, further pointing to contradictions between the actual emotions experienced during an event and the memory of them. In addition to producing errors in forecasts about the future, this discrepancy has incited economists to redefine different types of utility and happiness(see section on economics).
Another problem that can arise with affective forecasting is that people tend to misremember their past predictions. Meyvis, Ratner, and Levav predicted that people forget how they predicted an experience would be like beforehand, and thought their predictions were the same as their actual emotions. Because of this, people do not realize that they made a mistake in their predictions, and will then continue to misforecast similar situations in the future. Meyvis et al. ran 5 studies to test whether or not this is true. They found in all of their studies, when people were asked to recall their previous predictions they instead write how they currently feel about the situation. This shows that they do not remember how they thought they would feel, and makes it impossible for them to learn from this event for future experiences.
Emotional evanescence refers to the general tendency for emotions to fade or decrease in intensity over time, which forecasters often overlook. Some research suggests that all errors in affective forecasting derive solely from initial intensity bias, or the flawed estimation of an initial response to future events. This bias may be mediated by context-specific variables. For example, forecasters who report being more in love with their partners exhibit greater errors in the forecasts of whether or not they will break up. Other research suggests that accuracy in affective forecasting is greater for positive affect than negative affect. A final related bias is fading affect bias, in which the emotions associated with unpleasant memories fade more quickly than the emotion associated with positive events.
Psychologists have traditionally focused on identifying how and why errors in affective forecasting arise, contributing significantly to the understanding of how different biases influence the process.
Psychological immune system
Gilbert and Wilson coined the term "psychological immune system" to encompass a number of biases and mechanisms that protect people from experiencing extreme negative emotions. This label draws on an analogy with the biological immune system. These processes affect how the people process, transform or construct information, making the existing state of affairs more bearable and the alternatives more appealing. The mechanisms of the psychological immune system act without conscious awareness, so people usually fail to anticipate its effects. This is one reason why people are poor at affective forecasting: they typically underestimate the extent to which these processes will shield them from a negative event.
Preliminary studies have incorporated findings on cognitive biases and affective forecasting to investigate ways of "debiasing" forecasts. Approaches vary depending on which bias is targeted. For example, one study successfully "defocused" (i.e. reduced the impact of focalism) participants. Researchers asked participants to imagine how happy a winner of the lottery and a recently diagnosed HIV patient would be. The researchers were able to attenuate focalism by exposing participants to detailed and mundane descriptions of each person's life. These participants subsequently estimated similar levels of happiness for the HIV patient as well as the lottery-winner, as opposed to control participants, who made unrealistically disparate predictions of happiness. Wilson et al. ran another study that demonstrated that defocalizing helps people make better predictions. They ran several studies involving college students and had half of the students complete a diary, before making their predictions about how the outcome of a football game would affect them. Since they were reminded of other things in their daily life that will affect their happiness, their predictions were much closer to what their actual feelings were in the days following the football game. They realized that the outcome of the game would have a low influence on their overall happiness. If people do as this study suggests and consider their other everyday activities when they are predicting how a future event will affect them, will help them make more accurate forecasts.
A study conducted by Emanuel et al. focused on decreasing impact bias employing mindfulness techniques. They ran a study regarding people’s forecasts about their feelings two weeks after the 2008 Presidential election, while also looking at the different facets of mindfulness to see which is most involved in impact bias. The researchers found that the participants who had a greater tendency towards observing, a facet of mindfulness, made more accurate predictions on what their feelings would be two weeks after the election than those that used other techniques. This study shows that if people “observe”, or are more aware of their emotions and their whole
Hoerger in his study mentioned above, found that since many affective forecasting errors can be caused since people do not take into account their coping strategies when making their forecasts. He suggested that if people identify their coping strategies, they can become more aware of them and the effect that they can have on their emotions following an emotional event, and then alter their forecasts appropriately
In improving forecasting errors cause by misremembering, Meyvis, et al. in the same study from above also looked to see there was a way to correct these forecasting errors that people tend to make. They found that if they reminded the participants of their original predictions, and point out how these predictions were different than how they actually feel currently, they will better remember their actual feelings the next time they were asked to recall them. They found that if you simply help people realize that they mispredicted how they would feel about an event, they would be able to remember their predictions better and more importantly, learn from them in the future.
Psychologists are interested in what types of factors mediate affective forecasting. Research suggests links between forecasting processes and extraversion and neuroticism, possibly because these personality traits affect baseline moods and both experienced and anticipated emotional reactions. Other research has found that working memory and the perceived importance of a future event increase impact bias, but only for some individuals. Other individual traits that lead to differences in forecasting accuracy are levels of attachment anxiety and emotional intelligence. Culture may also mediate affective forecasting. People from east Asian cultures exhibit less susceptibility to both impact bias and focalism. Research has also investigated motivational components of affective forecasting, suggesting that impact bias may be a result of an effort to motivate ourselves towards achieving goals.
Research in affective forecasting errors complicate conventional interpretations of utility maximization, which presuppose that to make rational decisions, people must be able to make accurate forecasts about future experiences or utility. Whereas economics formerly focused largely on utility in terms of a person's preferences, the realization that forecasts are often inaccurate suggests that simple measuring preferences at a time of choice may be an incomplete concept of utility. Thus, economists such as Daniel Kahneman, have incorporated differences between affective forecasts and later outcomes into corresponding types of utility. Whereas a current forecast reflects expected or predicted utility, the actual outcome of the event reflects experienced utility.
Affective forecasting is an important component of studying human decision making. Research in affective forecasts and economic decision making include investigations of durability bias in consumers, predictions of public transit satisfaction, and environmentally friendly decisions. Broadly, the tendencies people have to make biased forecasts deviate from rational models of decision making. One application of affective forecasting research is in economic policy. Knowledge that forecasts, and therefore, decisions, are affected by biases as well as other factors (such as framing effects), can be used to design policies that maximize the utility of people's choices. This approach is not without its critics, however, as it can also be seen to justify economic paternalism.
Prospect theory describes how people make decisions. It differs from expected utility theory in that it takes into account the relativity of how people view utility and incorporates loss aversion, or the tendency to react more strongly to losses rather than gains. Some researchers suggest that loss aversion is in itself an affective forecasting error, since people often overestimate the impact of future losses.
Happiness and well-being
Economic definitions of happiness are tied to concepts of welfare and utility, and researchers are often interested in how to increase levels of happiness in the population. Affective forecasting provides a unique challenge to answering this question, and economists are split between offering more choices to maximize happiness, versus offering experiences that contain more objective or experienced utility. Applying findings from affective forecasting research to happiness also raises methodological issues: should happiness measure the outcome of an experience, or the choice that is satisfied as a result of a forecast? For example, although professors may forecast that getting tenure would significantly increase their happiness, research suggests that in reality, happiness levels between professors who are or are not awarded tenure are insignificant. Affective forecasting conflicts such as this one have also influenced theories of hedonic adaptation, which compares happiness to a treadmill, in that it remains relatively stable despite our forecasts.
Similar to how some economists have drawn attention to how affective forecasting violates assumptions of rationality, legal theorists point out that inaccuracies in these forecasts have implications in law that have remained overlooked. For example, jury awards for tort damages are based on compensating victims for pain, suffering, and loss of quality of life. However, findings in affective forecasting errors have prompted some to suggest that juries are overcompensating victims, since their forecasts overestimate the negative impact of damages on the victims' lives. Some scholars suggest implementing jury education to attenuate potentially inaccurate predictions, drawing upon research that investigates how to decrease inaccurate affective forecasts. In addition to influencing legal discourse on emotions, tort damages, and welfare, Jeremy Blumenthal cites additional implications of affective forecasting in civil jury compensation, contract law, sexual harassment, health law, and capital sentencing. The application of affective forecasting, and its related research, to legal theory reflects a wider effort to address how emotions affect the legal system.
Affective forecasting has implications in health decision making and medical ethics and policy. Research in health-related affective forecasting suggests that nonpatients consistently underestimate the quality of life associated with chronic health conditions and disability. The so-called "disability paradox" states the discrepancy between self-reported levels of happiness amongst chronically ill people versus the predictions of their happiness levels by healthy people. The implications of this forecasting error in medical decision making can be severe, because judgments about future quality of life often inform health decisions. Inaccurate forecasts can lead to patients refusing life-saving treatment in cases when the treatment would involve a drastic change in lifestyle, for example, the amputation of a leg. A patient who falls victim to focalism would fail to take into account all the aspects of their life that would remain the same after losing a limb. Although Halpern and Arnold suggest interventions to foster awareness of forecasting errors and improve medical decision making amongst patients, the lack of direct research in the impact of biases in medical decisions provides a significant challenge.
Research also indicates that affective forecasts about future quality of life are influenced by the forecaster's current state of health. Whereas healthy individuals associate future low health with low quality of life, less healthy individuals do not forecast necessarily low quality of life when imagining having poorer health. Thus, patient forecasts and preferences about their own quality of life may conflict with public notions. Because a primary goal of healthcare is maximizing quality of life, knowledge about patients' forecasts can potentially inform policy on how resources are allocated.
Some doctors suggest that research findings in affective forecasting errors merit medical paternalism. Others argue that although biases exist and should support changes in doctor-patient communication, they do not unilaterally diminish decision-making capacity and should not be used to endorse paternalistic policies. This debate captures the tension between medicine's emphasis on protecting the autonomy of the patient and an approach that favors intervention in order to correct biases.
- George Loewenstein
- Happiness economics
- Hedonic treadmill
- List of cognitive biases
- List of memory biases
- Prospect theory
- Welfare economics
- Wilson, Timothy D.; Daniel T. Gilbert (2003). "Affective Forecasting". Advances in Experimental Social Psychology 35: 345–411. doi:10.1016/S0065-2601(03)01006-2.
- Gilbert, D.T.; E.C. Pinel; T.D. Wilson; S.J. Blumberg; T.P. Wheatley (1998). "Immune neglect: A source of durability bias in affective forecasting". Journal of Personality and Social Psychology 75 (3): 617–638. doi:10.1037/0022-3518.104.22.1687. PMID 9781405.
- Kahneman, Daniel; Richard H. thaler (Winter 2006). "Utility Maximization and Experienced Utility". Journal of Economic Perspectives 20 (1): 221–234. doi:10.1257/089533006776526076. Retrieved 3 March 2012.
- Woodzicka, Julie A.; Marianne LaFrance (Spring 2001). "Real Versus Imagined Gender Harassment". Journal of Social Issues 57 (1): 15–30. doi:10.1111/0022-4537.00199.
- Finkenauer, Catrin; Marcello Gallucci; Wilco W. van Dijk; Monique Pollmann (August 2007). "Investigating the Role of Time in Affective Forecasting: Temporal Influences on Forecasting Accuracy". Personality and Social Psychology Bulletin 33 (8): –. doi:10.1177/0146167207303021.
- Ayton, Peter; Alice Pott; Najat Elwakili (February 2007). "Affective forecasting: Why can't people predict their emotions?". Thinking & Reasoning 13 (1): 62–80. doi:10.1080/13546780600872726.
- Eastwick, Paul W.; Eli J. Finkel; Tamar Krishnamurti; George Loewenstein (May 2008). "Mispredicting distress following romantic breakup: Revealing the time course of the affective forecasting error". Journal of Experimental Social Psychology 44 (3): 800–807. doi:10.1016/j.jesp.2007.07.001.
- Liberman, Nira; Michael D. Sagristano; Yaacov Trope (2002). "The effect of temporal distance on level on mental construal". Journal of Experimental Social Psychology 38 (6): 523–534. doi:10.1016/S0022-1031(02)00535-8.
- Hsee, Christopher K; Reid Hastie; Jingqiu Chen (May 2008). "Hedonomics: Bridging decision research with happiness research". Perspectives on Psychological Science 3 (3): 224–243. doi:10.1111/j.1745-6924.2008.00076.x.
- Tella, R. D.; R. MacCulloch (2006). "Some uses of happiness data in economics". The Journal of Economic Perspectives 20 (1): 25–46. doi:10.1257/089533006776526111.
- Halpern, Jodi; Robert M. Arnold (October 2008). "Affective Forecasting: An Unrecognized Challenge in Making Serious Health Decisions". Journal of General Internal Medicine 23 (10): 1708–1712. doi:10.1007/s11606-008-0719-5. PMC 2533375. PMID 18665428.
- Kahneman, Daniel; Amos Tversky (27 September 1974). "Judgment under Uncertainty: Heuristics and Biases". Science 185 (4157): 1124–1131. doi:10.1126/science.185.4157.1124. PMID 17835457.
- Hausman, Daniel M. (2010). "Hedonism and Welfare Economics". Economics and Philosophy 26 (3): 321–344. doi:10.1017/S0266267110000398.
- Swedloff, Rick; Peter H. Huang (Spring 2010). "Tort Damages and the New Science of Happiness". Indiana Law Journal 85 (2): 553–597.
- Wood, Stacy L.; James R. Bettman (2007). "Predicting Happiness: How Normative Feeling Rules Influence (and Even Reverse) Durability Bias". Journal of Consumer Psychology 17 (3): 188–201. doi:10.1016/S1057-7408(07)70028-1.
- Levine, Linda J.; Lench, Heather C.; Kaplan, Robin L.; Safer, Martin A. (1 January 2012). "Accuracy and artifact: Reexamining the intensity bias in affective forecasting.". Journal of Personality and Social Psychology 103 (4): 584–605. doi:10.1037/a0029544.
- Buehler, Roger; Cathy McFarland (November 2001). "Intensity Bias in Affective Forecasting: The Role of Temporal Focus". Personality and Social Psychology Bulletin 27 (11): 1480–1493. doi:10.1177/01461672012711009.
- Hoerger, Michael; Stuart W. Quirk; Richard E. Lucas; Thomas H. Carr (August 2010). "Cognitive determinants of affective forecasting errors". Judgment and Decision Making 5 (5): 365–373. PMC 3170528. PMID 21912580.
- Gilbert, Daniel T.; Pinel, Elizabeth C.; Wilson, Timothy D.; Blumberg, Stephen J.; Wheatley, Thalia P. (1 January 1998). "Immune neglect: A source of durability bias in affective forecasting.". Journal of Personality and Social Psychology 75 (3): 617–638. doi:10.1037/0022-3522.214.171.1247. PMID 9781405.
- Wilson, Timothy D.; Daniel T. Gilber (June 2005). "Affective Forecasting: Knowing What to Want". Current Directions in Psychological Science 14 (3): 131–134. doi:10.1111/j.0963-7214.2005.00355.x.
- Wilson, Timothy D.; Wheatley, Thalia; Meyers, Jonathan M.; Gilbert, Daniel T.; Axsom, Danny (1 January 2000). "Focalism: A source of durability bias in affective forecasting.". Journal of Personality and Social Psychology 78 (5): 821–836. doi:10.1037/0022-35126.96.36.1991. PMID 10821192.
- Wilson, Timothy D.; Thalia Wheatley; Jonathan M. Meyers; Daniel T. Gilbert; Danny Axsom (May 2000). "Focalism: A source of durability bias in affective forecasting". Journal of Personality and Social Psychology 78 (5): 821–836. doi:10.1037/0022-35188.8.131.521. PMID 10821192.
- Laurie E. Wasko; Cynthia L. S. Pury (2009). Shane J. Lopez, ed. The Encyclopedia of Positive Psychology. West Sussex: Blackwell Publishing. p. 24.
- Schkade, David A.; Daniel Kahneman (1998). "Does Living in California Make People Happy? A Focusing Illusion in Judgments of Life Satisfaction". Psychological Science 9 (5): 340–346. doi:10.1111/1467-9280.00066.
- Hoerger, Michael; Stuart W. Quirk; Richard E. Lucas; Thomas H. Carr (February 2009). "Immune neglect in affective forecasting". Journal of Research in Personality 43 (1): 91–94. doi:10.1016/j.jrp.2008.10.001.
- Bolger, N.; Zuckerman A. (1995). "A Framework for Studying Personality in the Stress Process". Journal of Personality and Social Psychology 69: 890–902. doi:10.1037/0022-35184.108.40.2060.
- Hoerger, M.; Quirk S.W.; Lucas R.E.; Carr T.H. (2009). "Immune Neglect in Affective Forecasting". Journal of Research in Personality 43: 91–94. doi:10.1016/j.jrp.2008.10.001.
- Hoerger, M. (2012). "Coping strategies and Immune Neglect in Affective Forecasting: Direct Evidence and Key Moderators". Judgement and Decision Making 7 (1): 86–96.
- Daniel T. Gilbert, Matthew D. Lieberman, Carey K. Morewedge and Timothy D. Wilson. The Peculiar Longevity of Things Not So Bad. Psychological Science January 2004 vol. 15 no. 1 14-19
- Lowenstein, George; Ted O'donoghue; Matthew Rabin (November 2003). "Projection Bias in Predicting Future Utility". The Quarterly Journal of Economics 118 (4): 1209–1248. doi:10.1162/003355303322552784.
- Loewenstein, G; D. Prelec; C. Shatto. "Hot/cold intrapersonal empathy gaps and the under-prediction of curiosity". Unpublished manuscript, Carnegie Mellon University, Pittsburgh, PA.
- Sayette, Michael A.; George Loewenstein; Kasey M. Griffin; Jessica J. Black (Sep 2008). "Exploring the cold-to-hot empathy gap in smokers". Psychological Science 19 (9): 926–932. doi:10.1111/j.1467-9280.2008.02178.x. PMC 2630055. PMID 18947359.
- Wesp, Richard; Joshua Sandry; Anthony Prisco; Pamela Sarte (2009). "Affective forecasts of future positive events are tempered by consideration of details". American Journal of Psychology 122 (2): 167–174. PMID 19507424.
- Read, Daniel; Read, N.L. "Time discounting over the lifespan". Organizational Behavior and Human Decision Processes 94 (1): 22–32. doi:10.1016/j.obhdp.2004.01.002.
- Kassam, Karim S.; Gilbert, Daniel T.; Boston, Andrew; Wilson, Timothy D. (1 November 2008). "Future anhedonia and time discounting". Journal of Experimental Social Psychology 44 (6): 1533–1537. doi:10.1016/j.jesp.2008.07.008.
- Zauberman, Gal; Kim, B. Kyu; Malkoc, Selin A; Bettman, James R (1 August 2009). "Discounting Time and Time Discounting: Subjective Time Perception and Intertemporal Preferences". Journal of Marketing Research 46 (4): 543–556. doi:10.1509/jmkr.46.4.543.
- Kassam, Karim S.; Daniel T. Gilbert; Andrew Boston; Timothy D. Wilson (November 2008). "Future anhedonia and time discounting". Journal of Experimental Social Psychology 44 (6): 1533–1537. doi:10.1016/j.jesp.2008.07.008.
- Frederick, Shane; George Loewenstein,; Ted O'Donoghue (June 2002). "Time Discounting and Time Preference: A Critical Review". Journal of Economic Literature XL: 351–401. doi:10.1257/jel.40.2.351.
- Wilson, Timothy D.; Douglas J. Lisle; Dolores Kraft; Christopher G. Wetzel (April 1989). "Preferences as expectation-driven inferences: Effects of affective expectations on affective experience". Journal of Personality and Social Psychology 56 (4): 519–530. doi:10.1037/0022-35220.127.116.119. PMID 2709307.
- Geers, Andrew L.; G. Daniel Lassiter (July 1999). "Affective Expectations and Information Gain: Evidence for Assimilation and Contrast Effects in Affective Experience". Journal of Experimental Social Psychology 35 (4): 394–413. doi:10.1006/jesp.1999.1377.
- Morewedge,, Carey K; Daniel T. Gilbert; Timothy D. Wilson (August 2005). "The Least Likely of Times: How Remembering the Past Biases Forecasts of the Future". Psychological Science 16 (8): 626–630. doi:10.1111/j.1467-9280.2005.01585.x. PMID 16102065.
- Meyvis, T.; Ratner R.K.; Levav J.M. (2010). "Why Don't We Learn to Accurately Forecast Feelings? How Misremembering Our Predictions Blinds Us to Forecasting Errors". Journal of Experimental Psychology: General 139 (4): 579–589. doi:10.1037/a0020285.
- T. D. Wilson; D.T. Gilbert; D.B. Centerbar (2002). J. Carillo & I. Brocas, ed. Economics and psychology. Oxford: Oxford UP. pp. 209–233.
- Walker, W. Richard; John J. Skowronski; Charles P. Thompson (2003). "Life Is Pleasant—and Memory Helps to Keep It That Way!". Review of General Psychology (Educational Publishing Foundation) 7 (2): 203–210. doi:10.1037/1089-2618.104.22.168. Retrieved 2009-08-27.
- Gilbert, D.T.; Blumberg, S.J.; Pinel, E.C.; Wilson, T.D.; Wheatley, T.P. (1998). "Immune neglect: A source of durability bias in affective forecasting". Journal of Personality and Social Psychology 75 (3): 617–638. doi:10.1037/0022-3522.214.171.1247. PMID 9781405.
- Gertner, Jon (September 7, 2003). "The Futile Pursuit of Happiness". New York Times. Retrieved 2009-08-29. "Gilbert says. "We've used the metaphor of the 'psychological immune system' -- it's just a metaphor, but not a bad one for that system of defenses that helps you feel better when bad things happen.""[dead link]
- Kay, Aaron C.; Maria C. Jimenez; Joim T. Jost (2002). "Sour Grapes, Sweet Lemons, and the Anticipatory Rationalization of the Status Quo". Personality and Social Psychology Bulletin (Society for Personality and Social Psychology) 28 (9): 1300–1312. doi:10.1177/01461672022812014.
- Wilson, T.D.; Wheatley, T.; Meyers, J. M.; Gilbert, D. T.; Axsom, D (2000). "Focalism: A source of durability bias in affective forecasting". Journal of Personality and Social Psychology 78 (5): 821–836. doi:10.1037/0022-35126.96.36.1991. PMID 10821192.
- Emanuel, Amber S.; John A. Updegraff; David A. Kalmbach; Jeffrey A. Cisela (Jul 2010). "The role of mindfulness facets in affective forecasting". Personality and Individual Differences 49 (7): 815. doi:10.1016/j.paid.2010.06.012.
- Hoerger, M. (1 January 2012). "Coping Strategies and Immune Neglect in Affective Forecasting: Direct Evidence and Key Moderators". Judgement and Decision Making 7 (1): 86–96.
- Meyvis, Tom; Ratner R.K.; Levav J. (2010). "Why Don’t We Learn to Accurately Forecast Feelings? How Misremembering Our Predictions Blinds Us to Past Forecasting Errors". Journal of Experimental Psychology: General 139 (4): 579–589. doi:10.1037/a0020285.
- Hoerger, Michael; Stuart W. Quirk (December 2010). "Affective forecasting and the Big Five". Personality and Individual Differences 49 (8): 972–976. doi:10.1016/j.paid.2010.08.007. PMC 3183582. PMID 22021944.
- Tomlinson, J. M.; C. L. Carmichael; H. T. Reis; A. Aron (2010). "Affective forecasting and individual differences: Accuracy for relational events and anxious attachment". Emotion 10 (3): 447–453. doi:10.1037/a0018701. PMID 20515233.
- Dunn, Elizabeth W; Marc A Brackett; Claire Ashton-James; Elyse Schneiderman; Peter Salovey (Jun 2007). "On Emotionally Intelligent Time Travel: Individual Differences in Affective Forecasting Ability". Personality and Social Psychology Bulletin 33 (1): 85–93. doi:10.1177/0146167206294201. PMID 17178932.
- Lam, Kent C.H.; Roger Buehler; Cathy McFarland; Michael Ross; Irene Cheung (Sep 2005). "Cultural Differences in Affective Forecasting: The Role of Focalism". Personality and Social Psychology Bulletin 31 (9): 1296–1309. doi:10.1177/0146167205274691. PMID 16055648.
- Hartnett, Jessica Lynn (2009). More affect, less forecast: How mood contributes to affective forecasting. Northern Illionois University. ISBN 978-1-109-18415-0.
- Hoover, Gina (2011). "A Motivational Account of the Impact Bias". The Ohio State University. Retrieved 9 March 2012.
- DellaVigna, Stefano (2009). "Psychology and Economics: Evidence from the Field". Journal of Economic Literature 47 (2): 315–372. doi:10.1257/jel.47.2.315.
- Welsch, Heinz (15 September 2009). "Implications of happiness research for environmental economics". Ecological Economics 68 (11): 2735–2742. doi:10.1016/j.ecolecon.2009.06.003.
- Pedersen, Tore (2009). Affective Forecasting: Predicting Future Satisfaction with Public Transport. Karlstad: Karlstad University. ISBN 978-91-7063-273-0.
- Kahneman, Daniel; Amos Tversky (Mar 1979). "Prospect Theory: An analysis of decision under risk". Econometrica 47 (2): 263–292. doi:10.2307/1914185.
- Kermer, Deborah A.; Erin Driver-Linn; Timothy D. Wilson; Daniel T. Gilbert (2006). "Loss Aversion Is an Affective Forecasting Error". Psychological Science 17 (8): 649–653. doi:10.1111/j.1467-9280.2006.01760.x. PMID 16913944.
- Maroney, Terry A. (April 2006). "Law and Emotion: A Proposed Taxonomy of an Emerging Field". Law and Human Behavior 30 (2): 119–142. doi:10.1007/s10979-006-9029-9. PMID 16786403.
- Blumenthal, Jeremy A. (2005). "Law and the Emotions: The Problems of Affective Forecasting". Indiana Law Journal 80: 155–238. doi:10.2139/ssrn.497842. Retrieved 3 March 2012.
- Blumenthal, Jeremy (2009). "Affective forecasting and capital sentencing: reducing the effect of victim impact statements". American Criminal Law Review 46 (1): 107+.
- Winter, Laraine; Miriam S. Moss; Christine Hoffman (April 2009). "Affective Forecasting and Advance Care PlanningAffective Forecasting and Advance Care Planning". Journal of Health Psychology 14 (3): 447–456. doi:10.1177/1359105309102201. PMID 19293306. Retrieved 3 March 2012.
- Loewenstein, George (2005). "Projection bias in medical decision making". Med Decis Making 25 (1): 96–105. doi:10.1177/0272989X04273799. PMID 15673587.
- Rhodes, Rosamond; James J. Strain (2008). "Affective Forecasting and Its Implications for Medical Ethics". Cambridge Quarterly of Healthcare Ethics 17 (1): 54–65. doi:10.1017/S0963180108080067. PMID 18462545.
- Gligorov, Nada (2009). "Reconsidering the Impact of Affective Forecasting". Cambridge Quarterly of Healthcare Ethics 18 (2): 166–173. doi:10.1017/S0963180109090276. PMID 19263600.
- Loewenstein, G; PA Ubel; N Schwarz; D Smith (July 2004). "Misimagining the unimaginable: the disability paradox and health care decision making". Health Psychology 24 (4): 57–62. doi:10.1037/0278-6133.24.4.s57.
- Fiske, Susan T. (2004). Social Beings: A Core Motives Approach to Social Psychology. Wiley. ISBN 978-0-471-45151-8.
- Gilbert, Daniel T. (2006). Stumbling on happiness. Alfred A. Knopf. ISBN 978-1-4000-7742-7.
- Sanna, Lawrence J.; Schwarz, Norbert (2004). "Integrating Temporal Biases: The Interplay of Focal Thoughts and Accessibility Experiences". Psychological Science (American Psychological Society) 15 (7): 474–481. doi:10.1111/j.0956-7976.2004.00704.x. PMID 15200632.
- Wilson, Timothy D. (2002). Strangers to Ourselves: Discovering the Adaptive Unconscious. Belknap Press of Harvard University Press. ISBN 0-674-01382-4.
- Hsee, Christopher K.; Hastie, Reid (2006). "Decision and experience: why don't we choose what makes us happy?". Trends in Cognitive Sciences 10 (1): 31–37. doi:10.1016/j.tics.2005.11.007. PMID 16318925.