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Sleeper Effect

Sleeper effect definition.

A sleeper effect in persuasion is a delayed increase in the impact of a persuasive message. In other words, a sleeper effect occurs when a communication shows no immediate persuasive effects, but, after some time, the recipient of the communication becomes more favorable toward the position advocated by the message. As a pattern of data, the sleeper effect is opposite to the typical finding that induced opinion change dissipates over time.

Sleeper Effect Discovery and Original Interpretation

After the war, Hovland returned to his professorship at Yale University and conducted experiments on the sleeper effect to determine its underlying causes. According to Hovland, a sleeper effect occurs as a result of what he called the dissociation discounting cue hypothesis—in other words, a sleeper effect occurs when a persuasive message is presented with a discounting cue (such as a low-credible source or a counterargument). Just after receiving the message, the recipient recalls both message and discounting cue, resulting in little or no opinion change. After a delay, as the association between message and discounting cue weakens, the recipient may remember what was said without thinking about who said it.

History of the Sleeper Effect Research

The Hovland research gave the sleeper effect scientific status as a replicable phenomenon and the dissociation discounting cue hypothesis credibility as the explanation for this phenomenon. As a result, the sleeper effect was discussed in almost every social psychology textbook of the 1950s and 1960s, appeared in related literatures (such as marketing, communications, public opinion, and sociology), and even obtained some popular notoriety as a lay idiom.

However, as the sleeper effect gained in notoriety, researchers found that it was difficult if not impossible to obtain and replicate the original Hovland findings. For example, Paulette Gillig and Tony Greenwald published a series of seven experiments that paired a persuasive message with a discounting cue. They were unable to find a sleeper effect. They were not the only ones unable to find a sleeper effect, prompting the question “Is it time to lay the sleeper effect to rest?”

The Differential Decay Hypothesis

Two sets of researchers working independently of each other were able to find reliable empirical conditions for producing a sleeper effect. In two sets of experiments conducted by Charles Gruder, Thomas Cook, and their colleagues and by Anthony Pratkanis, Greenwald, and their colleagues, reliable sleeper effects were obtained when (a) message recipients were induced to pay attention to message content by noting the important arguments in the message, (b) the discounting cue came after the message, and (c) message recipients rated the credibility of the message source immediately after receiving the message and cue. For example, in one experiment, participants underlined the important arguments as they read a persuasive message. After reading the message, subjects received a discounting cue stating that the message was false and then rated the trustworthiness of the message source. This set of procedures resulted in a sleeper effect.

The procedures developed by these researchers are sufficiently different from those of earlier studies to warrant a new interpretation of the sleeper effect. As a replacement for the dissociation hypothesis, a differential decay interpretation was proposed that hypothesized a sleeper effect occurs when (a) the impact of the message decays more slowly than the impact of the discounting cue and (b) the information from the message and from the discounting cue is not immediately integrated to form an attitude (and thus the discounting cue is already dissociated from message content).

The procedures associated with a reliable sleeper effect and the differential decay hypothesis do not often occur in the real world. However, one case in which these conditions are met is when an advertisement makes a claim that is subsequently qualified or modified in a disclaimer (often given in small print and after the original message). In such cases, the disclaimer may not be well integrated with the original claim and thus its impact will decay quickly, resulting in the potential for a sleeper effect.

Other Sleeper Effects

Although much of the research on the sleeper effect has focused on the discounting cue manipulation, researchers have developed other procedures for producing sleeper effects including (a) delayed reaction to a fear-arousing message, (b) delayed insight into the implications of a message, (c) leveling and sharpening of a persuasive message over time, (d) dissipation of the effects of forewarning of persuasive intent, (e) group discussion of a message after a delay, (f) the dissipation of reactance induced by a message, (g) delayed internalization of the values of a message, (h) wearing-off of initial annoyance with a negative or tedious message, (i) delayed acceptance of an ego-attacking message, and (j) delayed impact of minority influence. Although these other procedures for obtaining a sleeper effect have been less well researched, they may indeed be more common in everyday life than are sleeper effects based on the differential decay hypothesis.

References:

• Gillig, P. M., & Greenwald, A. G. (1974). Is it time to lay the sleeper effect to rest? Journal of Personality and Social Psychology, 29, 132-139.
• Gruder, C. L., Cook, T. D., Hennigan, K. M., Flay, B. R., Alessis, C., & Halamaj, J. (1978). Empirical tests of the absolute sleeper effect predicted from the discounting cue hypothesis. Journal of Personality and Social Psychology, 36, 1061-1074.
• Hovland, C. I., Janis, I. L., & Kelley, H. H. (1953). Communication and persuasion. New Haven, CT: Yale University Press.
• Pratkanis, A. R., Greenwald, A. G., Leippe, M. R., & Baumgardner, M. H. (1988). In search of reliable persuasion effects: III. The sleeper effect is dead. Long live the sleeper effect. Journal of Personality and Social Psychology, 54, 203-218.

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Sleeper effect.

 “Sleeper effect” describes a phenomenon in which messages from sources with originally low credibility cause opinion change over time. The credibility of a source as perceived by receivers of its message constitutes a central issue in the theory of persuasion, in particular with regard to its impact on attitude change. A highly credible communicator (e.g., by virtue of trust, expertise, or reliability) commands an increased probability that the receivers of a message will accept and absorb the persuasive intent of the communication. However, the effect of credibility varies during the course of persuasive effects over time. In general, the impact of a persuasive message peaks immediately after exposition and declines over time. The sleeper effect describes a contrary phenomenon for messages from low-credibility sources. Here, the immediate effect is overruled by the long-term effect: The sleeper effect is thus defined as the absolute increase in attitude change over time for receivers of a low credibility message (Hovland et al. 1949).

Increase in agreement for low-credibility communication in the long term might be due to the diminishing of initial skepticism over time. Generally, arguments and other content supporting a communicator’s conclusion are subject to being forgotten over time. For a credible communicator, receivers of a message will show a fairly high agreement immediately after exposure, but this will gradually decline over time. For a non-credible communicator, on the other hand, skepticism or antipathy will lead the receivers of the message to show little initial agreement with the communicator’s position. Over time, however, the effects of low-credibility communication take a different course: “If then the discounted source is forgotten more quickly than the content (or ‘dissociated’ from the content) agreement with the recommended opinion should increase with time” (Hovland et al. 1953, 254).

Hovland & Weiss (1951) tested the sleeper effect in an experiment by using identical communications for four different topics. These stimuli were then presented to half each of the test persons by sources considered trustworthy and untrustworthy respectively. Opinion questionnaires were presented to the subjects before the communication, immediately after the communication, and four weeks afterward. The results show that initially the communications presented by the untrustworthy source were “discounted” by the audience and had less effect on opinion than those presented by the trustworthy sources. With the passing of time, the initial differences attributable to the source disappeared. After a period of four weeks, the amount of opinion change retained from the two sources was approximately equal: “Thus there was a forgetting effect when the presentation was by a trustworthy communicator and a sleeper effect when the communication was presented by a negative communicator” (Hovland et al. 1953, 255). Hovland and Weiss’s results indicate that there is a decreased tendency over time to reject the communication by an untrustworthy communicator. “This may or may not require that the individual must be less likely with the passage of time to associate spontaneously the content with the source” (Hovland & Weiss 1953, 648). Some evidence for this was provided by Kelman and Hovland (1953), who reinstated the source at the time of the delayed opinion test by repeating the introduction of the communicator before passing out the questionnaire. As soon as the source was reactivated in the recipient’s memory by the playback, the original effects were reproduced: the highly credible source yielded a greater impact on attitude change than the less credible source.

Even today, the sleeper effect remains controversial . From a methodological point of view, the experimental design has been criticized (Capon & Hulbert 1973; Gillig & Greenwald 1974), since the original studies included no control groups, which – given that measurement took place at three points in time – would have been necessary to control for a measurement contamination. Still, Praktkanis et al. (1988) found supporting evidence for the sleeper effect given the following presuppositions: subjects have to (1) be aware of the central arguments of a message, (2) receive the disrating stimulus (i.e., the low trustworthiness of a source of opposing arguments) after the reception of the message, and (3) evaluate the credibility of the communicator after having received the disrating stimulus. Such a procedure facilitates the message and the disrating stimulus being central to the effects of the communication. Here, the sleeper effect occurs only if the effect of the disrating stimulus fades sooner than the effect of the message. This interpretation of a different course of effects depending on source credibility opposes the dissociation hypothesis elaborated in the original and in subsequent studies. A further explanation is provided on the basis of the effect of forgetting (forgetting model). Here, immediately after reception, the message of a trustworthy source is assumed to be more readily remembered than that of an untrustworthy source. Over time, as both kinds of messages are forgotten, the difference in the persuasive effects of messages presented by high- and low-credibility sources diminishes. Differences in the effects of high- and low credibility sources can be observed only temporarily.

Given the findings of the studies discussed, the sleeper effect has to be summarized as a relative effect . Over time, the advantage of high-credibility sources diminishes. The sleeper effect suggests only a temporary impact of credibility. Highly credible message sources do indeed create additional attitude change, but this is leveled out over time as either (1) dissociation or (2) forgetting occur. In the long term, a convergence of the effects of the low- and the high-credibility sources is found.

References:

• Allen, M., & Stiff, J. (1989). Testing three models for the sleeper effect. Western Journal of Speech Communication , 53, 411–426.
• Capon, N., & Hulbert, J. (1973). The sleeper effect – an awakening. Public Opinion Quarterly , 37, 33–358.
• Gillig, P., & Greenwald, A. (1974). Is it time to leave the sleeper effect to rest? Journal of Personality and Social Psychology , 29, 132–139.
• Gruder, C., Cook, T., Hennigan, F., Flay, B., Alessis, C., & Halamaj, J. (1978). Empirical tests of the absolute sleeper effect predicted from the discounting cue hypothesis. Journal of Personality and Social Psychology , 36, 1061–1074.
• Hovland, C. I., & Weiss, W. (1951). The influence of source credibility on communication effectiveness. Public Opinion Quarterly , 15, 635–650.
• Hovland, C. I., Lumsdaine, A. A., & Sheffield, F. D. (1949). Experiments on mass communication . Princeton, NJ: Princeton University Press.
• Hovland, C. I., Janis, J. L., & Kelley, H. H. (1953). Communication and persuasion . New Haven, CT: Yale University Press.
• Kelman, H., & Hovland, C. I. (1953). “Reinstatement” of the communicator in delayed measurement of opinion change. Journal of Abnormal and Social Psychology , 48, 327–335.
• Praktkanis, A., Greenwald, A., Leippe, M., & Baumgardner, M. (1988). In search of reliable persuasion effects: III. The sleeper effect is dead, long live the sleeper effect. Journal of Personality and Social Psychology , 54, 203–218.
• Weiss, W. (1953). A “sleeper” effect in opinion change. Journal of Abnormal and Social Psychology , 48, 173–180.

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59 Theories and Effects of Political Humor: Discounting Cues, Gateways, and the Impact of Incongruities

Dannagal Goldthwaite Young (Ph.D., University of Pennsylvania, 2007) is an Associate Professor of Communication at the University of Delaware where she studies the content, audience, and effects of political humor. Her research on media effects and the cognitive implications political humor has appeared in numerous books and journals including Media Psychology, Political Communication, Popular Communication, International Journal of Press/Politics, Journal of Broadcasting and Electronic Media, and Mass Media and Society. In 2011, she launched Breaking Boundaries, a website and symposium dedicated to the interdisciplinary study of entertainment and politics, funded by the University of Delaware’s Center for Political Communication. Young is also an improvisational comedian, performing regularly with the improv comedy troupe, ComedySportz Philadelphia, since 1999.

• Published: 02 September 2014
• This version: January 2018

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As both an art form and a mode of persuasive discourse, the use of political humor dates back to ancient Greece and Rome. For centuries politicians, citizens, and elites have marveled at and even feared its powerful—and magical—influence on public opinion. By reflecting on various approaches to the study of political humor’s content, audience, and impact, this chapter offers scholars multiple ways to consider the effects of political humor on individuals and society. It culminates with a consideration of the latest advances in the study of political humor and humor theory and poses challenges to those in the field to better explicate micro-level processes that incorporate structural elements of the text and characteristics of the audience.

As both an art form and a mode of persuasive discourse, political humor dates back to ancient Greece and Rome. For centuries politicians, citizens, and elites have marveled at and feared its powerful—and magical—influence on public opinion ( Caufield, 2007 ; Test, 1991 ). Writing almost four hundred years bc , the Athenian playwright Aristophanes, “the comic genius of political criticism” ( Schutz, 1977 , 10), explored themes of status, power, and war, all within the frame of a play that rendered his satire both humorous and incendiary. Socrates, the great ironic “sage satyr” ( Schutz, 1977 , 79), offered subtle critiques of Athenian society through the voice of a playful clown. In spite of this rich history, scholars have only just begun to quantify the impact of political humor on attitudes, cognitions, and behaviors.

Scholars from linguistics, psychology, and sociology have developed theories to account for humor’s role in society and impact on the audience. The core empirical work on the impact of political humor has emerged over the last decade from the disciplines of communication, political science, and psychology. In the late 1990s, as political candidates appeared on entertainment programs and talk shows, media effects scholars began studying the impact of nontraditional forms of political information on the audience. While first included under such umbrella terms as “soft news” ( Baum, 2003 ) or “talk shows” ( Davis and Owen, 1998 ), political humor soon became a dedicated area of media effects research.

The rise in scholarly attention to political humor can be attributed to the increasing prevalence of hybrid forms of political information over the past twenty years. This includes the frequency of political themes in the monologues of late-night comedians like David Letterman and Jay Leno throughout the 1990s; the emergence of dedicated political satire programs on the cable network Comedy Central ( The Daily Show with Jon Stewart, now Trevor Noah, which launched in 1999; the Colbert Report with Stephen Colbert; and The Nightly Show with Larry Wilmore ); and alternative entertainment political formats like Politically Incorrect with Bill Maher (first introduced on Comedy Central in 1993) or Last Week Tonight with John Oliver on HBO, as well as the rise of online political humor sites such as Funny or Die (launched 2006) or the Onion News Network (launched 2007). As chronicled by Geoff Baym (2009a) , these hybrid forms of political content have their roots in the deregulation of the media industry in the 1980s and the simultaneous rise in digital technologies in the 1990s. Together, these changes fostered a media environment in which the once-formal distinction between news and entertainment disappeared (see also Williams and Delli Carpini, 2002 ). As news programs had to compete with entertainment shows for ratings, news executives increasingly adopted entertainment production norms. Meanwhile, the rise in digital technologies meant that media conglomerates could efficiently repurpose content across their many outlets and platforms (see Jenkins, 2006 ). Hence, entertainment creators increasingly experimented with political themes—leading to the late 1990s influx of hybrid political entertainment genres.

The Content of Political Humor

When scholars discuss the content of contemporary political humor, they are usually referring to a set of texts ranging from the political jokes of late-night comedians like Stephen Colbert, Jimmy Fallon, and Jimmy Kimmel, to online political parodies, to the playful cultural critiques on the animated series The Simpsons, to longer ironic or satirical segments from the likes of Jon Stewart, Trevor Noah, and John Oliver. Early studies of the content of late-night comedy monologues suggested that late-night political jokes tended to focus on the executive branch and were almost “devoid’ of policy content, focusing instead on personalities and weaknesses of individual politicians ( Niven, Lichter, and Amundson, 2003 ). Recent research on the content of televised political humor complicates these initial observations. The themes included in the content of The Daily Show , for example, are often more issue-oriented than those of Leno or Letterman ( National Annenberg Election Survey, 2004 ). In fact, scholars have found comparable treatment of substantive issues across the content of The Daily Show and network news broadcasts during the same time period ( Fox, Koloen, and Sahin, 2007 ).

These competing findings reflect an evolving political media landscape, but also an imperative need to understand what we’re talking about when we say “political humor.”

“Political humor” is an umbrella term that encompasses any humorous text dealing with political issues, people, events, processes, or institutions. Within that broad category, political satire occupies a specific role. According to humor scholar George Test (1991) , political satire is playful and is designed to elicit laughter, while simultaneously casting judgment. It is this function of “casting judgment” that separates satire from broader notions of political humor. Jokes and texts that treat political topics in a lighthearted manner but offer no criticism of institutions, policies, or societal norms do not constitute satire. Rather, satire questions the existing political or social order, usually by juxtaposing the existing imperfect reality with visions of what could or should be. So, while satire can be biting and even aggressive in tenor, the underlying premise of a satirical text is often optimistic, as it suggests we (collectively) deserve better. In the words of Bloom and Bloom (1979) , “The satirist who goes about his task skillfully gives the reader a double reward: the pleasure of an aesthetic experience coupled with the reasonable hope that a stable political order may be attainable” (1979, 38).

Parody, a subcategory of humor that often overlaps with satire, relies on the audience’s prior knowledge of an original text or concept by exaggerating its most familiar aspects ( Gray, 2005 ; Gray, Jones, and Thompson, 2009 ). Caricatures, or visual exaggerations of a known person’s most identifiable characteristics, are an example of parody. Other examples include impersonations of political figures as well as programs and texts that exaggeratedly (or ironically) mimic a political concept, event, or genre. The Colbert Report with Stephen Colbert, for instance, constitutes parody, as the structure of his mock cable -news program and his very persona are based on Bill O’Reilly’s No Spin Zone on Fox News (see Baym, 2009a ). While parodies are not always satirical, they can be. Friendly political impersonations, such as those of Rich Little in the 1970s and 1980s, offer physical and verbal exaggerations without casting judgment. Other parodies, such as Saturday Night Live comedian Tina Fey’s impersonation of vice-presidential candidate Sarah Palin, constitute political satire. Fey’s Palin impersonation not only exaggerated the Alaskan governor’s folksy accent and winking appearance, but also criticized her conservative issue positions with statements such as, “I think that Global warming is … just God huggin’ us closer” ( Tina Fey and Amy Poehler, 2008 ).

In addition to satire and parody, it is important to consider the role played by irony in a political context. Irony is present when a text exposes a gap between what is stated and what is meant. Bergson notes: “Sometimes we state what ought to be done, and pretend to believe that this is just what is actually being done; then we have irony” (1921, 127). Irony is a common rhetorical tool of the satirist. Just as satirical texts present critiques of society’s ills through a humorous lens, irony offers a useful mechanism to playfully expose the gap between the way things are and the way things should be. Jonathan Swift’s (1729)   A Modest Proposal , for example, proposes a detailed plan to remedy the economic and social problems of Ireland by feeding poor malnourished children to Ireland’s upper class. The text is both ironic , as Swift certainly does not mean what he says, and satirical , as the act of comprehending the text requires the reader to question the dispassionate rational perspective underlying his economic argument. Similarly, The Colbert Report is a complex example of satirical irony (see Lamarre, Landreville, and Beam, 2009 ). Colbert’s character rails against liberal policies under the guise of an ill-informed right-wing pundit, who doesn’t let facts get in the way of “truth.” This playful inversion of reality (the real Colbert believes the opposite of what he states on the show) forces the audience to see the conservative arguments made by his character as shortsighted and ill-informed at best, or hypocritical and malevolent at worst.

Several additional approaches to the categorization of political humor have helped make sense of this rich body of content. The Roman satirists Horatio and Juvenal codified two broad subgenres of political satire: Horatian satire was lighthearted and playful, and Juvenalian satire articulated outrage and pessimism about the evils of society through sarcasm and irony. These categories continue to inform how political communication researchers think about political humor’s content and impact ( Holbert et al., 2011 ). Integrating a more generalizable vocabulary into the study of political humor, Paletz’s (1990) typology considers it as a function of four elements: target, focus, acceptability, and presentation. Together these dimensions determine how a humorous political text ranks on a spectrum, from “supportive” of the existing political order to “subversive.”

The Audience of Contemporary Political Humor

Much of the interest in political humor as a source of political influence stems from its perceived accessibility to broad audiences. During the past decade several reports from the Pew Center for the People and the Press concluded that young people, more so than older people, were increasingly reporting learning about politics from comedy shows ( Pew, 2004 ). At the same time, young people were reporting lower rates of learning from traditional news programming. Yet the contention that young people are abandoning traditional news in favor of comedy programming is not supported by existing research ( Young and Tisinger, 2006 ). Youthful late-night comedy viewers are more likely to be consuming news on cable networks, on the radio, and online than their non-comedy-viewing counterparts. Cross-sectional studies also contradict the assumption of the “politically disengaged” audience, as late-night comedy viewers, particularly those of the Daily Show , are more politically knowledgeable, more participatory, and more attentive to politics than non-late-night viewers ( Brewer and Cao, 2006 ; Brewer, Young, and Morreale, 2013 ; Cao, 2010 ; Cao and Brewer, 2008 ; Young and Tisinger, 2006 ).

Humor Theory

For centuries, philosophers, psychologists, and sociologists have attempted to untangle the mystery that is “humor.” Why do people enjoy humor? How does humor work? In addressing these questions, scholars have pursued several broad theoretical perspectives. Superiority theory, the roots of which are in the writings of Hobbes (1650) , proposes that humor capitalizes on the “sudden glory” of realizing that we may be superior to someone else. Release or tension theories in humor research are an extension of concepts from Freudian psychology. Here humor is conceptualized as a “safety valve” that expels excess energy or passions that might otherwise transform into sexual or aggressive energy (see Raskin, 1985 , for a review). Finally, the class of humor theories most often integrated into cognitive models of media effects is “incongruity theory.” While incongruity theory has been elaborated upon by Koestler (1964) and Suls (1972) , among others, the approach is often attributed to Kant’s observation that “laughter is an affection arising from the sudden transformation of a strained expectation into nothing” (2007, 133). This notion of unmet expectations has been adapted by cognitive scholars who see humor as the intersection of two incompatible schemas in memory.

Perhaps because of its compatibility with concepts such as mental models, schemas, and associative networks in memory, much of the recent empirical work on the cognitive impact of political humor has been theoretically grounded in incongruity theory ( Nabi, Moyer-Guse, and Byrne, 2007 ; Young, 2008 ). Incongruity theory assumes that a humorous text begins with one apparent or conventional script: an initial story or set of predictable constructs ( Raskin, 1985 ). Side by side with the conventional script is one that is hidden until it intersects with the first ( Koestler, 1964 ). Humor is experienced when the listener becomes aware of the two coexisting incompatible scripts or frames ( Attardo, 1997 ) and has to reinterpret the old information in light of the new ( Giora, 1991 ). This is ultimately the unique element of humor as a form of discourse: the participatory role of the audience in “reconciling” the incongruity and interpreting the original schema in light of this new frame of reference ( Koestler, 1964 ). Because the audience of a humorous text must participate in its construction and appreciation, the audience is complicit in the creation of its meaning.

The Question of Impact

Persuasion, priming, and cognitive elaboration.

While Athenian society viewed satirists as possessing a magical persuasive power ( Caufield, 2007 ), historians do not agree on the amount of influence these humorous texts actually had on Athenian citizens. Lord argues that Aristophanes’s audience could not “distinguish between the caricature [of Socrates] and the reality” (1925, 40), hence leading to Socrates’s conviction and execution. Stow (1942) , on the other hand, argues that Aristophanes’s impact on the citizens of Athens was negligible—his power was in revealing the sentiments of Athenians, not in shaping them.

Contemporary empirical research remains focused on this same question: Is political humor an agent of influence or merely a barometer of public opinion? If the audience is complicit in the creation of meaning through humor, could that enhance its persuasive capacity? Intuitively we know that topics treated in a humorous way are often perceived as less offensive than when presented seriously. If humor can playfully present information or argument without eliciting a negative audience reaction, then employing it could be a promising way to incite attitude change. Indeed, research consistently indicates that humor reduces counterargumentation, or argument scrutiny, in response to the premise of that humorous text ( Nabi, Moyer-Guse, and Byrne, 2007 ; Young, 2008 ). However, the mechanism responsible for this phenomenon remains elusive. On the one hand, some scholars suggest that the complex task of reconciling incongruity reduces cognitive resources available to scrutinize message arguments ( Young, 2008 ). On the other, some studies suggest that the reduction in argument scrutiny is a result of the listener discounting the message as “just a joke,” a mechanism referred to as a discounting cue ( Nabi, Moyer-Guse, and Byrne, 2007 ). While this debate may seem tedious, the implications are profound. If humor’s ability to suspend argument scrutiny of the listener stems from the listener’s decision to treat the text as “just a joke,” then the potential power of humor depends on the audience’s willingness to play along. If, however, the reduction in counterargumentation is a result of humor’s drain on cognitive resources, then the listener is at the mercy of the humorous text.

In spite of political humor’s documented ability to suspend argument scrutiny, researchers have yet to find strong and consistent evidence of humor’s persuasive capacity. Young (2004) found more negative appraisals of candidates’ most caricatured personality traits as an outcome of viewing late-night comedy programming, particularly among those low in political knowledge. Similarly, Morris (2009) documented more negative ratings of Republican candidates among viewers of The Daily Show during the 2004 Republican conventions, consistent with the tenor of the show’s content during that time. Research has also demonstrated that exposure to political humor can increase the salience of certain issues or constructs in the minds of the audience ( Moy, Xenos, and Hess, 2006 ; Young, 2006 ). Here, the focus is not on attitude change per se, but rather on the priming of certain issues, events, or traits that could affect subsequent decision-making processes (see Iyengar and Kinder, 1987 ).

Learning, Recall, and Information Seeking from Political Humor

In addition to examinations of humor’s role in persuasion, scholars have studied how political humor affects information acquisition—both directly and indirectly. To date, studies suggest that exposure to political humor may be associated with information recognition and a viewer’s sense of being informed ( Hollander, 2005 ). However, experimental research indicates that exposure to late-night comedy may result in lower acquisition of detailed factual and issue knowledge than traditional news viewing ( Kim and Vishak, 2008 ). Complicating these findings is the observation that viewers of late-night comedy programs consistently score higher on political knowledge tests than nonviewers, even in the face of controls ( Cao, 2008 ; National Annenberg Election Survey, 2004 ). Overall, it seems that political humor audiences likely come to the viewing experience with above average political knowledge, but the direct impact of that exposure on information acquisition depends on the nature of the humorous content and on how viewers conceptualize that content ( Feldman, 2013 ). One of the strongest examples of satirical programming serving this educational role comes to us from the ironic satirist Stephen Colbert. Hardy, Gottfried, Winneg, and Jamieson (2014) found evidence that citizens watching The Colbert Report had significantly greater understanding of the complexities of the 2012 campaign finance reform debate. Here, Stephen Colbert had engaged in months of humorous segments that digested the issue for his viewers, going so far as to launch a super PAC himself to try to understand the limits of campaign finance reform. Viewers came away feeling more knowledgeable and, more important, actually possessed more knowledge on the issue.

Additional research has moved beyond direct learning models to assess political humor’s possible “gateway” effect ( Baum, 2003 ). According to Baum, “soft news” (including political humor) serves as a gateway to politics for viewers who are otherwise politically inattentive. By covering politics in an entertaining way, these programs may motivate politically inattentive viewers to seek out additional political information. Cross-sectional research supports Baum’s general model, with evidence that viewers of late-night comedy are more attentive to politics ( Cao, 2010 ; Young and Tisinger, 2006 ) and that exposure to political humor among politically inattentive audiences is associated with increased attention to high-profile political stories ( Cao, 2010 ) as well as issue-specific news items ( Feldman, Leiserwitz, and Maibach, 2011 ). Time series analyses reveal that viewers of late-night comedy programming experience a steeper increase in news attention than noncomedy viewers during primary campaigns ( Feldman and Young, 2008 ). Also consistent with the gateway hypothesis, experimental work by Xenos and Becker (2009) illustrates enhanced attentiveness to news after exposure to political humor programming among less politically interested viewers. In this same study, politically inattentive viewers experienced higher rates of learning from subsequent news exposure. Together, these findings speak to the potential of political humor to increase viewers’ attention to politics, hence indirectly fostering certain kinds of political learning, particularly among those with the least political interest from the start.

Political Participation, Discussion, Engagement, and Trust in Government

At the heart of this effects research is a question of how political humor might affect democracy. The US Supreme Court has consistently upheld parody and satire as protected forms of expression, a fact that speaks to humor’s privileged role in a democratic society. As conceptualized by literary scholars Bloom and Bloom, satire is intended to “plead with man for a return to his moral senses” (1979, 38). When successful, they state, satire can “effect a gradual moral reawakening, a reaffirmation of positive social and individual values” (17). If these contentions were true, exposure to political satire, such as The Daily Show , should result in higher rates of political participation and discussion and other characteristics of an engaged citizenry, such as attention to politics or political efficacy (see Jones, 2009 ).

Cross-sectional studies consistently find that the audience of The Daily Show with Jon Stewart participates in politics more ( Cao and Brewer, 2008 ; Hoffman and Young, 2011 ) and is more likely to discuss politics with friends, family, and coworkers than are nonviewers ( Young and Esralew, 2011 ). Using panel data, Landreville, Holbert, and Lamarre (2010) demonstrated that Daily Show viewers experienced increases in political discussion, a process mediated by increased debate viewing. Such findings suggest that Baum’s (2003) gateway mechanism might extend beyond attention and learning, to include other beneficial democratic behaviors like political discussion. Moy, Xenos, and Hess (2005) found that late-night comedy viewing in general (which includes exposure to Leno and Letterman) was associated with increased vote intention and political discussion, though these effects were limited to political sophisticates. In an experimental context, Becker (2014) found that consumption of political satire that targets one’s outgroup does indeed increase political efficacy, especially among viewers who consume political humor to reduce anxiety. Although not all of this research establishes causality between exposure and efficacy/participation/discussion, experimental and time-series studies of political attention and information seeking have pointed to a causal relationship, with exposure to political humor fostering these democratically healthy outcomes ( Feldman, Leiserwitz, and Maibach, 2011 ; Feldman and Young, 2008 ; Xenos and Becker, 2009 ).

Because of contemporary political humor’s frequent criticism of politicians and governing institutions, some fear that routine exposure to such critical examinations of government may erode citizens’ trust in institutions and faith in the democratic process ( Baumgartner and Morris, 2006 ; Hart and Hartelius, 2007 ). While isolated studies have found that viewers of The Daily Show are less trusting of government ( Baumgartner and Morris, 2006 ), questions remain regarding whether a lack of government trust is necessarily a bad thing for democracy, as government trust is often lowest among our most politically active and engaged citizens ( Cappella and Jamieson, 1997 ; de Vreese and Semetko, 2002 ; de Vreese, 2005 ). If the fundamental proposition of political satire is that we deserve—and can attain—something better, then it is logical that audiences would see this message as both an indictment of the existing political order and a call to strive for its improvement. In fact, work by Lee and Kwak (2014) suggests that political satire can elicit strong negative emotions from viewers, as they become frustrated with targeted government policies, and that this negative emotion then spurs political action.

The reason for scholars’ fundamentally different conclusions about satire’s role in a democratic society may stem from the polysemy inherent in humor. The meaning of humor is not in the text itself. Instead, it is in the reconciliation of the incongruity which, in turn, is at the mercy of whatever the listener brings to the text. Perhaps this is why we find such differences in the effects of political humor as a function of various individual-level characteristics: political knowledge ( Young, 2004 ), interest in politics ( Xenos and Becker, 2009 ), age ( Cao, 2008 ), and political ideology ( Lamarre, Landreville, and Beam, 2009 ). With different experiences and understandings of politics, these distinct groups will likely construct different meanings from political humor, thereby fostering different processes and different outcomes.

Illustrative of the importance of a listener’s cognitive contribution to meaning construction in humor are Lamarre, Landreville, and Beam’s (2009) findings regarding the perceived meaning of The Colbert Report among conservatives and liberals. The authors found that liberals interpreted Colbert’s ironic performance accurately—as a criticism of conservative policies and values. Meanwhile, conservatives found humor in Colbert’s show, but interpreted it literally, as an exaggerated indictment of liberal politics. Hence, selective perception altered the audience’s construction of Colbert’s meaning. Such findings demonstrate the importance of exploring individual differences as moderating variables in studies of humor’s impact.

Where We Are and Where We’re Going

At present, political humor’s impact on knowledge, attitudes, and behaviors is far from clear. Reports of humor disrupting argument scrutiny, but not necessarily leading to attitude change, suggest that whatever counterargument-disruption mechanism is operative in humor might suspend other forms of processing as well. Humor’s limited ability to foster detailed information recall, in spite of its positive impact on construct recognition ( Hollander, 2005 ) and overall impressions of political constructs ( Kim and Vishak, 2008 ), illustrates a similar phenomenon. Perhaps political humor activates online, rather than memory-based, processing (see Kim and Vishak, 2008 ), rendering it suitable for impression formation and heuristic evaluation, but not for central message processing or detailed information acquisition (see Baum, 2003 ). These micro-level processes need to be better explicated, perhaps through the integration of physiological measurements or with novel imaging techniques emerging from neuroscience ( Coulson, 2001 ; Coulson and Williams, 2005 ).

Because the comprehension of and meaning derived from political humor depend on the cognitive contribution of the audience, future work on political humor’s impact ought to link detailed analyses of humorous texts to audience characteristics, psychology, and viewing motivations. In particular, future work ought to develop effects mechanisms that emphasize the importance of the structure elements of the humorous texts and the individual-level characteristics of the audience:

Structural elements of the humorous text: Since humorous texts are incomplete until reconciled by the audience, the nature of the incongruity helps determine what kind of contribution a listener will make and hence what that text will ultimately come to mean. In the case of a punchline-oriented late-night joke, the incongruity might simply be a pun or play on words that unexpectedly highlights a candidate’s physical or personality flaws. In the case of satirical irony, the incongruity is presented by the gap between what is said and what is meant—or between what reality is and what it ought to be. To better understand the potential power of humor to shape audiences, scholars must dissect these underlying incongruities and link them with cognitive contributions made to reconcile them.

Individual-level characteristics: Once an incongruity is presented, the audience takes over in constructing the text’s meaning. The cognitive contribution made by the listener depends on what he or she brings to the table: political knowledge, political beliefs or ideology (selective perception), as well as psychological characteristics and viewing motivations. Viewers’ own orientations toward such programs (Do they consider them straight entertainment, or do they see them as holding some informational content?) ( Feldman, 2013 ) shape the extent and nature of mental effort that they will dedicate to processing such programming, hence influencing the outcomes of exposure as well. Future studies need to further integrate uses and gratifications approaches ( Katz, Blumler, and Gurevitch, 1974 ) into studies of political humor effects. By understanding why people consume political humor, we can better capture the various cognitive processes underlying different viewing experiences.

To pursue some of the core questions regarding political humor’s role in a democratic society, researchers may need to look across discipline and method. Whether or not political satire is good or bad for democracy has proven exceptionally difficult to address with empirical effects studies. For example, operationalizing political cynicism with three items designed to measure trust in government might not adequately capture the meaning of contemporary political satire. If viewers come away from Stewart or Colbert critically challenging the current system, but striving for something better, perhaps qualitative methods (focus groups, long-form interviews, ethnographies, or textual analysis) would help us better understand these complex processes.

Indeed, humanistic studies of political humor have contributed rich theoretical and historical understanding to the approach being taken by scholars across epistemological boundaries ( Holbert and Young, 2013 ). Qualitative and cultural research has chronicled how and why the once-strict divide between entertainment and news no longer exists ( Baym, 2009a ; Williams and Delli Carpini, 2002 ), and that scholars should explore political humor not as an alternative to political information, but as an alternative form of political information ( Baym, 2009b ). Work by Baym ( 2005 , 2009a ) highlights how political humor challenges the notion that journalistic practices such as objectivity and sensationalism are necessary or beneficial to society. Work by Jones (2009) and Van Zoonen (2005) suggests that by addressing political themes outside the traditional elite model of political discourse, political humor might invite more people into the political conversation.

As political comedians capitalize on advances in digital technologies to translate their message across platform and genre ( Jenkins, 2006 ), scholars will benefit from the integration of qualitative and quantitative approaches to the study of these phenomena. Baym and Shah (2011) , for example, tracked the flow and context of digital segments of The Colbert Report across the Internet landscape. Their work illustrates how activists and organizations repurpose relevant clips to help attain informational, community-building, and deliberative goals. Such innovative approaches will advance our understanding of newly emerging political humor phenomena. For example, in October 2010 Stewart and Colbert mobilized people from around the country to travel to Washington, D.C., to playfully restore civility to political discourse. Through social networking sites and broad media appearances, the shows’ hosts gathered a crowd of more than 200,000 people ( Tavernise and Stelter, 2010 ). The rally—a mix of music festival, variety show, and political commentary—stumped journalists and politicos, who struggled to make sense of the event. And just as the rally did not fit neatly within the news/entertainment dichotomy, neither did it fit neatly into linear models of media effects.

In just the past year, numerous examples of political humor operating across platforms highlight the need for scholars to work across methodological and epistemological traditions to understand what this all means. In September 2010 Stephen Colbert appeared in character to ironically testify before Congress on the issue of immigration reform. Throughout 2010 and 2011 Jon Stewart engaged in satirical critiques of Fox News on his own show, while appearing as a guest on Bill O’Reilly’s No Spin Zone (on Fox) to debate and mock the host. In March 2011 Colbert launched the ironically self-aggrandizing Colbert super PAC, a political action committee designed to raise unlimited funds to help “make a better a better tomorrow, tomorrow” ( colbertpac.com ).

To anchor this body of research in generalizable concepts, we must formally recognize that humor arises not only from audience perception, but from structural elements within the text that invite or signal that audience participation. The act of returning to the basic concept of incongruity and audience reconciliation will encourage scholars to build upon existing theory to advance our understanding of micro-level processes involved in humor comprehension. Finally, the complexity of the multiplatform digital environment means that linear sender-receiver models of effects will not be adequate to capture the full scope of political humor’s impact. Instead, scholars of political humor will increasingly be called upon to embrace diverse methods and innovative approaches. Only through collaborative and discursive research models will the political meaning and significance of this diverse set of humorous texts and performances be adequately understood.

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Psychologenie

Psychology 101: Understanding the Sleeper Effect With Examples

The sleeper effect is a commonly observed psychological phenomenon that helps us understand and explain perception and change in attitudes of people with regards to other people, products, entities, etc. This piece elaborates on this effect in order to make the topic easy to grasp.

Did You Know?

The sleeper effect was discovered by Carl Hovland, Arthur Lumsdaine, and Fred Sheffield in 1949, in the course of their study that examined the impact of a World War II propaganda movie on American soldiers.

During World War II, a series of documentary films were commissioned by the US government in order to explain to the American soldiers the reason behind the involvement of US forces in the war. Carl Hovland and his colleagues were curious to study the long-term effects of such propagandized messages. During the course of their study, they found that the soldiers initially dismissed the information in the films, as it was clearly part of a propaganda, and hence, was not a very reliable source of information. When reexamined after a substantial period of time, the researchers were astounded to find that, instead of an increase in the rejection of the information, the soldiers showed an increase in its acceptability. This led Hovland and his colleagues to posit that, the passage of time had caused the soldiers to forget the source of the information, and in turn lead to the acceptance it. This was the first documented instance of the sleeper effect.

Sleeper Effect

Normally, the persuasiveness of information gradually decreases over a period of time. Often, this information is associated with cues such as source credibility and morality. Some of these cues are positive, while some are negative. Messages accompanied by positive cues are usually readily accepted and believed by people, while those associated with negative cues (discounting cues) are viewed suspiciously and sometimes even dismissed.

However, it has been observed in many studies that despite the initial rejection of the message, people tend to get persuaded over time, leading to an increase in the acceptance of that message. This phenomenon of delayed persuasion is called the sleeper effect.

However, for the sleeper effect to manifest, three basic conditions must be met. They are:

• The message itself should be persuasive
• The discounting cue must initially suppress attitude change
• The discounting cue must become dissociated from the message over time

It must be noted that the effect is seen to disappear if the audience is reminded of the source.

How Does It Work?

There exist three hypotheses that try to explain the rationale behind this effect.

The sleeper effect caused Hovland and his colleagues to believe that acceptance was possible due to the non-remembrance of the discounting cue. To test this hypothesis, the researchers conducted a series of experiments which were made up of two sets of people each. Each set was told the same message or information, but one was associated with a highly credible source, whereas the other was associated with an untrustworthy source. Following up on the two groups showed that, initially, the message from the credible source was readily accepted as compared to the message from the unreliable source. After a certain period of time, the subjects were again examined. This examination revealed that there was a significant increase in the acceptability of the message related to the discounting cue. This led to the hypothesis that the effect was observed due to the fact that the subjects had forgotten the unreliable nature of the source. However, later examinations revealed that, despite the increase in acceptability, most of the subjects still remembered the non-credible source of the message.

Dissociation

Due to the limitation of the above hypothesis, the researchers proposed a new theory, one which claimed that the subjects didn’t forget the source entirely, but merely possessed a weakened association with the message, or in other words, the message and discounting cue were dissociated. Since the association of the source and message is weakened over time, so as to be dissociated, recall of the message would not cause the recall of the source as well, thereby causing a delayed acceptance/persuasion, which results in the sleeper effect.

Differential Decay

Hovland and his colleagues succeeded in elaborating many facets of this effect, but they managed to ignore one aspect, that is, why the discounting cue was readily forgotten or less accessible, but not the message itself. This query was investigated by Greenwald, Pratkanis, and their team, in a series of experiments designed to determine the specific parameters required for the occurrence of the sleeper effect. Their experimental results suggest that, the message and the cue decay at different rates. After exposure, the effect or memory of the cue degrades or decays much faster, as compared to the effect of the message. Also, interestingly, the sequence of the cue and message also played an important role in bringing about the sleeper effect. The effect was seen only in cases where the cue followed the message and reinforced the message content.

Real-life Examples

➠ During political election campaigns, often, the candidates of the opposing party are targeted via negative remarks, advertisements, or news. This largely affects the undecided voters, who initially dismiss these occurrences as being slanderous attempts, but later, due to the sleeper effect, retain only the memory of the message but not the source, causing them to vote against the defamed candidates.

➠ Young children of divorced parents, who show no signs of mental stress or trauma as a result of the divorce, often exhibit difficulties regarding relationships, trust, and intimacy during early adulthood.

➠ The prevalent idea that autism is caused by MMR vaccines is due to the sleeper effect caused by an unreliable source in the form of an incorrect scientific study.

➠ The sleeper effect is evident in the case of word of mouth marketing. Product reviews are often spread this way. It may either be a friend telling you about it, or a salesman. The same can occur on websites and forums as well.

➠ Reading about facts, statistics, or anecdotes from an unreliable source may also result in the presentation of this effect.

➠ Social media applications can act as platforms for the propagation of untruths or propaganda against or in support of an individual or entity.

The only effective way to overcome any and all effects of the sleeper effect is to question and investigate the source of your knowledge. If a certain piece of information reaches you, you must determine the soundness of the source, and verify the validity of the information before applying it in any way.

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In search of reliable persuasion effects: III. The sleeper effect is dead. Long live the sleeper effect

Affiliation.

• 1 Board of Psychology, University of California, Santa Cruz 95064.
• PMID: 3346811
• DOI: 10.1037//0022-3514.54.2.203

The sleeper effect in persuasion is a delayed increase in the impact of a message that is accompanied by a discounting cue. Despite a long history, the sleeper effect has been notoriously difficult to obtain or to replicate, with the exception of a pair of studies by Gruder et al. (1978). We conducted a series of 16 computer-controlled experiments and a replication of the Gruder et al. study to demonstrate that a sleeper effect can be obtained reliably when subjects (a) note the important arguments in a message, (b) receive a discounting cue after the message, and (c) rate the trustworthiness of the message communicator immediately after receiving the discounting cue. These operations are sufficiently different from those used in earlier studies to justify a new differential decay interpretation of the sleeper effect, in place of the dissociation hypothesis favored by most previous sleeper effect researchers. According to the differential decay interpretation, a sleeper effect occurs when message and discounting cue have opposite and near-equal immediate impacts that are not well-integrated in memory. The effect occurs, then, if the impact of the discounting cue decays faster than that of the message.

Publication types

• Research Support, U.S. Gov't, Non-P.H.S.
• Research Support, U.S. Gov't, P.H.S.
• Mental Recall*
• Minicomputers
• Persuasive Communication*
• Reaction Time*
• Set, Psychology

Grants and funding

• MH 32317/MH/NIMH NIH HHS/United States

Sleeper Effect

The sleeper effect is a psychological phenomenon whereby a highly persuasive message, paired with a discounting cue, causes an individual to be more persuaded by the message (rather than less persuaded) over time.

When people are normally exposed to a highly persuasive message (such as an engaging or persuasive television ad), their attitudes toward the advocacy of the message display a significant increase. Over time, however, their newly formed attitudes seem to gravitate back toward the position held prior to receiving the message, almost as if they were never exposed to the communication in the first place. This pattern of normal decay in attitudes has been documented as the most frequently observed longitudinal pattern in persuasion research (Eagly & Chaiken, 1993). In contrast, some messages are often accompanied with a discounting cue (e.g., a message disclaimer, a low-credibility source) that would arouse a recipient’s suspicion of the validity of the message and suppress any attitude change that might occur with exposure to the message alone. Furthermore, when people are exposed to a persuasive message followed by a discounting cue, people tend to be more persuaded over time; this is referred to as the sleeper effect (Hovland & Weiss, 1951; Cook & Flay, 1978). For example, in political campaigns during important elections, undecided voters often see negative advertisements about a party or candidate running for office. At the end of the advertisement, they also might notice that the opposing candidate paid for the advertisement. Presumably, this would make voters question the truthfulness of the advertisement, and consequently, they may not be initially persuaded. However, even though the source of the advertisement lacked credibility, voters will be more likely to be persuaded later (and ultimately, vote against the candidate in the advertisement). This pattern of attitude change has puzzled social psychologists for nearly half a century, primarily due to its counter-intuitive nature and for its potential to aid in understanding attitude processes (Eagly & Chaiken, 1993). In addition, it has been the most widely studied phenomenon in persuasion research (Kumkale & Albarracín, 2004; see also Cook & Flay, 1978).

Controversy surrounding the existence of a “sleeper effect”

One of the more challenging aspects that the sleeper effect posed to some researchers in early studies was that the mere difficulty in obtaining the effect (e.g. Capon & Hulbert, 1973; Gillig & Greenwald, 1974). After attempting to replicate the effect and failing, some researchers went as far as suggesting that it might be better to accept the null hypothesis and conclude that the sleeper effect does not exist (Gillig & Greenwald, 1974). However, Cook and his associates (Cook, Gruder, Hennigan, & Flay, 1979) responded by suggesting that previous studies failed to obtain the sleeper effect because the requirements for a strong test were not met. Specifically, they argued that the sleeper effect will occur only if: (a) the message is persuasive, (b) the discounting cue has a strong enough impact to suppress initial attitude change, (c) enough time has passed between immediate and delayed post-tests, and (d) the message itself still has an impact on attitudes during the delayed post-test. Experimental studies conducted did, in fact, provide support for the sleeper effect occurring under such theoretically relevant conditions (Gruder, Cook, Hennigan, Flay, Alessis, & Halamaj, 1978). Furthermore, the sleeper effect did not occur when any of the four requirements were not met.

Past hypotheses on how the sleeper effect occurs

Because the sleeper effect has been considered to be counter-intuitive at face value, researchers since the early 1950s have attempted to explain how and why it occurs.

Figure A: Forgetting Figure B: Dissociation Figure C: Differential-Decay

Forgetting and dissociation

Hovland, Lumsdaine, and Sheffield (1949) first discovered the effect in a well-known study that demonstrated the delayed impact of a World War II propaganda film on American soldiers. In a subset of conditions that caused participants to question the credibility of the source in the film, participants later reported a slight increase in persuasion (much to the researchers’ surprise). After examining the results, they initially hypothesized that forgetting of the discounting cue (in this case, the non-credible source) was driving the effect. However, this premise turned out to be incorrect, because the recall measures indicated that recipients of the message were remembering the source of the communication. Consequently, Hovland and Weiss (1951) modified the forgetting hypothesis to one of dissociation. According to this reasoning, the sleeper effect occurs because the association between the discounting cue and the message in one’s memory becomes severed over time; hence, when the message is recalled for purposes of producing an attitude, the source is not readily associated.

Differential decay

Years later, Pratkanis, Greenwald, Leippe, and Baumgardner (1988) offered an alternative hypothesis that differed from Hovland and his colleagues. They argued that the conditions under which the sleeper effect is more likely to occur were not highlighted under the dissociation hypothesis. In addition, the requirements for a sleeper effect laid out by Gruder et al. (1978) did not detail the empirical conditions necessary to observe the sleeper effect. Based on a series of 17 experiments, the researchers proposed a theory of differential decay; that is, they suggested that the sleeper effect occurs because the impact of the cue decays faster than the impact of the message. Consequently, an overall increase in attitude change is observed at a later time. Moreover, they found that a critical requirement needed to observe the sleeper effect included the discounting cue following (rather than preceding) the message. This relatively complicated literature has been synthesized recently in a meta-analysis (see Kumkale & Albarracin, 2004).

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Discounting

The basic idea, theory, meet practice.

TDL is an applied research consultancy. In our work, we leverage the insights of diverse fields—from psychology and economics to machine learning and behavioral data science—to sculpt targeted solutions to nuanced problems.

Rather than accepting certain behaviors or events, we like to make explanations for them. If you receive an A+ on an assessment, are you more likely to say it was due to your inherent talent or your hard work? Could it be a mix of both?

When faced with more than one possible explanation for an event or behavior, humans discount, or minimize, the importance of each reason. 1 If one explanation seems plausible, we will disregard the other potential factors as irrelevant. The discounting principle is part of Kelley’s Covariation Model of attribution theory, a model for explaining how humans determine the causes of certain events or behaviors.

It is natural to ask (a) whether there are other ways to take plausible causes into account and (b) what might be all the possible forms of such ‘taking into account. – Harold Kelley on applying the discounting principle in his 1972 article, “Causal Schemata and the Attribution Process”

Attribution theory: A theory about how people make causal explanations for events or behaviors.

Covariation: As one variable increases in value, the corresponding variable also increases in value. Alternatively, as one variable decreases in value, the corresponding variable also decreases in value.

Discounting principle: If there is a good explanation for an effect, people will disregard other possible factors as irrelevant.

Augmentation principle: If there is a good explanation for a failure, then to explain success, people require an especially strong explanatory factor to compensate for said failure.

Attribution theory developed within social psychology to address questions of social perception and self-perception. 1 In terms of social perception, research deals with questions like whether behavior reflects who we are as a person, or environmental factors. As for self-perception, attribution theory focuses on people’s judgements of themselves, such as their abilities, feelings, or attractiveness.

Harold Kelley has made several significant contributions to attribution theory in the 1960s and 1970s, starting with his Covariation Model. 1 Kelley’s 1967 Covariation Model was developed to judge whether a specific action should be attributed to characteristics of the person (a dispositional attribution) or to characteristics of their environment (a situational attribution). As indicated by its name, the model focuses on the covariation that occurs when an effect is attributed to one of its possible causes. 2 According to Kelley’s Covariation Model, people make attributions based on past experiences and will look for either:

• Multiple necessary causes , where both A and B are necessary to produce an effect; or
• Multiple sufficient causes , where either A or B is sufficient to produce an effect.

Kelley introduced the discounting principle in his 1971 chapter “Attribution in social interaction”. 3 He outlined the use of discounting to explain how job candidates present themselves in interviews. When candidates present themselves in an ideal manner, observers suggest that they could either be showing their true personalities or be conforming to contextual demands. However, when candidates present themselves in a less than ideal manner, observers conclude that they are showing their true colors since it does not make sense for them to purposely act that way.

Essentially, discounting is a trade-off between two possible explanations: if one is stronger, the other is discounted. 3 In the interview scenario, the trade-off is between dispositional and situational attributions. Kelley specified that the discounting principle applies when there is a good explanation for an effect. On the other hand, the augmentation principle specifies that if there is a good explanation for failure, then an especially strong facilitatory factor is needed to explain success. Unlike attributions to single causes, discounting and augmentation focus on the competition between multiple causal factors: the stronger explanation will win. 4

To conceptualize Kelley’s work on the discounting versus augmentation principle, imagine two tennis players who paired up for a doubles tournament and won the finals. 4 Player A won a series of singles tournaments, so they are individually perceived to be a great player. Applying the discounting principle, if Player A’s past performances are sufficient for explaining the pair’s win, then Player B’s contribution will be discounted. However, if Player B and Player C are paired up for the doubles tournament, with Player C being an amateur player, then Player B’s contributions would be augmented.

Harold H. Kelley

An American social psychologist and professor at the University of California, Los Angeles, Kelley was most known for his contributions to attribution theory and interpersonal processes. 5 Kelley’s scientific contributions have received numerous awards and honors from the American Psychological Association, the American Sociological Association, and the Society of Experimental Social Psychology, among others. A 2002 survey in Review of General Psychology ranked Kelley as the 43rd most cited psychologist of the 20th century. 6

Consequences

Since its conception, the discounting principle has been confirmed in many experiments among both adults and children. 7,8 Kelley’s work on discounting has been applied to a variety of fields including judgement and decision making, health perceptions, 9 and social dynamics. 10 Developmental effects have also been found; as children age, they become more skilled at differentiating whether an effect is due to a single cause or multiple causes. 11,12

Considering Kelley’s distinction between the discounting and augmentation principle, and the corresponding distinction between dispositional and situational attributions, discounting can be linked to locus of control. As a result, the discounting principle has influenced subsequent research on the relationship between the covariation model and locus of control, especially when factoring in self-esteem. 13 How do these factors influence perceived causes of success and failures of others’ job seeking activities? How can the covariation model and discounting be used to explain the relationship between explanations, attributions, and perceptions? 14

Controversies

Subsequent work on discounting has found that observers seem to be prone to correspondence bias, which is the tendency to draw inferences about a person’s unique and enduring dispositions from behaviors that could be completely explained by situational factors. 15 While this is the opposite of Kelley’s discounting principle, which warns observers to not attribute an effect to a single cause (such as a dispositional factor) when another explanation (like a situational factor) is plausible, the correspondence bias is more evident in Western cultures. This could be explained by the fact that people in Eastern cultures may be more inclined to discount the influence of one’s disposition on behavior and focus on situational contexts instead. 16

Attributions, discounting, and life satisfaction

In 1983, Norbert Schwarz and Gerald Clore set out to study whether people’s judgments of their life satisfaction could be influenced by their mood at the time of judgement. 17 The researchers asked participants to do a filler sound perception task in a soundproof room, before participants were randomly assigned to write three pages about either a positive or negative event. This task was used as a mood induction, either priming participants to feel positive or negative as a result of their writing.

After the mood induction, participants were asked to rate how satisfied they were with their life overall, on a scale of 1 to 10. 17 Not surprisingly, those who wrote about negative events ranked lower life satisfaction than those who wrote about positive events. Ultimately, the researchers found that asking participants to write vivid and detailed descriptions of either negative or positive life events influenced not only participants’ moods in the moment, but also their judgments of how satisfying their life was.

Now, you might be wondering about the purpose of the filler task in the soundproof room. There was another layer to the study, such that some participants were also warned that being in the soundproof room could make certain people feel tense or depressed. 17 When the room effects were not mentioned, the results of life satisfaction were the same as before. However, when the room effects were mentioned, the writing task which served as a mood manipulation had no effects on people’s ratings of life satisfaction.

When given the chance to attribute a bad mood to a certain cause - regardless of its contribution to participants’ quality of life - the description task was discounted in influencing judgments of overall well-being. 17 In other words, people discounted aspects of their lives as a cause of their bad mood when the soundproof room, another explanatory factor, was emphasized.

Availability heuristic, discounting and misattributions

Nortbert Schwarz, the same researcher from the previous case study, also set out with a team of psychologists to study the availability heuristic and discounting. 18 One of the most known heuristics in the field of judgement and decision making, the availability heuristic is when people estimate the frequency or likelihood of an event based on the ease with which information comes to mind. The easier it is to recall something, the more frequent it seems.

Extending the availability heuristic, the researchers showed that if you give people a reason why thinking about something may be hard, then they will discount the applicability of recall on their judgements. 18 40 participants were asked to either recall 6 or 12 instances when they behaved assertively. Additionally, there was either no background music playing while participants came up with examples, or there was background music playing for which the researchers apologized, as it could be a bit distracting. After coming up with 6 or 12 examples, participants were then asked to rate how assertive they are.

The researchers found that when there was no background music, then those who recalled 6 examples rated themselves as more assertive than those who had recalled 12 examples. 18 The more examples asked of participants, the more difficult it was to recall so many events, which then influenced participants to question their assertiveness. However, when there was background music playing, there was no difference between recalling 6 or 12 examples on determinants of assertiveness. These results show that when there was a misattribution error and people had an excuse for their difficulties coming up with 12 examples, their poor recall was discounted.

Related TDL Content

Fundamental attribution error

When making judgements about other people’s behaviors, we tend to emphasize dispositional attributions and de-emphasize situational attributions. No, this isn’t Kelley’s discounting principle - it’s the fundamental attribution error. Social psychology consists of many concepts that relate to and build off one another, as is the case here. Read through this piece to learn more about misattributions and our social blindness.

Hyperbolic discounting

The discounting principle in social psychology can get mixed up with hyperbolic discounting, a concept in behavioral economics. While they are not the same, they both involve choosing one item over another, whether a figurative or literal item. Take a look at this article to learn why we prefer immediate gratification!

• Kelley, H. H. (1973). The processes of causal attribution. American Psychologist, 28 (2), 107-128.
• Kelley, H. H. (1967). Attribution theory in social psychology. In Nebraska Symposium on Motivation. University of Nebraska Press.
• Kelley, H. H. (1971). Attribution in social interaction. In Attribution: Perceiving the Causes of Behavior. General Learning Press.
• Van Overwalle, F. (2006). Discounting and augmentation of dispositional and causal attributions. Psychologica Belgica, 46 (3), 211-234.
• In Memoriam: Harold H. Kelley. (2007, December 15). The University of California. http://www.universityofcalifornia.edu/senate/inmemoriam/HaroldH.Kelley.htm
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Sleeper effect

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The sleeper effect is a psychological phenomenon whereby a highly persuasive message, paired with a discounting cue, causes an individual to be more persuaded by the message (rather than less persuaded) over time.

Figure A: Normal Decay Figure B: Sleeper Effect

When people are normally exposed to a highly persuasive message (such as an engaging or persuasive television ad), their attitudes toward the advocacy of the message display a significant increase. Over time, however, their newly formed attitudes seem to gravitate back toward the position held prior to receiving the message, almost as if they were never exposed to the communication in the first place. This pattern of normal decay in attitudes has been documented as the most frequently observed longitudinal pattern in persuasion research (Eagly & Chaiken, 1993). In contrast, some messages are often accompanied with a discounting cue (e.g., a message disclaimer, a low-credibility source) that would arouse a recipient’s suspicion of the validity of the message and suppress any attitude change that might occur with exposure to the message alone. Furthermore, when people are exposed to a persuasive message followed by a discounting cue, people tend to be more persuaded over time; this is referred to as the sleeper effect (Hovland & Weiss, 1951; Cook & Flay, 1978). For example, in political campaigns during important elections, undecided voters often see negative advertisements about a party or candidate running for office. At the end of the advertisement, they also might notice that the opposing candidate paid for the advertisement. Presumably, this would make voters question the truthfulness of the advertisement, and consequently, they may not be initially persuaded. However, even though the source of the advertisement lacked credibility, voters will be more likely to be persuaded later (and ultimately, vote against the candidate in the advertisement). This pattern of attitude change has puzzled social psychologists for nearly half a century, primarily due to its counter-intuitive nature and for its potential to aid in understanding attitude processes (Eagly & Chaiken, 1993). In addition, it has been the most widely studied phenomenon in persuasion research (Kumkale & Albarracín, 2004; see also Cook & Flay, 1978).

• 1 Controversy surrounding the existence of a "sleeper effect"
• 2.1 Forgetting and dissociation
• 2.2 Differential decay
• 4 References
• 5 Further reading

Controversy surrounding the existence of a "sleeper effect" [ ]

One of the more challenging aspects that the sleeper effect posed to some researchers in early studies was the mere difficulty in obtaining the effect (e.g. Capon & Hulbert, 1973; Gillig & Greenwald, 1974). After attempting to replicate the effect and failing, some researchers went as far as suggesting that it might be better to accept the null hypothesis and conclude that the sleeper effect does not exist (Gillig & Greenwald, 1974). However, Cook and his associates (Cook, Gruder, Hennigan, & Flay, 1979) responded by suggesting that previous studies failed to obtain the sleeper effect because the requirements for a strong test were not met. Specifically, they argued that the sleeper effect will occur only if: (a) the message is persuasive, (b) the discounting cue has a strong enough impact to suppress initial attitude change, (c) enough time has passed between immediate and delayed post-tests, and (d) the message itself still has an impact on attitudes during the delayed post-test. Experimental studies conducted did, in fact, provide support for the sleeper effect occurring under such theoretically relevant conditions (Gruder, Cook, Hennigan, Flay, Alessis, & Halamaj, 1978). Furthermore, the sleeper effect did not occur when any of the four requirements were not met.

Past hypotheses on how the sleeper effect occurs [ ]

Because the sleeper effect has been considered to be counter-intuitive at face value, researchers since the early 1950s have attempted to explain how and why it occurs.

Figure A: Forgetting Figure B: Dissociation Figure C: Differential-Decay

Forgetting and dissociation [ ]

Hovland , Lumsdaine, and Sheffield (1949) first discovered the effect in a well-known study that demonstrated the delayed impact of a World War II propaganda film on American soldiers. In a subset of conditions that caused participants to question the credibility of the source in the film, participants later reported a slight increase in persuasion (much to the researchers’ surprise). After examining the results, they initially hypothesized that forgetting of the discounting cue (in this case, the non-credible source) was driving the effect. However, this premise turned out to be incorrect, because the recall measures indicated that recipients of the message were remembering the source of the communication. Consequently, Hovland and Weiss (1951) modified the forgetting hypothesis to one of dissociation. According to this reasoning, the sleeper effect occurs because the association between the discounting cue and the message in one’s memory becomes severed over time; hence, when the message is recalled for purposes of producing an attitude, the source is not readily associated.

Differential decay [ ]

Years later, Pratkanis, Greenwald, Leippe, and Baumgardner (1988) offered an alternative hypothesis that differed from Hovland and his colleagues. They argued that the conditions under which the sleeper effect is more likely to occur were not highlighted under the dissociation hypothesis. In addition, the requirements for a sleeper effect laid out by Gruder et al. (1978) did not detail the empirical conditions necessary to observe the sleeper effect. Based on a series of 17 experiments, the researchers proposed a theory of differential decay; that is, they suggested that the sleeper effect occurs because the impact of the cue decays faster than the impact of the message. Consequently, an overall increase in attitude change is observed at a later time. Moreover, they found that a critical requirement needed to observe the sleeper effect included the discounting cue following (rather than preceding) the message.

This relatively complicated literature has been synthesized recently in a meta-analysis (see Kumkale & Albarracin, 2004).

See also [ ]

• Disinformation
• Framing (social sciences)
• Misinformation
• Psychological manipulation

References [ ]

• Capon, N. & Hulbert, J., "The Sleeper Effect — An Awakening", Public Opinion Quarterly , Vol.37, No.3, (Autumn 1973), pp. 333–358.
• Cook, T. D. & Flay, B. R., "The Persistence of Experimentally-Induced Attitude Change", Advances in Experimental Social Psychology , Vol.11, (1978), pp. 1–57.
• Cook, T. D., Gruder, C. L., Hennigan, K. M., & Flay, B. R., "History of the Sleeper Effect: Some Logical Pitfalls in Accepting the Null Hypothesis", Psychological Bulletin , Vol.86, No.4, (July 1979), pp. 662–679.
• Eagly, A.K., & Chaiken, S. , The Psychology of Attitudes , Harcourt Brace Jovanovich, (Fort Worth), 1993.
• Gillig, P.M., & Greenwald, A.G. (1974), "Is it Time to Lay the Sleeper Effect to Rest?", Journal of Personality and Social Psychology , Vol.29, No.1, (January 1974), pp. 132–139.
• Gruder, C.L., Cook, T.D., Hennigan, K.M., Flay, B.R., Alessis, C., & Halamaj, J. "Empirical Tests of the Absolute Sleeper Effect Predicted from the Discounting Cue Hypothesis", Journal of Personality and Social Psychology , Vol.36, No.10, (October 1978), pp. 1061–1074.
• Hovland, C.I., Lumsdale, A.A. & Sheffield, F.D, Experiments on Mass Communication: Studies in Social Psychology in World War II: Volume III , Princeton University Press, (Princeton), 1949.
• Hovland, C.I., Weiss, W., "The Influence of Source Credibility on Communication Effectiveness", Public Opinion Quarterly , Vol.15, No.4, (Winter 1951), pp. 635–650.
• Kumkale, G.T., & Albarracín, D., "The Sleeper Effect in Persuasion: A Meta-Analytic Review", Psychological Bulletin , Vol.130, 1, (January 2004), pp. 143–172.
• Pratkanis, A.R., Greenwald, A.G., Leippe, M.R. & Baumgardner, M.H., "In Search of Reliable Persuasion Effects: III. The Sleeper Effect is Dead. Long Live the Sleeper Effect", Journal of Personality and Social Psychology , Vol.54, No.2, (February 1988), pp. 203–218.

Further reading [ ]

• Ajzen, I., "Persuasive Communication Theory in Social Psychology: A Historical Perspective", pp. 1–27 in Manfredo, M.J. (ed.), Influencing Human Behavior: Theory and Applications in Recreation, Tourism, and Natural Resources Management , Sagamore Publishing, (Champaign), 1992. [1]
• Catton, W.R., "Changing Cognitive Structure as a Basis for the “Sleeper Effect”", Social Forces , Vol.38, No.4, (May 1960), pp.348-354.
• Cohen, A.R., "Need for Cognition and Order of Communication as Determinants of Opinion Change", pp. 79–97 in Hovland, C.I. (ed.), The Order of Presentation in Persuasion , Yale University Press, (New Haven), 1957.
• Hannah, D.B. & Sternthal, B., "Detecting and Explaining the Sleeper Effect", The Journal of Consumer Research , Vol.11, No.2, (September 1984), pp. 632–642.
• Hovland, C.I., "Introduction", pp. 1–10 in Hovland, C.I. (ed.), The Order of Presentation in Persuasion , Yale University Press, (New Haven), 1957.
• Hovland, C., "Reconciling Conflicting Results Derived From Experimental and Survey Studies of Attitude Change", American Psychologist , Vol.14, No.1, (January 1959), pp. 8–17.
• Hovland, C.I., Janis, I.L. & Kelley, H.H., Communication and Persuasion: Psychological Studies of Opinion Change , Yale University Press, (New Haven), 1953.
• Lariscy, R.A.W. & Tinkham, S.F., "The Sleeper Effect and Negative Political Advertising", Journal of Advertising , Vol.28, No.4, (Winter 1999), pp. 13–30.
• Mazursky, D. & Schul, Y., "In the Aftermath of Invalidation: Shaping Judgment Rules on Learning that Previous Information was Invalid", Journal of Consumer Psychology , Vol.9, No.4, (2000), pp. 213–222.
• Mazursky, D. & Schul, Y., "The Effects of Advertisement Encoding on the Failure to Discount Information: Implications for the Sleeper Effect", Journal of Consumer Research , Vol.15, No.1, (June 1988), pp. 24–36.
• McGuire, W.J., "Creative Hypothesis Generating in Psychology: Some Useful Heuristics", Annual Review of Psychology , Vol.48, No.1, (February 1997), pp. 1–30.
• Priester, J., Wegener, D., Petty, R. & Fabrigar, L., "Examining the Psychological Process Underlying the Sleeper Effect: The Elaboration Likelihood Model Explanation", Media Psychology , (1999), Vol.1, No.1, pp. 27–48.
• Schulman, G.I. & Worrall, C., "Salience Patterns, Source Credibility, and the Sleeper Effect", Public Opinion Quarterly , Vol.34, No.3, (Autumn 1970), pp. 371–382.
• Sitton, S.C. & Griffin, S., "The Sleeper Effect in Reconstructive Memory", Journal of General Psychology , Vol.103, No.1, (July 1980), pp. 21–25.
• Underwood, J. & Pezdek, K., "Memory Suggestibility as an Example of the Sleeper Effect", Psychonomic Bulletin and Review , Vol.5, No.3, (September 1998), pp. 449–453.
• Weiss, W., "A “Sleeper” Effect in Opinion Change", Journal of Abnormal and Social Psychology , Vol.48, No.2, (April 1953), pp. 173–180.
• Wilson, T.D., Lindsey, S. & Schooler, T.Y., "A Model of Dual Attitudes", Psychological Review , Vol.107, No.1, (January 2000), pp. 101–126.
• 1 Pregnancy fetishism
• 2 Race and intelligence (test data)
• 3 Filipino psychology

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Experimental Reductions of Delay Discounting and Impulsive Choice: A Systematic Review and Meta-Analysis

Many behaviors posing significant risks to public health are characterized by repeated decisions to forego better long-term outcomes in the face of immediate temptations. Steeply discounting the value of delayed outcomes often underlies a pattern of impulsive choice. Steep delay discounting is correlated with addictions (e.g., substance abuse, obesity) and behaviors such as seatbelt use and risky sexual activity. As evidence accumulates suggesting steep delay discounting plays a causal role in these maladaptive behaviors, researchers have begun testing methods for reducing discounting. In this first systematic and comprehensive review of this literature, the findings of 92 articles employing different methodologies to reduce discounting are evaluated narratively and meta-analytically. While most of the methods reviewed produced significant reductions in discounting, they varied in effect sizes. Most methods were ideal for influencing one-off choices (e.g., framing and priming manipulations) although other successful manipulations, such as episodic future thinking, could be incorporated into existing therapies designed to produce longer-lasting changes in decision-making. The largest and longest-lasting effects were produced by learning-based manipulations; although, translational research is needed to determine the generality and clinical utility of these methods. Methodological shortcomings in the existing literature and suggestions for ameliorating these issues are discussed. This review reveals a variety of methods with translational potential, which, through continued refinement, may prove effective in reducing impulsive choice and its associated maladaptive decisions that negatively impact quality of life

In our daily lives, we encounter intertemporal choice opportunities that tempt us toward the “dark side.” Do you stay up longer binge-watching Game of Thrones or do you go to sleep so you can be rested, focused, and productive at work tomorrow? Do you enjoy another cocktail now or do you opt for the benefits of a sober drive home at the close of the evening? Do you choose fried instead of baked chicken, preferring the immediate crunch over the desire to lose weight? These encounters with immediate temptation that are at odds with our long-term interests are commonplace. Examined in isolation, the outcomes of these choices may be trivial; but when combined into a temporally extended pattern of behavior, they can influence wealth, health, and psychological well-being ( Rachlin, 1995 ; Schroeder, 2007 ).

Delay discounting describes the devaluation of an outcome because it is delayed ( Madden & Johnson, 2010 ). To illustrate, Figure 1 depicts how two individuals, represented by the dashed and solid curves, discount the value of a larger-later reward (LLR). Across human and nonhuman species, discounting functions are hyperbolic (or approximately so), which is revealed by a steep decline in reward value at short delays, and a more shallow decline at longer delays ( Green & Myerson, 2004 ; Kirby & Herrnstein, 1995 ; Madden, Bickel, & Jacobs, 1999 ; Mazur, 1987 ). This form holds regardless of reward type (real, hypothetical, drug, food, etc.; Friedel, DeHart, Madden, & Odum, 2014, Johnson & Bickel, 2002 ; Jiruma, Myerson, Hilgard, Braver, & Green, 2009) or delay type (e.g., Jimura et al., 2009; Johnson, Herrman, & Johnson, 2015). At time t in Figure 1 , a smaller-sooner reward (SSR) is available immediately and its undiscounted value is given by the height of the bar. The subjective value of the LLR is given by the height of the discounting function at t . All else being equal, the steep discounter will choose the subjectively more valuable SSR – the impulsive choice. By contrast, for the individual whose choices are described by the dashed curve, the subjective value of the LLR exceeds that of the SSR at t , and hence the LLR (the self-control choice) is selected. 1

Discounted value of a larger-later reward (LLR) plotted as a function of time to reward delivery. At time t the smaller-sooner reward (SSR) is available immediately while the LLR reward is delayed. Solid and dashed curves show high- and low-rate hyperbolic delay discounting.

Among humans, steeply discounting the future is correlated with maladaptive preferences for SSRs that pose significant public health concerns. For example, steep delay discounting is associated with substance use and dependence, including cigarette smoking ( Baker, Johnson, & Bickel, 2003 ; Bickel, Odum, & Madden, 1999 ; Mitchell, 1999 ), problematic alcohol use ( MacKillop et al., 2010 ; Vuchinich & Simpson, 1998 ) and alcohol dependence ( Mitchell, Fields, D’Esposito, & Boettiger, 2005 ; Petry, 2001 ), heroin use ( Kirby & Petry, 2004 ; Kirby, Petry, & Bickel, 1999 ), and illicit stimulant use ( Heil, Johnson, Higgins, & Bickel, 2006 ; Kirby & Petry, 2004 ; Monterosso et al., 2007 ). In addition, steep delay discounting is related to obesity ( Fields, Sabet, Peal, & Reynolds, 2011 ; Jarmolowicz et al., 2014 ; see Amlung et al., 2016 for review); pathological gambling ( Alessi & Petry, 2003 ; Dixon, Marley, & Jacobs, 2003 ); and sub-clinical yet impactful health-related behaviors such as wearing sunscreen, using seatbelts, visiting the dentist, early sexual activity, and relationship infidelity ( Daugherty & Brase, 2010 ; Reimers, Maylor, Stewart, & Chater, 2009 ).

Accumulating evidence of the predictive validity of steepness of discounting suggests an etiological role in the development of addictions. Longitudinal studies illustrate that steep discounting is predictive of the initiation of cigarette smoking in adolescents ( Audrain-McGovern et al., 2009 ), future alcohol use in adolescents ( Fernie et al., 2013 ; Khurana et al., 2014 ), and increases in drug use in young adulthood (cigarette, marijuana, and alcohol use; Brody et al., 2014 ); for an exception to these findings see Isen, Sparks, and Iacono (2014) . Further, discounting does not increase after initiation of cigarette smoking in adolescence ( Audrain-McGovern et al., 2009 ), counter to expectation if steeper discounting was due to nicotine exposure. Also consistent with an etiological role, steep delay discounting is often predictive of poor outcomes during ( Stanger et al., 2012 ; Washio et al., 2011 ) and after substance-abuse treatment ( MacKillop & Kahler, 2009 ; Sheffer et al., 2014 ), relapse after spontaneously quitting ( Yoon et al., 2007 ), as well as relapse in analogue laboratory treatment settings ( Mueller et al., 2009 ).

Non-human animal research provides some support for the hypothesis that steep delay discounting precedes and predicts drug taking. High-impulsive rats more often initiate ( Perry, Larson, German, Madden, & Carroll, 2005 ) and escalate ( Anker, Perry, Gliddon, & Carroll, 2009 ) cocaine self-administration, and show more persistent demand for nicotine and cocaine when the price of the drug increases (i.e., responses per dose; Diergaarde, Van Mourik, Pattij, Schoffelmeer, & De Vries, 2012 ; Koffarnus & Woods, 2013 ). However, the relation between impulsive-choice and other drugs is inconsistent (see Stein & Madden, 2013 for review).

Extensive research has evaluated the alternative possibility that steep delay discounting is a result of problem drug use. The findings are discrepant: human and nonhuman discounting can be increased, decreased, or unaffected by a wide variety of drugs and doses, with little consistency between published studies (see de Wit & Mitchell, 2010 ; Stein & Madden, 2013 ; Weafer, Mitchell, & De Wit, 2014 , for discussion and reviews). If acute or chronic drug use influences delay discounting, the effects are complicated by poorly understood genetic factors, dose of drug, drug type, baseline levels of discounting, and the discounting task itself ( Stein & Madden, 2013 ; Weafer et al., 2014 ).

Thus, the weight of the current evidence favors (but does not establish) an etiological role of delay discounting in addictions and health-impacting behaviors. Bickel and colleagues (2012) suggested steep delay discounting is a trans-disease process underlying these maladaptive behaviors. This proposal and its implications have been echoed by researchers calling for interventions to reduce steepness of delay discounting as a preventive measure for those at risk of addictions (e.g., Gray & MacKillop, 2015 ; Volkow & Baler, 2015 ) or as a component of a comprehensive treatment for those already afflicted with health deficits caused by persistent patterns of impulsive choice ( Schroeder, 2007 ).

If the above hypotheses are supported empirically, then it will be important to identify effective methods for experimentally reducing delay discounting and impulsive choice. These experimental manipulations are also important in evaluating the causal role, if any, of delay discounting on the maladaptive behaviors with which it correlates. The present review and meta-analysis identified and evaluated the efficacy of methods used to reduce delay discounting or impulsive choice. The few existing reviews of this literature have either not been systematic (i.e., explicitly defined and replicable search and inclusionary procedures; e.g., Gray & MacKillop, 2015 ; Lempert & Phelps, 2016 ) or were not comprehensive ( Koffarnus, Jarmolowicz, Mueller, & Bickel, 2013 ). The present comprehensive review focuses on environmental manipulations (i.e., non-pharmacological or neurological) designed to reduce delay discounting or impulsive choice.

Identification of Studies

Studies employing an experimental manipulation designed to reduce the steepness of delay discounting or prevalence of impulsive choice were identified via one of two methods. First, the PsycINFO and PLoS One 2 databases were searched on March 1, 2016, limiting results to peer-reviewed papers written in English. Articles were included if the abstracts contained at least one term in each of two groups of terms: 1 [intertemporal choice OR delay discounting OR delayed gratification OR “impulsive choice” OR “impulsive decision making” OR “intertemporal decision making”], 2 [expectancy OR certainty OR manipulat* OR train* OR improv* OR intervention OR chang* OR alter* OR effect* OR affect* OR reduc* OR increas*]. The PsycINFO and PLoS One searches yielded 1,186 and 47 records, respectively. Second, references of articles identified and included in the review were searched, with 19 additional articles identified.

Exclusion Criteria

The 1,233 articles were screened according to six exclusion criteria (see Figure 2 ). First, articles needed to include at least one experimental manipulation that was neither pharmacological nor neural (e.g., lesion). The rationale for this exclusion was, in part, practical. Including the large number of studies attempting to influence impulsive choice through pharmacological or neural manipulations would make the review unwieldy, and reviews on these topics already exist (e.g., Stein & Madden, 2013 ; Weafer et al., 2014 ; Winstanley, 2010 ). Second, articles were excluded if they did not include a control or comparison procedure (e.g., a control group or pre-intervention baseline) or if they were case studies. Third, if delay discounting or impulsive choice was not unambiguously measured (e.g., studies in which the effects of delay and effort were confounded) the article was excluded. Fourth, articles were excluded if their therapeutic potential was limited to select contexts; e.g., experimental manipulations of reward magnitude or sign (gains vs. losses), participant income, and commodity type (monetary vs. food rewards) are impractical in clinical and certain field settings. Fifth, articles were excluded if there were confirmed violations of the assumptions of inferential statistical tests (e.g., violations of normality) and the authors did not respond to requests to provide individual-participant data for the purpose of non-parametric re-analyses.

Diagram depicting the number of articles retrieved, included, and excluded following the criteria developed for the present review.

Articles that passed the first five criteria ( n = 113) were categorized based on the type of experimental manipulation (e.g., framing, cueing, priming) and the direction of the hypothesized effect (increase/decrease). A sixth criterion excluded categories of manipulations that were uniformly hypothesized to increase delay discounting/impulsive choice ( n = 22; e.g., sexual cues hypothesized to increase delay discounting in men; Van den Bergh, Dewitte, & Warlop, 2008 ).

Computation of Effect Size

Effect sizes were calculated when the necessary data were provided in-text, could be obtained from the published graphs using GraphClick, or were provided by the corresponding author. Authors were contacted if the article was published within the last 10 years, reasoning that older data were unlikely to be retained. If data were not provided after two requests, the article was retained for narrative purposes but not included in meta-analysis or graphical displays of effect sizes.

Effect size was first calculated using Cohen’s d , but depending on the study design, the effect size was calculated differently. For between-subjects designs, when the means and standard deviations (or standard errors of the mean) were available, Cohen’s d was calculated as follows:

where x ̅ 1 and x ̅ 2 are the means for each of two groups of interest, n 1 and n 2 are the sample sizes for those groups, and SD 1 and SD 2 are the standard deviations of the mean for each of those groups, respectively ( Lakens, 2013 ). If the results of a t -test were reported for a between-subjects comparison, then the following formula was used:

where t is the test statistic, and n 1 and n 2 are as described above ( Lakens, 2013 ). When the number of participants/subjects assigned to each group was not specified, the sample size per group was approximated as the total number of subjects in the analytic sample divided by the number of groups being compared.

For within-subjects designs, the standardizer of the mean difference was the average of the standard deviations across measurements, which was calculated as follows:

where x ̅ diff is the difference in means across two assessments, and SD 1 and SD 2 are as described previously. Equation 3 was chosen so effect sizes across between- and within-subjects designs would be more comparable (see Lakens, 2013 for discussion).

For studies using impulsive choice assessments with dichotomous outcomes (e.g., single-item discounting questions) the proportions of individuals choosing the SSR or LLR were used in lieu of averages for calculating effect size. Specifically, the proportions were treated as means (of a distribution of 0s and 1s), with their difference divided by the pooled standard deviation of these means; this version of Cohen’s d btw was calculated as outlined in DeCoster (2009) :

In Equation 4 , the subscripts 1 and 2 represent the two groups being compared; n is as in previous calculations, p is the percentage of participants selecting the target response (e.g., LLR), and q is 1 – p . For within-subjects designs with a proportional dependent measure, Cohen’s d win was calculated similarly to d btw-prop (but with subscripts referring to measurements across baseline and intervention assessments):

The formula for d win-prop was extrapolated from the calculation for d btw-prop ; although note that both formulas result in the same values. These particular formulas for proportion data were chosen over others (e.g., Cohen’s h ; Cohen, 1988 ) because, based on simulations (not reported here), they yielded effect sizes of a more comparable range to that of Cohen’s d btw and d win .

After calculating the appropriate version of Cohen’s d , effect sizes were corrected for small sample sizes because Cohen’s d tends to overestimate effect sizes when groups are small ( Cumming, 2011 ). To correct for small sample size bias, Hedge’s g was calculated by multiplying d by one of the following correction factors ( Cumming, 2011 ; Lakens, 2013 ). For between-subjects designs, the correction ( j ) was:

and for within-subjects designs, the correction was:

For consistency across studies, the correction was applied regardless of sample size. Thus, the final reported effect sizes, and those used in meta-analyses, are all Hedge’s g .

Next, the sampling variance of the effect sizes was calculated using the following formula ( Morris & DeShon, 2002 ):

where ñ is equal to ( n 1 * n 2 )/ ( n 1 + n 2 ) for between-subjects designs and N for within-subjects designs. All others terms in equation 8 are as previously described and are the same across study types with the exception that the second term ( N – 2/ N – 4) is ( N – 1/ N – 3) for within-subjects designs. While variance calculations for within-subjects designs more typically incorporate the correlation between repeated measurements, the majority of studies employing within-subjects designs did not report this information. Application of Equation 8 to within-subject data is identical to the within-subject calculation of variance suggested by Morris and DeShon (2002) while assuming a correlation of 0.5 between measurements.

Finally, effect sizes were subjected to meta-analysis to broadly examine which categories and subcategories of manipulations were successful for reducing steepness of discounting. Meta-analysis was conducted using the metafor package ( Viechtbauer, 2010 ) in R ( R Core Team, 2013 ). The efficacy of the different categories of manipulations was examined using a mixed-effects model, including category as a moderator and no intercept. Next, similar models were conducted for each category but with subcategory included as a moderator (unless there were no subcategories, which reduced to a simple random-effects model). All effect sizes were included except where noted in the footnote below. 3 A measure of effect size heterogeneity ( I 2 ) indicates the percentage of study variability, or the amount of variability in effect sizes not accounted for by chance ( J. P. T. Higgins, Thompson, Deeks, & Altman, 2003 ).

Because of the heterogeneity in experimental procedures both between- (e.g., momentary framing vs. months-long training regimens) and within-categories (e.g., methods of measuring and quantifying impulsive choice), as well as the settings (e.g., controlled laboratory vs. outpatient clinic) and participant populations (e.g., economics students vs. laboratory rats), moderators of effect size other than category and subcategory were not examined. For these reasons, we also did not conduct comparisons of effect sizes across categories, nor did we provide metrics of publication bias. We chose not to provide the latter because in many instances there were varying numbers of papers for which effect sizes could not be calculated, which would ultimately bias the resulting measure. Thus, the meta-analytic technique primarily served to provide an objective method of determining manipulation efficacy, which was contextualized within narrative review.

After applying the exclusion criteria, 92 papers qualified for review. These articles fell into the nine categories of experimental manipulations shown in Figure 3 (symbols represent effect sizes from each paper). Some categories were divided into the sub-categories outlined in the legend. The primary meta-analysis revealed that overall, the interventions were successful in reducing discounting, Q (9) = 224.68, p < .0001. Moderate heterogeneity in effect sizes reveal differences across studies ( I 2 = 64%). In the sections below, we discuss the significance of, and theory behind each of the categories and their subcategories as relevant. The categories are organized sequentially from applied to translational to basic research. As such, the first six sections summarize research conducted exclusively with human participants, while the remaining sections include human and nonhuman research subjects.

Effect sizes (Hedge’s g btw or g win ) by manipulation type. The effect sizes are either averages by publication (when a single study had more than one experiment or condition examining the same manipulation) or individual effect sizes (when a publication reported the result of one study or found a significant moderator of the effect). Larger effect sizes reflect greater preference for larger, delayed outcomes. Horizontal lines reflect the median effect size for that category; symbols for effect sizes are jittered to reduce overlap. Gray symbols indicate that the effect was not statistically significant.

Clinical Interventions

Twelve studies meeting the inclusion criteria examined the effects of clinical interventions; see Table 1 and Figure 4 . For most of these studies, delay discounting was not a primary target of the intervention, but changes in discounting were examined because of their relevance to the problem behavior(s) (e.g., addictions). Clinical interventions overall produced significant reductions in discounting ( B = 0.23, SE = 0.08; z = 2.78, p = .005), with magnitude and significance varying by subcategory. After accounting for subcategories, study heterogeneity was small to moderate ( I 2 = 38%) suggesting that a relatively larger percentage of variability in effect sizes was due to manipulation type, rather than study heterogeneity.

Effect sizes ( g btw or g win ; filled and open circles, respectively) and 95% confidence intervals for manipulations in the Clinical category. Only studies for which effect sizes could be calculated are included. Larger effect sizes reflect greater preference for larger, delayed outcomes.

Effect sizes for Clinical Manipulations: Estimated subcategory averages (and SEM) from the Clinical manipulation-only meta-analytic model and individual-publication effect sizes.

n.s., No statistically significant effect; AUC, area under the discounting curve; CNC, could not calculate effect size

Mindfulness-Based Approaches

Mindfulness is nonjudgmental awareness of present-moment events (e.g., thoughts, sensations), which can be employed as a stand-alone intervention or within mindfulness-based therapies such as Acceptance and Commitment Therapy (ACT; Hayes, Strosahl, & Wilson, 1999 ). Overall, the reduction in discounting produced by mindfulness interventions only approached significance ( z = 1.89, p = .059). Mindful-eating produced a small decrease in discounting of hypothetical food, but not money ( Hendrickson & Rasmussen, 2013 ), whereas a brief course of ACT produced significant within-subjects reductions in discounting ( Morrison, Madden, Odum, Friedel, & Twohig, 2014 ). In the latter, however, the between-group difference (ACT vs. wait-list control) did not achieve statistical significance. Continued research to clarify outcome-specificity of effects while employing better controls (e.g., sham-therapy in lieu of wait-list controls) is warranted.

Contingency Management

In contingency management (CM) of substance abuse, rewards (e.g., money, vouchers) are provided contingent on biologically confirmed drug abstinence ( Dallery, Glenn, & Raiff, 2007 ; S. T. Higgins & Petry, 1999 ; Silverman et al., 1996 ). If steep delay discounting is a consequence of frequent drug use, then abstinence-producing interventions like CM should decrease delay discounting (unless such changes are permanent). Three studies examining the effect of CM for reducing cigarette smoking revealed inconsistent effects, although when combined they produced significant decreases in discounting ( z = 2.39, p = .02). In one study, a five-day CM intervention reduced cigarette smoking and discounting of delayed monetary- and cigarette-rewards ( Yi et al., 2008 ). However, Weidberg et al. (2015) found that a longer course of CM decreased discounting in women only, and regression to the mean likely accounted for the effect. Yoon, Higgins, Bradstreet, Badger, and Thomas (2009) reported no effect of CM on delay discounting.

Other Substance Use Treatments

The effects of multi-component substance use treatments on delay discounting have been examined in five studies. Three of these found no significant reductions in steepness of discounting ( Aklin, Tull, Kahler, & Lejuez, 2009 ; De Wilde, Bechara, Sabbe, Hulstijn, & Dom, 2013 ; Littlefield et al., 2015 ), but when combined they produced small, significant effects ( z = 2.16, p = .03). In the two studies in which significant reductions were observed, CM was a component of the intervention ( Landes, Christensen, & Bickel, 2012 ; Lee, Stanger, & Budney, 2015 ).

Two additional studies evaluated the effects of more specific treatment components on discounting. A financial-planning-based treatment for cocaine use (e.g., clients were encouraged to restrict current spending and to plan for future expenses) nominally reduced delay discounting ( p = .052; Black & Rosen, 2011 ). Likewise, counseling clients to increase engagement in non-substance related activities (e.g., those related to educational and career goals) reduced drug value and use, but not discounting ( Dennhardt, Yurasek, & Murphy, 2015 ).

Overall, substance-use treatments do not consistently reduce discounting, and their overall utility is modest (model-estimated d = .16). Heterogeneity in procedures and treatments makes it difficult to rectify these inconsistencies. When considered in light of the hypothesis that regular drug use produces neuroadaptations that increase delay discounting (e.g., Mendez et al., 2010 ; Yi, Mitchell, & Bickel, 2010 ), these findings offer no simple support for the position that drug abstinence would reverse these effects. Perhaps the neuroadaptations are longer lasting than the treatments in these studies (2 to 36 weeks) or that treatment-produced abstinence (not simply being in treatment) coincides with reductions in delay discounting. The latter analysis was conducted in only one study in this review ( Weidberg et al., 2015 ) and they found no relation between smoking abstinence and reductions in discounting. Future substance-use treatment studies which include delay discounting or impulsive choice as a dependent measure should conduct much needed mediation analyses to evaluate the causal pathway that delay discounting might hold.

Episodic Future Thinking

Episodic future thinking (EFT) is the act of vividly imagining one’s future, which involves episodic simulation (pre-experiencing an event in its entirety: associated feelings, sensations, emotions, etc.) as opposed to generating semantic details (facts, general knowledge; Atance & O’Neill, 2001 ). When applied to delay discounting, participants are first asked to identify and vividly imagine positive future events (e.g., Daniel, Stanton, & Epstein, 2013b ; Peters & Büchel, 2010 ) and then are cued to imagine these events while completing a delay discounting task. EFT putatively reduces discounting by increasing the salience of future events or response-outcomes that would otherwise not be considered ( Dassen, Jansen, Nederkoorn, & Houben, 2016 ; Lin & Epstein, 2014 ), and/or that it inhibits hyper-valuation of immediate rewards ( Snider, LaConte, & Bickel, 2016 ). Based on the ten studies in this review, EFT produces sizeable ( B = 0.38, SE = .09), significant reductions in discounting ( z = 4.02, p < .0001) with little study variability ( I 2 = 3%); see Table 2 and Figure 5 .

Effect sizes ( g btw or g win ; filled and open circles, respectively) and 95% confidence intervals for Episodic Future Thinking manipulations. Only studies for which effect sizes could be calculated are included. Larger effect sizes reflect greater preference for larger, delayed outcomes

Effect sizes for Episodic Future Thinking Manipulations: Individual-publication effect sizes.

EFT procedures have been shaped by empirical findings regarding moderators of its efficacy. First, the episodic thinking must be future-oriented: EFT reduces delay discounting relative to present ( Lin & Epstein, 2014 ), past ( Dassen et al., 2016 ), and temporally-neutral thinking ( Daniel, Stanton, & Epstein, 2013a ; Daniel et al., 2013b ). Second, EFT produces larger effects when future events are more vividly imagined ( Palombo, Keane, & Verfaellie, 2015 ; Peters & Büchel, 2010 ) and personally/emotionally relevant ( Benoit, Gilbert, & Burgess, 2011 ). Thus, when engaging in EFT participants are often encouraged to imagine many details about the future events (e.g., where will this happen, what will you see/smell/hear; Dassen et al., 2016 ; Kwan et al., 2015a; Palombo, Keane, & Verfaellie, 2015b) and are explicitly instructed to imagine the cued events while completing the discounting task (cf. Peters & Büchel, 2010 ). The presented cues are often temporally-matched with the LLR (e.g., “Graduation in 1 year” is shown when the LLR is “$100 in 1 year”).

Some findings have called into question the necessity of episodic prospection for EFT benefits. Kwan et al. (2015) reported that EFT reduced delay discounting in amnesiacs with serious deficits in episodic prospective ability, and that changes in delay discounting were unrelated to the extent of these deficits. Notably, participants in Kwan et al. identified personally-relevant future events. By contrast, Palombo et al. (2015) , who supplied future events to participants, reported no beneficial effect of EFT in a similar sample. Because vividness and personal/emotional relevance are related to EFT’s efficacy ( Benoit, Gilbert, & Burgess, 2011b ; Palombo et al., 2015 ; Peters & Büchel, 2010 ), this procedural difference may account for the discrepant results. Kwan et al. (2015) also suggested that personal cues may enable other types of future prospection (e.g., semantic), which may similarly enhance future perspective.

The importance of other aspects of typical EFT procedures are less well researched. Where one study suggested benefits of EFT were dependent upon imagining positive-valence future events ( Liu, Feng, Chen, & Li, 2013 ), another found that imagining neutral-valence events reduced steepness of delay discounting ( Lin & Epstein, 2014 ). These studies used different discounting tasks and dependent measures, so additional research is needed to resolve the issue of valence.

Some research has evaluated individual-differences that moderate the effects of EFT. EFT is less effective in those with low working memory capacity ( Lin & Epstein, 2014 ), low goal persistence ( Daniel et al., 2013a ), and high consideration of the future ( Benoit et al., 2011a ); i.e., those who are already future-oriented do not benefit as much from EFT (but see Daniel et al., 2013a for a failure to replicate with a different measure of time perspective). Given working memory deficits among substance-dependent individuals (e.g., Bechara & Martin, 2004 ), EFT interventions may not be as successful for such populations.

The modally discussed psychological mechanism by which EFT reduces delay discounting is increasing future orientation or broadening temporal horizon (e.g., Lin & Epstein, 2014 ; Snider et al., 2016 ). The one study meeting inclusion criteria which has evaluated this hypothesis indicated EFT did not increase future orientation ( Dassen et al., 2016 ). 4 The authors speculated that the Consideration of Future Consequences scale ( Strathman, Gleicher, Boninger, & Edwards, 1994 ), a measure of future orientation, is not sensitive to state changes.

An alternative account of the effect of EFT on delay discounting is that it may be a byproduct of demand characteristics ( Rung & Madden, 2018 ). That is, if the experimental hypothesis is deduced by the participant, then he/she may behave in accord with it ( Orne, 1962 ; see Nichols & Maner, 2008 for a demonstration of such bias)–i.e., choose the LLR. Presenting future cues (e.g., “vacation in 2 years”) delay-matched to the LLR (e.g., $10 now vs. $100 in 2 years) increases concerns for a demand-characteristic effect. Indeed, the majority of participants who read a description of typical EFT procedures deduced that the experimenter expected the participant to choose the LLR ( Rung & Madden, 2018 ). Evaluating the contribution (if any) of demand characteristics to the effect of EFT should be a priority for future research.

Framing manipulations vary the description of an intertemporal choice while holding functionally equivalent the outcomes across different descriptions/frames ( Kühberger, 1998 ). For instance, in a classic example of framing (the Asian disease problem; Tversky & Kahneman, 1981 ), participants choose how to address the outbreak of a disease. One frame indicates the number who can be saved, and the other the number who will die. Across frames, the outcomes are the same, but choice is influenced by the gain/loss framing. Ten studies meeting the inclusion criteria examined the effects of different choice frames (see Table 3 and Figure 6 ), which produced medium-to-large ( B = 0.47, SE = .06), significant reductions in discounting ( z = 7.48, p < .0001). Across framing studies, there was a moderate degree of study heterogeneity ( I 2 = 68%).

Effect sizes ( g btw or g win ; filled and open circles, respectively) and 95% confidence intervals for Framing manipulations. Only studies for which effect sizes could be calculated are included. Larger effect sizes reflect greater preference for larger, delayed outcomes.

Effect sizes for Framing Manipulations: Estimated subcategory averages (and SEM) from the Framing manipulation-only meta-analytic model and individual-publication effect sizes.

Framing of Time

Describing LLRs as an outcome to be delivered on a specific date (e.g., $100 obtained on _____ [insert date 1 year from today]) instead of delayed by the same interval of time (e.g., $100 in 1 year) consistently and significantly reduces delay discounting ( z = 5.39, p < .0001) ( Read, Frederick, Orsel, & Rahman, 2005 ; LeBoeuf, 2006). This finding has been replicated ( DeHart & Odum, 2015 ; Dshemuchadse, Scherbaum, & Goschke, 2013 ; Klapproth, 2012 ), with effects of medium to large magnitude ( B = 0.45).

Four theoretical accounts of the date-delay framing effect are noteworthy. First, date framing may shift attention from the delay and increase sensitivity to the difference in the SSR and LLR monetary amounts ( LeBoeuf, 2006 ; Read et al., 2005 ). Second, presenting dates may interfere with computational strategies or heuristics typically used to judge the subjective values of delayed outcomes ( Read et al., 2005 ). Third, the effect could be due to subadditivity: specifying the delay as a date prevents participants from considering, for example, six separate 1-week delays when the delay is specified as “6 weeks” ( LeBoeuf, 2006 ; Read, 2001 ). While not included in tables or graphs herein (exclusion criteria 6), support for the subadditivity hypothesis is mixed: one study provides support (days vs. date conditions in DeHart & Odum, 2015 ) and two provide evidence against (Experiment 5 in LeBoeuf, 2006 ; Experiment 1 in Read et al., 2005 ). Finally, date-delay framing may reduce subjective estimates of time duration (see Experiment 6 in LeBoeuf, 2006 ). For example, drug dependent populations overestimate time durations (e.g., Wittmann, Leland, Churan, & Paulus, 2007 ), and Klapproth (2012) reported that date framing reduced delay discounting so much in this population ( Mdn g = 0.96) that their discounting rates were not significantly different from those of a non-drug-using comparison group.

Framing of Outcomes

The remaining framing studies manipulated the presentation of the SSR and LLR outcomes themselves, which produced significant reductions in discounting ( z = 4.32, p < .0001). In the most common outcome frame, the explicit zero manipulation, the mutual exclusivity of SSR and LLR alternatives is highlighted by noting that selecting one alternative means nothing will be received at the time the foregone option would have been obtained ( Magen, Dweck, & Gross, 2008 ). For example, instead of choosing between $50 now vs. $100 in 1 year, the zero outcomes are made explicit by reframing the choice as $50 now and $0 in 1 year vs. $0 now and $100 in 1 year. Explicit-zero framing significantly reduces discounting with medium to large effects ( Magen et al., 2008 ; Radu, Yi, Bickel, Gross, & McClure, 2011 ; Wu & He, 2012 ). While the zero is typically made explicit in both the SSR and LLR alternatives, Wu and He (2012) found that the delayed zero ($50 now and $0 in 1 year ) is largely responsible for the effect. Presenting the immediate zero alone produced no significant reduction.

Magen et al. (2008) proposed that a preference for improving sequences can explain the explicit zero effect (e.g., Loewenstein & Prelec, 1993 ). That is, choosing the LLR arranges an improving sequence from $0 now to money in the future, whereas the SSR yields a decreasing sequence from something now to nothing later. This account was challenged by Radu et al. (2011) : explicit zeros reduce past discounting, in which preference for the larger more-distal reward produces a decreasing sequence (e.g., $100 26-days ago and $0 one hour ago). Combined with the null effect of the present zero ( Wu & He, 2012 ), the improving sequence hypothesis appears refuted. Radu and colleagues (2011) suggest instead that the explicit-zero increases temporal attention to more distal outcomes, thereby broadening the temporal window across which choice outcomes are integrated. That the explicit-zero effect is muted among those high in future time perspective ( average g = 0.04; Wu & He, 2012 ) is consistent with this account.

Other outcome framing manipulations were infrequent ( n = 2). In Grace and McLean (2005) , the LLR was presented as two amounts: the amount of the SSR plus the difference between the SSR and LLR amounts. For example, a choice between $150 now vs. $200 in 1 year was reframed as $150 now vs. $150 plus a $50 bonus, both delivered in 1 year. Segregating the LLR significantly reduced discounting. Imuta, Hayne, and Scarf (2014) found similar reductions in impulsive choice when children were shown the SSR (stickers), and then additional stickers were added to comprise the LLR. Grace and McLean (2005) explained the effect as the result of diminishing marginal utility of rewards; i.e., the subjective value of a reward increases as a concave function of objective amount ( Galanter, 1962 ). Therefore, when the LLR is separated into two outcomes, the value of each is calculated separately and the SS amount + bonus is subjectively more valuable than the single-quantity LLR. Given the initial successes of these manipulations, additional empirical attention appears warranted.

Perspective Taking

Making decisions on behalf of a group or another person does not, overall, significantly affect delay discounting ( z = 0.09, p = .93; I 2 = 84%; see Table 4 and Figure 7 ). Instructional differences may account for some discrepant between-study effects (see Ziegler & Tunney, 2012 ), but at this time there are too few studies representing the different instruction types (and potentially important participant characteristics) to objectively support this via evaluation of moderator(s).

Effect sizes ( g btw or g win ; filled and open circles, respectively) and 95% confidence intervals manipulations in the Perspective Taking category. Only studies for which effect sizes could be calculated are included. Larger effect sizes reflect greater preference for larger, delayed outcomes.

Effect sizes for Perspective Manipulations: Estimated subcategory averages (and SEM) from the Perspective manipulation-only meta-analytic model and individual-publication effect sizes.

n.s. , No statistically significant effect; AUC, area under the discounting curve; CNC, could not calculate effect size

Priming involves experimental manipulations of participants’ affect or cognitive content, typically arranged through a preliminary task and often framed as part of a different experiment than the discounting task. While priming manipulations produced modest ( B = 0.24, SE = 0.06), significant reductions in discounting ( z = 4.18, p < .0001) their effects are often context-specific (see Table 5 and Figure 8 ). The latter is both empirically (see below) and statistically supported by moderate study heterogeneity ( I 2 = 42%).

Effect sizes ( g btw or g win ; filled and open circles, respectively) and 95% confidence intervals manipulations in the Priming category. Only studies for which effect sizes could be calculated are included. Larger effect sizes reflect greater preference for larger, delayed outcomes, and arrows on the end of a confidence interval indicate that the limits extended beyond the axes.

Effect sizes for Priming Manipulations: Estimated subcategory averages (and SEM) from the Priming manipulation-only meta-analytic model and individual-publication effect sizes.

Affect Priming

Affect priming typically involves the presentation of emotion-inducing stimuli (e.g., pictures, words) or directed remembering (e.g., think of a positive event in your past). Across six papers, positive-affect priming had small ( B = 0.17, SE = .09), inconsistent, and overall non-significant effects on discounting ( z = 1.87, p = .06). Only one of these papers reported significant reductions in discounting: Pyone and Isen (2011) found that positive-affect primes reduced impulsive choice in three of four experiments, and that the effects were dependent on the magnitude or delay of the larger-later rewards 5 . The remaining five papers either reported no significant effect of positive affect ( DeSteno, Li, Dickens, & Lerner, 2014 ; Luo, Ainslie, & Monterosso, 2014 ; Moore, Clyburn, & Underwood, 1976 ; Shimoni, Asbe, Eyal, & Berger, 2016 ) or the opposite among extraverted participants ( Hirsh, Guindon, Morisano, & Peterson, 2010 ). DeSteno et al. (2014) , however, argued that the specific feeling of gratitude should increase altruism (which is generally motivated by long-term interests) and thereby reduce delay discounting. Consistent with this hypothesis, priming gratefulness produced moderate reductions in discounting.

Mortality Priming

Studies inducing thoughts of one’s mortality, either now or in the future, have a small ( B = 0.30, SE = .19) nonsignificant impact on delay discounting ( z = 1.56, p = .12; see Table 5 and Figure 5 ). Any significant effects of mortality priming on discounting are complicated by the specific primes used and participant characteristics (e.g., high vs. low SES; high vs. low disgust-sensitivity; Griskevicius, Tybur, Delton, & Robertson, 2011 ; Kelley, Crowell, Tang, Harmon-Jones, & Schmeichel, 2015 )

Temporal Priming

The few studies examining effects of temporal primes on steepness of discounting have produced modest ( B = 0.25, SE = .10) but significant reductions in discounting ( z = 2.41, p = .02; see Table 5 and Figure 5 ). For example, Zauberman, Kim, Malkoc, & Bettman (2009) demonstrated that nonlinear perception of time could partially account for the hyperbolic shape of the delay discounting function (see also McKerchar et al., 2009). From this, they hypothesized that priming attention to time would shift discounting from hyperbolic (steep declines at short delays that give way to shallow declines at long delays) to exponential (constant rate of discounting at all delays). Temporal priming was achieved by having participants estimate a variety of time durations (i.e., how long does it take to…). Consistent with a shift from hyperbolic to exponential discounting, time-primed participants discounted modestly less at a one-month, but not a three-month delay to the LLR. This effect was not, however, replicated in a follow-up experiment in the same report.

Construal Primes

Construal-level theory ( Trope & Liberman, 2003 ) posits that information processing occurs on a continuum from concrete (detailed, context-dependent, focused on the present situation) to abstract (broad, decontextualized, focused beyond the present situation). Applied to intertemporal choice, the SSR is imminent so it should be construed at a concrete level, whereas the LLR should be construed relatively abstractly (general thoughts about the nonspecific life-context in which the LLR would be received). While construal primes vary in their implementations, they yield the most consistent and significant effects of all primes reviewed herein ( B = .26, SE = .07; z = 3.56, p = .0004).

Malkoc, Zauberman, & Bettman (2010) hypothesized that abstract construal of the SSR (vs. the concrete default) should render the discounting function more exponential. In so doing, discounting should decrease in the shorter range of LLR delays, similar to the effects of temporal priming in Zauberman et al. (2009) . In support of their hypothesis, priming abstract thinking prior to an intertemporal choice task produced a small reduction in discounting at brief, but not long delays relative to concrete and control primes (i.e., a significant interaction with delay).

In contrast, Kim et al. (2013) suggested the mismatch in construal across the SSR (concrete) and LLR (abstract) impedes comparison of these outcomes and increases impulsive choice. Across three experiments, providing concrete visual and/or verbal details about immediate and delayed Paris vacations reduced discounting. Nisan (1974) conducted a similar concrete-construal manipulation by visually (vs. verbally) presenting the SSR and LLR prior to the choice. 6 Visual presentation decreased impulsive choice in 7-year olds, but had no effect in younger or older children who were putatively too impulsive or self-controlled, respectively, to benefit from the manipulation.

Viewed from the perspective of construal-level theory, EFT might be conceptualized as an all-concrete manipulation. That is, if the default construal of the SSR is concrete, then thinking vividly about the LLR may render its construal more concrete (e.g., I will be getting married in two years when I receive the $1,000). If this analysis is correct, then EFT represents a subset of construal-based manipulations, in which its mechanism is construal-level parity.

Cueing involves the presentation of a functional stimulus prior to decision-making. The function of the stimulus may be acquired through ontogenetic learning or phylogenetic evolution. While few studies have evaluated the ability of cues to reduce discounting, those that have show promise. Combined, the five studies examining cueing effects produce significant ( z = 4.75, p < .0001) and moderate reductions in discounting ( B = 0.63, SE = 0.13). The large degree of study heterogeneity in this category ( I 2 = 88%) is likely attributed to studies examining effects of learned cues, which had sparse representation (see discussion below).

Humans show an affinity for looking at natural landscapes depicting resource abundance ( Purcell, Peron, & Berto, 2001 ), which has beneficial effects on affect and attention ( Bowler, Buyung-Ali, Knight, & Pullin, 2010 ) and slows the perception of how quickly time passes ( Rudd, Vohs, & Aaker, 2012 ). Three studies report medium to large reductions in delay discounting following presentation of nature cues (see Table 6 and Figure 9 ; z = 2.81, p = .005). Nature cueing produces reductions in discounting both in-lab (i.e., nature photos, Berry et al., 2015 ; Berry, Sweeney, Morath, Odum, & Jordan, 2014 ; van der Wal et al., 2013 ) and in-vivo (i.e., spending time outdoors, van der Wal et al., 2013 ). Some mechanisms for the effects of nature cues have been evaluated: the studies above found no differences in session-time estimation across nature- and urban-cue conditions ( Berry et al., 2014 ), and discounting rate was not significantly correlated with time estimation ( Berry et al., 2015 ) nor with changes in affect ( van der Wal et al., 2013 ). Thus, the prevailing hypothesis is that nature cues signal a safe, rich environment in which waiting is evolutionarily adaptive. Given that exposure to nature cues consistently reduces discounting with medium-to-large significant effects, further research evaluating dose- and the duration of its effects is warranted.

Effect sizes ( g btw or g win ; filled and open circles, respectively) and 95% confidence intervals manipulations in the Cueing category. Only studies for which effect sizes could be calculated are included. Larger effect sizes reflect greater preference for larger, delayed outcomes, and arrows on the end of a confidence interval indicate that the limits extended beyond the axes.

Effect sizes for Cueing Manipulations: Estimated subcategory averages (and SEM) from the Cueing manipulation-only meta-analytic model and individual-publication effect sizes.

AUC, area under the discounting curve

Cues acquiring meaning as a function of learning history can also affect delay discounting; however, the small number of studies in this subcategory ( n = 2) combined with the large differences in the magnitude of their effects, and sample sizes used (which produced an extremely wide confidence interval in one case) rendered their combined efficacy non-significant ( z = 0.38, p = .71). Given the substantial differences in the theory driving these studies, this non-significant result should be interpreted with caution. In brief, Sellitto and di Pellegrino (2014) demonstrated small, significant reductions in discounting by presenting cues established to recruit enhanced top-down cognitive control. By contrast, cues trained with “better than” and “worse than” functions substantially decreased delay discounting in pathological gamblers when the “better than” cue was paired with the LLR ( Dixon and Holton, 2009 ). As discussed in the context of EFT, in the latter study participants may have deduced the experimenter’s intent during the discounting task, so demand characteristics are a concern.

Thirteen studies falling into the broad category of contextual manipulations produced significant reductions in impulsive choice ( B = 0.37, SE = 0.05; z = 6.90, p < .0001; see Table 7 and Figure 10 ). Given the breadth of this category and theories behind the approaches, the moderate to large degree of study variability is unsurprising ( I 2 = 68%). Context manipulations involve changing features of the choice scenario that do not fall within the scope of framing manipulations; i.e., these manipulations do not produce economically equivalent outcomes across conditions. Where possible, similar manipulations are grouped together.

Effect sizes ( g btw or g win ; filled and open circles, respectively) and 95% confidence intervals manipulations in the Context category. Only studies for which effect sizes could be calculated are included. Larger effect sizes reflect greater preference for larger, delayed outcomes, and arrows on the end of a confidence interval indicate that the limits extend beyond the axes.

Effect sizes for Contextual Manipulations: Estimated subcategory averages (and SEM) from the Contextual manipulation-only meta-analytic mode and individual-publication effect sizes.

n.s., No statistically significant effect; AUC, area under the discounting curve

The Context category is the first in which nonhuman animal research is presented. It is appropriate to review animal studies with those on humans because experimental reductions of nonhuman impulsive choice have often proven effective in reducing human impulsive choice (e.g., Mazur & Logue, 1978 ; Schweitzer & Sulzer-Azaroff, 1988 ). For the sake of stimulating future research, we note where manipulations have proven effective in animals but have not yet been evaluated in humans. Effect sizes for several animal studies are not reported because the number of subjects was too small to evaluate assumptions of normality and thus appropriately calculate effect sizes.

Adding Delays

The hyperbolic shape of the delay-discounting function predicts that impulsive choice will be reduced if a common delay is added to the delivery of both the SSR and LLR (e.g., $5 now vs. $10 in 2 weeks becomes $5 in 1 week vs. $10 in 3 weeks). Overall, this technique is successful for reducing impulsive choice ( z = 6.34, p < .0001). It is robust in pigeons, whether implemented by adding a common delay ( Ainslie & Herrnstein, 1981 ) or fixed-interval schedule ( Siegel & Rachlin, 1995 ) prior to the delivery of both rewards. Similarly, if at the beginning of a trial (when SSR and LLR are both delayed) pigeons are allowed to pre-commit to the LLR, they will do so ( Rachlin & Green, 1972 ). Green, Myerson, and Macaux (2005) replicated this effect in humans when adding a common delay to the SSR and LLR alternatives in a discounting task. They found the effect held with various added delay durations (e.g., 5 years and 10 years), LLR reward magnitudes (e.g., $200 to $250,000); and others have found it generalizes to cigarette smokers and different reward types (hypothetical or potentially real money; S. H. Mitchell & Wilson, 2012 ). Dai and Fishbach (2013) replicated this general effect in one of three experiments; the lack of significance in the other two cases was attributed to the small difference between the SSR and LLR rewards (approx. $5 USD).

In a variant of the adding-delays procedure in humans, Dai and Fishbach (2013) produced consistently large reductions in impulsive choice by simply informing participants of the choice alternatives before the choice-point. The authors hypothesized that this pre-choice waiting produced a sense of investment, which increased the subjective value of the LLR. Changes in the perceived value of the LLR completely mediated the manipulation effect, providing strong evidence for this account. This finding deserves continued exploration.

Adding Response Requirements

Four animal studies have examined the effects of adding a response requirement prior to the selection of SSR and LLR outcomes. Due to the small number of studies contributing effect sizes in this category ( n = 2) and the small-samples in each, the effect was large ( B = 2.14) but non-significant ( z = 1.83, p = .07). Siegel and Rachlin (1995) reported that adding a pre-choice response requirement that could be completed across either choice-key significantly reduced pigeons’ impulsive choice, and similar reductions have been observed in rats whether the pre-choice response requirement is arranged on a non-choice lever ( Mazur, 2012 ), independent requirements are arranged on each choice-lever ( Huskinson & Anderson, 2013 ), or only on the SSR alternative ( Fortes, Vasconcelos, & Machado, 2015 ). To date, this manipulation has not been investigated in humans.

From a theory-evaluation perspective, a shortcoming of existing studies that add common pre-choice response requirements is the confounding of added delay with added responses. Siegel and Rachlin (1995) reported reductions in impulsive choice whether the addition was response- or time-based; but, added delays were not yoked to time spent completing the response requirement. Theory aside, adding pre-choice response requirements has produced greater reductions in impulsive choice than adding delays alone (see individual effect sizes in Table 7 ), which underscores the need to investigate the therapeutic potential of this effect in humans.

Adding Outcomes

Decreases in discounting have been found by adding outcomes to the choice scenario ( z = 4.22, p = .0001), although with smaller effects than the above contextual manipulations ( B = 0.24). Kowal and Faulker (2016) offered mixed evidence for the discounting-reducing effects of adding a third alternative (a decoy) to the usual two-choice task. Adding a decoy that was the same size as the LLR but delivered after a longer delay; or one that was smaller than the SSR and more delayed than the LLR reduced delay discounting. However, the effects were not robust across task sequences and/or were dependent on data exclusions. Urminsky and Kivetz (2011) found reductions in impulsive choice by adding a small, near-immediate reward (referred to as a token ) to both the SSR and LLR alternatives; but this effect was often confined to conditions in which SSR and LLR magnitudes were similar. Scholten and Read (2014) reduced discounting to a modest, although significant degree by using a token immediate payment to arrange an improving sequence of events on the LLR (pay now, big reward later). Relatively larger, significant reductions were obtained when a token payment was used to create a deteriorating sequence on the SSR (small reward now, pay token amount later).

Trustworthiness

Increasing trustworthiness of the LLR source significantly reduces discounting ( z = 4.46, p <. 0001), although not consistently so. Mahrer (1956) increased trustworthiness of an experimenter by initially having him/her deliver all promised rewards to children. Subsequently, children in this high-trust group made less impulsive choices, but the effect did not generalize to a novel experimenter. Michaelson, De la Vega, Chatham, and Munakata (2013) reduced discounting by simply describing hypothetical LLR-providers as trustworthy, but the effect was only significant in a within-subjects manipulation (not between-).

Michaelson et al. (2013) and Mahrer (1956) also decreased the trustworthiness of the source of the LLR, and this produced consistent increases in impulsive choice. The aforementioned findings parallel reductions in impulsive choice when the probability of LLR receipt is reduced in human ( Vanderveldt, Green, & Myerson, 2015 ) and nonhuman research ( Mazur, 1985 ). As these manipulations were hypothesized to increase impulsive choice, these studies did not meet the inclusion criteria of this review and are not presented in tables or figures.

Learning-Based Approaches

A variety of learning-based approaches to reducing delay discounting have produced some of the most reliable and large reductions in delay discounting ( B = 0.62, SE = 0.08; z = 7.43, p < .0001; see Table 8 and Figure 11 ). The moderate to large degree of study variability ( I 2 = 60%) reflects the variety of learning-based approaches employed.

Effect sizes ( g btw or g win ; filled and open circles, respectively) and 95% confidence intervals for Learning manipulations. Only studies for which effect sizes could be calculated are included. Larger effect sizes reflect greater preference for larger, delayed outcomes, and arrows on the end of a confidence interval indicate that the limits extended beyond the axes.

Effect sizes for Learning-Based Manipulations: Estimated subcategory averages (and SEM) from the Learning manipulation-only meta-analytic model and individual-publication effect sizes.

Reward bundling

In accord with Figure 1 , if the discounted value of the LLR is less than the undiscounted value of the SSR, the impulsive choice will be made. However, if a single choice determines not just the next outcome, but a bundle of the same outcomes (e.g., choosing the SSR locks the decision-maker into three SSRs delivered over the next three trials) this should reduce impulsive choice (for quantitative details of this prediction see Stein, Smits, Johnson, Liston, & Madden, 2013 ).

Several studies have evaluated this bundling prediction, typically finding successful reductions in discounting ( z = 4.57, p < .0001). Kirby and Guastello (2001) found that bundling five rewards together (monetary or food), caused a large percentage of participants to reverse an initial preference for an SSR to LLR. This effect has been replicated in rats ( Ainslie & Monterosso, 2003 ) and human cigarette smokers ( Hofmeyr, Ainslie, Charlton, & Ross, 2011 ), although the latter failed to find an effect of reward bundling in nonsmokers. Stein, Smits, et al. (2013) evaluated if experience with bundled rewards would reduce impulsive choice when outcomes were subsequently unbundled. Relative to a no-bundle control group, experience with 9-reward bundles (but not 3-reward bundles) decreased impulsive choice for unbundled SS and LL rewards. There was no significant relation between impulsive choice in the bundling (training) phase and impulsive choice during testing (unbundled rewards), suggesting that greater exposure to delays during bundling exposure (i.e., the 9-reward bundle group) appeared responsible for the reduction in unbundled impulsive choice.

Delay fading/exposure

Exposure to delayed rewards via fading techniques or prolonged experience has produced large ( B = 0.98) and significant effects ( z = 3.13, p = .002) on impulsive choice. Taking a systematic approach to exposing animals to delayed reinforcers, Mazur and Logue (1978) first established pigeons’ preference for large food rewards vs. small food rewards that were both delayed by 6 s. Then, the delay to the smaller reward was gradually reduced (faded out) while maintaining preference for the LLR until the SSR was available immediately. Compared to a no-fading control group, delay-fading produced far fewer impulsive choices. This effect was maintained when the position of the SSR and LLR keys (i.e., left/right) was reversed and when the pigeons were retested 11 months later ( Logue & Mazur, 1981 ). The short-term impulsive-choice reducing effects of delay-fading have been replicated in a laboratory setting in children identified as impulsive or hyperactive ( Schweitzer & Sulzer-Azaroff, 1988 ).

Stein et al. (2013) evaluated the effects of delay exposure without choice opportunities during training. In their study, delay-exposed rats completed 4 months of daily sessions in which pressing a single lever delivered two food pellets after a 17.5 s delay. In both a post-training impulsive-choice assessment and a 9-week follow-up, delay-exposed rats chose a three-pellet LLR delayed by 15 s (vs. 1 pellet now) near-exclusively, relative to a group of immediacy-exposed rats. A weakness of the study is the lack of a no-training control group, which leaves the possibility that immediate reward exposure increased impulsive choice during training. The single experiment evaluating the effects of delay exposure in human children produced positive effects in some conditions, but it is unclear if the increase in self-control was present when compared to a no-training control group ( Eisenberger & Adornetto, 1986 ).

Delay timing and amount discrimination

Inaccurate interval timing occurs when the duration of a delay is over- or under-estimated; if an interval’s duration is overestimated, time is perceived as passing slowly, which should increase impulsive choice. However, few studies support this hypothesis (see Baumann & Odum, 2012 for an exception). Instead, the amount of unsystematic variability in timing from trial to trial (timing precision) correlates with impulsive choice in rats ( Marshall, Smith, & Kirkpatrick, 2014 ; McClure, Podos, & Richardson, 2014 ). Timing imprecision may undermine the ability to discriminate when (and perhaps whether ; e.g., McGuire & Kable, 2012 ) rewards will be delivered. Smith et al. (2015) evaluated the effects of interventions to improve timing precision in rats (differential reinforcement of low rate [DRL], fixed-interval [FI], or variable-interval [VI] schedules). These interventions improved timing precision and reduced impulsive choice ( z = 2.30, p = .02).

By contrast, an extended history of choosing between small and large rewards (to train amount discrimination) produced a transitory, but not lasting reduction in impulsive choice ( z = 0.53, p = .59; Marshall & Kirkpatrick, 2016 ). This long-term null effect is consistent with the finding that reward magnitude sensitivity is unrelated to impulsive choice in rats ( Marshall et al., 2014 ).

Working-memory training

Observing that individuals who steeply discount delayed rewards also tend to score poorly on tests of working memory, and reasoning that working memory is important in imagining one’s future experiences, Bickel, Yi, Landes, Hill and Baxter (2011) provided working-memory training to treatment-seeking stimulant-addicts, while a control group received sham training. While Bickel et al. found this training reduced rates of delay discounting, Renda, Stein, and Madden (2015) failed to replicate this finding in rats, even though working-memory training improved working memory ( g = 2.21). As it stands, working memory training has no significant overall effect on discounting ( z = 1.09, p = .28), but given the small number of studies on the topic and potential cross-species differences, the effects of working-memory training are in need of further investigation. Particularly useful would be investigating if improvements in working memory mediate changes in delay discounting. That 8 of 13 sham-trained participants in Bickel et al. (2011) had higher discounting rates at the post-training evaluation, and working-memory training had no effect on transfer tests of working memory (i.e., working memory assessments not used during training) raises the possibility of a Type I error.

Six studies examined the effects of social learning – that is, how observing a model choose a larger-later consequence influences observer decision-making. Unfortunately, effect sizes could only be calculated for two of these studies, with one being the sole study finding a null effect ( Gilman, Curran, Calderon, Stoeckel, & Eden Evins, 2014 ); thus, modeling yielded a non-significant effect on discounting in the meta-analysis ( z = 1.01, p = .31) despite most reporting positive effects on impulsive choice. For example, Bandura and Mischel (1965) evaluated how viewing (or reading about) adult models choosing and subsequently explaining their reasons for selecting LLRs, affected decisions of 4 th and 5 th grade children. They found that 10% fewer children selected the SSR after observing or reading about a model, relative to a no-model control group; the effect persisted 4–5 weeks later at a reassessment of choice. While this effect has been replicated with children ( Atwood, Ruebush, & Everett, 1978 ; Staub, 1972 ) and prison inmates ( Stumphauzer, 1972 ) a recent study revealed that adults seeing LLR choices made by virtual peers on a computer screen did not reduce delay discounting ( Gilman et al., 2014 ).

Instruction-based procedures

In two studies, asking participants to consider the consequences of (and reasons for) their choices reduced impulsive choice ( z = 3.64, p = .0003). For instance, in Nisan and Koriat (1984) , kindergarteners who were instructed to generate reasons why another child might choose the LLR (after the participant just chose the SSR) subsequently increased their selection of the LLR. Likewise, experimenter-provided reasons why another child chose the LLR (e.g., “because he wanted lots, and two tomorrow is more than one today”) also shifted preference toward the LLR. Similarly, Staub (1972) found that instructing children about the positive consequences of choosing the LLR increased the number of LLR choices.

Environmental Enrichment/Deprivation

Childhood trauma is predictive of substance use in humans ( Chassin, Ritter, Trim, & King, 2003 ) and is correlated with steep delay discounting in men ( van den Berk-Clark, Myerson, Green, & Grucza, 2018 ). This relation, and the finding that early isolation impairs response-inhibition in rats ( Hall, 1998 ), motivated research examining the effects of rats’ rearing environments on impulsive choice. Despite good external validity, rearing manipulations overall had no significant impact on impulsive choice ( B = .27, SE = 0.19; z = 1.44, p = .15; see Table 9 and Figure 12 ). Similarity in animal laboratory procedures may explain the low study variability ( I 2 = 0%; i.e., the observed variability is due to chance). The exception is a study by Perry, Stairs, and Bardo (2008) , who reported lower discounting among socially-enriched rats, relative to rats raised in isolation. However, the effect was only evaluated for 5 days and it appeared to lessen over time. Hellemans et al. (2005) reported temporary benefits of enrichment that did not continue through steady-state assessment. Thus, there is little evidence that environmental enrichment decreases delay discounting.

Effect sizes ( g btw ) and 95% confidence intervals for Environmental Enrichment/Deprivation manipulations. Only studies for which effect sizes could be calculated are included. Larger effect sizes reflect greater preference for larger, delayed outcomes

Effect sizes for Environmental Enrichment/Deprivation: Individual-publication effect sizes.

n.s., No statistically significant effect; CNC, could not calculate effect size

Working under the assumption that steeply discounting future outcomes plays a role in the development and/or continuation of maladaptive behaviors ( Bickel et al., 2012 ; Bickel, Mackillop, Madden, Odum, & Yi, 2015 ), a large number of studies have investigated environmental manipulations designed to reduce delay discounting or impulsive choice. Given the infancy of this effort, the majority of the research reviewed above was conducted in laboratory settings. Where clinical work has been conducted, delay discounting is often an incidental dependent measure and the effects of the intervention have been inconsistent or not compared to adequate control groups. It is appropriate, therefore, that laboratory research with human and nonhuman subjects continues to explore and refine methods to more consistently reduce delay discounting.

The effect sizes in Figure 3 , and the forest plots in Figures 4 – 12 , allow for informal cross-category and -experiment comparisons of effect sizes. Although efforts were made to select an effect size measure that was comparable across different types of experiments and different sample sizes, there are several reasons to interpret these data with caution. First, a wide variety of tasks, reward amounts/types, delay durations, and associated dependent measures were used in the reviewed experiments. If these task/reward/delay/measure combinations are differentially sensitive to experimental manipulations, comparison of effect sizes must be done judiciously. Second, effect sizes could not be calculated for some studies reviewed. Thus, some manipulations that produce potentially useful reductions in delay discounting are under-represented (e.g., modeling) in the figures. Third, comparing effect sizes across laboratory and clinical settings must consider that greater control of extraneous variables will increase effect sizes; this may be particularly true in nonhuman animal research, although a preliminary analysis of differences in effect sizes across humans and non- revealed no significant differences. 7 We believe the latter supports the applicability of research on discounting with non-human subjects for efforts to improve the human condition. Finally, the effect sizes entail some degree of error. In many instances we estimated information (e.g., means and variability via GraphClick software; assuming a moderate correlation between repeated measures) as opposed to directly retrieving it. The effect sizes and results of the meta-analysis, therefore, should be considered a means of providing relative, as opposed to absolute information of size and precision.

With these cautions in mind, we note that several brief experimental manipulations have produced across-laboratory reductions in delay discounting. For example, a short period of time spent with nature (cueing) or engaged in episodic future thinking (EFT) consistently reduces delay discounting unless the imagined future event has a negative valence (Liu et al., 2014). Likewise, arranging the environment so decisions are made when neither the SSR nor LLR are immediately available (adding delays, adding response requirements) produces large reductions in impulsive choice. Perhaps the shortest duration manipulation producing reliable reductions in impulsive choice (barring ceiling effects) are the date framing and explicit-zero framing interventions. As currently implemented, these manipulations do not produce, nor are they designed to produce, long-term changes in impulsive choice. That is, making explicit that selecting the SSR entails nothing will be received at a future date (explicit zero framing) can reduce a one-off impulsive choice, but this reframing is unlikely to influence a later decision to binge watch Netflix instead of getting the sleep needed to perform better the next day.

Such acute manipulations need not have limited utility. Many important decisions are one-offs and, in these cases, acute manipulations are all that is needed. Consider the decision to save money for retirement or to start a college savings account for a child. Once a savings plan is initiated, it is rarely reversed ( Samuelson & Zeckhauser, 1988 ) so influencing choice just once can profoundly affect one’s life. Using a version of the Adding Delays manipulation, Thaler and Benartzi’s (2004) Save More Tomorrow™ program strategically delays contributions to a retirement savings plan until a later date - when the employee receives their first pay raise. Similar programs might use the effective discounting manipulations reviewed in this paper to influence other important one-off choices. For example, sales of energy efficient appliances might be increased by simply specifying the amount and date by which energy-bill savings will be realized (the date framing effect; DeHart & Odum, 2015 ; Dshemuchadse et al., 2013 ; Klapproth, 2012 ; LeBoeuf, 2006 ; Read et al., 2005 ). Additional increases might be produced by explicitly noting the lack of savings if the cheaper appliance were purchased (the explicit zero effect; Magen et al., 2008 ; Radu et al., 2011 ; Wu & He, 2012 ). These effective means of acutely reducing delay discounting should empower researchers to creatively arrange choice contexts that nudge behavior in advantageous directions.

Unexplored to date is the development of interventions designed to teach decision-makers to consistently reframe for themselves intertemporal choice alternatives so as to minimize impulsive choices. Training might begin by teaching participants to recognize intertemporal choice contexts, to identify the SSR and LLR, and then to apply one or more of the strategies summarized in this review. Beyond learning to reframe the choice alternatives, participants might learn to bring to mind social experiences for which the individual is grateful ( DeSteno et al., 2014 ) or engage in EFT activities (e.g., Lin & Epstein, 2014 ).

Indeed, evidence supporting the development of such therapeutic interventions comes from the EFT literature. Sze, Daniel, Kilanowski, Collins, and Epstein (2015) trained overweight parent-child dyads in EFT and audio-recorded their future-thinking cues (including how losing weight would enhance the future event) so they could be later accessed to prompt EFT activities prior to meals. Following a 4-week trial, parents assigned to EFT reduced their BMI and percent overweight more than those assigned to a nutrition-education group that also received daily prompting ( p = .01). This is an encouraging finding serving as proof of concept that individuals prone to making impulsive choices can learn to self-initiate therapeutic behaviors prior to making a decision with health implications.

Learning-based approaches that produce large and long-lasting reductions in delay discounting have a long history, mostly in the animal laboratory where arranging extended training programs is more feasible than with free-ranging humans. One study that met the current inclusion criteria demonstrated the efficacy of a variant of delay-fading training in children with impulse control issues ( Schweitzer & Sulzer-Azaroff, 1988 ), and several studies that did not meet our inclusion criteria have replicated this effect (e.g., Dixon et al., 1998 ; Fisher, Thompson, Hagopian, Bowman, & Krug, 2000 ). Similarly, bundling greatly reduces delay discounting and impulsive choice in rats ( Ainslie & Monterosso, 2003 ; Stein, Smits, et al., 2013 ) and humans ( Hofmeyr et al., 2011 ; Kirby & Guastello, 2001 ). Nonetheless, most of this translational research remains laboratory based, with few human studies evaluating duration of efficacy or generalization to novel settings, commodities, etc. Where duration and/or efficacy have been evaluated in small- N studies, the findings with fading-related manipulations are encouraging ( Dunkel-Jackson, Dixon, & Szekely, 2016 ; Neef, Bicard, & Endo, 2001 ).

Given these encouraging findings, future research should explore the efficacy of procedures such as delay fading (Mazur & Logue, 1987) or delay-exposure training ( Stein et al., 2013 ) in preschool settings. As above, children might first be taught to discriminate situations in which SSRs and LLRs are available, and then given supported opportunities to select the LLR, experience the delay, and obtain the better of the two outcomes. It may be particularly important to ensure the LLR is always received, given that impulsive choice is a maximization strategy when the source of the delayed reward is untrustworthy ( Mahrer, 1956 ; Michaelson et al., 2013 ; Mazur, 1985 ). Embedding these didactic and experiential-learning techniques into, for example, the Head Start curriculum in the U.S. could prove an effective preventive measure in children at risk of substance use and abuse.

As these translational efforts begin, it is important to be cognizant of the distinction between “statistically significant” and “clinically significant,” and to consider the different interpretations of effect sizes. For example, as the definition of the standardized mean difference entails–an effect size of, for example, 0.5, would mean that those in the intervention group on average improved by half a standard deviation. However, such an interpretation is best reserved for within-subjects designs; more appropriate to a between-subjects design, an effect size of 0.5 means that 70% of the participants in the intervention group scored better (e.g., fewer impulsive choices) than the control group and thus not all participants may benefit from such an intervention. Many of the effect sizes in Figure 3 – 12 are smaller than this; it is important to consider all of these implications when evaluating effect sizes. In addition, to further assist researchers in evaluating the practical significance of an interventions’ efficacy, we urge researchers to graphically represent individual participant data (see e.g., Kwan et al., 2015 ).

Theoretical Issues

Within most of the categories of intervention strategies reviewed above, there is no clear theoretical understanding of how the manipulation influences delay discounting. Inclusion of formal mediation analyses is rare, although discussion of potential mediators is not. Similarly, in the absence of a straightforward way to measure some hypothesized mediators, few studies have designed procedures to disambiguate between similar processes of change.

There are at least two benefits to uncovering the processes by which delay discounting and impulsive choice are reduced. First, understanding how a manipulation affects discounting will facilitate more efficient translation to clinical interventions. In the absence of this knowledge, ineffective components may be carried forward, effective components omitted, and interventions may be applied in contexts or with populations who would not benefit. Second, further identifying the processes by which delay discounting is momentarily or permanently changed may guide the exploration of the neurological bases of discounting and aid in the development of better quantitative models of the discounting process.

Identifying the processes underlying changes in delay discounting requires an empirical base of studies with strong internal validity. At present, some of the promising interventions reviewed above have threats to internal validity and these must be addressed before translational research is undertaken. As noted above, a prominent threat to internal validity is demand characteristics. These are of particular concern when the experimental manipulation overlaps in content with the discounting task (e.g., EFT cues whose future time corresponds with the delay to the LLR) or when control procedures do not equate for expectancy of change (e.g., wait-list control groups). Several tactics are available for addressing these issues of internal validity ( Boot, Simons, Stothart, & Stutts, 2013 ). For example, control tasks could equate demand characteristics across groups (e.g., semantic future thinking vs. episodic future thinking; Chiou & Wu, 2017 ) or less transparent/less easily faked delay-discounting tasks could be used.

Methodological Recommendations

As research in delay discounting and impulsive choice continues to explore methods for reducing these important choices, it will be important for the purpose of between-experiment comparison to standardize the assessment and quantification of these behaviors. This is particularly important in human laboratory research, as these findings are most likely to influence which manipulations will be translated to clinical trials. Because i) several different tasks are available for quickly obtaining delay discounting rates (e.g., Du, Green, & Myerson, 2002 ; Kirby & Maraković, 1995 ; Rachlin, Raineri, & Cross, 1991 ), ii) these rates tend to significantly correlate across tasks ( Epstein et al., 2003 ; Hardisty, Thompson, Krantz, & Weber, 2013 ; Holt, Green, & Myerson, 2012 ; Koffarnus & Bickel, 2014 ; Kowal, Yi, Erisman, & Bickel, 2007 ), iii) preferences between SSRs and LLRs may be derived from these discounting rates, and iv) discounting rates do not appear to be systematically different whether the rewards are real or hypothetical ( Johnson & Bickel, 2002 ; Lagorio & Madden, 2005 ) we recommend that future studies evaluating therapeutic manipulations use a delay-discounting task, rather than an impulsive-choice task (see Madden & Johnson, 2010 for a primer on these tasks and quantitative methods). Such standardization of the dependent measure will facilitate evaluation of the relative merits of different approaches to reducing delay discounting.

With the exception of the Multiple-Choice Questionnaire ( Kirby et al., 1999 ), delay discounting tasks yield indifference points that may be plotted and fit with a discounting equation. The variety of these equations and the theoretical underpinnings of each are beyond the scope of this review. Suffice it to say, comparison across published articles and scholarly disciplines (e.g., economics and psychology) would be facilitated if a common metric of delay discounting were used. The area under the curve (AUC) formed by indifference points ( Myerson, Green, & Warusawitharana, 2001 ) is a candidate metric, because it is already frequently used and is usually normally distributed, facilitating the use of parametric statistical analyses. AUC is theoretically neutral and may be applied to any data set.

Last, we provide several suggestions, based on our observations in conducting this review, that would improve the meta-analyzability of this literature for future reviews. First, and most simply, researchers should take care to publish data needed in the calculation of effect sizes. This includes clear and explicitly identified descriptive statistics (i.e., measures of central tendency and variability), specific identification of group sizes, and correlations between repeated measurements. Where effect sizes are reported, the authors must also identify the method of calculation because there are several calculations for Cohen’s d for within-subjects designs, and they can yield very different effect size estimates.

Our second suggestion is to incorporate more systematic approaches into the study of manipulations of discounting. The heterogeneity in the execution of experimental manipulations, the measures used, the settings, and participants/species precluded identification of meaningful moderators of effect sizes between-categories (e.g., only two categories used both humans and non-; clinical populations were seldom recruited outside of clinical manipulations). In many instances there were similar issues within-category (e.g., most human studies used hypothetical monetary outcomes, with very few using non-monetary or [potentially-] real outcomes). In other words, most potential moderators were confounded with categories themselves, or too infrequently represented to yield meaningful analysis. These statements are supported by the generally moderate degree of effect-size variability at the category and subcategory level. A more systematic approach in planning future studies will facilitate our understanding of the efficacy of the manipulations, their limitations, and relative utility. To this end, researchers will need to work collectively to build a cohesive body of work, rather than a collection of studies.

Summary & Conclusions

Several methods for reducing delay discounting and impulsive choice were reviewed. Although some promising manipulations have been identified, very little translational research has adapted these techniques in clinical or practical settings. Some manipulations, like EFT, may be easily integrated into talk-based therapies (e.g., Acceptance & Commitment Therapy; Hayes et al., 1999 ), and substance-abuse treatment trials that include a behavior-therapy component should consider this integration. Because framing manipulations so consistently shift choice toward the LLR, future research should evaluate the efficacy of these manipulations in influencing one-off choices that affect the health and well-being of decision-makers, and the world in which they live. Finally, learning-based manipulations enjoy a robust empirical base and have shown successful initial translation to humans (e.g., delay fading; Schweitzer & Sulzer-Azaroff, 1988 ). These encouraging findings should be further developed into a practical curriculum for broad-scale dissemination and evaluation of long-term outcomes. Such resiliency-building programs hold promise for improving the pattern of decision-making that underlies physical and ecological health ( Volkow & Baler, 2015 ).

Acknowledgments

This research was supported in part by a grant from the National Institute on Drug Abuse awarded to the second author (R21 DA042174).

1 It should be noted that delay discounting is a description of a pattern of choices between SSRs and LLRs; it is not an explanation. There are several factors that can explain why delayed outcomes are discounted in value. For example, delays typically come with opportunity costs (Paglieri, 2013; Stevens & Stephens, 2010) and often either signal a local collection risk (the LLR is promised, but not delivered; e.g., Mahrer, 1956 ) or a stable collection risk in the phylogenetic history of the species (Stevens & Stephens, 2010). It is also important to note that sensitivity to delay is not the only factor that can influence impulsive decision-making. For example, if sensitivity to differences in reward magnitude declines, preference for a LLR will shift toward the SSR. Likewise, failure to couple the LLR with the response that produced it will render the SSR as the only functional response-outcome contingency ( Killeen, 2011 ). These complexities open considerably the range of experimental variables that can influence the SSR- and LLR-choices from which a delay-discounting function is derived. Several of these variables appear in the review that follows.

2 A separate search was conducted in PLoS One because some eligible papers published in that journal did not appear in the PsycINFO data base. The functionality of the PLoS One search engine was more limited so the search procedures were slightly modified from that used in PsycINFO.

3 If a study yielded > 3 effect sizes with the same participants/subjects, or if a study contributed > 8 effect sizes in total, individual effect sizes were omitted from the meta-analysis such that the total number of effect sizes for those participants/studies did not exceed the previously stated criteria. Sometimes, the effects of a manipulation were examined over a wide range of parameters (e.g., larger-later reward amounts/delays), which inflated the number of effect sizes for a given publication. To control for inflation of effect sizes due to unknown correlations within participants/studies, individual effect sizes from qualifying studies ( n = 4) were omitted such that the mean of the effect sizes for that study remained similar to that with all effect sizes, and that the effect sizes across levels of the moderating variable (e.g, larger-later reward delays) remained represented. These cases are noted in the corresponding effect size tables.

4 The only other known EFT study that measured temporal horizon did not meet the inclusion criteria. Cheng, Shein, and Chiou (2012) found that engaging in an EFT-like exercise produced greater future orientation as measured with the Zimbardo Time Perspective Inventory (ZTPI; Zimbardo & Boyd, 1999 ), and that ZTPI scores mediated the effect of EFT in reducing steepness of discounting. The paper was excluded from this review because the authors confirmed that assumptions of their statistical analyses were violated but failed to make available individual participant data for supplemental analyses.

5 Nonsignificant effects in Pyone and Isen (2011) were often observed when strong preferences were produced by choice parameters. For example, positive affect primes did not decrease impulsive choice when the LLR amount was only nominally larger than the SSR (e.g., $25 now vs. $30 in 4 weeks), or when the LLR was strongly preferred without the prime because the difference in reward amounts was large (e.g., $25 now vs. $50 in 4 weeks). In Figure 9 , these differences are indicated as “Easier” vs. “Harder” magnitude pairs.

6 Other studies have manipulated the presence vs. absence of rewards, but used appetitive rewards designed to increase impulsive choice. These studies did not meet the inclusion criteria of the present review.

7 To conduct a comparison of effect sizes using humans ( n = 6) and non-humans ( n = 5) as research subjects, the Delay-Fading/Exposure and Bundling subcategories (Learning category) were collapsed. Then, “Population” (human vs. non-) was examined as the sole moderator of effect sizes. Studies using non-humans ( B = −0.13, SE = 0.48) did not yield significantly different effect sizes from those with humans ( z = −0.27, p = 0.79). We call this comparison preliminary because Population is necessarily confounded with an array of procedural differences. A systematic line of research on the subject would need to be developed to rigorously test this difference.

None of the authors have any real or potential conflict(s) of interest, including financial, personal, or other relationships with organizations or pharmaceutical/biomedical companies that may inappropriately influence the research and interpretation of the findings. All authors have contributed substantively to this review and have read and approved this final manuscript.

Portions of this research were presented at the 2016 annual convention for the Association for Behavior Analysis International in Chicago, IL, USA.

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