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. Author manuscript; available in PMC: 2017 Jan 1.
Published in final edited form as: Appetite. 2015 Oct 22;96:268–279. doi: 10.1016/j.appet.2015.09.035

Managing temptation in obesity treatment: a neurobehavioral model of intervention strategies

Bradley M Appelhans 1,2,*, Simone A French 3, Sherry L Pagoto 4, Nancy E Sherwood 5
PMCID: PMC4684710  NIHMSID: NIHMS730057  PMID: 26431681

Abstract

Weight loss outcomes in lifestyle interventions for obesity are primarily a function of sustained adherence to a reduced-energy diet, and most lapses in diet adherence are precipitated by temptation from palatable food. The high nonresponse and relapse rates of lifestyle interventions suggest that current temptation management approaches may be insufficient for most participants. In this conceptual review, we discuss three neurobehavioral processes (attentional bias, temporal discounting, and the cold-hot empathy gap) that emerge during temptation and contribute to lapses in diet adherence. Characterizing the neurobehavioral profile of temptation highlights an important distinction between temptation resistance strategies aimed at overcoming temptation while it is experienced, and temptation prevention strategies that seek to avoid or minimize exposure to tempting stimuli. Many temptation resistance and temptation prevention strategies heavily rely on executive functions mediated by prefrontal systems that are prone to disruption by common occurrences such as stress, insufficient sleep, and even exposure to tempting stimuli. In contrast, commitment strategies are a set of devices that enable individuals to manage temptation by constraining their future choices, without placing heavy demands on executive functions. These concepts are synthesized in a conceptual model that categorizes temptation management approaches based on their intended effects on reward processing and degree of reliance on executive functions. We conclude by discussing the implications of our model for strengthening temptation management approaches in future lifestyle interventions, tailoring these approaches based on key individual difference variables, and suggesting high-priority topics for future research.

Keywords: Diet adherence, Obesity, Lifestyle intervention, Food reward, Temporal discounting, Commitment, Executive function

Introduction

Obesity is a risk factor for multiple health conditions (Abdullah, Peeters, de Courten, & Stoelwinder, 2010; Bogers et al., 2007; Guh et al., 2009; Hubert, Feinleib, McNamara, & Castelli, 1983; Renehan, Tyson, Egger, Heller, & Zwahlen, 2008; Strazzullo et al., 2010) and affects about one-third of adults and one-sixth of children in the U.S. (Ogden, Carroll, Kit, & Flegal, 2014). Obesity contributes to 9% of all medical expenditures in the U.S. (Trogdon, Finkelstein, Feagan, & Cohen, 2012), and this figure is projected to grow substantially in coming decades (Finkelstein et al., 2012). Effective and affordable approaches to prevention and treatment are urgently needed.

The current front-line therapy for obesity consists of comprehensive lifestyle intervention focused on dietary modification, physical activity, and behavior change strategies (Jensen et al., 2014). Roughly 50% of lifestyle intervention participants lose at least 5-10% of initial body weight (Dansinger, Tatsioni, Wong, Chung, & Balk, 2007; Pi-Sunyer et al., 2007), which is the minimum benchmark for conferring clinically meaningful improvements in cardiometabolic risk factors (Dow et al., 2013; Liu, Wharton, Sharma, Ardern, & Kuk, 2013; Wing et al., 2011). The remaining half of lifestyle intervention participants are nonresponders with respect to this outcome. In addition to high nonresponse rates, relapse is common (perhaps the norm). About one-third to one-half of lost weight is regained within one year of treatment discontinuation (Barte et al., 2010; Curioni & Lourenco, 2005; Franz et al., 2007). Even with ongoing, long-term intervention, only about 3.2 kg or 4-5% of lost weight is maintained (Look AHEAD Research Group, 2014; Middleton, Patidar, & Perri, 2012). Increasing response rates and reducing relapse are top priorities for behavioral obesity treatment (MacLean et al., 2015).

One path toward improved weight loss outcomes involves strengthening intervention strategies to help participants manage temptation from the highly palatable but unhealthy foods that permeate modern society. Weight loss outcomes are largely a function of sustained behavioral adherence to any reduced-energy diet (Alhassan, Kim, King, & Gardner, 2008; Dansinger, Gleason, Griffith, Selker, & Schaefer, 2005; Fitzpatrick et al., 2014; Heymsfield et al., 2007), and are not meaningfully affected by the type of diet one follows (Ajala, English, & Pinkney, 2013; Hu et al., 2012; Wycherley, Moran, Clifton, Noakes, & Brinkworth, 2012). Most dietary lapses are precipitated by temptation from palatable food (Cleobury & Tapper, 2014; McKee, Ntoumanis, & Taylor, 2014; Thomas, Doshi, Crosby, & Lowe, 2011). Thus, a promising route to better weight loss outcomes would be to design interventions that not only include diet plans with high acceptability and feasibility (Makris & Foster, 2011; Pagoto & Appelhans, 2013), but also arm patients with effective temptation management strategies.

In this review, we examine the neurobehavioral underpinnings of temptation, and highlight three processes that undermine diet adherence. We then review temptation management strategies in terms of their intended effects on temptation and the demand each strategy places on executive functions. Based on these considerations, we construct a model of temptation management strategies that we hope will guide future efforts to improve weight loss outcomes, particularly in participants who do not respond or relapse with traditional lifestyle interventions.

Food reward as the basis of temptation

Eating is regulated by two distinct but interconnected neurobehavioral systems: a homeostatic system and a reward-based system. In the homeostatic system, food is a component of a physiological-behavioral homeostatic feedback loop that governs energy balance (reviewed elsewhere; Berthoud, 2012; Harrold, Dovey, Blundell, & Halford, 2012; Hussain & Bloom, 2013; Rui, 2013; Woods & D'Alessio, 2008). In contrast, the reward system influences eating in response to the sensory experience of food. Two dimensions of reward have been distinguished in the literature (Berridge, 2009; Berridge, Ho, Richard, & DiFeliceantonio, 2010; Berridge & Kringelbach, 2008; Berridge & Robinson, 2003; Fulton, 2010; Kringelbach, Stein, & van Hartevelt, 2012). Liking reflects the hedonic aspect of reward and applies to the sensory pleasure associated with eating palatable food. Wanting, in contrast, manifests as appetitive motivation, desire, craving, and temptation; it is the dimension of reward that challenges self-control. Wanting underlies engagement in a variety of appetitive behaviors, including sexual activity (Georgiadis & Kringelbach, 2012), gambling (Joutsa et al., 2012; van Holst, van den Brink, Veltman, & Goudriaan, 2010), and substance abuse (Koob & Volkow, 2010; Pulvirenti & Koob, 1990; Robinson & Berridge, 1993; Schacht, Anton, & Myrick, 2013). When applied to food, wanting provides the motivational drive that supports adaptive foraging and hunting behaviors in environments of scarcity (Alcaro & Panksepp, 2011), but contributes to overeating in modern environments characterized by an abundance of hyper-palatable foods that can be obtained with minimal effort. Though liking and wanting may not be phenomologically distinct in most day-to-day human experiences (Havermans, 2011), it is increasingly recognized that reward, rather than energy homeostasis, is the primary driver of overeating in modern society (Lowe & Butryn, 2007). Individual differences in food reward processing (Finlayson, King, & Blundell, 2007b; Mela, 2006), reflected in a variety of behavioral (Appelhans et al., 2011b; Epstein, Carr, Lin, & Fletcher, 2011; Epstein, Leddy, Temple, & Faith, 2007; Finlayson, King, & Blundell, 2008; Finlayson, King, & Blundell, 2007a; Giesen, Havermans, Douven, Tekelenburg, & Jansen, 2010; Lansigan, Emond, & Gilbert-Diamond, 2015; Saelens & Epstein, 1996) and biological (Burger & Berner, 2014; Burger & Stice, 2014; Demos, Heatherton, & Kelley, 2012; Guo, Simmons, Herscovitch, Martin, & Hall, 2014; Jonsson et al., 1999; Stice, Spoor, Bohon, & Small, 2008; Stice, Yokum, Burger, Epstein, & Small, 2011; Volkow et al., 2008) measures, are implicated in obesity risk. While the notion of “food addiction” remains controversial (Blundell & Finlayson, 2011; Smith & Robbins, 2013; Wise, 2013), frequent encounters with tempting foods in the modern environment, which activate the brain’s reward circuitry and trigger an appetitive motivational cascade, continuously challenge dieters’ self-control and adherence.

The neurobehavioral profile of temptation

The influences of several neurobehavioral processes emerge during the experience of temptation: reward-driven attentional biases, temporal discounting, and the cold-hot empathy gap. Collectively, these processes constitute an altered neurobehavioral profile that undermines diet adherence.

Attentional bias

Reward is a strong modulator of cognitive control (Braver, 2012), having both bottom-up and top-down influences of reward on attention allocation. Motivationally salient stimuli in the environment preferentially attract reactive attention, and conversely, one’s motivational state affects the extent to which attention is proactively directed to seeking out reward-related stimuli (Anderson, 2013; Anderson, Laurent, & Yantis, 2011, 2013; Awh, Belopolsky, & Theeuwes, 2012; Chiew & Braver, 2014; Della Libera & Chelazzi, 2006; Wang, Duan, Theeuwes, & Zhou, 2014). As a rewarding stimulus, food elicits several forms of attentional bias, including greater susceptibility to distraction by food cues, more rapid detection of food cues in the visual field, and greater difficulty disengaging attention from food cues (Pool, Brosch, Delplanque, & Sander, 2014). These biases are enhanced in a state of hunger or food craving (Castellanos et al., 2009; Kemps & Tiggemann, 2009; Loeber, Grosshans, Herpertz, Kiefer, & Herpertz, 2013; Piech, Pastorino, & Zald, 2010; Smeets, Roefs, & Jansen, 2009; Werthmann, Roefs, Nederkoorn, & Jansen, 2013), particularly for palatable, energy-dense foods (Doolan, Breslin, Hanna, Murphy, & Gallagher, 2014). Attentional biases may potentiate the appetitive pursuit of rewards by keeping individuals locked onto rewarding stimuli until they are consumed (Alcaro, Huber, & Panksepp, 2007; Berridge, 2004). Thus, for obese individuals participating in lifestyle interventions, palatable food may act as a “motivational magnet” (Berridge et al., 2010) that monopolizes attention and triggers lapses in diet adherence. Studies have consistently linked attentional biases toward food with obesity (Castellanos et al., 2009; Doolan et al., 2014; Hendrikse et al., 2015; Nijs, Franken, & Muris, 2010; Nijs, Muris, Euser, & Franken, 2010; Werthmann et al., 2011) and weight gain (Calitri, Pothos, Tapper, Brunstrom, & Rogers, 2010; Yokum, Ng, & Stice, 2011), however, there is a need for well-controlled studies that can determine whether these associations stem from stable individual differences in attentional processing, or a greater acquired salience for food cues among those prone to obesity.

Temporal discounting

Mechanistically, engaging in health behaviors often involves pursuing the more valuable, long-term rewards associated with wellness over immediate gratification from various temptations. This dynamic characterizes healthy eating and weight control (Appelhans, 2009; Epstein, Salvy, Carr, Dearing, & Bickel, 2010; Herman & Polivy, 2003), abstaining from substance use (Amlung & MacKillop, 2011; Audrain-McGovern et al., 2009; Bickel, Odum, & Madden, 1999; Fernie et al., 2013; Friedel, DeHart, Madden, & Odum, 2014; Green & Lawyer, 2014; Heil, Johnson, Higgins, & Bickel, 2006; Hoffman et al., 2006; Kirby & Petry, 2004; Kirby, Petry, & Bickel, 1999; Reynolds, Richards, Horn, & Karraker, 2004; Robles, Huang, Simpson, & McMillan, 2011; Vuchinich & Simpson, 1998) and risky sexual activity (Dariotis & Johnson, 2015; Herrmann, Hand, Johnson, Badger, & Heil, 2014; Johnson & Bruner, 2012; Jones & Sullivan, 2014), and other health behaviors (Axon, Bradford, & Egan, 2009; Bradford, 2010; Daugherty & Brase, 2010). Yet these choices are rarely straightforward, even for those who value their future health. Humans discount the value of future rewards relative to opportunities for immediate gratification, a process known as temporal discounting. Delayed rewards are discounted in value as a hyperbolic function of time such that the desire for a reward spikes just before it is received (Figure 1). As a result, an individual presented with the choice between two future rewards may initially prefer the more highly valued option, but experience a preference reversal if the less preferred reward becomes available immediately. Thus, temporal discounting accounts for “short-sighted” decisions that conflict with an individual’s long-term interests (Ainslie, 1975; Loewenstein, Read, & Baumeister, 2003).

Figure 1.

Figure 1

Due to hyperbolic temporal discounting, an individual’s preference for a large, delayed reward (e.g., weight control) can reverse if a smaller but immediate reward (e.g., dessert) becomes available. These preference reversals (occurring at the intersection of the two valuation curves), are characteristic of impulsive, short-sighted decisions.

Most dietary lapses can be interpreted as preference reversals (Appelhans, 2009; Epstein et al., 2010; Herman & Polivy, 2003). In general, the most palatable, rewarding foods are high in calories, fat, salt, and sugar, and are poor in nutrients (Drewnowski, 1995; Kessler, 2009), which places immediate gratification from food in direct opposition to healthy eating and weight control. A dieter generally prefers weight loss to food reward when both options are considered as future outcomes. However, relative to the point of decision, the weight loss benefits of individual food choices are perpetually perceived as occurring in the future, whereas gratification from food is immediate. The tendency to steeply discount future rewards is associated with higher body weight (Epstein et al., 2014; Fields, Sabet, Peal, & Reynolds, 2011; Jarmolowicz et al., 2014; Weller, Cook, Avsar, & Cox, 2008) and weight gain (Kishinevsky et al., 2012), and appears to contribute to overeating among those who are particularly sensitive to food’s rewarding properties (Appelhans et al., 2011a; Best et al., 2012; Rollins, Dearing, & Epstein, 2010).

Cold-hot empathy gap

“Hot” visceral states such as hunger, thirst, sexual arousal, and craving are characterized by increased wanting of stimuli (e.g., food, water, sexual stimulation, drugs) that can resolve these drives (Alcaro & Panksepp, 2011; Levy, Thavikulwat, & Glimcher, 2013). Yet humans have difficulty anticipating the often powerful effects of visceral states (including temptation) on their decisions and behaviors (Gilbert, Gill, & Wilson, 2002; Loewenstein, 1996). This inability to “empathize” with one’s self in a different visceral state plays out in two ways. The hot-cold empathy gap is apparent when individuals in a hot, motivated state overestimate the degree to which they will value a reward in a non-motivated, neutral, “cold” state. For example, individuals make less healthy food choices when hungry (a hot state) compared to when satiated (a cold state), even when choosing what to eat next week (Read & van Leeuwen, 1998).

Conversely, the cold-hot empathy gap describes the tendency of an individual in a cold state to underestimate the impact of future hot visceral states on their decisions and behavior. Just as hungry subjects overvalue foods that they will consume in the future, satiated subjects underestimate the value food will have to them in the future when they are hungry (Fisher & Rangel, 2014; Gilbert et al., 2002). Similar examples of cold-hot empathy gaps have been documented during sexual arousal (Ariely & Loewenstein, 2006), drug craving (Giordano et al., 2002; Sayette, Loewenstein, Griffin, & Black, 2008), and other visceral states (Van Boven, 2013). The cold-hot empathy gap is particularly relevant to weight management because it implies that dieters routinely overestimate their capacity to resist temptation.

Temptation management in hot versus cold states.

The hypothesized roles of attentional bias, temporal discounting, and the cold-hot empathy gap in dietary lapses are synthesized in Figure 2. The contribution of each process evolves over time as dietary lapses unfold. The cold-hot empathy gap precludes an individual from preparing for encounters with temptation before they occur, whereas attentional biases are thought to initiate and perpetuate the hot states that are associated with preference reversals and culminate in dietary lapses.

Figure 2.

Figure 2

Diagram of dietary lapses under temptation. Encountering palatable food cues elicits enhanced attentional focus on food, and “hot” visceral states associated with a preference for immediate gratification. Due to the cold-hot empathy gap, individuals have difficulty appreciating these changes in advance. It is as though multiple selves (current and future) with competing priorities and different behavioral tendencies exist within the same individual at different times. Different sets of temptation management strategies are likely to be effective in cold versus hot states.

The shifting neurobehavioral profile of dieters from cold to hot (tempted) states underscores the importance of distinguishing between intervention strategies focused on resisting temptation while it is experienced, and those focused on avoiding temptation altogether. Strategies focused on resisting temptation are implemented by individuals when they are already in a hot state – in the “heat of the moment.” These temptation resistance strategies rely heavily on effortful inhibition, align with the lay concept of “willpower,” and include “urge surfing” [allowing cravings to pass without acting on them (Bowen & Marlatt, 2009; Forman & Butryn, 2015; Forman, Butryn, Hoffman, & Herbert, 2009)], “urge suppression” [inhibiting or ignoring a craving (Siep et al., 2012)], and cognitive reappraisal (Siep et al., 2012; Stice et al., 2015).

In contrast to temptation resistance strategies, temptation prevention strategies focus on avoiding or minimizing temptation. For example, stimulus control strategies involve identifying and modifying environmental factors that trigger problem behaviors, such as removing tempting foods from the home to prevent overeating (Butryn, Webb, & Wadden, 2011; Poelman et al., 2015). Other interventions emphasize scheduling and planning as strategies to manage temptation (Gillison et al., 2015; Kiernan et al., 2013; Murawski et al., 2009; Perri et al., 2001). The effectiveness of temptation prevention strategies may hinge on whether they are implemented in a cold state. For example, stimulus control strategies focused on ridding the home of tempting, unhealthy foods may be successful only to the extent that one is not tempted at the supermarket (Pagoto & Appelhans, 2015).

Both temptation resistance and prevention strategies are commonly featured in lifestyle interventions (Diabetes Prevention Program Research Group, 2002; Forman et al., 2009; Forman et al., 2007; Gorin et al., 2013; Poelman et al., 2015), however, their uptake and utilization by subjects and their impact on diet adherence (independent of the overall intervention package) have not been characterized. The nonresponse and relapse rates of existing lifestyle interventions, in which these two classes of temptation management strategies are mainstream, suggest the need to systematically study and improve upon these approaches.

The role of executive functions in temptation management.

Thus far, we have distinguished temptation management strategies based on their intent of resisting versus preventing temptation, yet they can also be categorized based on the demand they place upon executive functions. Managing temptation represents an aspect of self-regulation (Ent, Baumeister, & Tice, 2015), which refers to psychological and behavioral processes that individuals utilize while actively pursuing goals, including health-related goals such as dietary modification and weight loss (Hall & Marteau, 2014; Mann, de Ridder, & Fujita, 2013; Rasmussen, Wrosch, Scheier, & Carver, 2006). Successful self-regulation, in turn, heavily depends on executive functions (Barkley, 2001; Hofmann, Schmeichel, & Baddeley, 2012), which are high-level, top-down cognitive processes that are critical for overriding “automatic” or default actions (Mesulam, 2002) and maintaining goal-directed behavior (Miller & Cohen, 2001).

Multiple competing models of executive function have been proposed, each varying in the number and types of distinct executive functions posited (Barkley, 2001; Cummings, 1995; Domenech & Koechlin, 2015; Jurado & Rosselli, 2007). However, at least some consensus exists around the notion of three core executive function domains (Diamond, 2013; Miyake et al., 2000). Inhibitory control refers to the effortful suppression of impulses at the behavioral, cognitive, and affective levels, and is essential for suppressing extraneous or unwanted thoughts, focusing attention on relevant stimuli, and curbing impulsive behaviors (Baumeister, 2014; Filevich, Kuhn, & Haggard, 2012; Munakata et al., 2011). Working memory represents the ability to hold and manipulate information “in one’s mind” (Baddeley, 2010), whereas cognitive flexibility refers to the ability to entertain alternative perspectives and anticipate future outcomes (Diamond, 2013). The three core facets of executive function interact heavily to support higher-order abilities such as planning, problem-solving, pursuing goals, reasoning, and monitoring progress based on feedback (Bechara, Damasio, Damasio, & Anderson, 1994; Diamond, 2013; Munakata et al., 2011). Executive functions are largely mediated by different regions of the prefrontal cortex and their cortical and subcortical connections with other neural networks (Banich & Depue, 2015; Coutlee & Huettel, 2012; Domenech & Koechlin, 2015; Linden, 2007; Miller & Cohen, 2001) .

Temptation resistance strategies, which focus on overcoming temptation while in a hot state, are most directly related to the inhibitory control facet of executive function (Filevich et al., 2012; Munakata et al., 2011). Performance-based measures of inhibitory control, such as stop-signal and go/no-go tasks, are consistently associated with dietary intake and vulnerability to overeating and obesity (Ely, Winter, & Lowe, 2013; Hall, 2012; Hall, Lowe, & Vincent, 2014; Reinert, Po'e, & Barkin, 2013; Vainik, Dagher, Dube, & Fellows, 2013). One study involving undergraduate students found that hunger (a hot state) had a stronger effect on food choice among those with lower inhibitory control as measured by a stop-signal task (Nederkoorn, Guerrieri, Havermans, Roefs, & Jansen, 2009). Behavioral tasks purported to measure inhibitory control engage prefrontal brain regions implicated in executive functioning (Criaud & Boulinguez, 2013; Simmonds, Pekar, & Mostofsky, 2008; Swick, Ashley, & Turken, 2011; Zandbelt, Bloemendaal, Hoogendam, Kahn, & Vink, 2013). Active suppression of cravings during exposure to palatable food cues, which is analogous to the intervention strategies “urge suppression” and “urge surfing,” also engages prefrontal brain regions commonly implicated in executive functioning (Siep et al., 2012; Yokum & Stice, 2013).

As temptation prevention strategies are implemented in a cold state and are aimed at circumventing rather than resisting temptation, they are (almost by definition) not reliant on inhibitory control. Instead, temptation prevention strategies may recruit the working memory and cognitive flexibility components of executive function, which support higher level abilities such as problem-solving, planning, and goal pursuit. For example, stimulus control strategies require prospective thinking to identify and avoid exposure to foods that may challenge future self-control (Seligman, Railton, Baumeister, & Sripada, 2013). Another temptation management approach involves identifying adaptive behavioral responses through structured problem-solving exercises, which invoke reasoning and divergent thinking abilities within the working memory and cognitive flexibility domains (Diamond, 2013; McClure & Bickel, 2014). Relatively few studies have examined associations between working memory or cognitive flexibility measures and diet- or obesity-related outcomes, and published associations have been less consistent than with inhibitory control (Vainik et al., 2013).

Executive functions, particularly within the inhibitory control domain, are notoriously susceptible to disruption by a host of factors commonly encountered in daily life (Heatherton & Wagner, 2011; Hofmann et al., 2012). Performance on tests of executive function or self-control decline with exposure to stress (Pabst, Schoofs, Pawlikowski, Brand, & Wolf, 2013; Tryon, Carter, Decant, & Laugero, 2013), increased cognitive demands (Gathmann, Pawlikowski, Scholer, & Brand, 2014; Gunn & Finn, 2015; Hinson, Jameson, & Whitney, 2003; Starcke, Pawlikowski, Wolf, Altstotter-Gleich, & Brand, 2011), and insufficient sleep (Reynolds & Schiffbauer, 2004; Rossa, Smith, Allan, & Sullivan, 2014; Whitney & Hinson, 2010). Even more troubling are studies suggesting that executive functions often fail when they are needed most, such as during “hot” states characterized by visceral arousal (Ariely & Loewenstein, 2006; Loewenstein, 1996; Metcalfe & Mischel, 1999) or with mere exposure to a tempting stimulus (Hagger et al., 2013; Heatherton & Wagner, 2011). Inhibitory control may also be subject to fatigue or depletion such that the act of exercising inhibitory control reduces inhibitory strength in subsequent situations (Hagger, Wood, Stiff, & Chatzisarantis, 2010; also see Carter & McCullough, 2014). Virtually all of the factors that have been shown to interfere with executive functioning have also been implicated as triggers of overeating or dietary lapses in laboratory or clinical studies (Beebe et al., 2013; Harris, Bargh, & Brownell, 2009; Houben, Nederkoorn, & Jansen, 2012; Markwald et al., 2013; Torres & Nowson, 2007; Ward & Mann, 2000; Zimmerman & Shimoga, 2014). Interpreted through the lens of dual-system models in which executive/inhibitory and impulsive/appetitive systems vie for control of behavior (Appelhans, 2009; Bechara, 2005; Bickel, Jarmolowicz, Mueller, Gatchalian, & McClure, 2012; Evans & Stanovich, 2013; Hall & Fong, 2007; Hall & Fong, 2010; Heatherton & Wagner, 2011; Jentsch & Taylor, 1999; Strack & Deutsch, 2004), transient disruptions of executive function disinhibit the appetitive system, resulting in impulsive behaviors including dietary lapses. Incorporating temptation management strategies that are robust to executive function disruptors could greatly improve diet adherence in the context of lifestyle interventions, particularly for those with intrinsically low levels of executive function or who are exposed frequently to executive function disruptors in daily life.

Addressing executive function disruptors in lifestyle interventions

Two themes dominate the literature on how to improve temptation management approaches. One theme focuses on enhancing executive functions. For example, inhibitory control training and computerized working memory training programs have shown promising effects for reducing laboratory food intake or impulsive choice (Bickel, Yi, Landes, Hill, & Baxter, 2011; Houben & Jansen, 2011, 2015). Other groups are exploring rehearsal of episodic prospection (imagining future experiences) to reduce impulsivity, also with promising preliminary results (Daniel, Said, Stanton, & Epstein, 2015; Daniel, Stanton, & Epstein, 2013a, 2013b; Lin & Epstein, 2014; Peters & Buchel, 2010; Radu, Yi, Bickel, Gross, & McClure, 2011). Additional research is needed to determine if these benefits are long-lived and translate into sustained behavior change in real-world settings.

The other major theme in the literature calls for greater incorporation of strategies that are minimally dependent on executive function, and therefore unaffected by executive function disruption. Regulatory approaches (e.g., taxing sugar-sweetened beverages) (Farley, 2012; Novak & Brownell, 2012) and “choice architecture” interventions that use contextual manipulations to nudge behavior (Hollands et al., 2013; Levy, Riis, Sonnenberg, Barraclough, & Thorndike, 2012; Skov, Lourenco, Hansen, Mikkelsen, & Schofield, 2013; Thorndike, Riis, Sonnenberg, & Levy, 2014) are both motivated by the desire to avoid or minimize temptation without relying on individuals’ executive functions. However, these approaches are implemented by external agents and are not germane to lifestyle interventions. In contrast, some lifestyle interventions include commitment strategies, which are mechanisms that enable individuals manage temptation by voluntarily constraining his or her own future choices (thus commitment is a form of self-regulation). Two varieties of commitment strategies are commonly distinguished. Strict commitment involves constraining one’s future choices to more valuable, delayed rewards (Rachlin, 2000). Examples of strict commitment include enrolling in residential addiction and obesity treatment programs that limit one’s access to temptations (Kelly & Kirschenbaum, 2011; Reif et al., 2014), or placing temptations (e.g., junk food, cigarettes) in time-locking safes that open only after an extended delay (www.thekitchensafe.com).

Strict commitment is not always feasible in the real world as placing irrevocable constraints on one’s own behavior is logistically difficult (non-institutionalized adults can usually opt out of prior commitments to themselves). For this reason, most real-world commitment strategies work not by rigidly constraining future choices, but by attaching a cost to impulsive choices. Such commitment by punishment involves pairing temptations with some form of punishment, such as a financial penalty or loss of time, in order to minimize temptation at the point of decision (Green & Rachlin, 1996). A classic example of commitment by punishment is the medication disulfuram (Antabuse), which reduces the allure of alcohol at the point of decision by inducing a highly aversive reaction when it is consumed (Bell & Smith, 1949). Another commonly utilized commitment by punishment strategy involves financial contracting, whereby an individual enters into a binding agreement to forfeit an amount of money if they fail to meet a specified behavioral goal (Halpern, Asch, & Volpp, 2012). Financial contracting for weight loss has been found effective in a number of studies (Forster, Jeffery, Sullivan, & Snell, 1985; Jeffery, 2012; Jeffery, Bjornson-Benson, Rosenthal, Lindquist, & Johnson, 1984; John et al., 2011; Kullgren et al., 2013). Similarly, social contracting may involve publicly committing to a behavioral goal, thereby exposing one’s (future) self to embarrassment before family and friends if temptation prevails (Rieger et al., 2014). Both forms of commitment enable “cold” individuals to make a better choice for their future selves by bypassing or minimizing temptation, obviating the need to exercise inhibitory control strategies that are prone to disruption (Marteau, Hollands, & Fletcher, 2012).

To date, most applications of commitment to obesity treatment have involved financial contracting (Halpern et al., 2012; Rogers, Milkman, & Volpp, 2014; Schwartz et al., 2014). Some published interventions have included components that, while not explicitly described as such, capitalize on the principle of commitment. For example, several interventions have incorporated home-delivery of healthy meals (Appelhans et al., 2013; Gorin, Raynor, Niemeier, & Wing, 2007), which allows individuals in a cold state to make healthier food choices for their future selves (Hanks, Just, & Wansink, 2013; Milkman, Rogers, & Bazerman, 2010). The development of novel commitment strategies that support diet adherence represents an exciting and potentially fruitful undertaking.

A two-dimensional model of temptation management

Based on the foregoing considerations, we propose a two-dimensional framework for classifying temptation management strategies according to their intended effects on reward processing (Table 1, organized horizontally) and the dependence of each strategy on executive functions (Table 1, organized vertically). The top row of Table 1 includes the temptation resistance and prevention strategies that are common in existing lifestyle interventions. Temptation resistance strategies are implemented by an individual in a hot state with the goal of resisting temptation, whereas temptation prevention strategies are implemented in a cold state with the aim of avoiding or minimizing future temptation. Both strategies are dependent on executive functions, with temptation resistance strategies most heavily invoking inhibitory control and temptation prevention strategies relying on working memory and cognitive flexibility. The second row of Table 1 lists examples of commitment strategies that enable individuals to either avoid/minimize or resist temptation while placing minimal demands on executive function. Individuals must only initiate or enroll in these interventions (while in a cold state), rather than apply strategies independently in daily life. The bottom row of Table 1 includes regulatory strategies and choice architecture interventions that are implemented by external agents and require virtually no executive function involvement on the part of the individual.

Table 1.

Two-dimensional model of temptation management. Representative examples of temptation management strategies are listed according to their intended impact on reward processing (organized horizontally) and the demands that each strategy places on executive functions (organized vertically).

Intended impact on reward processing
Demand on executive
functions
Prevent temptation Resist temptation
High: Individuals are
trained to apply
strategies independently
  • Stimulus control

  • Home environment restructuring

  • Problem-solving

  • Urge surfing

  • Urge suppression

  • Working memory training

  • Episodic prospection


Minimal: Commitment;
individuals initiate or
enroll in interventions
  • Strict commitment
    • - Immersive therapies (residential programs)
    • - Pre-ordering healthy foods
  • Commitment by punishment
    • - Financial contracting
    • - Social contracting

None: Regulatory or
choice architecture
interventions enacted by
external agents
  • Choice restriction

  • Contextual “nudges”

  • “Sin taxes” on less healthy, but more tempting choices

Conclusions and Future Directions

The two-dimensional model of temptation management offers a conceptual framework for systematically selecting temptation management strategies for inclusion in lifestyle interventions. The neurobehavioral profile of temptation, combined with exposure to factors that disrupt executive function, preclude success in managing temptation through resistance alone for most dieters. Temptation prevention strategies can avoid or minimize exposure to temptation, but these strategies are also sensitive to executive function disruption and can only be applied to temptations that are anticipated in advance. Existing lifestyle interventions generally include some combination of temptation prevention and resistance strategies, and their high rates of nonresponse and relapse provide a motive for the field to either bolster these strategies against executive function disruptors, or explore the value of integrating commitment strategies that depend only minimally on executive functions. Lifestyle interventions seeking to incorporate a comprehensive approach to temptation management would include strategies that complement each other in terms of their intended effects on reward processing and executive function demands (the top four cells of Table 1).

Our review identified several gaps in the scientific literature on temptation management. The temptation management strategies included in most lifestyle interventions have not been systematically evaluated independent of other treatment components. As a result, their uptake, implementation, and effectiveness remain uncertain. This information is critical to refining temptation management approaches, and ultimately, to improving lifestyle interventions more generally. Studies that test utilization of specific temptation management strategies as mediators of weight loss outcomes, and directly compare different temptation management approaches in populations of interest (e.g., treatment nonresponders, those with low executive function or high sensitivity to food reward) would be particularly valuable towards this end.

Research on temptation management is currently hindered by a lack of reliable and valid measurement approaches. Use of individual temptation management strategies might be measured in multiple formats, including behavioral response/choice tasks where subjects “play” for access to immediate and delayed rewards, self-report instruments, ecological momentary assessments, or with systems for objectively monitoring the use of commitment devices. Regardless of format, such measures would be most useful when adapted to specific contexts (e.g., temptation from high-calorie foods). The availability of appropriate measurement tools could help answer several high-priority research questions, such as:

  • − Which temptation management approaches are most modifiable through intervention?

  • − Do individuals have stable preferences for temptation resistance versus prevention strategies, and are such preferences predictive of successful weight loss?

  • − Is an individual’s willingness to apply commitment strategies contingent on their recognition of their vulnerability to temptation (overcoming the cold-hot empathy gap)?

Tailoring the delivery of temptation management strategies based on key individual difference variables may improve success rates in lifestyle interventions. Individuals vary substantially in their food reward sensitivity (Epstein et al., 2007), intrinsic executive functioning capacities (Braver, Cole, & Yarkoni, 2010), and exposure to various executive function disruptors. Given valid measures of these factors, it would be possible to tailor temptation management approaches to match individuals’ neurobehavioral profiles. For example, one of us (NES) is currently examining whether the provision of healthy meals (an example of commitment) has greater benefits for diet adherence and weight loss among individuals who are non-responders to weight loss treatment and score lower on neuropsychological tests of executive function. Temptation management approaches could also be tailored based on treatment response. For example, an adaptive intervention may initially emphasize traditional temptation resistance and prevention strategies, and reserve alternative strategies with lower executive function demands but potentially greater costs (e.g., commitment devices) for non-responders or those prone to relapse. Sequential multiple assignment randomized trial (Almirall, Nahum-Shani, Sherwood, & Murphy, 2014) and fractional factorial (Collins, Dziak, Kugler, & Trail, 2014) study designs would be useful for identifying appropriate sequences and combinations of temptation management strategies.

An exciting array of innovations in temptation management is on the horizon. Interventions that enhance individuals’ executive function and self-regulatory skills are now being explored (discussed above). New variants of commitment are also being explored. For example, Rachlin’s (2000) “soft commitment” involves grouping choices or behaviors into long-term patterns (rather than constraining them) so that any single opportunity to succumb to immediate gratification is considered part of a temporally-extended trend that is less vulnerable to discounting effects (Myrseth & Fishbach, 2009; Rachlin, 2000; Rogers & Bazerman, 2008). Choices can be grouped through various mechanisms, such as adopting “personal policies” (e.g., “I never snack after 7:00 PM”), or making decisions in sets in advance of consumption (e.g., planning a week’s worth of meals in advance). Choices can also be grouped through “pattern setting” (Rachlin, 2015) which involves yoking one’s future consumption of a temptation to that at a particular instance. For example, rather than directly attempting to reduce one’s soda intake, a dieter could commit to always consuming the same amount of soda on weekdays as he or she does the prior Sunday. Reductions in soda intake would be expected over several weeks, not because the individual is striving to limit soda consumption, but because beverage choices on Sundays acquire significance as setting a precedent for future consumption. Human laboratory studies support hypothesized effects of soft commitment on decision-making and behavior (Camilleri & Newell, 2013; Myrseth & Fishbach, 2009; Read, Loewenstein, & Rabin, 1999), but more research is needed to determine whether this approach translates as an effective obesity intervention strategy.

Technology may inspire new directions in temptation management. Mobile technology could allow interventionists to provide real-time support to individuals during hot states, or enable the development of new, more feasible commitment devices. For example, smartphone-based payment methods could be harnessed to enable individuals to block their ability to purchase fast food, online social networks could serve as forums for people to make public or financial commitments (e.g., DietBet.com), and mobile applications used to arrange transportation could allow an individual to commit to traveling to the gym immediately after work on certain days. Such approaches would not have been possible even 5 years ago, and the present may be an ideal time for innovation in temptation management.

Acknowledgements

The development of this manuscript was supported by grants R01HL117804 and R21HL121861 from the National Institutes of Health (NHLBI). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The sponsor had no role in the writing of the manuscript or in the decision to submit the manuscript for publication. We thank Dr. Howard Rachlin for his input on portions of this manuscript.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Competing interests

BMA has received grant funding from Hillshire Brands Foundation. SP has received speaking funds from Weight Watchers, Int. All authors receive grant funding from the National Institutes of Health. The other authors declare that they have no competing interests.

Author contributions

BMA conceptualized and drafted the initial manuscript. All authors participated in refining the perspectives discussed in the manuscript, and critically revised the manuscript for important intellectual content. All authors have approved of the manuscript in its final form.

References

  1. Abdullah A, Peeters A, de Courten M, Stoelwinder J. The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies. Diabetes Res Clin Pract. 2010;89(3):309–19. doi: 10.1016/j.diabres.2010.04.012. [DOI] [PubMed] [Google Scholar]
  2. Ainslie G. Specious reward: a behavioral theory of impulsiveness and impulse control. Psychol Bull. 1975;82(4):463–96. doi: 10.1037/h0076860. [DOI] [PubMed] [Google Scholar]
  3. Ajala O, English P, Pinkney J. Systematic review and meta-analysis of different dietary approaches to the management of type 2 diabetes. Am J Clin Nutr. 2013;97(3):505–16. doi: 10.3945/ajcn.112.042457. [DOI] [PubMed] [Google Scholar]
  4. Alcaro A, Huber R, Panksepp J. Behavioral functions of the mesolimbic dopaminergic system: an affective neuroethological perspective. Brain Res Rev. 2007;56(2):283–321. doi: 10.1016/j.brainresrev.2007.07.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Alcaro A, Panksepp J. The SEEKING mind: primal neuro-affective substrates for appetitive incentive states and their pathological dynamics in addictions and depression. Neurosci Biobehav Rev. 2011;35(9):1805–20. doi: 10.1016/j.neubiorev.2011.03.002. [DOI] [PubMed] [Google Scholar]
  6. Alhassan S, Kim S, King AC, Gardner CD. Dietary adherence and weight loss success among overweight women: results from the A to Z Weight Loss Study. Int J Obes. 2008;32(6):985–91. doi: 10.1038/ijo.2008.8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Almirall D, Nahum-Shani I, Sherwood NE, Murphy SA. Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research. Transl Behav Med. 2014;4(3):260–74. doi: 10.1007/s13142-014-0265-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Amlung M, MacKillop J. Delayed reward discounting and alcohol misuse: the roles of response consistency and reward magnitude. J Exp Psychopathol. 2011;2(3):418–31. [PMC free article] [PubMed] [Google Scholar]
  9. Anderson BA, Laurent PA, Yantis S. Reward predictions bias attentional selection. Front Hum Neurosci. 2013;7:262. doi: 10.3389/fnhum.2013.00262. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Anderson BA, Laurent PA, Yantis S. Value-driven attentional capture. Proc Natl Acad Sci USA. 2011;108(25):10367–71. doi: 10.1073/pnas.1104047108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Anderson BA. A value-driven mechanism of attentional selection. J Vis. 201313(3) doi: 10.1167/13.3.7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Appelhans BM. Neurobehavioral inhibition of reward-driven feeding: implications for dieting and obesity. Obesity. 2009;17(4):640–7. doi: 10.1038/oby.2008.638. [DOI] [PubMed] [Google Scholar]
  13. Appelhans BM, Lynch EB, Martin MA, Nackers LM, Cail V, Woodrick N. Feasibility and acceptability of Internet grocery service in an urban food desert, Chicago, 2011-2012. Prev Chronic Dis. 2013;10:E67. doi: 10.5888/pcd10.120299. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Appelhans BM, Woolf K, Pagoto SL, Schneider KL, Whited MC, Liebman R. Inhibiting food reward: delay discounting, food reward sensitivity, and palatable food intake in overweight and obese women. Obesity. 2011;19(11):2175–82. doi: 10.1038/oby.2011.57. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Ariely D, Loewenstein G. The heat of the moment: The effect of sexual arousal on sexual decision making. J Behav Decis Mak. 2006;19(2):87–98. [Google Scholar]
  16. Audrain-McGovern J, Rodriguez D, Epstein LH, Cuevas J, Rodgers K, Wileyto EP. Does delay discounting play an etiological role in smoking or is it a consequence of smoking? Drug Alcohol Depend. 2009;103(3):99–106. doi: 10.1016/j.drugalcdep.2008.12.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Awh E, Belopolsky AV, Theeuwes J. Top-down versus bottom-up attentional control: a failed theoretical dichotomy. Trends Cog Sci. 2012;16(8):437–43. doi: 10.1016/j.tics.2012.06.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Axon RN, Bradford WD, Egan BM. The role of individual time preferences in health behaviors among hypertensive adults: a pilot study. J Am Soc Hypertens. 2009;3(1):35–41. doi: 10.1016/j.jash.2008.08.005. [DOI] [PubMed] [Google Scholar]
  19. Baddeley A. Working memory. Curr Biol. 2010;20(4):R136–40. doi: 10.1016/j.cub.2009.12.014. [DOI] [PubMed] [Google Scholar]
  20. Banich MT, Depue BE. Recent advances in understanding neural systems that support inhibitory control. Curr Opin Behav Sci. 2015;1:17–22. [Google Scholar]
  21. Barkley RA. The executive functions and self-regulation: an evolutionary neuropsychological perspective. Neuropsychol Rev. 2001;11(1):1–29. doi: 10.1023/a:1009085417776. [DOI] [PubMed] [Google Scholar]
  22. Barte JC, ter Bogt NC, Bogers RP, Teixeira PJ, Blissmer B, Mori TA, et al. Maintenance of weight loss after lifestyle interventions for overweight and obesity, a systematic review. Obes Rev. 2010;11(12):899–906. doi: 10.1111/j.1467-789X.2010.00740.x. [DOI] [PubMed] [Google Scholar]
  23. Baumeister RF. Self-regulation, ego depletion, and inhibition. Neuropsychologia. 2014;65:313–9. doi: 10.1016/j.neuropsychologia.2014.08.012. [DOI] [PubMed] [Google Scholar]
  24. Bechara A. Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nature Neurosci. 2005;8(11):1458–63. doi: 10.1038/nn1584. [DOI] [PubMed] [Google Scholar]
  25. Bechara A, Damasio AR, Damasio H, Anderson SW. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition. 1994;50(1-3):7–15. doi: 10.1016/0010-0277(94)90018-3. [DOI] [PubMed] [Google Scholar]
  26. Beebe DW, Simon S, Summer S, Hemmer S, Strotman D, Dolan LM. Dietary intake following experimentally restricted sleep in adolescents. Sleep. 2013;36(6):827–34. doi: 10.5665/sleep.2704. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Bell RG, Smith HW. Preliminary report on clinical trials of Antabuse. Can Med Assoc J. 1949;60(3):286–8. [PMC free article] [PubMed] [Google Scholar]
  28. Berridge KC. Motivation concepts in behavioral neuroscience. Physiol Behav. 2004;81(2):179–209. doi: 10.1016/j.physbeh.2004.02.004. [DOI] [PubMed] [Google Scholar]
  29. Berridge KC. 'Liking' and 'wanting' food rewards: brain substrates and roles in eating disorders. Physiol Behav. 2009;97(5):537–50. doi: 10.1016/j.physbeh.2009.02.044. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Berridge KC, Ho CY, Richard JM, DiFeliceantonio AG. The tempted brain eats: pleasure and desire circuits in obesity and eating disorders. Brain Res. 2010;1350:43–64. doi: 10.1016/j.brainres.2010.04.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Berridge KC, Kringelbach ML. Affective neuroscience of pleasure: reward in humans and animals. Psychopharmacology. 2008;199(3):457–80. doi: 10.1007/s00213-008-1099-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Berridge KC, Robinson TE. Parsing reward. Trend Neurosci. 2003;26(9):507–13. doi: 10.1016/S0166-2236(03)00233-9. [DOI] [PubMed] [Google Scholar]
  33. Berthoud HR. The neurobiology of food intake in an obesogenic environment. Proc Nutr Soc. 2012;71(4):478–87. doi: 10.1017/S0029665112000602. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Best JR, Theim KR, Gredysa DM, Stein RI, Welch RR, Saelens BE, et al. Behavioral economic predictors of overweight children's weight loss. J Consult Clin Psychol. 2012;80(6):1086–96. doi: 10.1037/a0029827. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Bickel WK, Jarmolowicz DP, Mueller ET, Gatchalian KM, McClure SM. Are executive function and impulsivity antipodes? A conceptual reconstruction with special reference to addiction. Psychopharmacology. 2012;221(3):361–87. doi: 10.1007/s00213-012-2689-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Bickel WK, Odum AL, Madden GJ. Impulsivity and cigarette smoking: delay discounting in current, never, and ex-smokers. Psychopharmacology. 1999;146(4):447–54. doi: 10.1007/pl00005490. [DOI] [PubMed] [Google Scholar]
  37. Bickel WK, Yi R, Landes RD, Hill PF, Baxter C. Remember the future: working memory training decreases delay discounting among stimulant addicts. Biol Psychiatry. 2011;69(3):260–5. doi: 10.1016/j.biopsych.2010.08.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Blundell JE, Finlayson G. Food addiction not helpful: the hedonic component - implicit wanting - is important. Addiction. 2011;106(7):1216–8. doi: 10.1111/j.1360-0443.2011.03413.x. discussion 9-20. [DOI] [PubMed] [Google Scholar]
  39. Bogers RP, Bemelmans WJ, Hoogenveen RT, Boshuizen HC, Woodward M, Knekt P, et al. Association of overweight with increased risk of coronary heart disease partly independent of blood pressure and cholesterol levels: a meta-analysis of 21 cohort studies including more than 300 000 persons. Arch Int Med. 2007;167(16):1720–8. doi: 10.1001/archinte.167.16.1720. [DOI] [PubMed] [Google Scholar]
  40. Bowen S, Marlatt A. Surfing the urge: brief mindfulness-based intervention for college student smokers. Psychol Addict Behav. 2009;23(4):666–71. doi: 10.1037/a0017127. [DOI] [PubMed] [Google Scholar]
  41. Bradford WD. The association between individual time preferences and health maintenance habits. Med Decis Making. 2010;30(1):99–112. doi: 10.1177/0272989X09342276. [DOI] [PubMed] [Google Scholar]
  42. Braver TS. The variable nature of cognitive control: a dual mechanisms framework. Trends Cog Sci. 2012;16(2):106–13. doi: 10.1016/j.tics.2011.12.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Braver TS, Cole MW, Yarkoni T. Vive les differences! Individual variation in neural mechanisms of executive control. Curr Opin Neurobiol. 2010;20(2):242–50. doi: 10.1016/j.conb.2010.03.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Burger KS, Berner LA. A functional neuroimaging review of obesity, appetitive hormones and ingestive behavior. Physiol Behav. 2014;136:121–7. doi: 10.1016/j.physbeh.2014.04.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Burger KS, Stice E. Greater striatopallidal adaptive coding during cue-reward learning and food reward habituation predict future weight gain. NeuroImage. 2014;99:122–8. doi: 10.1016/j.neuroimage.2014.05.066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Butryn ML, Webb V, Wadden TA. Behavioral treatment of obesity. Psychiatr Clin North Am. 2011;34(4):841–59. doi: 10.1016/j.psc.2011.08.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Calitri R, Pothos EM, Tapper K, Brunstrom JM, Rogers PJ. Cognitive biases to healthy and unhealthy food words predict change in BMI. Obesity. 2010;18(12):2282–7. doi: 10.1038/oby.2010.78. [DOI] [PubMed] [Google Scholar]
  48. Camilleri AR, Newell BR. The long and short of it: closing the description-experience "gap" by taking the long-run view. Cognition. 2013;126(1):54–71. doi: 10.1016/j.cognition.2012.09.001. [DOI] [PubMed] [Google Scholar]
  49. Carter EC, McCullough ME. Publication bias and the limited strength model of self-control: has the evidence for ego depletion been overestimated? Front Psychol. 2014;5:823. doi: 10.3389/fpsyg.2014.00823. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Castellanos EH, Charboneau E, Dietrich MS, Park S, Bradley BP, Mogg K, et al. Obese adults have visual attention bias for food cue images: evidence for altered reward system function. Int J Obes. 2009;33(9):1063–73. doi: 10.1038/ijo.2009.138. [DOI] [PubMed] [Google Scholar]
  51. Chiew KS, Braver TS. Dissociable influences of reward motivation and positive emotion on cognitive control. Cogn Affect Behav Neurosci. 2014;14(2):509–29. doi: 10.3758/s13415-014-0280-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Cleobury L, Tapper K. Reasons for eating 'unhealthy' snacks in overweight and obese males and females. J Hum Nutr Diet. 2014;27(4):333–41. doi: 10.1111/jhn.12169. [DOI] [PubMed] [Google Scholar]
  53. Collins LM, Dziak JJ, Kugler KC, Trail JB. Factorial experiments: efficient tools for evaluation of intervention components. Am J Prev Med. 2014;47(4):498–504. doi: 10.1016/j.amepre.2014.06.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Coutlee CG, Huettel SA. The functional neuroanatomy of decision making: prefrontal control of thought and action. Brain Res. 2012;1428:3–12. doi: 10.1016/j.brainres.2011.05.053. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Criaud M, Boulinguez P. Have we been asking the right questions when assessing response inhibition in go/no-go tasks with fMRI? A meta-analysis and critical review. Neurosci Biobehav Rev. 2013;37(1):11–23. doi: 10.1016/j.neubiorev.2012.11.003. [DOI] [PubMed] [Google Scholar]
  56. Cummings JL. Anatomic and behavioral aspects of frontal-subcortical circuits. Ann NY Acad Sci. 1995;769:1–13. doi: 10.1111/j.1749-6632.1995.tb38127.x. [DOI] [PubMed] [Google Scholar]
  57. Curioni CC, Lourenco PM. Long-term weight loss after diet and exercise: a systematic review. Int J Obes. 2005;29(10):1168–74. doi: 10.1038/sj.ijo.0803015. [DOI] [PubMed] [Google Scholar]
  58. Daniel TO, Said M, Stanton CM, Epstein LH. Episodic future thinking reduces delay discounting and energy intake in children. Eat Behav. 2015;18:20–4. doi: 10.1016/j.eatbeh.2015.03.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Daniel TO, Stanton CM, Epstein LH. The future is now: comparing the effect of episodic future thinking on impulsivity in lean and obese individuals. Appetite. 2013;71:120–5. doi: 10.1016/j.appet.2013.07.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Daniel TO, Stanton CM, Epstein LH. The future is now: reducing impulsivity and energy intake using episodic future thinking. Psychol Sci. 2013;24(11):2339–42. doi: 10.1177/0956797613488780. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Dansinger ML, Gleason JA, Griffith JL, Selker HP, Schaefer EJ. Comparison of the Atkins, Ornish, Weight Watchers, and Zone diets for weight loss and heart disease risk reduction: a randomized trial. JAMA. 2005;293(1):43–53. doi: 10.1001/jama.293.1.43. [DOI] [PubMed] [Google Scholar]
  62. Dansinger ML, Tatsioni A, Wong JB, Chung M, Balk EM. Meta-analysis: the effect of dietary counseling for weight loss. AnnInt Med. 2007;147(1):41–50. doi: 10.7326/0003-4819-147-1-200707030-00007. [DOI] [PubMed] [Google Scholar]
  63. Dariotis JK, Johnson MW. Sexual discounting among high-risk youth ages 18-24: Implications for sexual and substance use risk behaviors. Exp Clin Psychopharmacol. 2015;23(1):49–58. doi: 10.1037/a0038399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Daugherty JR, Brase GL. Taking time to be healthy: Predicting health behaviors with delay discounting and time perspective. Pers Individ Dif. 2010;48(2):202–7. [Google Scholar]
  65. Della Libera C, Chelazzi L. Visual selective attention and the effects of monetary rewards. Psychol Sci. 2006;17(3):222–7. doi: 10.1111/j.1467-9280.2006.01689.x. [DOI] [PubMed] [Google Scholar]
  66. Demos KE, Heatherton TF, Kelley WM. Individual differences in nucleus accumbens activity to food and sexual images predict weight gain and sexual behavior. J Neurosci. 2012;32(16):5549–52. doi: 10.1523/JNEUROSCI.5958-11.2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Diabetes Prevention Program (DPP) Research Group The Diabetes Prevention Program (DPP): description of lifestyle intervention. Diabetes Care. 2002;25(12):2165–71. doi: 10.2337/diacare.25.12.2165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Diamond A. Executive functions. Annu Rev Psychol. 2013;64:135–68. doi: 10.1146/annurev-psych-113011-143750. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Domenech P, Koechlin E. Executive control and decision-making in the prefrontal cortex. Curr Opin Behav Sci. 2015;1(0):101–6. [Google Scholar]
  70. Doolan KJ, Breslin G, Hanna D, Murphy K, Gallagher AM. Visual attention to food cues in obesity: an eye-tracking study. Obesity. 2014;22(12):2501–7. doi: 10.1002/oby.20884. [DOI] [PubMed] [Google Scholar]
  71. Dow CA, Thomson CA, Flatt SW, Sherwood NE, Pakiz B, Rock CL. Predictors of improvement in cardiometabolic risk factors with weight loss in women. J Am Heart Assoc. 2013;2(6):e000152. doi: 10.1161/JAHA.113.000152. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Drewnowski A. Energy intake and sensory properties of food. Am J Clin Nutr. 1995;62(5 Suppl):1081s–5s. doi: 10.1093/ajcn/62.5.1081S. [DOI] [PubMed] [Google Scholar]
  73. Ely AV, Winter S, Lowe MR. The generation and inhibition of hedonically-driven food intake: behavioral and neurophysiological determinants in healthy weight individuals. Physiol Behav. 2013;121:25–34. doi: 10.1016/j.physbeh.2013.03.026. [DOI] [PubMed] [Google Scholar]
  74. Ent MR, Baumeister RF, Tice DM. Trait self-control and the avoidance of temptation. Pers Individ Dif. 2015;74:12–5. [Google Scholar]
  75. Epstein LH, Carr KA, Lin H, Fletcher KD. Food reinforcement, energy intake, and macronutrient choice. Am J Clin Nutr. 2011;94(1):12–8. doi: 10.3945/ajcn.110.010314. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Epstein LH, Jankowiak N, Fletcher KD, Carr KA, Nederkoorn C, Raynor HA, et al. Women who are motivated to eat and discount the future are more obese. Obesity. 2014;22(6):1394–9. doi: 10.1002/oby.20661. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Epstein LH, Leddy JJ, Temple JL, Faith MS. Food reinforcement and eating: a multilevel analysis. Psychol Bull. 2007;133(5):884–906. doi: 10.1037/0033-2909.133.5.884. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Epstein LH, Salvy SJ, Carr KA, Dearing KK, Bickel WK. Food reinforcement, delay discounting and obesity. Physiol Behav. 2010;100(5):438–45. doi: 10.1016/j.physbeh.2010.04.029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Evans JSBT, Stanovich KE. Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychol Sci. 2013;8(3):223–41. doi: 10.1177/1745691612460685. [DOI] [PubMed] [Google Scholar]
  80. Farley TA. The role of government in preventing excess calorie consumption: the example of New York City. JAMA. 2012;308(11):1093–4. doi: 10.1001/2012.jama.11623. [DOI] [PubMed] [Google Scholar]
  81. Fernie G, Peeters M, Gullo MJ, Christiansen P, Cole JC, Sumnall H, et al. Multiple behavioural impulsivity tasks predict prospective alcohol involvement in adolescents. Addiction. 2013;108(11):1916–23. doi: 10.1111/add.12283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Fields SA, Sabet M, Peal A, Reynolds B. Relationship between weight status and delay discounting in a sample of adolescent cigarette smokers. Behav Pharmacol. 2011;22(3):266–8. doi: 10.1097/FBP.0b013e328345c855. [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Filevich E, Kuhn S, Haggard P. Intentional inhibition in human action: the power of 'no'. Neurosci Biobehav Rev. 2012;36(4):1107–18. doi: 10.1016/j.neubiorev.2012.01.006. [DOI] [PubMed] [Google Scholar]
  84. Finkelstein EA, Khavjou OA, Thompson H, Trogdon JG, Pan L, Sherry B, et al. Obesity and severe obesity forecasts through 2030. Am J Prev Med. 2012;42(6):563–70. doi: 10.1016/j.amepre.2011.10.026. [DOI] [PubMed] [Google Scholar]
  85. Finlayson G, King N, Blundell J. The role of implicit wanting in relation to explicit liking and wanting for food: implications for appetite control. Appetite. 2008;50(1):120–7. doi: 10.1016/j.appet.2007.06.007. [DOI] [PubMed] [Google Scholar]
  86. Finlayson G, King N, Blundell JE. Is it possible to dissociate 'liking' and 'wanting' for foods in humans? A novel experimental procedure. Physiol Behav. 2007a;90(1):36–42. doi: 10.1016/j.physbeh.2006.08.020. [DOI] [PubMed] [Google Scholar]
  87. Finlayson G, King N, Blundell JE. Liking vs. wanting food: importance for human appetite control and weight regulation. Neurosci Biobehav Rev. 2007b;31(7):987–1002. doi: 10.1016/j.neubiorev.2007.03.004. [DOI] [PubMed] [Google Scholar]
  88. Fisher G, Rangel A. Symmetry in cold-to-hot and hot-to-cold valuation gaps. Psychol Sci. 2014;25(1):120–7. doi: 10.1177/0956797613502362. [DOI] [PubMed] [Google Scholar]
  89. Fitzpatrick SL, Coughlin JW, Appel LJ, Tyson C, Stevens VJ, Jerome GJ, et al. Application of latent class analysis to identify behavioral patterns of response to behavioral lifestyle interventions in overweight and obese adults. Int J Behav Med. 2014 doi: 10.1007/s12529-014-9446-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Forman EM, Butryn ML. A new look at the science of weight control: how acceptance and commitment strategies can address the challenge of self-regulation. Appetite. 2015;84:171–80. doi: 10.1016/j.appet.2014.10.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Forman EM, Butryn ML, Hoffman KL, Herbert JD. An open trial of an acceptance-based behavioral intervention for weight loss. Cogn Behav Pract. 2009;16(2):223–35. [Google Scholar]
  92. Forman EM, Hoffman KL, McGrath KB, Herbert JD, Brandsma LL, Lowe MR. A comparison of acceptance- and control-based strategies for coping with food cravings: an analog study. Behav Res Ther. 2007;45(10):2372–86. doi: 10.1016/j.brat.2007.04.004. [DOI] [PubMed] [Google Scholar]
  93. Forster JL, Jeffery RW, Sullivan S, Snell MK. A work-site weight control program using financial incentives collected through payroll deduction. J Occup Med. 1985;27(11):804–8. doi: 10.1097/00043764-198511000-00011. [DOI] [PubMed] [Google Scholar]
  94. Franz MJ, VanWormer JJ, Crain AL, Boucher JL, Histon T, Caplan W, et al. Weight-loss outcomes: a systematic review and meta-analysis of weight-loss clinical trials with a minimum 1-year follow-up. J Am Diet Assoc. 2007;107(10):1755–67. doi: 10.1016/j.jada.2007.07.017. [DOI] [PubMed] [Google Scholar]
  95. Friedel JE, DeHart WB, Madden GJ, Odum AL. Impulsivity and cigarette smoking: discounting of monetary and consumable outcomes in current and non-smokers. Psychopharmacology. 2014;231(23):4517–26. doi: 10.1007/s00213-014-3597-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  96. Fulton S. Appetite and reward. Front Neuroendocrinol. 2010;31(1):85–103. doi: 10.1016/j.yfrne.2009.10.003. [DOI] [PubMed] [Google Scholar]
  97. Gathmann B, Pawlikowski M, Scholer T, Brand M. Performing a secondary executive task with affective stimuli interferes with decision making under risk conditions. Cogn Process. 2014;15(2):113–26. doi: 10.1007/s10339-013-0584-y. [DOI] [PubMed] [Google Scholar]
  98. Georgiadis JR, Kringelbach ML. The human sexual response cycle: brain imaging evidence linking sex to other pleasures. Prog Neurobiol. 2012;98(1):49–81. doi: 10.1016/j.pneurobio.2012.05.004. [DOI] [PubMed] [Google Scholar]
  99. Giesen JC, Havermans RC, Douven A, Tekelenburg M, Jansen A. Will work for snack food: the association of BMI and snack reinforcement. Obesity. 2010;18(5):966–70. doi: 10.1038/oby.2010.20. [DOI] [PubMed] [Google Scholar]
  100. Gilbert DT, Gill MJ, Wilson TD. The future is now: Temporal correction in affective forecasting. Organ Behav Hum Decis Process. 2002;88(1):430–44. [Google Scholar]
  101. Gillison F, Stathi A, Reddy P, Perry R, Taylor G, Bennett P, et al. Processes of behavior change and weight loss in a theory-based weight loss intervention program: a test of the process model for lifestyle behavior change. Int J Behav Nutr Phys Act. 2015;12(1):2. doi: 10.1186/s12966-014-0160-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  102. Giordano LA, Bickel WK, Loewenstein G, Jacobs EA, Marsch L, Badger GJ. Mild opioid deprivation increases the degree that opioid-dependent outpatients discount delayed heroin and money. Psychopharmacology. 2002;163(2):174–82. doi: 10.1007/s00213-002-1159-2. [DOI] [PubMed] [Google Scholar]
  103. Gorin AA, Raynor HA, Fava J, Maguire K, Robichaud E, Trautvetter J, et al. Randomized controlled trial of a comprehensive home environment-focused weight-loss program for adults. Health Psychol. 2013;32(2):128–37. doi: 10.1037/a0026959. [DOI] [PMC free article] [PubMed] [Google Scholar]
  104. Gorin AA, Raynor HA, Niemeier HM, Wing RR. Home grocery delivery improves the household food environments of behavioral weight loss participants: results of an 8-week pilot study. Int J Behav Nutr Phys Act. 2007;4:58. doi: 10.1186/1479-5868-4-58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  105. Green L, Rachlin H. Commitment using punishment. J Exp Anal Behav. 1996;65(3):593–601. doi: 10.1901/jeab.1996.65-593. [DOI] [PMC free article] [PubMed] [Google Scholar]
  106. Green RM, Lawyer SR. Steeper delay and probability discounting of potentially real versus hypothetical cigarettes (but not money) among smokers. Behav Processes. 2014;108:50–6. doi: 10.1016/j.beproc.2014.09.008. [DOI] [PubMed] [Google Scholar]
  107. Guh DP, Zhang W, Bansback N, Amarsi Z, Birmingham CL, Anis AH. The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC Pubic Health. 2009;9:88. doi: 10.1186/1471-2458-9-88. [DOI] [PMC free article] [PubMed] [Google Scholar]
  108. Gunn RL, Finn PR. Applying a dual process model of self-regulation: The association between executive working memory capacity, negative urgency, and negative mood induction on pre-potent response inhibition. Pers Individ Dif. 2015;75:210–5. doi: 10.1016/j.paid.2014.11.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  109. Guo J, Simmons WK, Herscovitch P, Martin A, Hall KD. Striatal dopamine D2-like receptor correlation patterns with human obesity and opportunistic eating behavior. Mol Psychiatry. 2014;19(10):1078–84. doi: 10.1038/mp.2014.102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  110. Hagger MS, Leaver E, Esser K, Leung CM, Te Pas N, Keatley DA, et al. Cue-induced smoking urges deplete cigarette smokers' self-control resources. Ann Behav Med. 2013;46(3):394–400. doi: 10.1007/s12160-013-9520-8. [DOI] [PubMed] [Google Scholar]
  111. Hagger MS, Wood C, Stiff C, Chatzisarantis NL. Ego depletion and the strength model of self-control: a meta-analysis. Psychol Bull. 2010;136(4):495–525. doi: 10.1037/a0019486. [DOI] [PubMed] [Google Scholar]
  112. Hall PA. Executive control resources and frequency of fatty food consumption: findings from an age-stratified community sample. Health Psychol. 2012;31(2):235–41. doi: 10.1037/a0025407. [DOI] [PubMed] [Google Scholar]
  113. Hall PA, Fong GT. Temporal self-regulation theory: A model for individual health behavior. Health Psychol Rev. 2007;1(1):6–52. [Google Scholar]
  114. Hall PA, Fong GT. Temporal self-regulation theory: Looking forward. Health Psychol Rev. 2010;4(2):83–92. [Google Scholar]
  115. Hall PA, Lowe C, Vincent C. Executive control resources and snack food consumption in the presence of restraining versus facilitating cues. J Behav Med. 2014;37(4):587–94. doi: 10.1007/s10865-013-9528-3. [DOI] [PubMed] [Google Scholar]
  116. Hall PA, Marteau TM. Executive function in the context of chronic disease prevention: theory, research and practice. Prev Med. 2014;68:44–50. doi: 10.1016/j.ypmed.2014.07.008. [DOI] [PubMed] [Google Scholar]
  117. Halpern SD, Asch DA, Volpp KG. Commitment contracts as a way to health. BMJ. 2012;344:e522. doi: 10.1136/bmj.e522. [DOI] [PMC free article] [PubMed] [Google Scholar]
  118. Hanks AS, Just DR, Wansink B. Preordering school lunch encourages better food choices by children. JAMA Pediatr. 2013;167(7):673–482. doi: 10.1001/jamapediatrics.2013.82. [DOI] [PubMed] [Google Scholar]
  119. Harris JL, Bargh JA, Brownell KD. Priming effects of television food advertising on eating behavior. Health Psychol. 2009;28(4):404–13. doi: 10.1037/a0014399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  120. Harrold JA, Dovey TM, Blundell JE, Halford JC. CNS regulation of appetite. Neuropharmacology. 2012;63(1):3–17. doi: 10.1016/j.neuropharm.2012.01.007. [DOI] [PubMed] [Google Scholar]
  121. Havermans RC. "You Say it's Liking, I Say it's Wanting …". On the difficulty of disentangling food reward in man. Appetite. 2011;57(1):286–294. doi: 10.1016/j.appet.2011.05.310. [DOI] [PubMed] [Google Scholar]
  122. Heatherton TF, Wagner DD. Cognitive neuroscience of self-regulation failure. Trends Cog Sci. 2011;15(3):132–9. doi: 10.1016/j.tics.2010.12.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  123. Heil SH, Johnson MW, Higgins ST, Bickel WK. Delay discounting in currently using and currently abstinent cocaine-dependent outpatients and non-drug-using matched controls. Addict Behav. 2006;31(7):1290–4. doi: 10.1016/j.addbeh.2005.09.005. [DOI] [PubMed] [Google Scholar]
  124. Hendrikse JJ, Cachia RL, Kothe EJ, McPhie S, Skouteris H, Hayden MJ. Attentional biases for food cues in overweight and individuals with obesity: a systematic review of the literature. Obes Rev. 2015 doi: 10.1111/obr.12265. [DOI] [PubMed] [Google Scholar]
  125. Herman CP, Polivy J. Dieting as an exercise in behavioral economics. In: Loewenstein G, Read D, Baumeister RF, editors. Time and Decision: Economic and Psychological Perspectives on Intertemporal Choice. Russell Sage Foundation; New York: 2003. pp. 459–89. c2003. [Google Scholar]
  126. Herrmann ES, Hand DJ, Johnson MW, Badger GJ, Heil SH. Examining delay discounting of condom-protected sex among opioid-dependent women and non-drug-using control women. Drug Alcohol Depend. 2014;144:53–60. doi: 10.1016/j.drugalcdep.2014.07.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  127. Heymsfield SB, Harp JB, Reitman ML, Beetsch JW, Schoeller DA, Erondu N, et al. Why do obese patients not lose more weight when treated with low-calorie diets? A mechanistic perspective. Am J Clin Nutr. 2007;85(2):346–54. doi: 10.1093/ajcn/85.2.346. [DOI] [PubMed] [Google Scholar]
  128. Hinson JM, Jameson TL, Whitney P. Impulsive decision making and working memory. J Exp Psychol Learn Mem Cogn. 2003;29(2):298–306. doi: 10.1037/0278-7393.29.2.298. [DOI] [PubMed] [Google Scholar]
  129. Hoffman WF, Moore M, Templin R, McFarland B, Hitzemann RJ, Mitchell SH. Neuropsychological function and delay discounting in methamphetamine-dependent individuals. Psychopharmacology. 2006;188(2):162–70. doi: 10.1007/s00213-006-0494-0. [DOI] [PubMed] [Google Scholar]
  130. Hofmann W, Schmeichel BJ, Baddeley AD. Executive functions and self-regulation. Trends Cog Sci. 2012;16(3):174–80. doi: 10.1016/j.tics.2012.01.006. [DOI] [PubMed] [Google Scholar]
  131. Hollands GJ, Shemilt I, Marteau TM, Jebb SA, Kelly MP, Nakamura R, et al. Altering micro-environments to change population health behaviour: towards an evidence base for choice architecture interventions. BMC Public Health. 2013;13:1218. doi: 10.1186/1471-2458-13-1218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  132. Houben K, Jansen A. Chocolate equals stop. Chocolate-specific inhibition training reduces chocolate intake and go associations with chocolate. Appetite. 2015;87:318–23. doi: 10.1016/j.appet.2015.01.005. [DOI] [PubMed] [Google Scholar]
  133. Houben K, Jansen A. Training inhibitory control. A recipe for resisting sweet temptations. Appetite. 2011;56(2):345–9. doi: 10.1016/j.appet.2010.12.017. [DOI] [PubMed] [Google Scholar]
  134. Houben K, Nederkoorn C, Jansen A. Too tempting to resist? Past success at weight control rather than dietary restraint determines exposure-induced disinhibited eating. Appetite. 2012;59(2):550–5. doi: 10.1016/j.appet.2012.07.004. [DOI] [PubMed] [Google Scholar]
  135. Hu T, Mills KT, Yao L, Demanelis K, Eloustaz M, Yancy WS, Jr., et al. Effects of low-carbohydrate diets versus low-fat diets on metabolic risk factors: a meta-analysis of randomized controlled clinical trials. Am J Epidemiol. 2012;176(Suppl 7):S44–54. doi: 10.1093/aje/kws264. [DOI] [PMC free article] [PubMed] [Google Scholar]
  136. Hubert HB, Feinleib M, McNamara PM, Castelli WP. Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart Study. Circulation. 1983;67(5):968–77. doi: 10.1161/01.cir.67.5.968. [DOI] [PubMed] [Google Scholar]
  137. Hussain SS, Bloom SR. The regulation of food intake by the gut-brain axis: implications for obesity. Int J Obes. 2013;37(5):625–33. doi: 10.1038/ijo.2012.93. [DOI] [PubMed] [Google Scholar]
  138. Jarmolowicz DP, Cherry JB, Reed DD, Bruce JM, Crespi JM, Lusk JL, et al. Robust relation between temporal discounting rates and body mass. Appetite. 2014;78:63–7. doi: 10.1016/j.appet.2014.02.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  139. Jeffery RW, Bjornson-Benson WM, Rosenthal BS, Lindquist RA, Johnson SL. Behavioral treatment of obesity with monetary contracting: two-year follow-up. Addict Behav. 1984;9(3):311–3. doi: 10.1016/0306-4603(84)90027-3. [DOI] [PubMed] [Google Scholar]
  140. Jeffery RW. Financial incentives and weight control. Prev Med. 2012;55(Suppl):S61–7. doi: 10.1016/j.ypmed.2011.12.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  141. Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. J Am Coll Cardiol. 2014;63:2985–3023. doi: 10.1016/j.jacc.2013.11.004. 25 Pt B. [DOI] [PubMed] [Google Scholar]
  142. Jentsch JD, Taylor JR. Impulsivity resulting from frontostriatal dysfunction in drug abuse: implications for the control of behavior by reward-related stimuli. Psychopharmacology. 1999;146(4):373–90. doi: 10.1007/pl00005483. [DOI] [PubMed] [Google Scholar]
  143. John LK, Loewenstein G, Troxel AB, Norton L, Fassbender JE, Volpp KG. Financial incentives for extended weight loss: a randomized, controlled trial. J Gen Intern Med. 2011;26(6):621–6. doi: 10.1007/s11606-010-1628-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  144. Johnson MW, Bruner NR. The Sexual Discounting Task: HIV risk behavior and the discounting of delayed sexual rewards in cocaine dependence. Drug Alcohol Depend. 2012;123(1-3):15–21. doi: 10.1016/j.drugalcdep.2011.09.032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  145. Jones J, Sullivan PS. Impulsivity as a risk factor for HIV transmission in men who have sex with men: a delay discounting approach. J Homosex. 2014:1–16. doi: 10.1080/00918369.2014.987568. [DOI] [PubMed] [Google Scholar]
  146. Jonsson EG, Nothen MM, Grunhage F, Farde L, Nakashima Y, Propping P, et al. Polymorphisms in the dopamine D2 receptor gene and their relationships to striatal dopamine receptor density of healthy volunteers. Mol Psychiatry. 1999;4(3):290–6. doi: 10.1038/sj.mp.4000532. [DOI] [PubMed] [Google Scholar]
  147. Joutsa J, Johansson J, Niemela S, Ollikainen A, Hirvonen MM, Piepponen P, et al. Mesolimbic dopamine release is linked to symptom severity in pathological gambling. NeuroImage. 2012;60(4):1992–9. doi: 10.1016/j.neuroimage.2012.02.006. [DOI] [PubMed] [Google Scholar]
  148. Jurado MB, Rosselli M. The elusive nature of executive functions: a review of our current understanding. Neuropsychol Rev. 2007;17(3):213–33. doi: 10.1007/s11065-007-9040-z. [DOI] [PubMed] [Google Scholar]
  149. Kelly KP, Kirschenbaum DS. Immersion treatment of childhood and adolescent obesity: the first review of a promising intervention. Obes Rev. 2011;12(1):37–49. doi: 10.1111/j.1467-789X.2009.00710.x. [DOI] [PubMed] [Google Scholar]
  150. Kemps E, Tiggemann M. Attentional bias for craving-related (chocolate) food cues. Exp Clin Psychopharmacol. 2009;17(6):425–33. doi: 10.1037/a0017796. [DOI] [PubMed] [Google Scholar]
  151. Kessler DA. The End of Overeating. Rodale; New York: 2009. [Google Scholar]
  152. Kiernan M, Brown SD, Schoffman DE, Lee K, King AC, Taylor CB, et al. Promoting healthy weight with "stability skills first": a randomized trial. J Consult Clin Psychol. 2013;81(2):336–46. doi: 10.1037/a0030544. [DOI] [PMC free article] [PubMed] [Google Scholar]
  153. Kirby KN, Petry NM, Bickel WK. Heroin addicts have higher discount rates for delayed rewards than non-drug-using controls. J Exp Psychol Gen. 1999;128(1):78–87. doi: 10.1037//0096-3445.128.1.78. [DOI] [PubMed] [Google Scholar]
  154. Kirby KN, Petry NM. Heroin and cocaine abusers have higher discount rates for delayed rewards than alcoholics or non-drug-using controls. Addiction. 2004;99(4):461–71. doi: 10.1111/j.1360-0443.2003.00669.x. [DOI] [PubMed] [Google Scholar]
  155. Kishinevsky FI, Cox JE, Murdaugh DL, Stoeckel LE, Cook EW, 3rd, Weller RE. fMRI reactivity on a delay discounting task predicts weight gain in obese women. Appetite. 2012;58(2):582–92. doi: 10.1016/j.appet.2011.11.029. [DOI] [PubMed] [Google Scholar]
  156. Koob GF, Volkow ND. Neurocircuitry of addiction. Neuropsychopharmacol. 2010;35(1):217–38. doi: 10.1038/npp.2009.110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  157. Kringelbach ML, Stein A, van Hartevelt TJ. The functional human neuroanatomy of food pleasure cycles. Physiol Behav. 2012;106(3):307–16. doi: 10.1016/j.physbeh.2012.03.023. [DOI] [PubMed] [Google Scholar]
  158. Kullgren JT, Troxel AB, Loewenstein G, Asch DA, Norton LA, Wesby L, et al. Individual-versus group-based financial incentives for weight loss: a randomized, controlled trial. Ann Intern Med. 2013;158(7):505–14. doi: 10.7326/0003-4819-158-7-201304020-00002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  159. Lansigan RK, Emond JA, Gilbert-Diamond D. Understanding eating in the absence of hunger among young children: a systematic review of existing studies. Appetite. 2015;85:36–47. doi: 10.1016/j.appet.2014.10.032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  160. Levy DE, Riis J, Sonnenberg LM, Barraclough SJ, Thorndike AN. Food choices of minority and low-income employees: a cafeteria intervention. Am J Prev Med. 2012;43(3):240–8. doi: 10.1016/j.amepre.2012.05.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  161. Levy DJ, Thavikulwat AC, Glimcher PW. State dependent valuation: the effect of deprivation on risk preferences. PloS One. 2013;8(1):e53978. doi: 10.1371/journal.pone.0053978. [DOI] [PMC free article] [PubMed] [Google Scholar]
  162. Lin H, Epstein LH. Living in the moment: effects of time perspective and emotional valence of episodic thinking on delay discounting. Behav Neurosci. 2014;128(1):12–9. doi: 10.1037/a0035705. [DOI] [PMC free article] [PubMed] [Google Scholar]
  163. Linden DE. The working memory networks of the human brain. Neuroscientist. 2007;13(3):257–67. doi: 10.1177/1073858406298480. [DOI] [PubMed] [Google Scholar]
  164. Liu RH, Wharton S, Sharma AM, Ardern CI, Kuk JL. Influence of a clinical lifestyle-based weight loss program on the metabolic risk profile of metabolically normal and abnormal obese adults. Obesity. 2013;21(8):1533–9. doi: 10.1002/oby.20219. [DOI] [PubMed] [Google Scholar]
  165. Loeber S, Grosshans M, Herpertz S, Kiefer F, Herpertz SC. Hunger modulates behavioral disinhibition and attention allocation to food-associated cues in normal-weight controls. Appetite. 2013;71:32–9. doi: 10.1016/j.appet.2013.07.008. [DOI] [PubMed] [Google Scholar]
  166. Loewenstein G, Read D, Baumeister RF. Time and Decision: Economic and Psychological Perspectives on Intertemporal Choice. Russell Sage Foundation; New York: 2003. [Google Scholar]
  167. Loewenstein G. Out of control: visceral influences on behavior. Organ Behav Hum Decis Process. 1996;65(3):272–92. [Google Scholar]
  168. Look AHEAD Research Group Eight-year weight losses with an intensive lifestyle intervention: the Look AHEAD study. Obesity. 2014;22(1):5–13. doi: 10.1002/oby.20662. [DOI] [PMC free article] [PubMed] [Google Scholar]
  169. Lowe MR, Butryn ML. Hedonic hunger: a new dimension of appetite? Physiol Behav. 2007;91(4):432–9. doi: 10.1016/j.physbeh.2007.04.006. [DOI] [PubMed] [Google Scholar]
  170. MacLean PS, Wing RR, Davidson T, Epstein L, Goodpaster B, Hall KD, et al. NIH working group report: Innovative research to improve maintenance of weight loss. Obesity. 2015;23(1):7–15. doi: 10.1002/oby.20967. [DOI] [PMC free article] [PubMed] [Google Scholar]
  171. Makris A, Foster GD. Dietary approaches to the treatment of obesity. Psychiatr Clin North Am. 2011;34(4):813–27. doi: 10.1016/j.psc.2011.08.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  172. Mann T, de Ridder D, Fujita K. Self-regulation of health behavior: social psychological approaches to goal setting and goal striving. Health Psychol. 2013;32(5):487–98. doi: 10.1037/a0028533. [DOI] [PubMed] [Google Scholar]
  173. Markwald RR, Melanson EL, Smith MR, Higgins J, Perreault L, Eckel RH, et al. Impact of insufficient sleep on total daily energy expenditure, food intake, and weight gain. Proc Natl Acad Sci USA. 2013;110(14):5695–700. doi: 10.1073/pnas.1216951110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  174. Marteau TM, Hollands GJ, Fletcher PC. Changing human behavior to prevent disease: the importance of targeting automatic processes. Science. 2012;337(6101):1492–5. doi: 10.1126/science.1226918. [DOI] [PubMed] [Google Scholar]
  175. McClure SM, Bickel WK. A dual-systems perspective on addiction: contributions from neuroimaging and cognitive training. Ann N Y Acad Sci. 2014;1327:62–78. doi: 10.1111/nyas.12561. [DOI] [PMC free article] [PubMed] [Google Scholar]
  176. McKee HC, Ntoumanis N, Taylor IM. An ecological momentary assessment of lapse occurrences in dieters. Ann Behav Med. 2014;48(3):300–10. doi: 10.1007/s12160-014-9594-y. [DOI] [PubMed] [Google Scholar]
  177. Mela DJ. Eating for pleasure or just wanting to eat? Reconsidering sensory hedonic responses as a driver of obesity. Appetite. 2006;47(1):10–7. doi: 10.1016/j.appet.2006.02.006. [DOI] [PubMed] [Google Scholar]
  178. Mesulam MM. The human frontal lobes: transcending the default mode through contingent encoding. In: Stuss DT, Knight RL, editors. Principles of Frontal Lobe Function. Oxford University Press; Oxford: 2002. pp. 8–30. [Google Scholar]
  179. Metcalfe J, Mischel W. A hot/cool-system analysis of delay of gratification: dynamics of willpower. Psychol Rev. 1999;106(1):3–19. doi: 10.1037/0033-295x.106.1.3. [DOI] [PubMed] [Google Scholar]
  180. Middleton KM, Patidar SM, Perri MG. The impact of extended care on the long-term maintenance of weight loss: a systematic review and meta-analysis. Obes Rev. 2012;13(6):509. doi: 10.1111/j.1467-789X.2011.00972.x. [DOI] [PubMed] [Google Scholar]
  181. Milkman KL, Rogers T, Bazerman MH. I'll have the ice cream soon and the vegetables later: A study of online grocery purchases and order lead time. Marketing Letters. 2010;21(1):17–35. [Google Scholar]
  182. Miller EK, Cohen JD. An integrative theory of prefrontal cortex function. Annu Rev Neurosci. 2001;24:167–202. doi: 10.1146/annurev.neuro.24.1.167. Journal Article. [DOI] [PubMed] [Google Scholar]
  183. Miyake A, Friedman NP, Emerson MJ, Witzki AH, Howerter A, Wager TD. The unity and diversity of executive functions and their contributions to complex "Frontal Lobe" tasks: a latent variable analysis. Cogn Psychol. 2000;41(1):49–100. doi: 10.1006/cogp.1999.0734. [DOI] [PubMed] [Google Scholar]
  184. Munakata Y, Herd SA, Chatham CH, Depue BE, Banich MT, O'Reilly RC. A unified framework for inhibitory control. Trends Cog Sci. 2011;15(10):453–9. doi: 10.1016/j.tics.2011.07.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  185. Murawski ME, Milsom VA, Ross KM, Rickel KA, DeBraganza N, Gibbons LM, et al. Problem solving, treatment adherence, and weight-loss outcome among women participating in lifestyle treatment for obesity. Eat Behav. 2009;10(3):146–51. doi: 10.1016/j.eatbeh.2009.03.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  186. Myrseth KOR, Fishbach A. Self-control: A function of knowing when and how to exercise restraint. Curr Dir Psychol Sci. 2009;18(4):247–252. [Google Scholar]
  187. Nederkoorn C, Guerrieri R, Havermans RC, Roefs A, Jansen A. The interactive effect of hunger and impulsivity on food intake and purchase in a virtual supermarket. Int J Obes. 2009;33(8):905–12. doi: 10.1038/ijo.2009.98. [DOI] [PubMed] [Google Scholar]
  188. Nijs IM, Franken IH, Muris P. Food-related Stroop interference in obese and normal-weight individuals: behavioral and electrophysiological indices. Eat Behav. 2010;11(4):258–65. doi: 10.1016/j.eatbeh.2010.07.002. [DOI] [PubMed] [Google Scholar]
  189. Nijs IM, Muris P, Euser AS, Franken IH. Differences in attention to food and food intake between overweight/obese and normal-weight females under conditions of hunger and satiety. Appetite. 2010;54(2):243–54. doi: 10.1016/j.appet.2009.11.004. [DOI] [PubMed] [Google Scholar]
  190. Novak NL, Brownell KD. Role of policy and government in the obesity epidemic. Circulation. 2012;126(19):2345–52. doi: 10.1161/CIRCULATIONAHA.111.037929. [DOI] [PubMed] [Google Scholar]
  191. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA. 2014;311(8):806–14. doi: 10.1001/jama.2014.732. [DOI] [PMC free article] [PubMed] [Google Scholar]
  192. Pabst S, Schoofs D, Pawlikowski M, Brand M, Wolf OT. Paradoxical effects of stress and an executive task on decisions under risk. Behav Neurosci. 2013;127(3):369–79. doi: 10.1037/a0032334. [DOI] [PubMed] [Google Scholar]
  193. Pagoto S, Appelhans BM, et al. The challenge of stimulus control: a comment on Poelman. Ann Behav Med. 2015;49(1):3–4. doi: 10.1007/s12160-014-9661-4. [DOI] [PubMed] [Google Scholar]
  194. Pagoto SL, Appelhans BM. A call for an end to the diet debates. JAMA. 2013;310(7):687–8. doi: 10.1001/jama.2013.8601. [DOI] [PubMed] [Google Scholar]
  195. Perri MG, Nezu AM, McKelvey WF, Shermer RL, Renjilian DA, Viegener BJ. Relapse prevention training and problem-solving therapy in the long-term management of obesity. J Consult Clin Psychol. 2001;69(4):722–6. [PubMed] [Google Scholar]
  196. Peters J, Buchel C. Episodic future thinking reduces reward delay discounting through an enhancement of prefrontal-mediotemporal interactions. Neuron. 2010;66(1):138–48. doi: 10.1016/j.neuron.2010.03.026. [DOI] [PubMed] [Google Scholar]
  197. Piech RM, Pastorino MT, Zald DH. All I saw was the cake. Hunger effects on attentional capture by visual food cues. Appetite. 2010;54(3):579–82. doi: 10.1016/j.appet.2009.11.003. [DOI] [PubMed] [Google Scholar]
  198. Pi-Sunyer X, Blackburn G, Brancati FL, Bray GA, Bright R, Clark JM, et al. Reduction in weight and cardiovascular disease risk factors in individuals with type 2 diabetes: one-year results of the Look AHEAD trial. Diabetes Care. 2007;30(6):1374–83. doi: 10.2337/dc07-0048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  199. Poelman MP, Velema E, Seidell JC, Steenhuis IH. de Vet E, de Boer MR, editors. PortionControl@HOME: Results of a randomized controlled trial evaluating the effect of a multi-component portion size intervention on portion control behavior and body mass index. Ann Behav Med. 2015;49(1):18–28. doi: 10.1007/s12160-014-9637-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  200. Pool E, Brosch T, Delplanque S, Sander D. Where is the chocolate? Rapid spatial orienting toward stimuli associated with primary rewards. Cognition. 2014;130(3):348–59. doi: 10.1016/j.cognition.2013.12.002. [DOI] [PubMed] [Google Scholar]
  201. Pulvirenti L, Koob GF. The neural substrates of drug addiction and dependence. Funct Neurol. 1990;5(2):109–19. [PubMed] [Google Scholar]
  202. Rachlin H. The Science of Self-Control. Harvard University Press; Cambridge, Massachusetts: 2000. [Google Scholar]
  203. Rachlin H. Teleological behaviorism and its potential application in self-control. 2015 Manuscript under review. [Google Scholar]
  204. Radu PT, Yi R, Bickel WK, Gross JJ, McClure SM. A mechanism for reducing delay discounting by altering temporal attention. J Appl Behav Anal. 2011;96(3):363–85. doi: 10.1901/jeab.2011.96-363. [DOI] [PMC free article] [PubMed] [Google Scholar]
  205. Rasmussen HN, Wrosch C, Scheier MF, Carver CS. Self-regulation processes and health: the importance of optimism and goal adjustment. J Pers. 2006;74(6):1721–47. doi: 10.1111/j.1467-6494.2006.00426.x. [DOI] [PubMed] [Google Scholar]
  206. Read D, Loewenstein G, Rabin M. Choice Bracketing. Journal of Risk & Uncertainty. 1999;19(1-3):171–197. [Google Scholar]
  207. Read D, van Leeuwen B. Predicting hunger: The effects of appetite and delay on choice. Organ Behav Hum Decis Process. 1998;76(2):189–205. doi: 10.1006/obhd.1998.2803. [DOI] [PubMed] [Google Scholar]
  208. Reif S, George P, Braude L, Dougherty RH, Daniels AS, Ghose SS, et al. Residential treatment for individuals with substance use disorders: assessing the evidence. Psychiatr Serv. 2014;65(3):301–12. doi: 10.1176/appi.ps.201300242. [DOI] [PubMed] [Google Scholar]
  209. Reinert KR, Po'e EK, Barkin SL. The relationship between executive function and obesity in children and adolescents: a systematic literature review. J Obes. 2013;2013:820956. doi: 10.1155/2013/820956. [DOI] [PMC free article] [PubMed] [Google Scholar]
  210. Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet. 2008;371(9612):569–78. doi: 10.1016/S0140-6736(08)60269-X. [DOI] [PubMed] [Google Scholar]
  211. Reynolds B, Richards JB, Horn K, Karraker K. Delay discounting and probability discounting as related to cigarette smoking status in adults. Behav Processes. 2004;65(1):35–42. doi: 10.1016/s0376-6357(03)00109-8. [DOI] [PubMed] [Google Scholar]
  212. Reynolds B, Schiffbauer R. Measuring state changes in human delay discounting: an experiential discounting task. Behav Processes. 2004;67(3):343–56. doi: 10.1016/j.beproc.2004.06.003. [DOI] [PubMed] [Google Scholar]
  213. Rieger E, Treasure J, Swinbourne J, Adam B, Manns C, Caterson I. The effectiveness of including support people in a cognitive behavioural weight loss maintenance programme for obese adults: study rationale and design. Clin Obes. 2014;4(2):77–90. doi: 10.1111/cob.12042. [DOI] [PubMed] [Google Scholar]
  214. Robinson TE, Berridge KC. The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res Rev. 1993;18(3):247–91. doi: 10.1016/0165-0173(93)90013-p. [DOI] [PubMed] [Google Scholar]
  215. Robles E, Huang BE, Simpson PM, McMillan DE. Delay discounting, impulsiveness, and addiction severity in opioid-dependent patients. J Subst Abuse Treat. 2011;41(4):354–62. doi: 10.1016/j.jsat.2011.05.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  216. Rogers T, Bazerman MH. Future lock-in: Future implementation increases selection of 'should' choices. Organizational Behavior and Human Decision Processes. 2008;106(1):1–20. [Google Scholar]
  217. Rogers T, Milkman KL, Volpp KG. Commitment devices: using initiatives to change behavior. JAMA. 2014;311(20):2065–6. doi: 10.1001/jama.2014.3485. [DOI] [PubMed] [Google Scholar]
  218. Rollins BY, Dearing KK, Epstein LH. Delay discounting moderates the effect of food reinforcement on energy intake among non-obese women. Appetite. 2010;55(3):420–5. doi: 10.1016/j.appet.2010.07.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  219. Rossa KR, Smith SS, Allan AC, Sullivan KA. The effects of sleep restriction on executive inhibitory control and affect in young adults. J Adolesc Health. 2014;55(2):287–92. doi: 10.1016/j.jadohealth.2013.12.034. [DOI] [PubMed] [Google Scholar]
  220. Rui L. Brain regulation of energy balance and body weight. Rev Endocr Metab Disord. 2013;14(4):387–407. doi: 10.1007/s11154-013-9261-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  221. Saelens BE, Epstein LH. Reinforcing value of food in obese and non-obese women. Appetite. 1996;27(1):41–50. doi: 10.1006/appe.1996.0032. [DOI] [PubMed] [Google Scholar]
  222. Sayette MA, Loewenstein G, Griffin KM, Black JJ. Exploring the cold-to-hot empathy gap in smokers. Psychol Sci. 2008;19(9):926–32. doi: 10.1111/j.1467-9280.2008.02178.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  223. Schacht JP, Anton RF, Myrick H. Functional neuroimaging studies of alcohol cue reactivity: a quantitative meta-analysis and systematic review. Addict Biol. 2013;18(1):121–33. doi: 10.1111/j.1369-1600.2012.00464.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  224. Schwartz J, Mochon D, Wyper L, Maroba J, Patel D, Ariely D. Healthier by precommitment. Psychol Sci. 2014;25(2):538–46. doi: 10.1177/0956797613510950. [DOI] [PubMed] [Google Scholar]
  225. Seligman ME, Railton P, Baumeister RF, Sripada C. Navigating into the future or driven by the past. Perspect Psychol Sci. 2013;8(2):119–41. doi: 10.1177/1745691612474317. [DOI] [PubMed] [Google Scholar]
  226. Siep N, Roefs A, Roebroeck A, Havermans R, Bonte M, Jansen A. Fighting food temptations: the modulating effects of short-term cognitive reappraisal, suppression and up-regulation on mesocorticolimbic activity related to appetitive motivation. NeuroImage. 2012;60(1):213–20. doi: 10.1016/j.neuroimage.2011.12.067. [DOI] [PubMed] [Google Scholar]
  227. Simmonds DJ, Pekar JJ, Mostofsky SH. Meta-analysis of Go/No-go tasks demonstrating that fMRI activation associated with response inhibition is task-dependent. Neuropsychologia. 2008;46(1):224–32. doi: 10.1016/j.neuropsychologia.2007.07.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  228. Skov LR, Lourenco S, Hansen GL, Mikkelsen BE, Schofield C. Choice architecture as a means to change eating behaviour in self-service settings: a systematic review. Obes Rev. 2013;14(3):187–96. doi: 10.1111/j.1467-789X.2012.01054.x. [DOI] [PubMed] [Google Scholar]
  229. Smeets E, Roefs A, Jansen A. Experimentally induced chocolate craving leads to an attentional bias in increased distraction but not in speeded detection. Appetite. 2009;53(3):370–5. doi: 10.1016/j.appet.2009.07.020. [DOI] [PubMed] [Google Scholar]
  230. Smith DG, Robbins TW. The neurobiological underpinnings of obesity and binge eating: a rationale for adopting the food addiction model. Biol Psychiatry. 2013;73(9):804–10. doi: 10.1016/j.biopsych.2012.08.026. [DOI] [PubMed] [Google Scholar]
  231. Starcke K, Pawlikowski M, Wolf OT, Altstotter-Gleich C, Brand M. Decision-making under risk conditions is susceptible to interference by a secondary executive task. Cogn Process. 2011;12(2):177–82. doi: 10.1007/s10339-010-0387-3. [DOI] [PubMed] [Google Scholar]
  232. Stice E, Spoor S, Bohon C, Small DM. Relation between obesity and blunted striatal response to food is moderated by TaqIA A1 allele. Science. 2008;322(5900):449–52. doi: 10.1126/science.1161550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  233. Stice E, Yokum S, Burger K, Rohde P, Shaw H, Gau JM. A pilot randomized trial of a cognitive reappraisal obesity prevention program. Physiol Behav. 2015;138:124–32. doi: 10.1016/j.physbeh.2014.10.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  234. Stice E, Yokum S, Burger KS, Epstein LH, Small DM. Youth at risk for obesity show greater activation of striatal and somatosensory regions to food. J Neurosci. 2011;31(12):4360–6. doi: 10.1523/JNEUROSCI.6604-10.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  235. Strack F, Deutsch R. Reflective and impulsive determinants of social behavior. Pers Soc Psychol Rev. 2004;8(3):220–47. doi: 10.1207/s15327957pspr0803_1. [DOI] [PubMed] [Google Scholar]
  236. Strazzullo P, D'Elia L, Cairella G, Garbagnati F, Cappuccio FP, Scalfi L. Excess body weight and incidence of stroke: meta-analysis of prospective studies with 2 million participants. Stroke. 2010;41(5):e418–26. doi: 10.1161/STROKEAHA.109.576967. [DOI] [PubMed] [Google Scholar]
  237. Swick D, Ashley V, Turken U. Are the neural correlates of stopping and not going identical? Quantitative meta-analysis of two response inhibition tasks. NeuroImage. 2011;56(3):1655–65. doi: 10.1016/j.neuroimage.2011.02.070. [DOI] [PubMed] [Google Scholar]
  238. Thomas JG, Doshi S, Crosby RD, Lowe MR. Ecological momentary assessment of obesogenic eating behavior: combining person-specific and environmental predictors. Obesity. 2011;19(8):1574–9. doi: 10.1038/oby.2010.335. [DOI] [PubMed] [Google Scholar]
  239. Thorndike AN, Riis J, Sonnenberg LM, Levy DE. Traffic-light labels and choice architecture: promoting healthy food choices. Am J Prev Med. 2014;46(2):143–9. doi: 10.1016/j.amepre.2013.10.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  240. Torres SJ, Nowson CA. Relationship between stress, eating behavior, and obesity. Nutrition. 2007;23(11-12):887–94. doi: 10.1016/j.nut.2007.08.008. [DOI] [PubMed] [Google Scholar]
  241. Trogdon JG, Finkelstein EA, Feagan CW, Cohen JW. State- and payer-specific estimates of annual medical expenditures attributable to obesity. Obesity. 2012;20(1):214–20. doi: 10.1038/oby.2011.169. [DOI] [PubMed] [Google Scholar]
  242. Tryon MS, Carter CS, Decant R, Laugero KD. Chronic stress exposure may affect the brain's response to high calorie food cues and predispose to obesogenic eating habits. Physiol Behav. 2013;120:233–42. doi: 10.1016/j.physbeh.2013.08.010. [DOI] [PubMed] [Google Scholar]
  243. Vainik U, Dagher A, Dube L, Fellows LK. Neurobehavioural correlates of body mass index and eating behaviours in adults: a systematic review. Neurosci Biobehav Rev. 2013;37(3):279–99. doi: 10.1016/j.neubiorev.2012.11.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  244. Van Boven L, Loewentstein G, Dunning D, Nordgren LF. Changing places: a dual judgment model of empathy gaps in emotional perspective taking. In: Zanna Mark, Olson James., editors. Advances in Experimental Social Psychology. Academic Press; Burlington: 2013. pp. 117–71. [Google Scholar]
  245. van Holst RJ, van den Brink W, Veltman DJ, Goudriaan AE. Brain imaging studies in pathological gambling. Curr Psychiatry Rep. 2010;12(5):418–25. doi: 10.1007/s11920-010-0141-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  246. Volkow ND, Wang GJ, Telang F, Fowler JS, Thanos PK, Logan J, et al. Low dopamine striatal D2 receptors are associated with prefrontal metabolism in obese subjects: possible contributing factors. NeuroImage. 2008;42(4):1537–43. doi: 10.1016/j.neuroimage.2008.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  247. Vuchinich RE, Simpson CA. Hyperbolic temporal discounting in social drinkers and problem drinkers. Exp Clin Psychopharmacol. 1998;6(3):292–305. doi: 10.1037//1064-1297.6.3.292. [DOI] [PubMed] [Google Scholar]
  248. Wang L, Duan Y, Theeuwes J, Zhou X. Reward breaks through the inhibitory region around attentional focus. J Vis. 2014;14(12) doi: 10.1167/14.12.2. [DOI] [PubMed] [Google Scholar]
  249. Ward A, Mann T. Don't mind if I do: disinhibited eating under cognitive load. J Pers Soc Psychol. 2000;78(4):753–63. doi: 10.1037//0022-3514.78.4.753. [DOI] [PubMed] [Google Scholar]
  250. Weller RE, Cook EW, Iii, Avsar KB, Cox JE. Obese women show greater delay discounting than healthy-weight women. Appetite. 2008;51(3):563–9. doi: 10.1016/j.appet.2008.04.010. [DOI] [PubMed] [Google Scholar]
  251. Werthmann J, Roefs A, Nederkoorn C, Jansen A. Desire lies in the eyes: attention bias for chocolate is related to craving and self-endorsed eating permission. Appetite. 2013;70:81–9. doi: 10.1016/j.appet.2013.06.087. [DOI] [PubMed] [Google Scholar]
  252. Werthmann J, Roefs A, Nederkoorn C, Mogg K, Bradley BP, Jansen A. Can(not) take my eyes off it: attention bias for food in overweight participants. Health Psychol. 2011;30(5):561–9. doi: 10.1037/a0024291. [DOI] [PubMed] [Google Scholar]
  253. Whitney P, Hinson JM. Measurement of cognition in studies of sleep deprivation. Prog Brain Res. 2010;185:37–48. doi: 10.1016/B978-0-444-53702-7.00003-8. [DOI] [PubMed] [Google Scholar]
  254. Wing RR, Lang W, Wadden TA, Safford M, Knowler WC, Bertoni AG, et al. Benefits of modest weight loss in improving cardiovascular risk factors in overweight and obese individuals with type 2 diabetes. Diabetes Care. 2011;34(7):1481–6. doi: 10.2337/dc10-2415. [DOI] [PMC free article] [PubMed] [Google Scholar]
  255. Wise RA. Dual roles of dopamine in food and drug seeking: the drive-reward paradox. Biol Psychiatry. 2013;73(9):819–26. doi: 10.1016/j.biopsych.2012.09.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  256. Woods SC, D'Alessio DA. Central control of body weight and appetite. J Clin Endocrinol Metab. 2008;93(11 Suppl 1):S37–50. doi: 10.1210/jc.2008-1630. [DOI] [PMC free article] [PubMed] [Google Scholar]
  257. Wycherley TP, Moran LJ, Clifton PM, Noakes M, Brinkworth GD. Effects of energy-restricted high-protein, low-fat compared with standard-protein, low-fat diets: a meta-analysis of randomized controlled trials. Am J Clin Nutr. 2012;96(6):1281–98. doi: 10.3945/ajcn.112.044321. [DOI] [PubMed] [Google Scholar]
  258. Yokum S, Ng J, Stice E. Attentional bias to food images associated with elevated weight and future weight gain: an fMRI study. Obesity. 2011;19(9):1775–83. doi: 10.1038/oby.2011.168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  259. Yokum S, Stice E. Cognitive regulation of food craving: effects of three cognitive reappraisal strategies on neural response to palatable foods. Int J Obes. 2013;37(12):1565–70. doi: 10.1038/ijo.2013.39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  260. Zandbelt BB, Bloemendaal M, Hoogendam JM, Kahn RS, Vink M. Transcranial magnetic stimulation and functional MRI reveal cortical and subcortical interactions during stop-signal response inhibition. J Cogn Neurosci. 2013;25(2):157–74. doi: 10.1162/jocn_a_00309. [DOI] [PubMed] [Google Scholar]
  261. Zimmerman FJ, Shimoga SV. The effects of food advertising and cognitive load on food choices. BMC Public Health. 2014;14:342. doi: 10.1186/1471-2458-14-342. [DOI] [PMC free article] [PubMed] [Google Scholar]

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