Physiology & Behavior 222 (2020) 112878

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Physiology & Behavior

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Review Regulating food craving: From mechanisms to interventions T ⁎ Wendy Suna,b, Hedy Kobera, a Yale University, New Haven, CT 06510, United States b Harvard University, Boston, MA 02115, United States

ARTICLE INFO ABSTRACT

Keywords: Craving, defined here as a strong desire to eat, is a common experience that drives behavior. Here wediscussthe Craving concept of craving from historical, physiological, and clinical perspectives, and review work investigating the Cue reactivity effects of cue reactivity and cue-induced craving on eating and weight outcomes, as well as underlying neural Regulation mechanisms. We also highlight the significance of cue reactivity and craving in the context of our “toxicfood Choice environment” and the obesity epidemic. We then summarize our work developing the Regulation of Craving Value (ROC) task, used to test the causal effects of cognitive strategies on craving for food and drugs as wellasthe Training underlying neural mechanisms of such regulation. Next, we review our recent development of a novel ROC- based intervention that trains individuals to use cognitive strategies to regulate craving, with promising effects on subsequent food choice and caloric consumption. We end by discussing future directions for this important line of work.

At the 2019 annual meeting of the Society for the Study of Ingestive centuries. In the Buddhist Four Noble Truths, craving is said to be the Behaviors in Utrecht, the last author presented our lab's work on the cause of all suffering [5]; the philosopher Baruch Spinoza said that it is Regulation of Craving (ROC) task, as well as the development and va- “the very essence of man” [6]; and Bertrand Russell said that it prompts lidation of a cognitive training procedure based on this task. In this all human activity [7]. These thinkers join in the belief that craving is a review, we provide a synthesis of the background and motivation for very common experience (as confirmed by modern epidemiological those studies, summarize important relevant work, and discuss future directions for refining and disseminating this training. We will begin with a demonstration. Please look at this freshly baked brownie, and imagine that it is on a plate, right in front of you (Fig. 1). You can see there are chips and nuts on top, and warm fudge running down the sides. Now, please imagine picking up this warm brownie, bringing it close to your nose, and inhaling the sweet scent of chocolate. Imagine bringing it to your mouth and taking a bite. Can you feel the rich texture of the dough, and the indulgent taste? As you imagine this, you may feel your mouth watering, perhaps your heart rate quickens ever-so-slightly. And with this, you may notice that you really want to eat a brownie. If so, then what you are experi- encing is known as craving. Craving can be defined in a myriad of ways. In our conceptualiza- tion – as in most common conceptualizations – craving is defined as a strong desire, urge, or wanting [e.g., 1–4]. For example, in the current 5th edition of the Diagnostic and Statistical Manual of Mental Disorders [DSM-5; 3], craving is listed as a diagnostic criterion for substance use disorders and is defined as “a strong desire or urge to use drugs.” Fig. 1. Brownie for craving demonstration. Image courtesy of istockphoto.com/ As a human experience, craving has been troubling philosophers for portfolio/anthonysp.

⁎ Corresponding author. E-mail address: [email protected] (H. Kober). https://doi.org/10.1016/j.physbeh.2020.112878 Received 15 December 2019; Received in revised form 25 February 2020; Accepted 15 March 2020 Available online 13 April 2020 0031-9384/ © 2020 Elsevier Inc. All rights reserved. W. Sun and H. Kober Physiology & Behavior 222 (2020) 112878 studies showing that craving is experienced by ~99% of adults [8]) that levels of craving for specific foods, which includes questions such as“I also drives behavior [9]. have an intense desire to eat [one or more specific foods]” [e.g., 28–32]. Other studies have used the Food Craving Inventory to assess Craving as a learned response frequency of cravings for four specific categories of foods (sweets, , , and fast food fats); scores can also be calculated for food Psychologically, we consider craving to be a complex, multi- in general [e.g., 31,33–35]. Other scales have also been used to assess dimensional construct that can be understood using a learning-based frequency and intensity for food cravings, such as those developed by model of behavior. To conceptualize this, one could think back to the Weingarten and Elston [36,37], and Hill [37,38]. Further, many studies classical experiments of Russian physiologist Ivan Pavlov. In the late have assessed craving (including cue-induced craving) for specific foods 19th century, Pavlov began researching salivation in dogs in response by collecting responses to single-item, Likert-type or Visual Analog to feeding. He started with the fundamental observation that his dogs Scale, with questions such as “how much do you want to eat this food” naturally salivated in response to eating food, and often as soon as it or “rate the intensity of your craving” [e.g., 2,39,40-45]. was placed in front of them. Then, Pavlov started to ring a bell each time before placing food in front of the dogs. After a number of repeated bell-food pairings, Pavlov rang the bell without placing food in front of Food cues, craving, eating, and the “toxic food environment” the dogs, and observed that they salivated in response to the sound of the bell, even when food did not follow. Pavlov formalized his ob- In the literature, it has been consistently documented that food and servations into a paradigm now known as classical or Pavlovian con- drug cues increase craving. For example, an early meta-analysis by ditioning [10]. In this framework, ingested food is considered to be an Carter & Tiffany (1999) showed that across drugs of abuse, participants “unconditioned stimulus” and salivation is considered to be an “un- demonstrated significant increases in self-reported craving when ex- conditioned response” (that is, these “unconditioned” factors do not posed to drug-related vs. neutral stimuli [46]. Similar data have been depend on or require any learning; salivation is a natural response to reported for food, such that exposure to food cues significantly in- food). When Pavlov introduced the bell before food, he was adding a creases craving for food [e.g., 25,32,40,47-50]. Importantly, a large “neutral stimulus” that previously did not elicit a salivation response body of work has shown that food cue exposure not only increases from the dogs. After repeated pairing with the food (the unconditioned physiological responses and craving, but also increases eating. Further, stimulus), the bell became a “conditioned stimulus” that predicts food this body of work has also shown that the magnitude of cue reactivity presentation, and came to elicit a “conditioned response,” salivation. As and of cue-induced craving consistently and reliably predicts sub- such, bell-cued salivation is a learned stimulus-response connection. sequent eating as well as weight gain in both adults and children [9,51]. In the years since, research has repeatedly shown similar effects in We recently assessed this systematically in a meta-analysis that quan- animals as well as in humans. Indeed, after repeated exposure, food- tified the effects of food cue exposure, cue reactivity, and theexperi- related cues (e.g. labels/packaging for brownies, the sight of brownies) ence of craving on eating and weight-related outcomes in both the that are present at the time of eating acquire the ability to predict the short- and long-term [9]. Overall, we found medium effect sizes of food ingested food, becoming conditioned stimuli that evoke conditioned cue exposure, cue reactivity, and craving on eating and weight out- responses. Specifically, experimental work in animals and humans has comes, suggesting that cue exposure and craving lead to eating and shown that cues paired with food elicit strong conditioned responses, weight gain. Taken together, these data are consistent with the idea that including increased salivation [10,11], heart rate [12], gastric activity food cues increase craving, and that craving then leads to eating. An- [13], hormone secretions [14,15], neuron firing and release other meta-analysis focused on a subset of studies that used food ad- [16,17], and subsequent neural activity in regions typically associated vertisements as cues; this meta-analysis reported a small-to-moderate with reward [18]. effect size of advertisement exposure on eating, such that participants Food cues that become conditioned (e.g., food pictures, advertise- ate more after exposure to food advertising [51]. Together, these ments, labeling) are often external. However, for humans in particular, findings show that cue exposure increases eating and that the magni- conditioned food cues can also include internal factors such as the ex- tude of cue reactivity and craving reliably predicts eating and weight- perience of stress [19], negative [20], hormonal changes [21], related outcomes. This suggests that they are important targets for the and food-related thoughts [22]. Such conditioned cues reliably evoke prevention and treatment of obesity. conditioned bodily responses including increased salivation [11], heart Indeed, the implications of such findings extend to the natural rate [12,23], gastric activity [12,23] and neural activity in the ventral ecology, where food cues for energy-dense “unhealthy” foods – such as striatum [VS; a region typically associated with reward; 18,24]. Fur- advertisements for fast food – are abundant. We now live in an en- ther, these physiological conditioned responses are often accompanied vironment with pervasive advertisements (e.g., TV commercials, bill- by the conscious experience of craving, which in this context is termed boards) for low-, high-calorie foods [52,53]. According to the “cue-induced craving” [25,26]. Thus, we can think of craving as a type Rudd Center for Food Policy & Obesity, food companies spent $1.28 of cue reactivity, or as an emergent psychological property of physio- billion advertising snack foods in 2014, an increase of 4% from the logical cue reactivity, or more broadly as a conditioned response to food $1.23 billion spent in 2010 [54]. During this time, exposure to such cues. Accordingly, experimental work in humans has demonstrated that advertising increased by 10% and 29% for children and teens, respec- exposure to food cues strongly and consistently produces the conscious tively [54]. In addition, the number of advertisements viewed by adults experience of craving, and that these associations are learned quickly has steadily increased since 2007, growing from ~5500 in 2007 to [27]. To illustrate, even previously-neutral stimuli (e.g., serving trays) >7000 in 2014 [55]. On a population level, this increase in food cues can come to elicit cue-induced craving for chocolate when presented leads to increases in eating: not surprisingly, both adults and children alone, after just a single Pavlovian conditioning session in which they who live in a food environment with advertised, high-calorie foods (e.g. were paired with chocolate [e.g., 27]. fast food) eat those foods more frequently and have higher Body Mass Notably, craving can be experienced for specific foods (e.g., cho- Index [BMI; 56–59]. Some have deemed this a “toxic food environ- colate), for groups of foods (e.g., desserts), and for food in general. ment,” in which widespread food cues lead to increased food con- Across such contexts, craving is traditionally assessed by asking in- sumption and weight gain [60–62]. These outcomes have important dividuals to self-report their current desire to eat, or the current in- public health implications. Today, over two-thirds of the United States tensity of their craving. To this end, researchers have used both multi- population is overweight or obese, and obesity is the second leading item questionnaires and single-item scales. For example, some studies cause of preventable morbidity and mortality in the United States [63, have used the Food Cravings Questionnaire-State to assess current 64].

2 W. Sun and H. Kober Physiology & Behavior 222 (2020) 112878

Neural mechanisms underlying cue reactivity and craving environment by altering food prices (e.g., soda tax) or reducing avail- ability of foods failing to meet specific nutrition standards [e.g., junk Given the link between food cues, craving, and eating – and the pos- food bans; 85]. Other approaches have attempted to alter the choice sible role this has in the obesity epidemic – much neuroimaging work has architecture surrounding eating behaviors by “nudging” people towards been devoted to understanding the neural mechanisms underlying these healthy behaviors [i.e., by presenting food in smaller portions or having processes. This work has revealed that cue reactivity and cue-induced healthy optimal defaults in menus; 85–88]. Unfortunately, these in- craving express in brain regions that could serve as targets for modulation. terventions have had relatively little success in real-world settings be- Prior brain imaging work in drug users has shown that cue exposure cause they are limited in implementation and generalizability [89–91]. consistently increases craving along with neural activity in regions in- Further, even if such changes were successfully implemented, ulti- cluding the ventral tegmental area [VTA; e.g., 43,65], ventral striatum mately, most people would still be exposed to tempting food cues in [VS; e.g., 43,66], amygdala [65,67], insula [67,68], orbitofrontal cortex situations in which these nudge strategies have yet to be implemented [OFC; e.g., 43,65,66,67], and anterior cingulate cortex [ACC; e.g., (e.g., in our own kitchens). Therefore, it is critical to consider the me- 43,65,66], as well as its subgenual region [sgACC; e.g., 43,66]. These chanisms underlying this epidemic and identify targets for interven- regions have previously been associated with emotion [particularly, the tions on an individual level. amygdala, VS, OFC, ACC; 69–71] and the experience of drug effects, For example, how can we reduce craving in an environment rife which have been shown to activate the dopamine reward pathway [e.g., with food cues? Under tempting circumstances, we could exert self- 72,73]. Importantly, these regions have also been implicated in food cue control over our thoughts, feelings, and behaviors, which can be fa- exposure and craving: similar studies have been conducted with food cilitated through the application of cognitive strategies [92]. Con- stimuli, and the regions involved are largely similar to those for drugs, in sistently, cognitive strategies for the regulation of craving are an im- both healthy individuals and those with eating pathology [18,24,74]. portant component of cognitive-behavioral treatments (CBTs) for Over the past decade, several meta-analyses have been conducted to obesity and eating disorders [93], which are known to be effective elucidate the robustness and reliability of brain networks and regions [94,95]. We developed the Regulation of Craving (ROC) task to ex- involved in food cue reactivity. One meta-analysis identified the insula, perimentally measure the specific causal effect of regulation strategies a region commonly associated with gustatory/taste processing [75], to on craving, as well as the neural mechanisms underlying craving and its consistently respond to olfactory as well as visual food cues [76]. An- regulation [e.g., 42,43]. other meta-analysis focusing on response to visual food cues identified In the original study of the ROC task, cigarette smokers and healthy the OFC, insula, and fusiform gyrus, with modulating the re- adults were presented with photographs of cigarettes and foods that sponse in the amygdala and OFC, and energy content of the food had been previously shown to induce craving [42]. During each trial of modulating the response in the VS [77]. Other meta-analyses have fo- the ROC task (see Fig. 2 for schematic representation), participants cused on these processes in individuals with obesity, and found that were exposed to one of these cues, and were instructed to follow one of these individuals exhibit greater activation of VS and ACC, along with two instructions: NOW – focus on the immediate feelings associated lower activation in dorsolateral prefrontal cortex (dlPFC) in response to with consuming the item (e.g., it will taste good), or LATER – focus on visual food cues, compared with healthy individuals [78,79]. Studies the long-term negative consequences associated with repeated con- have also suggested that self-reported craving correlates with neural sumption (e.g., this increases my risk for heart disease). Then, partici- activity in regions including VS and OFC [e.g., 43,80]; results from one pants were asked to rate their craving for the specific food or cigarette study further suggest that neural response to food cues in the VS pre- they just saw, using a 1 (not at all) to 5 (very much) Likert-type scale dicts future weight gain [81]. Notably, this network of regions that are (Fig. 2). Importantly, the LATER instruction is a strategy drawn from linked with cue reactivity and craving lies along the reward-related CBTs for addiction, eating disorders, and obesity [e.g., 96–98]. In ad- dopamine pathway [82], overlaps with the default mode network [83], dition, its prospective thinking component is related more broadly to is commonly activated in both positive and negative emotion [e.g., Episodic Future Thinking (EFT), which involves engaging episodic 70,84], and can serve as a target for modulation. Indeed, these are the memory to vividly simulate future events [99,100], and has been shown regions in which we would expect to see reduced activity if craving is to reduce monetary delay discounting and caloric consumption effectively regulated. [101–103]. Behaviorally, participants reported significantly reduced cravings for both food and cigarettes after applying the LATER strategy Strategies and interventions to reduce craving and eating vs. the NOW instruction. In a follow up study, we used functional magnetic resonance imaging (fMRI) to probe the neural systems un- To address obesity on a population level, some public-health in- derlying these effects [43]. In this study, we replicated the original terventions have attempted to change the structure of the food results, finding significant reductions in craving in the LATER

Fig. 2. Regulation of food craving (ROC) task. Schematic representation of one trial from the original Regulation of Craving (ROC) task. Participants first saw a fixation cross, followed by an instruction. Specifically, NOW instructed participants to focus on the immediate feelings associated with consuming theitem(e.g.,it will taste good). LATER – a strategy drawn from CBT treatment protocols – indicated that they should focus on the long-term negative consequences associated with repeated consumption (e.g., this increases my risk for heart disease). Instructions were followed by an image, including unhealthy foods, after which participants rated their craving. Pizza image courtesy of istockphoto.com/portfolio/bhofack2.

3 W. Sun and H. Kober Physiology & Behavior 222 (2020) 112878 condition. Further, we found that this cognitive down-regulation of craving was associated with decreased activation in regions implicated in craving (e.g., VS & sgACC, vmPFC), along with recruitment of regions associated with cognitive control (e.g., dorsolateral and ventrolateral prefrontal cortex; dlPFC, vlPFC). In addition, these two neural effects were found to be related to each other: decreases in behavioral reports of craving correlated with decreases in VS activity and were inversely correlated with increases in dlPFC activity. Importantly, VS activity fully mediated the relationship between dlPFC activity and reported craving, suggesting that the dlPFC could exert an inhibitory effect on VS, which in turn affects the experience and reports of craving. The downregulatory effects of cognitive strategies on craving have been replicated many times, in both healthy populations and drug-using populations, for both food and drugs [44,104–113]. In addition, the role of the dlPFC has been well established in the context of emotion regulation, and regulation of craving for drugs and food [e.g., 43,71,109,114–117], as well as in EFT [100]. In subsequent studies, we also tested slightly different instruction words (e.g., NEGATIVE instead Fig. 3. Food choice task. Schematic representation of one trial from the Food of LATER) and replicated the original findings as well (see below). Choice Task. Before and after ROC-T, participants completed binary food These promising effects led us to develop training protocols tar- choices, most of which were between healthy and unhealthy foods (other geting cue reactivity and cue-induced craving, with the hopes that such choices were between healthy vs. healthy or between unhealthy vs. unhealthy preemptive training in cognitive strategies, prior to temptation, would foods). Pizza image courtesy of istockphoto.com/portfolio/bhofack2. Salad image courtesy of istockphoto.com/portfolio/semenovp. improve subsequent eating behavior [P50 DA009241; 44,118]. This approach has several advantages. First, cognitive strategies can be generated by the individual and are adaptable based on the situation, $1.75 less for unhealthy foods like brownies after using the NEGATIVE unlike externally-applied, situation-specific nudges. Second, rather than strategy. Notably, this magnitude of value change is far greater than modifying the environment, training in cognitive strategies aims to reported effects of other population-level interventions; it is atleast alter the internal architecture of food choices, including how in- 2.5–10x more than that of the proposed penny-per-ounce soda tax dividuals crave and value foods, allowing individuals to nudge them- [127]. Finally, these cognitive strategies were effective across in- selves toward change. Such training may also alter the subjective va- dividual differences in BMI. luation of healthy vs. unhealthy options during food choice. Indeed, it Based on these strategies (and ongoing work with cigarette smo- has been proposed that value computation of choice options is a key kers), we developed Regulation of Craving Training (ROC-T) as a novel factor in successful self-control [119–122]. Consistent with this idea, and brief intervention [44]. ROC-T contains both a framing (informa- computational work has suggested that food choices are driven (at least tional essay) and a training component, and it trains participants to use to some degree) by estimations of the benefit or value of possible out- cognitive strategies to either (a) increase craving for healthy foods comes. In turn, these estimations are based on (a) the value assigned to (POSITIVE ROC-T condition), or (b) decrease craving for unhealthy each item on various dimensions (e.g., the healthiness and tastiness of foods (NEGATIVE ROC-T condition; conditions are italicized; strategies foods), and (b) the weight assigned to each dimension (e.g., importance are not italicized). In the next set of studies, we tested whether ROC-T, of healthiness vs. tastiness) by any particular individual, at any parti- compared to a CONTROL no-training “look-only” condition, could cular timepoint [107,122–126]. In turn, this further suggests that change choices of healthy vs. unhealthy foods as well as food con- training in cognitive strategies – as described below – could alter either sumption. In these studies, participants completed a binary food choice the value of options or the weight placed on dimensions of health and task (e.g., healthy food vs. unhealthy food) before and after ROC-T (See taste, thus leading to sustainable change. Fig. 3 for schematic representation of one choice; see [44] for sche- To test this, we conducted several studies to develop an intervention matics of the full studies). In one of the studies, participants also to improve food choices and eating behavior [44]. First, we developed completed a “food taste test” following the second food choice task, in and tested two cognitive strategy instructions that frame foods as “bad which we measured caloric intake. Across studies, we found that after for you” or “good for you.” The NEGATIVE strategy involves thinking completing ROC-T, participants in both the POSITIVE and NEGATIVE about negative consequences of eating food (e.g. health consequences ROC-T conditions chose significantly more healthy vs. unhealthy foods, or disliking the taste), whereas the POSITIVE strategy involves thinking and that these participants consumed 93–121 fewer calories compared about the positive aspects of eating food (e.g. health benefits or liking to participants in the CONTROL condition. Finally, in another study, we the taste). Across two studies, we exposed participants to both healthy compared ROC-T to a framing-only control condition, and found that and unhealthy foods, and compared participants’ self-reported craving the training component of ROC-T is an important “active ingredient”; for these foods (assessed on a 1 to 5 Likert-type scale following each that is, the training component was necessary for maximal change in specific food) after following these instructions to a control LOOKin- food choice to take place. struction (i.e., just look at the food and respond naturally). Consistent with prior work, we found that the NEGATIVE strategy Future directions decreased craving for both unhealthy as well as healthy foods. In con- trast, the POSITIVE strategy increased craving for healthy as well as Taken together, this work shows that cognitive strategies can be unhealthy foods. Importantly, we found that both strategies also in- used effectively to regulate craving. Based on that initial insight, we fluenced the subjective valuation of both healthy and unhealthy foods, developed a targeted, mechanism-focused, cognitive strategy-based operationalized as willingness-to-pay (WTP), such that the POSITIVE intervention to prevent and reduce unhealthy eating that also alters the strategy increased WTP by 21–69 cents, and the NEGATIVE strategy valuation of healthy and unhealthy foods. We are currently pursuing decreased WTP by 56–80 cents, compared to the LOOK instruction. To several avenues of work to extend these findings. For example, we are illustrate this, participants in one of our studies were willing to pay testing the boundary conditions of ROC-T, such as optimal “dose” and $1.28 (US dollars) more for healthy foods like broccoli, after using the durability of effects under time pressure, stress, and cognitive load.In POSITIVE vs. the NEGATIVE strategy. They were also willing to pay addition, we are investigating the neural mechanisms underlying the

4 W. Sun and H. Kober Physiology & Behavior 222 (2020) 112878 promising effects of ROC-T. We are also beginning to test the longevity [9,51]. Thus, they are promising targets for the prevention and treat- of these effects, including the cumulative effects of repeated sessions ment of obesity. and their effect on long-term outcomes such as weight, and to consider Thus, we developed and tested an individual-level, internally-ap- the effects of ROC-T on other constructs such as food reinforcement. plied intervention based on prior work showing that cognitive strate- Further, dismantling and comparing the efficacy of specific com- gies for the regulation of craving successfully reduce self-reported ponents of the POSITIVE and NEGATIVE strategies is important to ex- craving and reward-related neural reactivity to unhealthy food cues plore. Overall, there is limited work examining the efficacy of different [e.g., 43]. We first demonstrated that cognitive strategies decrease strategies and their components in the context of craving, and existing craving for unhealthy foods by emphasizing their negative con- evidence does not clearly indicate that certain strategies are differen- sequences, increase craving for healthy foods by emphasizing their tially effective. For example, prior research in cigarette smokers has positive benefits, and importantly, change food valuation (willingness tested the efficacy of gain-framed (i.e., benefits of quitting smoking) vs. to pay) for both healthy and unhealthy foods [44]. In addition, we loss-framed messages (i.e., costs of continuing to smoke) on smoking showed that Regulation of Craving Training (ROC-T) – a novel inter- cessation. This work suggests that gain-framed messages may be more vention based on these cognitive strategies – significantly increased effective in the short-term [128–130], but not the long term [128], and healthy food choice and reduced caloric consumption [44]. Finally, we that this difference is influenced by gender and risk perceptions asso- found that the training component of ROC-T was necessary for maximal ciated with quitting smoking [130]. In our own work testing regulation change in food choice [44]. To extend these promising findings, we are of craving training (ROC-T), we found that the NEGATIVE ROC-T and now investigating the boundary conditions, neural mechanisms, and POSITIVE ROC-T condition were equally effective in improving food longevity of ROC-T. Taken together, these findings and future direc- choice across three of four studies [44]. Further, in another study tions have important theoretical, public health, and clinical implica- testing the regulation of food craving, participants were allowed to tions for the prevention and treatment of obesity and eating disorders. select cognitive strategies from a suggested list [113]. The authors re- ported that the most commonly applied strategies were (1) focusing on Funding the negative consequences of eating the food (as in our ROC task), and (2) imagining that something was wrong with the food [113]. However, This work was supported by the NIH grants P50 DA009241 (Project reduction in craving did not vary by strategy [113]. Thus, it is im- 3) and R01 DA043690 to HK. portant to explore additional strategies that may be differentially ef- fective (e.g., mindfulness), and to further ask whether there are in- Declaration of Competing Interest dividual differences in strategy choice and efficacy. In addition, because the food environment often presents more The authors declare no competing financial interests. HK provided complex situations than making binary decisions between “healthy” consulting services to Indivior, Inc, on topics unrelated to this manu- and “unhealthy” foods, one useful next step would be to investigate script or this work. foods that are often thought of as healthy but are also energy-dense (e.g., nuts, avocados) and thus might be detrimental to goals related to Acknowledgments weight loss. Thus, we hope to make ROC-T adaptable to different eating and weight goals. Ultimately, we hope that ROC-T can serve as a The authors thank many co-authors and fellow researchers for their standalone computerized intervention as well as “homework” as part of contributions to the research in this review; Italia Hanik, Nilo Vafay, longer interventions for healthy eating, weight loss, obesity, and po- Sophy Xiong, Sophie Shang and Elizabeth Sun for help copyediting; and tentially for eating disorders. Uri Berger, and David Saunders for their helpful comments on the manuscript. Conclusion References Craving – in the context of food – is defined as a strong desire to eat, and has troubled philosophers for centuries [5-7]. It is a common ex- [1] R.C. Havermans, “You say it’s liking, I say it’s wanting…”. on the difficulty of perience [8] that drives eating behavior and predicts weight gain over disentangling food reward in man, Appetite 57 (2011) 286–294. time [9]. Craving can be understood using a learning-based model of [2] C.E. Cornell, J. Rodin, H.P. 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