Goal - McGill
This is the pre-peer reviewed version of the following article: [Doucerain, M., & Fellows, L. K. (2012). Eating right: Linking food-related decision-making concepts from neuroscience, psychology, and education. Mind, Brain, and Education, 6(4), 206–219. doi:10.1111/j.1751-228X.2012.01159.x], which has been published in final form at:
Eating right: Linking food-related decision-making concepts from neuroscience, psychology and education.
Matthias Doucerain1,2 & Lesley K. Fellows2
1Harvard Graduate School of Education, Harvard University, Cambridge, MA 02138, USA
2Department of Neurology and Neurosurgery, McGill University, Montreal Neurological Institute, Montreal, Quebec, H3A 2B4, Canada
Abstract
This literature review uses four dimensions to classify and compare how food-related decision-making is conceptualized and experimentally assessed in neuroscience and other disciplines: (1) food-related decision-making other than the decision of what to eat that is part of each eating episode, (2) decision complexes other than the eating episode itself, (3) the evolution of food-related decision-making over time, and (4) the nature of food related decisions. In neuroscience in particular, food-related decision-making research has been dominated by studies exploring the influence of a wide range of factors on the final outcome, the type and amount of foods eaten. In comparison, the steps that are leading up to this outcome have only rarely been discussed. Neuroscientists should broaden their historically narrow conceptualization of food-related decision-making. Then neuroscience research could help group the numerous hypothesized influences for each of the decision complexes into meaningful clusters that rely on the same or similar brain mechanisms and that thus function in similar ways. This strategy could help researchers improve existing broad models of human food-related decision-making from other disciplines. The integration of neuroscientific and behavioral science approaches can lead to a better model of food-related decision-making grounded in the brain and relevant to design of more effective school and non-school lifestyle interventions to prevent and treat obesity in children, adolescents, and adults.
Keywords: decision making, eating, food choice, food intake, food selection, obesity
Obesity and its impact
The increase in obesity over the last several decades has been the subject of a vast literature (see e.g. Finucane et al., 2011; Flegal, Carroll, Odgen, & Curtin, 2010; Ogden, Carroll, Curtin, Lamb, & Flegal, 2010; Olds et al., 2011). The negative impact of this trend is well established, particularly for very high levels of BMI and for early life - and therefore likely long-term - exposure (Fontaine, Redden, Wang, Westfall, & Allison, 2003); as a corollary, an increase in the level of childhood obesity, a condition that is likely to persist into adulthood, is of particular concern to individuals and societies alike (Reilly et al., 2003; Lobstein, Baur, & Uauy, 2004). The consequences of obesity can be classified as physiological, psychological and economic: Physiological risks of obesity include increased rates of a wide range of medical conditions (see e.g. Dixon, 2010; Kopelman, 2000; Must et al., 1999; Pi-Sunyer, 1993). Psychological risks include stigmatization by others, resulting in an increasingly negative self-image in obese individuals (Dietz, 1998; Lobstein, Baur, & Uauy, 2004). Possibly driven by this psychological impact, obesity appears to be associated with lower income, lower marriage rates and lower socioeconomic status.
Educational relevance
The increasing prevalence of obesity and its impact on health and wellbeing are of particular relevance to education because adult eating patterns are established during childhood and adolescence (Dietz, 1997). Much eating during this period happens in schools, and both the foods available in school as well as the school social context strongly influence what children will eat (Schanzenbach, 2009; Vereecken, Bobelijn, & Maes, 2005). The most immediate and widespread type of risk in children is psychological in nature: Obese children are the subject of stereotyping by both teachers (Neumark-Sztainer, Story, & Harris, 1999) and peers (Janssen, Craig, Boyce, & Pickett, 2004). Obese children also miss more days of school than their normal-weight peers, a phenomenon also documented for children and adolescents with other chronic diseases (Schwimmer, Burwinkle, & Varni, 2003). Obesity is associated with lower grades, placement in special education or remedial classes, fewer years of schooling, and lower academic performance overall (Taras & Potts-Datema, 2005). The negative impact of obesity does not stop there: Even for students with equal credentials, obesity is associated with lower college acceptance rates (Lobstein, Baur, & Uauy, 2004).
Intervention landscape
Because of obesity’s prevalence and its massive impact, diverse interventions abound. Among existing interventions, surgery appears to be the most effective, but it is too invasive to become the treatment of choice for large parts of the population (Albrecht & Pories, 1999; Bruce & Mitchell, 2011; Sjöström, 2000). Pharmacological interventions are less invasive but less effective than surgery, and also have negative side effects (Cooke & Bloom, 2006; Finer, 2002). Large-scale environmental interventions such as changes in the food supply e.g. through taxation would address the likely causes of the obesity pandemic, but they are very difficult to implement politically (Glanz & Mullis, 1988; Story, Kaphingst, Robinson-O’Brien, & Glanz, 2008).
This leaves lifestyle interventions as the main choice for most societies. Unlike the other intervention types, lifestyle interventions are first and foremost educational interventions – their goal is to teach participants to live healthier lives. As such, many lifestyle interventions target children in schools (Gortmaker et al., 1999; Caballero et al., 2003; Neumark-Sztainer, Story, Hannan, & Rex, 2003), and they commonly employ educational tools such as health education curricula, nutritional games, and physical education classes. Unfortunately they often produce inconsistent results, and their effects tend not to persist in the long run (Jakicic et al., 2001; Lobstein, Baur, & Uauy, 2004).
The situation is not quite as dire as this assessment might suggest: we know that the effectiveness of lifestyle interventions is generally higher if they combine multiple approaches (Gonzalez-Suarez, Worley, Grimmer-Somers, & Dones, 2009), are theory-based (Bluford, Sherry, & Scanlon, 2007; Sharma, 2007), long-running (Lobstein, Baur, & Uauy, 2004), individualized (Brown & Summerbell, 2009; Stice, Shaw, & Marti, 2006), and if the focus is on prevention (particularly during childhood and in school and household settings) rather than treatment (Barlow, 2007). Indeed, the likely most promising way to improve interventions in a fragmented area of research such as food-related decision-making in humans is to compare and integrate existing effective interventions. However, if researchers and societies alike want to avoid a drawn-out and inefficient trial and error process towards intervention improvement, they face a number of considerable hurdles along this path to integration.
Hurdles to effective interventions
Comparing and integrating multiple interventions necessitates a range of comparable intervention characteristics (Opp & Wippler, 1990; Seipel, 1999). The theoretical basis underlying each intervention, if properly used, could provide these intervention characteristics. However, many interventions do not make their theoretical basis sufficiently explicit. Even if they do, they do not necessarily have the same object of investigation: While e.g. all interventions should be referring to some theory of changing food-related decision-making in humans, some only refer to a theory of human behavior change (not specific to eating) and others to a theory of food human decision-making (not particularly concerned with its change). Another hurdle is that theories underlying existing interventions are rarely general in nature; they are mostly specific. Both when comparing across general or specific theories, different levels of accuracy of individual theories as well as conflict between them can cause problems. Theory-comparisons involving specific theories introduce the additional problem-types of irrelevance (when theories do not address food-related decision-making that is problematic from the perspective of a given individual or group) and non-comparability (if two or more specific theories do not address at least some of the same food-related decision-making). Lastly, comparison and integration of interventions - even if compatible otherwise - can also be hampered by theories being expressed in different disciplinary ‘languages’ such as neuroscience or psychology and requiring (at times rather tedious) translation from one to the other.
Literature review goal
The present literature review aims to help overcome some of these hurdles to intervention integration by contributing to the development of an improved theory of food-related decision-making in humans. To do so, it reviews the fragmented and multidisciplinary literature on food-related decision-making in humans in a systematic way, extracting how food-related decision-making is conceptualized and experimentally assessed in individual publications as well as across contributing disciplines. To effectively deal with this multidisciplinary literature, the review selected one discipline – neuroscience – as its anchor, and compared its conceptualization and assessment to that of all others as a whole.
What is particularly exciting about this approach is that both neuroscience and behavioral science of food-related decision-making in humans have produced highly evolved theories that only partially overlap: The neuroscience literature has worked its way towards food-related decision-making from the gut up and is very concerned with the influences of particular neurochemicals (Berthoud, 2002). As such, it has a long history of informing surgical and pharmacological obesity interventions. However, so far it has rarely – if ever – been brought to bear in the lifestyle intervention arena even though much is known about e.g. the neuroscience of self-control and reward processing that could be employed quite readily.
Behavioral science, on the other hand, often informs lifestyle interventions. The behavioral science literature, in particular as exemplified by the keyword ‘food choice’, has started out by observing free-living humans and emphasizing a significantly wider range of influences on food intake (Buttriss et al., 2004). But while its list of influences is impressive, it is at times at risk of being perceived as no more than a laundry list for which mechanisms and interdependencies are far less well worked out than for the neuroscientific model. If successfully integrated, the neuroscientific and behavioral science approaches would result in a much improved model of food-related decision-making grounded in the brain that would support the design of the more effective school and non-school based lifestyle interventions to prevent and treat obesity in children, adolescents and adults that so many desire.
It should be noted that the disciplinary anchoring is not meant to imply primacy of neuroscience over other disciplines in understanding food-related decision-making in humans and battling obesity – far from it! The biggest rewards of this approach to theory integration will only be realized if any resulting insights into general theory development are translated back into the languages of the contributing disciplines to equally inform their research efforts.
Search Strategy
The literature review was conducted through an electronic search performed on Thomson Reuters’ Web of Knowledge. In the first step, all experimental neuroscience publications on food-related decision-making in humans, a total of just over 100 articles, were reviewed. In this and the following step, qualifiers are represented by a set of search terms (see table 1 below) that were jointly applied to either the title or topic fields. The results of this review of neuroscience publications on human decision-making were then contrasted with an equivalent review of experimental non-neuroscience publications on food-related decision-making in humans. Given the more than 2,000 hits of the unrestricted step two search and the intention to only expand on the existing detailed review in step one, (Times Cited) > 40 was used as an additional qualifier, resulting in a more manageable impact-weighted selection of around 150 additional publications for review.
Food: Title = ("Meal*" or "Food*" or "Eating" or "Obes*")
Decision-making: Title = ("Choice*" or "Selection*" or "Decision*" or "Judgment*")
Human: Topic = ("Human*" or "Men" or "Participant*" or "Patient*" or "Adult*" or "Child*" or "People*" or "Household*" or "Man" or "Women" or "Adolescent*" or "Student*" or "Parent*" or "Family")
Neuroscience: Topic = ("Brain*" or "Neuro*" or "Cortex" or "Cortical")
Table 1: Sets of search terms for each qualifier.
Publication selection
In the course of the search term selection process, a much wider list of search terms was considered for inclusion. For the given sets of search terms, each of the two searches produces a high proportion of relevant publications. An additional title, abstract, and if necessary full text level screening process ensured the elimination of all irrelevant papers nevertheless captured by these searches, such as the occasional papers not concerned with humans, papers which address decision-making by groups of agents, such as governments or corporations, rather than individuals, non-experimental papers, or papers exploring decision-making processes other than those directed at the consumption of food. However, this high relevance came at the cost of excluding search terms (and deciding on the application of search terms to the title rather than the broader topic level) which in addition to capturing proportionally many more irrelevant papers always also produced at least a few additional papers of relevance. To deal with this problem of non-inclusion at least in a partial manner, the database searches were supplemented with manual reviews of the reference lists of included relevant articles.
Analysis
The literature on food-related decision-making in humans is not a well-behaved or uniform literature: It is impossible to capture in a more or less complete form and without too many intrusions by a reasonable set of search terms. This is partially due to its multidisciplinarity, but the most important contributing factor is the complex and diverse nature of food-related decision-making itself.
Human food-related decision-making has been discussed using a range of different labels, including but not limited to ‘dietary choice’, ‘food choice’, ‘food selection’, or ‘nutrient selection’. The most broadly accepted – albeit rather restrictive – position seems to be that all of them are concerned with deciding what to eat. This position is exemplified by Buttriss’ et al. statement that food choice is defined as the “selection of foods for consumption” (Buttriss et al., 2004, p. 334). In its idealized form this decision-making process is taking place e.g. when we pick bite-size buffet items from a plate to put them into our mouth and eat. However, in the reality of most of our lives, settings where a fixed number of relatively discrete food choices exist and choosing and consuming co-occur in close temporal proximity and disconnected from other aspects of our lives represent only a fraction of eating situations overall. The majority of eating situations are significantly more complex (see figure 1 for a ‘simple’ example of this complexity) – and at least a subset of the literature reviewed here reflects this.
[pic]
Figure 1: Food-related decision-making example. The figure shows the evolution over time of the decision to consume a bowl of cereal for breakfast on a given morning (specific eating episode), including the various production, acquisition, preparation, and clean-up decision complexes along with their interdependencies.
While there is no single best way to appropriately broaden the definition of food choice to include food-related decision-making processes more generally, a number of dimensions of complexification appear particularly promising. Four such dimensions will serve as a rubric to classify and compare how food-related decision-making is conceptualized and experimentally assessed in all publications reviewed:
(1) Food-related decision-making other than the decision of what to eat that are part of each eating episodes:
This dimension is evaluated by assessing to what extent any given publication explores or addresses any one of a number of individual decisions, including whether, where, when, with whom, how long, how, how much, and why.
(2) Decision complexes other than the eating episode itself:
This dimension is evaluated by assessing to what extent any given publication explores or addresses any one of a number of individual decision complexes, including production, acquisition, transport, preparation, serving, storing, digestion, and clean up.
(3) The evolution of food-related decision-making over time.
(4) The nature of food-related decision-making.
Food-related decision-making research in neuroscience
The usage of food-related decision-making vocabulary in neuroscience in humans is relatively rare – only around 100 among the tens of thousands of neuroscience publications concerned with food intake, food perception and related processes contain these words in their title or abstract according to our Web of Science search. Even though small, this group of articles is quite diverse in terms of neuroscientific methods used, and includes patient studies, neurochemical studies, and neuroimaging methods such as functional magnetic resonance imaging, positron emission tomography, and electroencephalography – it thus represents a rather well-balanced sample of the discipline at large. Among this small group of articles, food-related decision-making is generally equated with either food intake, food preferences, or some combination thereof. Both of these aspects are also important foci beyond the articles that refer to food-related decision-making in an explicit sense.
When food-related decision-making is interpreted to mean food intake, which is the case in the majority of reviewed neuroscience papers, researchers have the choice between measuring it directly through identifying and weighing all food items consumed by participants (see e.g. Blundell & Rogers, 1980; Born et al., 2010; Greenwood et al., 2005; Moller, 1986) or indirectly through questionnaires and self-reports (see e.g. Atkinson, Waggoner, & Kaiser, 1988; Breum, Moller, Andersen, & Astrup, 1996; Cohen, Yates, Duong, & Convit, 2011; Lammers et al., 1996; Pijl et al., 1991; Roberts, 2008) – and both methods are used with roughly equal frequency and sometimes jointly. Consumption measurement is without doubt the more precise of the two methods, but it also significantly increases study complexity and in the process creates eating scenarios which are likely to differ in fundamental ways from those typically encountered by participants – except in the case of institutionalized participants with externally controlled food provision. Self-reports of food intake can take the form of 24-hour or longer-delay recalls or of food frequency and diet history questionnaires (Block, 1982; Acheson, Campbell, Edholm, Miller, & Stock, 1980). They have been criticized for underreporting intake, particularly in overweight and obese participants (Schoeller, 1990), and for dependence on question wording, format and context (Schwarz, 1999), but they can be improved through the use of cross-checks and interview or evaluation by experienced dieticians.
Food preferences are commonly assessed in conjunction with food intake in studies of nutrient selection and serve as a form of secondary confirmation of their main findings (Blundell & Hill, 1987; Blundell & Rogers, 1980; Hill & Blundell, 1986). However, the focus of this paragraph is studies in which the assessment of food preferences is the central study component. In these studies, participants are typically shown a series of images or text identifying one or more food items on a screen and have to express their preferences for them in an absolute or relative sense. The food items evaluated can be entire plates or even menus (see e.g. Arana et al., 2003 for a typical example) or they can take the form of easy-to-store-and-transport snacks (as e.g. in Plassmann, O’Doherty, & Rangel, 2007). Depending on the specific nature of the task, the expression of food preferences can be no more than the more or less accurate assessment of the temporary state of one of the many influences driving intake, or it can be almost equivalent to food intake itself, separated from it only by a possibly short period of time.
Some studies try to ensure quasi-equivalence of food preference assessment with food intake through realizing one or more of the preference judgments and ‘forcing’ participants to consume their selection before leaving the laboratory (see e.g. Camus et al., 2009; Hare et al., 2008; Hare, Camerer, & Rangel, 2009; Hare, Malmaud, & Rangel, 2011; Plassmann, O’Doherty, & Rangel, 2007). This is a good idea in principle, but it is plagued by the fact that in all studies that have taken this approach so far, the resulting eating events have been relatively minor one-off experiences. Most human beings can easily bring themselves to eat a small, non-disgusting item even if they wouldn’t do so under normal circumstances. The link of food preferences with food intake becomes weaker yet if consumption is not enforced in the laboratory (as in Linder et al., 2010) or if preference expression in the lab remains without any consequences for the participant (see e.g. Folley & Rark, 2010; Paulus & Frank, 2003; Arana et al., 2003; Hinton et al., 2004; Piech et al., 2009 & 2010).
The neuroscience literature through the eyes of the rubric
Food intake and food preference are clearly important in food-related decision-making - the former because it exemplifies the culmination of all food-related decision-making, and the latter because it is assumed to be one of the central factors driving food intake. As such, they are good focus areas of research in neuroscience. In this section, the four dimensions of the evaluation rubric will be used to probe which aspects of our extended definition of food-related decision-making neuroscientists have been exploring using their food intake- and food preference-centered approach.
(1) Food-related decision-making other than the decision of what to eat that is part of each eating episode:
As was described above, studies of food-related decision-making in neuroscience commonly assess food intake in one way or another. As such, they collectively address several food-related decisions, in particular what type of food is eaten, and how much is eaten overall and of the various food types.
When comparing across studies, it is important to be aware that food intake can be expressed in one of three units: in the form of meal names such as ‘beef Stroganoff’, ‘Kraft cheese dinner’ or ‘banana split’, in the form of food item names along with their form of preparation such as ‘steamed broccoli’ or ‘fried chicken liver’, or in the form of composite elements (macronutrients, electrolytes, vitamins and minerals) such as protein, carbohydrate or calcium, and that quantities can not always be easily translated from one unit into another.
Depending on the specific type of self-report questionnaire used (24 hour recall vs. diet history vs. food frequency), two other food-related decision types, whether food is eaten at all and when food was eaten, are also occasionally assessed along with what and how much was eaten, but where, with whom, how long, and how are generally not assessed. Food consumption measures on the other hand typically control (or can easily assess) all the postulated food-related decisions, often in somewhat artificial settings. However, even if food-related decisions other than what and how much were controlled or assessed in the articles under review, they were never the research focus.
(2) Decision complexes other than the eating episode itself:
With respect to decision complexes other than eating, the picture is even bleaker: Giving food away, storing, cleaning, production and transport have not been explored at all. Food preparation has been included in one neuroscientific study, but only as an imagined and supposedly affect-free contrast to eating choices (Piech et al., 2010) – an assumption that is somewhat questionable for anyone who regularly prepares foods and struggles with onion-cutting-tears, potato-peeling-boredom, or chicken-gutting-disgust. Real-life cooking experience was not assessed in the participants of this study. Certain aspects of digestion decisions might be informed by existing neuroscience studies of bulimics and their comparison with the normal population, but no studies with an explicit decision-making focus have been published to date. The most studied food-related decision complex other than eating is food acquisition, at least in its abstract preference-expression form. Presentation of food images has some similarities with supermarket-, speciality-store-, or café-type food choice, and the presentation of textual meal descriptions resembles restaurant-type decisions, all of which are common food acquisition scenarios. Bidding money on food items, as happens in several studies (Camus et al., 2009; Hare et al., 2008; Linder et al., 2010; Plassman, O’Doherty, & Rangel, 2007), also adds to the acquisition-realism. Unfortunately, results from acquisition and eating experiments – treating them as separate food-related decision complexes – have not yet been contrasted with one another.
(3) The evolution of food-related decision-making over time:
Time has played an important role in a subset of neuroscience studies of eating to the extent that they are evaluating how food intake is changing over time, usually as the result of some intervention of interest (Atkinson, Waggoner, & Kaiser, 1988; Breum, Moller, Andersen, & Astrup, 1996; Pijl et al., 1991; Russ, Ciavarella, Kaiser, & Atkinson, 1984). In all of these studies, only what or how much decisions from the decision complex for eating are compared across time. All other food-related decisions, no matter from which decision complex, are ignored. In general, food-related decision-making in neuroscience is simply not treated as dynamic decision-making. Instead, choice is explored as a static in-the-moment phenomenon, either because the study design collapses the decision-making process in time (as in basically all laboratory type studies reviewed here) or because the actual complex temporal dynamics of the underlying decision-making process, even though important for the measures taken, are not explored (as is the case for all papers that assess human food intake in real-life contexts).
(4) The nature of food related decisions:
The nature of food related decisions is actively debated in a small subgroup of the papers reviewed here. As laid out by one of the authors (Piech et al., 2010), one prominent family of decision-making models – dual-process models – differentiate between two types of conscious human choice: one more effortful, rule-based, and thus ‘cold’, and one more intuitive, heuristic, and thus ‘hot’. In all of the studies concerned with ‘hot’ vs. ‘cold’ decision-making discussed here, food is simply a convenient trigger for a supposedly ‘hot’ choice process. Three papers (Arana, 2003 et al.; Paulus & Frank, 2003; Piech et al., 2010) try to uncover the neural basis of ‘hot’ choice processes, either as compared to ‘cold’ ones (Paulus & Frank, 2003; Piech et al., 2010) or compared to a presumed choice-free situation (Arana et al., 2003). Beyond these papers, differences in the nature of the way decisions are made are not debated – even though the differences in study design occasionally favor one over the other due to restrictions in reflection time (e.g. fMRI studies) or due to special emphasis on one or more aspects that could or should be driving choice (e.g. Hare, Malmaud, & Rangel, 2011).
Food-related decision-making research outside neuroscience
The narrow view of food-related decision-making as synonymous with food intake or food preferences that features so prominently in the neuroscience literature plays an important – albeit much less dominant – role outside of neuroscience as well. Self-reports of food intake are used with a comparable high frequency (in more than 30 percent of the publications reviewed) both outside neuroscience and within. Experimental measurement of food intake and assessment of food preferences, however, are used noticeably less frequently outside neuroscience – in only ten to twenty percent of studies. Overall, the methodological landscape beyond the neuroscience of food-related decision-making in humans is much more diverse than within, which should not come as a big surprise given the wide range of contributing disciplines. Food-related decision-making processes are often probed directly and in a verbal way, through attempts of tapping into the thoughts, memories, and interpretations of research participants. And the common – but from the perspective of neuroscience novel – usage of tools like interviews and questionnaire assessments of food choice motives, both discussed in more detail below, reflect this.
Interviews are a powerful and very flexible tool to assess food-related decision-making that is particularly helpful when the subject of study is not yet very well defined; it can be paired with just about any understanding of food choice as long as it can be put into words (Strauss & Corbin, 1990). Interviewing approaches range from open to structured, but in the context of food-related decision-making typically take the form of semi-structured interviews, with a list of open-ended questions pre-defined in an interview guide, asked in a more or less standardized order. Interviews can vary tremendously in depth, breadth, and length, and some studies employ them to probe rather narrow food-related decision-making questions such as choice in a specific context (van der Merwe, Kempen, Breedt, & de Beer, 2010) or with respect to a specific eating style (James, 2004). However, the majority of studies reviewed here that rely on interviews ask very broad questions and attempt to understand food-related decision-making in a holistic way (Bisogni, Connors, Devine, & Sobal, 2002; Bove, Sobal, & Rauschenbach, 2003; Contento, Williams, Michela, & Franklin, 2006; Devine, Connors, Bisogni, & Sobal, 1998; Devine, Connors, Sobal, & Bisogni, 2003; Falk, Bisogni, & Sobal, 1996; Feunekes, de Graaf, Meyboom, & van Staveren, 1998; Furst, Connors, Bisogni, Sobal, & Falk, 1996; Stratton & Bromley, 1999), thereby catering to the method’s particular strengths. Interestingly, many of these broader studies come from the same group of researchers at Cornell University that is also responsible for putting forward one of the most complete models of food-related decision-making outside neuroscience (see e.g. Sobal & Bisogni, 2009).
Food intake is the outcome of a food-related decision-making process, while food preference is a ranking of different food items that can be viewed as one step removed from, but fundamental in determining what is consumed. Food choice motives are yet another step removed. They in turn explain food preferences, and as such have been speculated about in a diverse manner on many occasions both within the field of food-related decision-making research and beyond. Structured assessments of food choice motives are thus urgently needed, but seem to be slow to come about: Steptoe, Pollard, & Wardle developed a 36-item questionnaire that probes the importance of health, mood, convenience, sensory appeal, natural content, price, weight control, familiarity, and ethical concerns as influences on food preferences in 1995. While this questionnaire has received a fair bit of attention it has not yet become the standard of the field, as many researchers either perceive a need for modification to adapt it to their specific study context (Ares & Gambaro, 2007; Lindeman & Vaananen, 2000; Lockie, Lyons, Lawrence, & Grice, 2004) or come up with their own versions of a related set of questions (see e.g. Contento, Williams, Michela, & Franklin, 2006; French, Story, Neumark-Sztainer, Fulkerson, & Hannan, 2001; Furst, Connors, Bisogni, Sobal, & Falk, 1996; Lennernas et al., 1997; Magnusson, Arvola, Hursti, Aberg, & Sjoden, 2003; Neumark-Sztainer, Story, Perry, & Casey, 1999). The development of (and agreement on) a standard assessment of food choice motives is all the more important since assessment of the same material through interviews, a common current approach, is likely to be even more prone to demand characteristics and to produce even more imperfect data.
The non-neuroscience literature through the eyes of the rubric
When probing with the help of the same evaluation rubric which aspects of the extended definition of food-related decision-making have been explored outside neuroscience, a number of important differences emerge. While a subset of the non-neuroscience literature is relying on an approach not unlike the one used within neuroscience, several of the studies reviewed here take a different and in some cases much wider perspective – it is those studies in particular that will be described in more detail below.
(1) Food-related decision-making other than the decision of what to eat that are part of each eating episode:
One way in which the narrow definition of food choice underlying much neuroscientific research has been expanded in the non-neuroscientific literature is through the emphasis on other food-related decisions that typically co-occur with the decision of what to eat. Examples of such other decisions are whether, where, when, with whom, how long, how, and how much to eat, most of which have been identified in the series of broad interview-type studies already discussed above (Bisogni, Connors, Devine, & Sobal, 2002; Bove, Sobal, & Rauschenbach, 2003; Devine, Connors, Bisogni, & Sobal, 1998; Devine, Connors, Sobal, & Bisogni, 2003; Falk, Bisogni, & Sobal, 1996; Furst, Connors, Bisogni, Sobal, & Falk, 1996; for a good review see Sobal & Bisogni, 2009). These – and potentially more – decisions are described as forming a decision complex that jointly characterizes any given eating episode. It has been estimated that over 200 food-related decisions are made by the average person each day (Wansink & Sobal, 2007), though this finding does not yet seem to have been independently replicated and may be the result of the specific assessment technique used in this study.
The exact types of decisions to be included in the array of food-related decisions is debatable as well, as in the cited study they were only derived from a subset of the range of existing studies and not on the basis of principles: What about including decisions such as why as in eating because it is polite, because one is hungry, or because it is customary time for a meal, or about what kind of activity to pursue in parallel with eating? On the other hand, a potential further broadening increases the already significant degree of fragmentation of the proposed decision-making process into supposedly separable components – a problematic approach not only as a result of many of the food-related decisions being of limited separability but also because they are not or at least not always independent: In deciding to eat dinner at a colleague’s house (where, with whom), I may forgo decisions about other aspects of the eating episode (what, when, how much, how long). Or different food-related decisions might be bundled together, such as the traditional burgers & beer meal with a group of buddies at a local pub during any playoff appearance of your favorite football team, where attempts for modification along any one dimension might be met with social pressure against change. Wansink and Sobal also point out that it was common for participants to significantly underestimate the number of food-related decisions they made, presumably at least partially as a result of the unconscious nature of many of the decisions – an aspect that will be discussed in more detail in the paragraph on the nature of decision-making below. One type of decision seemingly ignored by researchers focusing on the neuroscience of food-related decision-making, but that has received particular attention in the non-neuroscience literature is the decision about with whom to eat, a reflection of the highly social nature of many eating episodes as well as the development of food-related decision-making and eating skills itself (see e.g. Birch, 1980; French, Story, Neumark-Sztainer, Fulkerson, & Hannan, 2001; Klesges, Stein, Eck, Isbell, & Klesges, 1991; Neumark-Sztainer, Story, Perry, & Casey, 1999).
(2) Decision complexes other than the eating episode itself:
Both the neuroscientific and non-neuroscientific literatures on food-related decision-making are predominantly focused on food intake, i.e. the eating episode, with a minor additional focus on food acquisition (notable exceptions beyond the broad interview-type studies are Pfau & Piekarski, 1997 and Wade, Milner, & Krondl, 1981). But while most food might ultimately be selected for consumption, eating is far from the only decision complex in which food-related decision-making takes place. Rozin (2006) describes it as “but a step in a series of behaviors organized for the quest for food” (Rozin, 2006, p. 19). Other food behaviors described in the literature include acquisition, preparation, serving, giving away, storing, and clean up (Sobal & Bisogni, 2009). Depending on one’s inclusiveness, one might want to add production, transport, and (to some degree) digestion to this list (Sobal, Kettel Khan & Bisogni, 1998). Each of these food behaviors can be treated as a decision complex in its own right, along with its own (partially overlapping and partially interdependent) list of food-related component decisions.
Food behavior decision complexes other than eating are interesting for a number of reasons, including their specific differences as well as the relationships between them; for the most part, they are also severely understudied. Unlike eating, which as its central element has the goal to change the internal state of the eater, almost all other food behaviors are mostly concerned with altering the external environment within which eaters reside and act. Whereas eating is typically studied in the form of eating on one’s own (in contrast to being fed), it is not at all atypical to explore the other food behaviors from a social perspective and with respect to others benefiting from their realization, implying decisions such as for whom. Lastly, somewhat objective quality criteria and an acknowledgment of the importance of learning and expertise exist for most of these food behaviors (decisions about how competent, how diligent, etc.), whereas we tend to view the capacity to eat as a universal skill, often overlooking how difficult it too is to learn and how far reaching the implications should it break down. As with the food-related decisions within each decision complex, decision complexes are not independent of one another. A person’s history with respect to each type of food behavior can provide us with important information when attempting to predict future instances of the same type of food behavior. Equally (since food behaviors can be organized broadly from the initial production of a food or food ingredient all the way to its consumption in a more or less refined state), recent food behaviors preceding any given one along this axis help predict subsequent food behaviors in important ways. These temporal dynamics will be discussed in more detail in the following paragraph.
(3) The evolution of food-related decision-making over time:
The passage of time plays an important role in decision-making at large: Decisions can be made about the past, present or future; decision-making processes play out over time; decisions take time to be made; and decisions can be concerned with time (Ariely, 2001). One way for decisions to play out over time is in the form of dynamic decision-making, which is defined by “three common characteristics: a series of actions must be taken over time to achieve some overall goal; the actions are interdependent so that later decisions depend on earlier actions; and the environment changes both spontaneously and as a consequence of earlier actions” (Busemeyer, 2001, p. 3903). Time is important for food-related decision-making in all of the ways mentioned above, but first and foremost, food-related decision-making of the type described in this paper is dynamic decision-making. When food-related decision-making is limited only to food choice, the decision of what to eat, it may be appropriate to treat it as located at one (or a series of) moment(s) in time, either with or without a significant temporal delay between food choice and the act of consumption, and to explore it using static decision tasks. But when other types of food-related decisions are included and in particular when food-related decision-making is seen as playing out over a series of interdependent decision complexes of various types, methods of dynamic decision research such as interactive computer programs (Brehmer, 1992) or simulations (Hammond et al., submitted) are essential. The relevant decision time frames are not seconds or minutes but rather hours, days, or even weeks and months, depending on a given individual’s reliance on the different decision complexes. Food-related decision-making is not continuous – it only happens during specific time intervals throughout the day. However, numerous such intervals typically exist each day, and at least a subset of influential variables such as hunger can be expected to change in a continuous manner.
It may thus be appropriate to view decision-making not as taking place but rather (or at least at times) as developing. In addition, human food-related decision-making likely serves multiple goals, including satiety, health, financial sustainability, etc., most of which are both temporary (concerned with one specific meal) as well as permanent (concerned with life as a whole) in nature. For the most part, non-neuroscience research on food-related decision-making is no more concerned with time than its neuroscientific counterpart, i.e. marginally and only as it affects other constructs. However, at least one proposed study explicitly tries to map out the temporal dynamics of food-related decision-making (Pfau & Piekarski, 1997), and two others adopt a life-course perspective in which the dynamics of food choice are seen as changing fundamentally from one period to another (Devine, Connors, Bisogni, & Sobal, 1998; Devine, Connors, Sobal, & Bisogni, 2003).
(4) The nature of food related decisions:
It is clear from the description of the complex web of food-related decisions that is woven into our days that making every possible decision in a conscious and effortful manner would be out of the question – it is at least not what we perceive as happening. As cited above, the average participant in Wansink and Sobal’s (2007) study estimated that they made only about 14 food-related decisions per day, less than 10% of the number of food-related decisions hypothesized to actually have taken place using a more structured question set; and there is no reason to believe that the ratio would be different if one were to look at other decision complexes. Participants might have forgotten about some of the decisions they worked out, but it is more likely that a good number of the hypothesized decisions either never took place or took place in a rather effort-free manner.
Let’s deal with the ‘never took place’ group first. Clearly, the fact that a specific aspect of an eating episode (or any other type of decision complex) could be actively decided does not mean it has to be. When you e.g. decide to eat at your desk at work, you may not know and not be able to influence how many (if any) and which ones of your colleagues will be present and potentially eating as well at that time, but they will provide an important social context nevertheless. Or you may decide to have dinner with your spouse at home after both of you have returned from work, and both of you know that that may be at any time during a rather longish time interval during the evening, but you may not attempt to reduce the uncertainty – in both cases certain food-related decisions (or decision opportunities) were de facto outside your control. Other examples for food-related decision-making that ‘never took place’ include outsourcing or bundling of decisions (as discussed in the section on ‘other decision complexes’ above).
Effort-free or unconscious decisions, the second explanation for the discrepancy between estimated and hypothesized daily decisions in Wansink and Sobal’s experiment, have been postulated by many theorists (see e.g. Dijksterhuis, 2004; Evans, 2008; Kahneman & Frederick, 2002; Sloman, 1996). In fact, there probably exist a number of different implicit processes that can result in decision-making, including heuristic processing which tends to be verbally encoded, simple, fast to learn, and at least initially conscious, and associative processing which in contrast to this tends to be affectively or perceptually encoded, potentially complex, slow to learn, and preconscious (Evans, 2008). But while at least a small subset of the research on the neuroscience of food-related decision-making addresses the issue of conscious versus unconscious processes directly, its importance has only been acknowledged – but not actively pursued – by non-neuroscientific researchers of food choice (e.g., Furst, Connors, Bisogni, Sobal, & Falk, 1996).
Implications of conceptual differences for neuroscientific research
Neuroscience has developed highly evolved models of food-related decision-making that are centered around the what and how much aspects of the moment of food intake and involve in particular homeostatic regulation and hedonic influences. However, in principle neuroscience is no more and no less able to speak about any of the other food-related decisions and decision complexes described in this paper: They all are the result of underlying neurological processes – it is simply the case that some of them at this point are mapped out better than others. Historically, this focus within neuroscience is quite understandable. When the serious scientific search for understanding human eating started and not much was known about its biological basis, it would have been unwise to invest energy into understanding temporally and behaviorally more remote processes. In addition, neurological processes that are closer to sensory input or behavioral output are much easier to disentangle than more abstract ones. The existence of presumably highly homologous animal models able to shed light on basic processes such as hunger, also helped to move research in this direction.
However, as the sophistication and accuracy of neurological models of the moment of food intake increases, all these arguments become less and less important and even counterproductive. This can be most clearly seen on the level of the resulting interventions. Neuroscience has a long history of informing pharmacological obesity interventions and has recently been increasingly involved in evaluating the effects of surgical obesity interventions, because both match rather closely its focus on food intake; but these interventions are quite invasive and have side effects that make them undesirable on a population level and as long-term treatments. In stark contrast to this neuroscience has only rarely – if ever – been brought to bear in the lifestyle intervention arena. And this is true even though these are much more likely to be the future interventions of choice since they can easily be pursued early in life and on a population-wide scale through educational institutions and families.
The time has come to broaden neuroscientific research to support lifestyle interventions. This adjustment produces a number of important challenges – how exactly are we to expand existing neuroscientific models? Our review of the non-neuroscientific literature on food-related decision-making in humans provides numerous indications for what such research might look like. In line with existing broad models of food-related decision-making such as Sobal & Bisogni (2009), neuroscientists should contrast decision-making processes across different decision complexes as well as across different decision types within each decision complex and develop models that can explain how a series of such interdependent decisions playing out over time comes to determine food intake in a given eating episode. Neuroscientific decision-making research at large has already identified a broad range of decision-relevant processes and mechanisms that go beyond those typically included in neuroscientific models of food choice, in particularly in the areas of social decision making (e.g. Sanfey, 2007), multi-attribute decision-making (e.g. Goel, 2010) and the neurochemistry of decision-making (e.g. Coates & Herbert, 2008). An expanded rubric of decision-relevant processes and mechanisms could help classify the broad group of influences identified by behavioral scientists according to their underlying biology and point out important relationships and functional overlaps. In addition, the history of medical case studies provides interesting leads with respect to the importance of various brain regions in the wider array of food-related decision-making, such as the loss of knowledge of how to eat in certain Alzheimer patients (Greenwood, 2005) or the loss of knowledge concerning what is edible in patients with the Kluver-Bucy syndrome (Lilly, Cummings, Benson & Frankel, 1983).
However, especially in light of recent over-interpretations of neuroscience’ influence on public policy (Seymour, 2012), neuroscientists should proceed with caution towards lifestyle intervention support. With respect to behavioral science researchers having worked in this area for decades, their approach should be one of collaboration and attempted complementation of existing models – not of replacing what has been put in place and providing quick-fixes.
Conclusion
Food-related decision-making research up to this point has been dominated by studies exploring the influence of a wide range of factors on the final outcome, the type and amount of foods eaten. In comparison, the steps leading up to this outcome have only rarely been discussed. However, as Ola Svenson, one of the pioneers of the process perspective on decision-making, pointed out, “human decision-making cannot be understood simply by studying final decisions” (Svenson, 1979). The present multi-disciplinary comparison applied a process perspective to food-related decision-making to identify overlaps and discrepancies between the two groups of disciplines. However, unlike in many other process views (Einhorn, 1981; Ford, 1989; Svenson, 1979, 1996), the focus here was not on separate processing stages leading up to one specific decision but rather on interdependent series of decisions and their shaping of both the decision maker and the environment.
Food intake regulation that acknowledges the temporal dynamic in bringing food choice about by including a wide range of food-related decisions offers a much richer intervention landscape than any attempt that is predominantly focused on the moment of food intake. Just as emotion regulation involves changes in emotion dynamics (Gross & Thompson, 2007), food intake regulation can be distributed over time, including situation selection, situation modification, attentional deployment, cognitive change and response modulation. Each of these different regulation techniques takes place in different contexts and at different moments in time, relying on different decision complexes, often removed from the moment of food intake. And each of these different decision complexes has different processing requirements that are not yet well understood.
The time has come for neuroscientists to broaden their historically narrow conceptualization of food-related decision-making in the ways outlined in this paper as well as by researchers from other disciplines at large. In return, neuroscience research can help group the numerous hypothesized influences for each of these decision complexes into meaningful clusters that rely on the same or similar neurological machinery and that thus function in similar ways, thereby helping to evolve existing broad models of human food-related decision-making such as Sobal & Bisogni (2009) towards a level of precision with respect to mechanisms and its underlying machinery that looks more like that of Berthoud (2002). And after some progress along this path of integration, the neuroscientific and behavioral science approaches can aspire to result in a much improved model of food-related decision-making grounded in the brain that would support the design of the more effective school and non-school based lifestyle interventions to prevent and treat obesity in children, adolescents and adults that so many desire.
Acknowledgements – This work was supported by an operating grant from the Fonds de la Recherche en Santé du Québec. We are grateful to Kurt Fischer and Jenny Thomson for feedback on previous versions of the manuscript and to Laurette Dubé and other members of the Brain-to-Society team for many valuable discussions along the way.
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