Running head: LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
1
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.1751228X.2012.01159.x], which has been published in final form at:
http://onlinelibrary.wiley.com/doi/10.1111/j.1751-228X.2012.01159.x/abstract
Eating right: Linking food-related decision-making concepts from neuroscience,
psychology and education.
Matthias Doucerain1,2 & Lesley K. Fellows2
1
Harvard Graduate School of Education, Harvard University, Cambridge, MA 02138, USA
2
Department of Neurology and Neurosurgery, McGill University, Montreal Neurological
Institute, Montreal, Quebec, H3A 2B4, Canada
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
2
Abstract
This literature review uses four dimensions to classify and compare how food-related decisionmaking 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 foodrelated 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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
3
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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
4
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 normalweight 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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
5
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 (GonzalezSuarez, 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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
6
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. Theorycomparisons 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 decisionmaking in humans. To do so, it reviews the fragmented and multidisciplinary literature on foodrelated decision-making in humans in a systematic way, extracting how food-related decisionmaking 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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
7
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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
8
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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
9
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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
10
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.
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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
11
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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
12
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 decisionmaking 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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
13
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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
14
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 decisionmaking neuroscientists have been exploring using their food intake- and food preferencecentered 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.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
15
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 affectfree 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-peelingboredom, 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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
16
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 foodrelated decision complex other than eating is food acquisition, at least in its abstract preferenceexpression 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 acquisitionrealism. 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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
17
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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
18
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 decisionmaking 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,
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
19
& 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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
20
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 foodrelated 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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
21
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 decisionmaking 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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
22
social nature of many eating episodes as well as the development of food-related decisionmaking 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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
23
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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
24
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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
25
dynamics of food-related decision-making (Pfau & Piekarski, 1997), and two others adopt a lifecourse 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 foodrelated 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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
26
the evening, but you may not attempt to reduce the uncertainty – in both cases certain foodrelated 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 nonneuroscientific 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.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
27
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
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
28
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 decisionmaking (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.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
29
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.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
30
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.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
31
References
Acheson, K., Campbell, I., Edholm, O., Miller, D., & Stock, M. (1980). The measurement of
food and energy-intake in man - an evaluation of some techniques. American Journal of
Clinical Nutrition, 33(5), 1147-1154.
Albrecht, R. J., & Pories, W. J. (1999). Surgical intervention for the severely obese. Best
Practice & Research Clinical Endocrinology & Metabolism, 13(1), 149-172.
Arana, F. S., Parkinson, J. A., Hinton, E., Holland, A. J., Owen, A. M., & Roberts, A. C. (2003).
Dissociable contributions of the human amygdala and orbitofrontal cortex to incentive
motivation and goal selection. Journal of Neuroscience, 23(29), 9632-9638.
Ares, G., & Gambaro, A. (2007). Influence of gender, age and motives underlying food choice
on perceived healthiness and willingness to try functional foods. Appetite, 49(1), 148158.
Ariely, D., & Zakay, D. (2001). A timely account of the role of duration in decision-making.
Acta Psychologica, 108(2), 187-207.
Atkinson, R. L., Waggoner, C. R., & Kaiser, D. L. (1988). Food-intake and nutrient selection in
obese humans on long-term naltrexone therapy. Nutrition Reports International, 37(3),
555-563.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
32
Barlow, S. E. (2007). Expert committee recommendations regarding the prevention, assessment,
and treatment of child and adolescent overweight and obesity: Summary report.
Pediatrics, 120, S164-S192.
Berthoud, H. R. (2002). Multiple neural systems controlling food intake and body weight.
Neuroscience and Biobehavioral Reviews, 26(4), 393-428.
Birch, L. L. (1980). Effects of peer models food choices and eating behaviors on preschoolers
food preferences. Child Development, 51(2), 489-496.
Bisogni, C. A., Connors, M., Devine, C. M., & Sobal, J. Y. (2002). Who we are and how we eat:
A qualitative study of identities in food choice. Journal of Nutrition Education and
Behavior, 34(3), 128-139.
Block, G. (1982). A review of validations of dietary assessment methods. American Journal of
Epidemiology, 115(4), 492-505.
Bluford, D. A. A., Sherry, B., & Scanlon, K. S. (2007). Interventions to prevent or treat obesity
in preschool children: A review of evaluated programs. Obesity, 15(6), 1356-1372.
Blundell, J. E., & Hill, A. J. (1987). Influence of tryptophan on appetite and food selection in
man. In S. Kaufman (Ed.), Amino Acids in Health and Disease: New Perspectives (pp.
403-419). New York, NY: Alan Liss.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
33
Blundell, J. E., & Rogers, P. J. (1980). Effects of anorexic drugs on food-intake, food selection
and preferences and hunger motivation and subjective experiences. Appetite, 1(2), 151165.
Born, J. M., Lemmens, S. G. T., Rutters, F., Nieuwenhuizen, A. G., Formisano, E., Goebel, R., et
al. (2010). Acute stress and food-related reward activation in the brain during food choice
during eating in the absence of hunger. International Journal of Obesity, 34(1), 172-181.
Bove, C. F., Sobal, J., & Rauschenbach, B. S. (2003). Food choices among newly married
couples: Convergence, conflict, individualism, and projects. Appetite, 40(1), 25-41.
Brehmer, B. (1992). Dynamic decision-making - human control of complex-systems. Acta
Psychologica, 81(3), 211-241.
Breum, L., Moller, S. E., Andersen, T., & Astrup, A. (1996). Long-term effect of
dexfenfluramine on amino acid profiles and food selection in obese patients during
weight loss. International Journal of Obesity, 20(2), 147-153.
Brown, T., & Summerbell, C. (2009). Systematic review of school-based interventions that focus
on changing dietary intake and physical activity levels to prevent childhood obesity: An
update to the obesity guidance produced by the national institute for health and clinical
excellence. Obesity Reviews, 10(1), 110-141.
Bruce, D. M., & Mitchell A. I. (2011). Surgery for obesity. Medicine, 39(1), 39-41.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
34
Busemeyer, J. (2001). Dynamic decision-making. In N. J. Smelcer & P. B. Baltes (Eds.),
International encyclopedia of the social and behavioral sciences (Vol. 6, pp. 3903-3908).
Oxford, UK: Elsevier Science.
Buttriss, J., Stanner, S., McKevith, B., Nugent, A. P., Kelly, C., Phillips, F., et al. (2004).
Successful ways to modify food choice: Lessons from the literature. Nutrition Bulletin,
29(4), 333-343.
Caballero, B., Clay, T., Davis, S. M., Ethelbah, B., Rock, B. H., Lohman, T., et al. (2003).
Pathways: A school-based, randomized controlled trial for the prevention of obesity in
American Indian schoolchildren. American Journal of Clinical Nutrition, 78(5), 10301038.
Camus, M., Halelamien, N., Plassmann, H., Shimojo, S., O'Doherty, J., Camerer, C., et al.
(2009). Repetitive transcranial magnetic stimulation over the right dorsolateral prefrontal
cortex decreases valuations during food choices. European Journal of Neuroscience,
30(10), 1980-1988.
Coates, J. M. & Herbert, J. (2008). Endogenous steroids and financial risk taking on a London
trading floor. Proceedings of the National Academy of Sciences, 105(16), 6167-6172.
Cohen, J. I., Yates, K. F., Duong, M., & Convit, A. (2011). Obesity, orbitofrontal structure and
function are associated with food choice: A cross-sectional study. BMJ Open, 1(2),
e000175.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
35
Contento, I. R., Williams, S. S., Michela, J. L., & Franklin, A. B. (2006). Understanding the food
choice process of adolescents in the context of family and friends. Journal of Adolescent
Health, 38(5), 575-582.
Cooke, D., & Bloom, S. (2006). The obesity pipeline: Current strategies in the development of
anti-obesity drugs. Nature Reviews Drug Discovery, 5(11), 919-931.
Devine, C. M., Connors, M., Bisogni, C. A., & Sobal, J. (1998). Life-course influences on fruit
and vegetable trajectories: Qualitative analysis of food choices. Journal of Nutrition
Education, 30(6), 361-370.
Devine, C. M., Connors, M. M., Sobal, J., & Bisogni, C. A. (2003). Sandwiching it in: Spillover
of work onto food choices and family roles in low- and moderate-income urban
households. Social Science & Medicine, 56(3), 617-630.
Dietz, W. H. (1997). Periods of risk in childhood for the development of adult obesity - what do
we need to learn? Journal of Nutrition, 127(9), S1884-S1886.
Dietz, W. H. (1998). Health consequences of obesity in youth: Childhood predictors of adult
disease. Pediatrics, 101(3), 518-525.
Dijksterhuis, A. (2004). Think different: The merits of unconscious thought in preference
development and decision-making. Journal of Personality and Social Psychology, 87(5),
586-598.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
36
Dixon, J. B. (2010). The effect of obesity on health outcomes. Molecular and Cellular
Endocrinology, 316(2), 104-108.
Einhorn, H. J., & Hogarth, R. M. (1981). Behavioral decision-theory - processes of judgment and
choice. Annual Review of Psychology, 32, 53-88.
Evans, J. S. B. T. (2008). Dual-processing accounts of reasoning, judgment, and social cognition.
Annual Review of Psychology, 59, 255-278.
Falk, L. W., Bisogni, C. A., & Sobal, A. (1996). Food choice processes of older adults: A
qualitative investigation. Journal of Nutrition Education, 28(5), 257-265.
Feunekes, G. I. J., de Graaf, C., Meyboom, S., & van Staveren, W. A. (1998). Food choice and
fat intake of adolescents and adults: Associations of intakes within social networks.
Preventive Medicine, 27(5), 645-656.
Finer, N. (2002). Pharmacotherapy of obesity. Best Practice & Research Clinical Endocrinology
& Metabolism, 16(4), 717-742.
Finucane, M. M., Stevens, G. A., Cowan, M. J., Danaei, G., Lin, J. K., Paciorek, C. J., et al.
(2011). National, regional, and global trends in body-mass index since 1980: Systematic
analysis of health examination surveys and epidemiological studies with 960 countryyears and 9.1 million participants. Lancet, 377(9765), 557-567.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
37
Fisher, J. O., & Birch, L. L. (1999). Restricting access to palatable foods affects children's
behavioral response, food selection, and intake. American Journal of Clinical Nutrition,
69(6), 1264-1272.
Flegal, K. M., Carroll, M. D., Ogden, C. L., & Curtin, L. R. (2010). Prevalence and trends in
obesity among US adults, 1999-2008. JAMA-Journal of the American Medical
Association, 303(3), 235-241.
Folley, B. S., & Park, S. (2010). Relative food preference and hedonic judgments in
schizophrenia. Psychiatry Research, 175(1-2), 33-37.
Fontaine, K. R., Redden, D. T., Wang, C. X., Westfall, A. O., & Allison, D. B. (2003). Years of
life lost due to obesity. JAMA-Journal of the American Medical Association, 289(2), 187193.
Ford, J. K., Schmitt, N., Schechtman, S. L., Hults, B. M., & Doherty, M. L. (1989). Process
tracing methods - contributions, problems, and neglected research questions.
Organizational Behavior and Human Decision Processes, 43(1), 75-117.
Frary, C. D., Johnson, R. K., & Wang, M. Q. (2004). Children and adolescents' choices of foods
and beverages high in added sugars are associated with intakes of key nutrients and food
groups. Journal of Adolescent Health, 34(1), 56-63.
French, S. A., Story, M., Fulkerson, J. A., & Hannan, P. (2004). An environmental intervention
to promote lower-fat food choices in secondary schools: Outcomes of the TACOS study.
American Journal of Public Health, 94(9), 1507-1512.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
38
French, S. A., Story, M., Neumark-Sztainer, D., Fulkerson, J. A., & Hannan, P. (2001). Fast food
restaurant use among adolescents: Associations with nutrient intake, food choices and
behavioral and psychosocial variables. International Journal of Obesity, 25(12), 18231833.
Friese, M., Hofmann, W., & Wänke, M. (2008). When impulses take over: Moderated predictive
validity of explicit and implicit attitude measures in predicting food choice and
consumption behaviour. British Journal of Social Psychology, 47, 397-419.
Furst, T., Connors, M., Bisogni, C. A., Sobal, J., & Falk, L. W. (1996). Food choice: A
conceptual model of the process. Appetite, 26(3), 247-265.
Gibson, E. L., & Desmond, E. (1999). Chocolate craving and hunger state: Implications for the
acquisition and expression of appetite and food choice. Appetite, 32(2), 219-240.
Glanz, K., & Mullis, R. M. (1988). Environmental interventions to promote healthy eating: A
review of models, programs, and evidence. Health Education Quarterly, 15(4), 395-415.
Goel, V. (2010). Neural basis of thinking: Laboratory problems versus real-world problems.
Wiley Interdisciplinary Reviews: Cognitive Science, 1, 613-621
Gonzalez-Suarez, C., Worley, A., Grimmer-Somers, K., & Dones, V. (2009). School-based
interventions on childhood obesity A meta-analysis. American Journal of Preventive
Medicine, 37(5), 418-427.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
39
Gortmaker, S. L., Peterson, K., Wiecha, J., Sobol, A. M., Dixit, S., Fox, M. K., et al. (1999).
Reducing obesity via a school-based interdisciplinary intervention among youth - planet
health. Archives of Pediatrics & Adolescent Medicine, 153(4), 409-418.
Greenwood, C. E., Tam, C., Chan, M., Young, K. W. H., Binns, M. A., & van Reekum, R.
(2005). Behavioral disturbances, not cognitive deterioration, are associated with altered
food selection in seniors with Alzheimer's disease. Journals of Gerontology Series ABiological Sciences and Medical Sciences, 60(4), 499-505.
Gross, J.J., & Thompson, R.A. (2007). Emotion regulation: Conceptual foundations. In J. J.
Gross (Ed.), Handbook of emotion regulation (pp. 3-24). New York, NY: Guilford Press.
Halford, J. C. G., Boyland, E. J., Hughes, G., Oliveira, L. P., & Dovey, T. M. (2007). Beyondbrand effect of television (TV) food advertisements / commercials on caloric intake and
food choice of 5-7-year-old children. Appetite, 49(1), 263-267.
Hammond, R. A., Ornstein, J. T., Fellows, L. K., Dubé, L., Levitan, R., & Dagher, A.
(submitted). A spatio-temporal model of food reward learning.
Hare, T. A., Camerer, C. F., & Rangel, A. (2009). Self-control in decision-making involves
modulation of the vmPFC valuation system. Science, 324(5927), 646-648.
Hare, T. A., Malmaud, J., & Rangel, A. (2011). Focusing attention on the health aspects of foods
changes value signals in vmPFC and improves dietary choice. Journal of Neuroscience,
31(30), 11077-11087.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
40
Hare, T. A., O'Doherty, J., Camerer, C. F., Schultz, W., & Rangel, A. (2008). Dissociating the
role of the orbitofrontal cortex and the striatum in the computation of goal values and
prediction errors. Journal of Neuroscience, 28(22), 5623-5630.
Hill, A. J., & Blundell, J. E. (1986). Model system for investigating the actions of anorectic
drugs: Effect of dextro fenfluramine on food intake nutrient selection food preferences
meal patterns hunger and satiety in healthy human subjects. Advances in the Biosciences,
60, 377-390.
Hinton, E. C., Parkinson, J. A., Holland, A. J., Arana, F. S., Roberts, A. C., & Owen, A. M.
(2004). Neural contributions to the motivational control of appetite in humans. European
Journal of Neuroscience, 20(5), 1411-1418.
Horgen, K. B., & Brownell, K. D. (2002). Comparison of price change and health message
interventions in promoting healthy food choices. Health Psychology, 21(5), 505-512.
Hursti, U. K. K., & Sjoden, P. O. (1997). Food and general neophobia and their relationship with
self-reported food choice: Familial resemblance in Swedish families with children of ages
7-17 years. Appetite, 29(1), 89-103.
Inzlicht, M., & Kang, S. K. (2010). Stereotype threat spillover: How coping with threats to social
identity affects aggression, eating, decision-making, and attention. Journal of Personality
and Social Psychology, 99(3), 467-481.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
41
Jakicic, J. M., Clark, K., Coleman, E., Donnelly, J. E., Foreyt, J., Melanson, E., et al. (2001).
Appropriate strategies for intervention weight loss and prevention of weight regain for
adults. Medicine and Science in Sports and Exercise, 33(12), 2145-2156.
James, D. C. S. (2004). Factors influencing food choices, dietary intake, and nutrition-related
attitudes among African Americans: Application of a culturally sensitive model. Ethnicity
& Health, 9(4), 349-367.
Janssen, I., Craig, W. M., Boyce, W. F., & Pickett, W. (2004). Associations between overweight
and obesity with bullying behaviors in school-aged children. Pediatrics, 113(5), 11871194.
Kahneman, D., & Frederick, S. (2002). Representativeness revisited: Attribute substitution in
intuitive judgment. In T. Gilovich, D. Griffin & D. Kahneman (Eds.), Heuristics and
biases: The psychology of intuitive judgment (pp. 49-81). New York, NY: Cambridge
University Press.
Kelder, S. H., Perry, C. L., Klepp, K. I., & Lytle, L. L. (1994). Longitudinal tracking of
adolescent smoking, physical-activity, and food choice behaviors. American Journal of
Public Health, 84(7), 1121-1126.
Klesges, R. C., Stein, R. J., Eck, L. H., Isbell, T. R., & Klesges, L. M. (1991). Parental influence
on food selection in young-children and its relationships to childhood obesity. American
Journal of Clinical Nutrition, 53(4), 859-864.
Kopelman, P. G. (2000). Obesity as a medical problem. Nature, 404(6778), 635-643.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
42
Krondl, M., Coleman, P., Wade, J., & Milner, J. (1983). A twin study examining the genetic
influence on food selection. Human Nutrition-Applied Nutrition, 37(3), 189-198.
Lahti-Koski, M., Pietinen, P., Heliovaara, M., & Vartiainen, E. (2002). Associations of body
mass index and obesity with physical activity, food choices, alcohol intake, and smoking
in the 1982-1997 FINRISK studies. American Journal of Clinical Nutrition, 75(5), 809817.
Lammers, G. J., Pijl, H., Iestra, J., Langius, J. A. E., Buunk, G., & Meinders, A. E. (1996).
Spontaneous food choice in narcolepsy. Sleep, 19(1), 75-76.
Lappalainen, R., & Epstein, L. H. (1990). A behavioral economics analysis of food choice in
humans. Appetite, 14(2), 81-93.
Lavin, J. H., French, S. J., & Read, N. W. (1997). The effect of sucrose- and aspartamesweetened drinks on energy intake, hunger and food choice of female, moderately
restrained eaters. International Journal of Obesity, 21(1), 37-42.
Leiter, L. A., Hrboticky, N., & Anderson, G. H. (1987). Effects of levo tryptophan on food
intake and selection in lean men and women. In R. J. Wurtman & J. J. Wurtman (Eds.),
Human Obesity (pp. 327-328). New York, NY: New York Academy of Sciences.
Lennernas, M., Fjellstrom, C., Becker, W., Giachetti, I., Schmitt, A., deWinter, A. M. R., et al.
(1997). Influences on food choice perceived to be important by nationally-representative
samples of adults in the European Union. European Journal of Clinical Nutrition, 51, S8S15.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
43
Lilly, R., Cummings, J. L., Benson, D. F., & Frankel, M. (1983). The human Kluver-Bucy
syndrome. Neurology, 33(9), 1141-1145.
Lindeman, M., & Vaananen, M. (2000). Measurement of ethical food choice motives. Appetite,
34(1), 55-59.
Linder, N. S., Uhl, G., Fliessbach, K., Trautner, P., Eiger, C. E., & Weber, B. (2010). Organic
labeling influences food valuation and choice. NeuroImage, 53(1), 215-220.
Lobstein, T., Baur, L., Uauy, R., & IASO International Obesity Task Force. (2004). Obesity in
children and young people: A crisis in public health. Obesity Reviews, 5 Suppl 1, 4-104.
Lockie, S., Lyons, K., Lawrence, G., & Grice, J. (2004). Choosing organics: A path analysis of
factors underlying the selection of organic food among Australian consumers. Appetite,
43(2), 135-146.
Lowry, R., Galuska, D. A., Fulton, J. E., Wechsler, H., Kann, L., & Collins, J. L. (2000).
Physical activity, food choice, and weight management goals and practices among US
college students. American Journal of Preventive Medicine, 18(1), 18-27.
Magnusson, M. K., Arvola, A., Hursti, U. K. K., Aberg, L., & Sjoden, P. O. (2003). Choice of
organic foods is related to perceived consequences for human health and to
environmentally friendly behaviour. Appetite, 40(2), 109-117.
Michela, J. L., & Contento, I. R. (1986). Cognitive, motivational, social, and environmental
influences on children’s food choices. Health Psychology, 5(3), 209-230.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
44
Moller, S. E. (1983). Tryptophan and tyrosine availability: Relation to food choice and sleeping
habits of man. A preliminary study. Human Neurobiology, 2(1), 45-8.
Moller, S. E. (1986). Carbohydrate/protein selection in a single meal correlated with plasma
tryptophan and tyrosine ratios to neutral amino acids in fasting individuals. Physiology
and Behavior, 38(2), 175-183.
Must, A., Spadano, J., Coakley, E. H., Field, A. E., Colditz, G., & Dietz, W. H. (1999). The
disease burden associated with overweight and obesity. JAMA-Journal of the American
Medical Association, 282(16), 1523-1529.
Neumark-Sztainer, D., Story, M., Hannan, P. J., & Rex, J. (2003). New moves: A school-based
obesity prevention program for adolescent girls. Preventive Medicine, 37(1), 41-51.
Neumark-Sztainer, D., Story, M., & Harris, T. (1999). Beliefs and attitudes about obesity among
teachers and school health care providers working with adolescents. Journal of Nutrition
Education, 31(1), 3-9.
Neumark-Sztainer, D., Story, M., Perry, C., & Casey, M. A. (1999). Factors influencing food
choices of adolescents: Findings from focus-group discussions with adolescents. Journal
of the American Dietetic Association, 99(8), 929-937.
Ogden, C. L., Carroll, M. D., Curtin, L. R., Lamb, M. M., & Flegal, K. M. (2010). Prevalence of
high body mass index in US children and adolescents, 2007-2008. JAMA-Journal of the
American Medical Association, 303(3), 242-249.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
45
Olds, T., Maher, C., Shi Zumin, Peneau, S., Lioret, S., Castetbon, K., et al. (2011). Evidence that
the prevalence of childhood overweight is plateauing: Data from nine countries.
International Journal of Pediatric Obesity, 6(5-6), 342-360.
Oliver, G., & Wardle, J. (1999). Perceived effects of stress on food choice. Physiology &
Behavior, 66(3), 511-515.
Oliver, G., Wardle, J., & Gibson, E. L. (2000). Stress and food choice: A laboratory study.
Psychosomatic Medicine, 62(6), 853-865.
Opp, K.-D. & Wippler, R. (Eds.) (1990). Empirischer Theorievergleich : Erklaerungen sozialen
Verhaltens in Problemsituationen. Opladen, GER: Westdeutscher Verlag.
Parsons, M. B., & Reid, D. H. (1990). Assessing food preferences among persons with profound
mental-retardation - providing opportunities to make choices. Journal of Applied
Behavior Analysis, 23(2), 183-195.
Patterson, B. H., & Block, G. (1988). Food choices and the cancer guidelines. American Journal
of Public Health, 78(3), 282-286.
Patterson, B. H., Harlan, L. C., Block, G., & Kahle, L. (1995). Food choices of whites, blacks,
and Hispanics: Data from the 1987 National Health Interview Survey. Nutrition and
Cancer, 23(2), 105-119.
Paulus, M. P., & Frank, L. R. (2003). Ventromedial prefrontal cortex activation is critical for
preference judgments. Neuroreport, 14(10), 1311-1315.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
46
Pfau, C. D., & Piekarski, J. (1997). Nutritional behavior of elderly persons in private households:
Times of food consumption and food choice. American Journal of Clinical Nutrition,
65(4), S1364-S1364.
Piech, R. M., Lewis, J., Parkinson, C. H., Owen, A. M., Roberts, A. C., Downing, P. E., et al.
(2009). Neural correlates of appetite and hunger-related evaluative judgments. PLoS One,
4(8), e6581.
Piech, R. M., Lewis, J., Parkinson, C. H., Owen, A. M., Roberts, A. C., Downing, P. E., et al.
(2010). Neural correlates of affective influence on choice. Brain and Cognition, 72(2),
282-288.
Pijl, H., de Meijer, P. H. E. M., Langius, J., Coenegracht, G. I. G. M., van den Berk, A. H. M.,
Shaw, P. K. C., et al. (2001). Food choice in hyperthyroidism: Potential influence of the
autonomic nervous system and brain serotonin precursor availability. Journal of Clinical
Endocrinology & Metabolism, 86(12), 5848-5853.
Pijl, H., Koppeschaar, H. P. F., Willekens, F. L. A., Dekamp, I. O., Veldhuis, H. D., & Meinders,
A. E. (1991). Effect of serotonin reuptake inhibition by fluoxetine on body-weight and
spontaneous food choice in obesity. International Journal of Obesity, 15(3), 237-242.
Pi-Sunyer, F. X. (1993). Medical hazards of obesity. Annals of Internal Medicine, 119(7), 655660.
Plassmann, H., O'Doherty, J., & Rangel, A. (2007). Orbitofrontal cortex encodes willingness to
pay in everyday economic transactions. Journal of Neuroscience, 27(37), 9984-9988.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
47
Pliner, P., Polivy, J., Herman, C. P., & Zakalusny, I. (1980). Short-term intake of overweight
individuals and normal weight dieters and non-dieters with and without choice among a
variety of foods. Appetite, 1(3), 203-213.
Reilly, J. J., Methven, E., McDowell, Z. C., Hacking, B., Alexander, D., Stewart, L., et al.
(2003). Health consequences of obesity. Archives of Disease in Childhood, 88(9), 748752.
Roberts, C. J. (2008). The effects of stress on food choice, mood and bodyweight in healthy
women. Nutrition Bulletin, 33(1), 33-39.
Rozin, P. (2006). The integration of biological, social, cultural and psychological influences on
food choice. In R. Shepherd & M. Raats (Eds.), The psychology of food choice (pp. 1939). Wallingford, UK: CABI Publishing.
Russ, C. S., Ciavarella, P. A., Kaiser, D. L., & Atkinson, R. L. (1984). Effects of naltrexone on
food intake and selection in obese humans. Federation Proceedings, 43(4), 4536.
Sanfey, A. G. (2007). Social decision-making: Insights from game theory and neuroscience.
Science, 318(5850), 598-602.
Schanzenbach, D. (2009). Do school lunches contribute to childhood obesity? Journal of Human
Resources, 44(3), 684-709.
Schoeller, D. (1990). How accurate is self-reported dietary energy-intake. Nutrition Reviews,
48(10), 373-379.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
48
Schulz, M., Nothlings, U., Hoffmann, K., Bergmann, M. M., & Boeing, H. (2005). Identification
of a food pattern characterized by high-fiber and low-fat food choices associated with
low prospective weight change in the EPIC-Potsdam cohort. Journal of Nutrition, 135(5),
1183-1189.
Schwarz, N. (1999). Self-reports - how the questions shape the answers. American Psychologist,
54(2), 93-105.
Schwimmer, J. B., Burwinkle, T. M., & Varni, J. W. (2003). Health-related quality of life of
severely obese children and adolescents. JAMA-Journal of the American Medical
Association, 289(14), 1813-1819.
Seipel, C. (1999). Strategien und Probleme des empirischen Theorievergleichs in den
Sozialwissenschaften: Rational Choice Theorie oder Persoenlichkeitstheorie? Opladen,
GER: Leske + Budrich.
Seymour, B. & Vlaev, I. (2012). Can, and should, behavioral neuroscience influence public
policy? Trends in Cognitive Sciences, 16(9), 449-451.
Sharma, M. (2007). Behavioural interventions for preventing and treating obesity in adults.
Obesity Reviews, 8(5), 441-449.
Shetty, P. S., Prentice, A. M., Goldberg, G. R., Murgatroyd, P. R., McKenna, A. P. M., Stubbs,
R. J., et al. (1994). Alterations in fuel selection and voluntary food-intake in response to
isoenergetic manipulation of glycogen stores in humans. American Journal of Clinical
Nutrition, 60(4), 534-543.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
49
Simmons, D., McKenzie, A., Eaton, S., Cox, N., Khan, M. A., Shaw, J., et al. (2005). Choice and
availability of takeaway and restaurant food is not related to the prevalence of adult
obesity in rural communities in Australia. International Journal of Obesity, 29(6), 703710.
Sjoberg, A., Hallberg, L., Hoglund, D., & Hulthen, L. (2003). Meal pattern, food choice, nutrient
intake and lifestyle factors in the Goteborg adolescence study. European Journal of
Clinical Nutrition, 57(12), 1569-1578.
Sjostrom, L. (2000). Surgical intervention as a strategy for treatment of obesity. Endocrine,
13(2), 213-230.
Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin,
119(1), 3-22.
Smith, J. A., & Epstein, L. H. (1991). Behavioral economic-analysis of food choice in obese
children. Appetite, 17(2), 91-95.
Sobal, J., Khan, L. K., & Bisogni, C. (1998). A conceptual model of the food and nutrition
system. Social Science & Medicine, 47(7), 853-863.
Sobal, J., & Bisogni, C. A. (2009). Constructing food choice decisions. Annals of Behavioral
Medicine, 38, S37-S46.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
50
Sparks, P., Conner, M., James, R., Shepherd, R., & Povey, R. (2001). Ambivalence about healthrelated behaviours: An exploration in the domain of food choice. British Journal of
Health Psychology, 6, 53-68.
Steptoe, A., Pollard, T. M., & Wardle, J. (1995). Development of a measure of the motives
underlying the selection of food - the food choice questionnaire. Appetite, 25(3), 267-284.
Stice, E., Shaw, H., & Marti, C. N. (2006). A meta-analytic review of obesity prevention
programs for children and adolescents: The skinny on interventions that work.
Psychological Bulletin, 132(5), 667-691.
Story, M., Kaphingst, K. M., Robinson-O'Brien, R., & Glanz, K. (2008). Creating healthy food
and eating environments: Policy and environmental approaches. Annual Review of Public
Health, 29, 253-272.
Stratton, P., & Bromley, K. (1999). Families' accounts of the causal processes in food choice.
Appetite, 33(1), 89-108.
Strauss, A. L. & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures
and research. Newbury Park, CA: Sage Publications.
Svenson, O. (1979). Process descriptions of decision-making. Organizational Behavior and
Human Performance, 23(1), 86-112.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
51
Svenson, O. (1996). Decision-making and the search for fundamental psychological regularities:
What can be learned from a process perspective? Organizational Behavior and Human
Decision Processes, 65(3), 252-267.
Taras, H., & Potts-Datema, W. (2005). Obesity and student performance at school. Journal of
School Health, 75(8), 291-295.
Tuschl, R. J., Laessle, R. G., Platte, P., & Pirke, K. M. (1990). Differences in food-choice
frequencies between restrained and unrestrained eaters. Appetite, 14(1), 9-13.
van der Merwe, D., Kempen, E. L., Breedt, S., & de Beer, H. (2010). Food choice: Student
consumers' decision-making process regarding food products with limited label
information. International Journal of Consumer Studies, 34(1), 11-18.
Vereecken, C. A., Bobelijn, K., and Maes, L. (2005). School food policy at primary and
secondary schools in Belgium-Flanders: does it influence young people's food habits?
European Journal of Clinical Nutrition, 59, 271–277.
Verger, P., Lanteaume, M. T., & Louissylvestre, J. (1992). Human intake and choice of foods at
intervals after exercise. Appetite, 18(2), 93-99.
Wade, J., Milner, J., & Krondl, M. (1981). Evidence for a physiological regulation of food
selection and nutrient intake in twins. American Journal of Clinical Nutrition, 34(2), 143147.
LINKING FOOD-RELATED DECISION-MAKING CONCEPTS
52
Wansink, B., & Sobal, J. (2007). Mindless eating - the 200 daily food decisions we overlook.
Environment and Behavior, 39(1), 106-123.
Wardle, J., Haase, A. M., Steptoe, A., Nillapun, M., Jonwutiwes, K., & Bellisle, F. (2004).
Gender differences in food choice: The contribution of health beliefs and dieting. Annals
of Behavioral Medicine, 27(2), 107-116.
Yanovski, S. Z., Leet, M., Yanovski, J. A., Flood, M., Gold, P. W., Kissileff, H. R., et al. (1992).
Food selection and intake of obese women with binge-eating disorder. American Journal
of Clinical Nutrition, 56(6), 975-980.
Zellner, D. A., Loaiza, S., Gonzalez, Z., Pita, J., Morales, J., Pecora, D., et al. (2006). Food
selection changes under stress. Physiology & Behavior, 87(4), 789-793.