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The determinants of food choice

Published online by Cambridge University Press:  01 December 2016

Gareth Leng*
Affiliation:
Centre for Integrative Physiology, University of Edinburgh, George Square, Edinburgh, EH8 9XD, UK
Roger A. H. Adan
Affiliation:
Department Translational Neuroscience, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
Michele Belot
Affiliation:
European University Institute, Via dei Roccettini 9, I-50014 San Domenico di Fiesole, Italy
Jeffrey M. Brunstrom
Affiliation:
Nutrition and Behaviour Unit, School of Experimental Psychology, University of Bristol, 12a Priory Road, Bristol BS8 1TU, UK
Kees de Graaf
Affiliation:
Division of Human Nutrition, Wageningen University & Research Centre, Wageningen, Stippeneng 4, 6708 WE, The Netherlands
Suzanne L. Dickson
Affiliation:
Department Physiology/Endocrine, Institute of Neuroscience and Physiology, The Sahlgrenka Academy at the University of Gothenburg, SE-405 30 Gothenburg, Sweden
Todd Hare
Affiliation:
Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Bluemlisalpstrasse 10, 8006 Zurich, Switzerland
Silvia Maier
Affiliation:
Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Bluemlisalpstrasse 10, 8006 Zurich, Switzerland
John Menzies
Affiliation:
Centre for Integrative Physiology, University of Edinburgh, George Square, Edinburgh, EH8 9XD, UK
Hubert Preissl
Affiliation:
Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen; German Center for Diabetes Research (DZD e.V.), Tübingen, Germany Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
Lucia A. Reisch
Affiliation:
Department of Intercultural Communication and Management, Copenhagen Business School, Porcelaenshaven 18a, DK – 2000 Frederiksberg, Denmark
Peter J. Rogers
Affiliation:
Nutrition and Behaviour Unit, School of Experimental Psychology, University of Bristol, 12a Priory Road, Bristol BS8 1TU, UK
Paul A. M. Smeets
Affiliation:
Division of Human Nutrition, Wageningen University & Research Centre, Wageningen, Stippeneng 4, 6708 WE, The Netherlands Image Sciences Institute, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
*Corresponding
* Corresponding author: Professor G. Leng, email gareth.leng@ed.ac.uk
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Abstract

Health nudge interventions to steer people into healthier lifestyles are increasingly applied by governments worldwide, and it is natural to look to such approaches to improve health by altering what people choose to eat. However, to produce policy recommendations that are likely to be effective, we need to be able to make valid predictions about the consequences of proposed interventions, and for this, we need a better understanding of the determinants of food choice. These determinants include dietary components (e.g. highly palatable foods and alcohol), but also diverse cultural and social pressures, cognitive-affective factors (perceived stress, health attitude, anxiety and depression), and familial, genetic and epigenetic influences on personality characteristics. In addition, our choices are influenced by an array of physiological mechanisms, including signals to the brain from the gastrointestinal tract and adipose tissue, which affect not only our hunger and satiety but also our motivation to eat particular nutrients, and the reward we experience from eating. Thus, to develop the evidence base necessary for effective policies, we need to build bridges across different levels of knowledge and understanding. This requires experimental models that can fill in the gaps in our understanding that are needed to inform policy, translational models that connect mechanistic understanding from laboratory studies to the real life human condition, and formal models that encapsulate scientific knowledge from diverse disciplines, and which embed understanding in a way that enables policy-relevant predictions to be made. Here we review recent developments in these areas.

Type
Conference on ‘New technology in nutrition research and practice’
Copyright
Copyright © The Authors 2016 

Health nudge interventions to steer people into healthier lifestyles are increasingly applied by governments worldwide( 1 , Reference Matjasko, Cawley and Baker-Goering 2 ). Nudges are approaches to law and policy that maintain freedom of choice, but which steer people in certain directions( Reference Sunstein and Thaler 3 ); they consist of small yet relevant behavioural stimuli such as simplification of information and choices, framing and priming of messages, feedback to one's behaviour, defaults and reminders and similar behavioural cues. Much of the health burden is caused by modifiable behaviours such as smoking, unhealthy food consumption and sedentary lifestyles, but neither decades of health information and education, nor attempts at hard regulation (such as fat taxes or sugar taxes), nor voluntary self-regulation of industry have markedly promoted healthier lifestyles or helped to stop the rise of non-communicable diseases. At the same time, there is increasing evidence that the purposeful design of the living and consumption environments, the ‘choice architecture’, is a key to changing nutritional and activity patterns( Reference Bucher, Collins and Rollo 4 ) and to maintaining healthier lifestyles. There is mounting evidence for the usefulness of WHO's motto: ‘make the healthier choice the easy choice’, through easier access, availability, priming and framing( Reference Wilson, Buckley and Buckley 5 ). More than 150 governments now use behavioral science, with an emphasis on nudges( Reference Ly and Soman 6 , Reference Sunstein 7 ). In these countries, nudging for health is regarded as an attractive option to make health policies more effective and efficient; a recent poll in six European countries found that health nudges are overwhelmingly approved by the people( Reference Reisch and Sunstein 8 ). This is the backcloth against which we set out to test nudging tools that might be useful add-ons to traditional health policies.

However, to produce policy recommendations that are likely to be effective, we need to be able to make valid, non-trivial predictions about the consequences of particular behaviours and interventions. For this, we need a better understanding of the determinants of food choice. These determinants include dietary components (e.g. highly palatable foods and alcohol), but also diverse cultural and social pressures, cognitive-affective factors (perceived stress, health attitude, anxiety and depression), and familial, genetic and epigenetic influences on personality characteristics. Our choices are influenced by how foods are marketed and labelled and by economic factors, and they reflect both habits and goals, moderated, albeit imperfectly, by an individual understanding of what constitutes healthy eating. In addition, our choices are influenced by physiological mechanisms, including signals to the brain from the gastrointestinal tract and adipose tissue, which affect not only our hunger and satiety but also our motivation to eat particular nutrients, and the reward we experience from eating.

To develop the evidence base necessary for effective policies, we need to build bridges across different levels of knowledge and understanding. This requires experimental models that can fill in the gaps in our understanding that are needed to inform policy, translational models that connect mechanistic understanding from laboratory studies to the real life human condition, and formal models that encapsulate scientific knowledge from diverse disciplines and which embed understanding in a way that enables policy-relevant predictions to be made.

State-of-the-art

Although it seems self-evident that changes in body weight reflect the choices an individual makes about what food to eat, how much to eat and how much to exercise, the long-term balance between energy intake and energy output is mainly determined by interacting physiological systems. Since the discovery of leptin in 1994 and ghrelin in 1999, we have gained a partial mechanistic understanding of how homeostatic and hedonic influences are coded and how they impact on eating behaviour, and we have an emerging understanding of the mechanisms by which particular food constituents influence hunger and satiety. The strong evolutionary conservation of these mechanisms has meant that knowledge from animal models translates well into understanding of human physiology: for example, mutations in genes that affect signalling in these pathways have very similar effects in rodents and human subjects.

Animal studies and human genetics studies have also framed the contributions of genetic and epigenetic influences on body weight. Body weight in people is estimated (from twin studies) to be about 80 % heritable( Reference Wardle, Carnell and Haworth 9 ) but the search for the genes responsible has (so far) revealed associations that account for only about 20 % of the inter-individual variation( Reference Locke, Kahali and Berndt 10 ). This has focused attention on other heritable mechanisms and particularly on the consequences of events in uterine and early post-natal life. Notably, stress and impaired nutrition during gestation and in early post-natal life are now known to have lifelong programming effects on physiology and metabolism.

Against this background of genetics and nurture, an individual's knowledge, preferences and behaviours, lifestyle and eating habits are all shaped by their environment. In our everyday consumption, we are far from rational agents; we do not use only evidence-based information when deciding which foods to buy, but are influenced by the wider information environment, which is shaped by cultural factors, including advertising and other media, and we are strongly influenced by earlier decisions and habits, even if these have not proven to be optimal.

Habits are preferences shaped by past choices. If dietary choices follow habitual patterns, then we need to understand how these arise. Children often have a say in what they eat (at school they often choose what to eat at lunch), but they may be unable to correctly assess the costs and benefits of different options. In that context, imitative or impulsive behaviour may dominate, making them vulnerable to peer pressure and the supply of food in their direct environment. Once habits are in place, they shape preferences and future choices. The habitual pattern of behaviour has implications for policy interventions: effective interventions must be continued for long enough to affect preferences in the longer run.

Emotional and environmental cues also have a large role. We are influenced by how product information is presented, even whether the name sounds healthy. At the point of purchase, a number of decision heuristics and biases undermine rational decision behaviour. The anchor effect leads us to overvalue the information we obtained first; the source effect draws greater attention to the source of information and leads to assumptions about its credibility that may be false; and herd behaviour makes us adopt products that others are purchasing. Furthermore, we are poor at estimating probabilities and objective risks; we overestimate our capacity for self-control, and underestimate the health risks associated with the choices we make. Conversely, we cheat in our mental book-keeping: ‘Today I ate too much, but I'll just eat less tomorrow’( Reference Sunstein and Thaler 3 ). We tend to select current enjoyment (ice cream now) over conditions we wish for later (slim and fit), which behavioural economists explain in terms of the temporal discounting of future conditions( Reference Benabou and Tirole 11 ).

The decision-making situation has a large effect, as demonstrated in human ecology models. The triple A factors (affordability, availability and accessibility) have a major impact on decisions( Reference Maas, de Ridder and de Vet 12 ), and help to explain the attitude–behaviour gap( Reference Reisch, Gwozdz, Brunsø and Scholderer 13 ). Marketers have long understood that how a product is positioned in the store (e.g. as a ‘stopper’ at eye level) has a major impact. The same is true for the perception of rapid availability (ready-to-eat dishes) and the brand's potential of reward. In fact, most preferences appear to be less stable than postulated in neo-classical models; many are formed at the place where the decision is made. This is why behavioral economists speak of constructive preferences.

Decision heuristics and biases apply in situations involving uncertainty, which is true of most real decision-making. In our everyday consumption we are far from rational (in the sense of following our best intentions). During the search phase of the consumption process, we only perceive selective product characteristics and because of our limited processing capacities, we restrict our search criteria to just a few (‘seven plus or minus two’). The presence of many alternatives is more likely to confuse us than to generate optimal decisions (choice overload or hyperchoice). Another key finding from behavioural economics is the power of default options, such as the standard menu in a cafeteria. People generally follow the default option, even when given an opt-out. This finding is robust in diverse decision areas as organ donation, purchase of organic apples and the use of green electricity, and across a wide range of methods (experiments, questionnaires, secondary evaluations). For this reason, a number of incentive systems have been developed based upon ‘hard’ and ‘soft’ defaults( Reference Sunstein and Reisch 14 ).

Hedonic processes and reward are important drivers for our decisions and are strong enough to overrule homeostatic needs. Food selection and intake in human subjects is largely driven by an interaction of homeostatic control and reward signals. This interaction involves a complex involvement of higher cognitive functions including memory, learning and evaluation of different options.

In summary, we need to understand exactly what conscious and unconscious factors bias our choices and subvert our best intentions. We need to understand how our homeostatic and higher cortical processes support healthy eating, and how these mechanisms come to be undermined. Our policies on healthy eating must be framed in this setting if they are to be effective. It is also crucial to know what real individual responses to policy instruments and actions can be expected, and to customise our ‘policy toolbox’ accordingly.

The evidence-based policy approach, currently pursued at all policy levels, is based upon empirical data and valid models of behaviour and effect( Reference Bogenschneider and Corbett 15 ). It relies on learning policy cycles of test–learn–adapt–share that tests policies in pilot applications and assesses their efficacy and cost-benefits before they are rolled out( 16 ). The most important policy measures are those that rely on optimized information (not more information, but more useful and intuitively understandable information). For an integrated, policy-focused understanding of food choices, we need to optimise information in four key areas: early life experiences; environmental factors and impulsive choice behaviour; emotions and decision making; and how choices change with age.

Early life experiences

Early life programming can influence stress responses, food choice and weight gain into adult life. The consequences of early life events for cardiovascular and weight-related morbidity have been studied in detail in the Dutch famine birth cohort, and are associated with changes in the methylation of certain genes in people conceived during the Hunger Winter of 1944–45( Reference Tobi, Goeman and Monajemi 17 ). However, even modest differences in food intake or food choices in early life may have lifelong repercussions, and the metabolic status of the mother during gestation influences the brain dynamics of the fetus( Reference Linder, Schleger and Kiefer-Schmidt 18 ). Obesity is most prevalent in lower socio-economic groups, and this is likely to reflect genetics (assortative mating), epigenetics and environmental factors, including a childhood diet of energy-dense foods( Reference Wang and Lim 19 ).

Obesity has been rising among European children, and it disproportionately affects those in low socio-economic groups. However, we do not know the mechanistic link between stress and/or poor nutrition in early life and obesity in adult life, and in particular, we do not know whether this is mediated by programming effects on the reward systems that affect food choice in adult life. Understanding this is critical, for not only are children in low socio-economic groups most affected by obesity, but they are also particularly resistant to healthy food campaigns. In 2004, one London borough, after a healthy food campaign, introduced changes in the meals offered in primary schools, shifting from low-budget processed meals towards healthier options. The effect on educational outcomes was analysed using a difference in differences approach, using the neighbouring Local Education Authorities as a control group. Outcomes improved in English and Science, and authorised absences (linked to illness and health) fell by 14 %( Reference Belot and James 20 ). However, the children that benefited least were those from the lowest socio-economic groups; those most in need of support.

Stress in early life is also a concern, because it can have programming effects that heighten responsiveness to stress in adult life, contributing further to weight gain( Reference Brunton 21 ). Stress is a feature of modern life, particularly in the workplace. Some people eat less when stressed, but most eat more: one large study over 19 years in more than 10 000 participants( Reference Brunner, Chandola and Marmot 22 ) found that employees experiencing chronic work stress had a 50 % increased risk of developing central adiposity. How stress impacts on appetite and weight gain has been extensively studied in rodent models, which appear to mimic the human situation well. In rodents, whereas acute stress is anorexigenic, chronic stress can lead to weight gain( Reference Rabasa and Dickson 23 ). Chronic stress is related to chronic stimulation of the hypothalamo–pituitary adrenal axis, comprising neuroendocrine neurons in the hypothalamus that regulate the secretion of adenocorticotrophic hormone from the anterior pituitary, which in turn regulates glucocorticoid secretion from the adrenal gland. The hypersecretion of glucocorticoids (cortisol in man, corticosterone in rodents) is implicated in obesity at several levels. Intake of high energy foods suppresses the hyperactivity of the hypothalamo–pituitary adrenal axis, leading to what has been called comfort eating. The underlying mechanisms are well established: glucocorticoids stimulate behaviours mediated by the dopamine reward pathway, resulting in increased appetite for palatable foods( Reference Maier, Makwana and Hare 24 ); stress also releases endogenous opioids, which reinforce palatable food consumption and promote non-homeostatic eating. Conversely, comfort food ingestion decreases hypothalamo–pituitary adrenal axis activity( Reference Dallman, Pecoraro and Akana 25 ); thus if stress becomes chronic, then eating patterns become a coping strategy. Beyond stress, which affects most of the population at some time, about 7 % of the European population suffers from depression every year. A common symptom is an alteration in food intake, and this can result in a vicious circle of weight gain and depression( Reference Stunkard, Faith and Allison 26 ).

While early life experience has a major impact upon health throughout life, little is known about how stress, poor nutrition and metabolic challenges like gestational diabetes in early life influences later food selection and valuation, and this is key to defining the timing and nature of policy interventions.

Environmental factors, food reward and impulsive choice behaviour

Many aspects of modern diet might contribute to the obesity epidemic, including the composition and palatability of modern food, its availability and affordability, how it is marketed, the modern environment, contemporary food culture and gene–environment interactions. These impact on the reward component of eating that is key to impulsive choice behaviour; the behaviour that governs momentary choices to eat high or low energy foods. The motivation to eat competes with other motivations via a highly conserved neural circuitry, the reward circuitry. One key part of this is the nucleus accumbens, which integrates homeostatic, hedonic and cognitive aspects of food intake( Reference Kelley, Baldo and Pratt 27 , Reference Adan, Vanderschuren and la Fleur 28 ), and this circuit involves the neurotransmitter dopamine. The nucleus accumbens receives a dense dopamine input from the ventral tegmental area. This does not code ‘reward’ in the sense of subjective pleasure; rather, it mediates incentive salience (attractiveness) and motivational properties of positive stimuli and events( Reference Berridge 29 ). The dopamine system is regulated by cues that signal the availability of rewards as well as actual reward: dopamine neurons fire in a way that reflects the reward value and the dopamine that is released in the striatum has a key role in habit formation, while that released in the orbitofrontal cortex is involved in decision-making.

Human brain imaging studies using positron emission tomography and functional MRI (fMRI) confirm that these mechanisms function similarly in human subjects as in rodents. Thus the central nervous system response to palatable foods differs from that to bland foods and responses of subjects that crave palatable foods differ from those who do not. Importantly, cravings for palatable food activate similar brain regions and involve the same chemical messengers in human subjects as in rats. In the striatum, the availability of dopamine D2 receptors is reduced in severely obese subjects( Reference Benton and Young 30 ), and people who show blunted striatal activation during food intake are at greater risk of obesity, particularly those with compromised dopamine signalling( Reference Stice, Spoor and Bohon 31 ).

Mammals pursue behaviour that is likely to yield them the greatest reward at that time; when fat stores are high, the rewarding power of food is less and they are more motivated to pursue other rewards. Thus hedonic and homeostatic mechanisms interact, and this takes place at defined brain sites. Importantly, endocrine signals such as ghrelin, insulin and leptin are not merely regulators of energy homeostasis, but also influence the reward circuitry to increase the incentive value of food( Reference Egecioglu, Jerlhag and Salomé 32 Reference Kullmann, Heni and Veit 34 ) and impulsive choice behaviour( Reference Anderberg, Hansson and Fenander 35 ). The consequences are striking: the one intervention of consistent effectiveness for weight loss in the morbidly obese is bariatric surgery and this works not by restricting intake or absorption, but by reducing the incentives to eat via changes in endocrine signalling to the brain( Reference Leng 36 , Reference Frank, Oberndorfer and Simmons 37 ). This shows that morbid obesity is resistant to interventions because of a dysfunction of gut-brain signalling and is important for policy. Blame and shame strategies that deny the underlying pathology are destined to be ineffective, and may be counterproductive by promoting comfort eating. It is also important because these endocrine signals vary with time of day and according to the timing of meals. This opens a window of opportunity by which changing meal patterns, when we eat rather than how much, can influence both how we utilise the energy intake and our appetite.

Emotions and decision-making

Eating is triggered by many factors, including the sight, smell and memory of food, and anticipation of food is associated with activation of well-defined regions of the hypothalamus( Reference Johnstone, Fong and Leng 38 ). The sensory characteristics of food are also important in food choice, and these can be well studied by fMRI( Reference Lundström, Boesveldt and Albrecht 39 ). Visual attention can be rapidly cued by food items, particularly items with high calorific content, and attentional responding to these is magnified in overweight individuals, suggesting that heightened attention to high-energetic food cues promotes greater intake. Animal studies also indicate a major role for learning; associations are formed between the sensory characteristics of a food and its post-ingestive effects. Over time, these generate flavour preferences, and may also control meal size.

The sight of appetizing food modulates brain activity in consistent ways: viewing food items enhances activation both in visually-related brain regions and in regions associated with reward (orbitofrontal cortex, parahippocampal gyrus and the insula) in both adults and children( Reference Van Meer, van der Laan and Adan 40 , Reference van der Laan, de Ridder and Viergever 41 ). Visually-driven responses to food are linked to increased connectivity between the ventral striatum, the amygdala and anterior cingulate in individuals at risk of obesity, hence differences in interactions within the appetitive network may determine the risk of obesity. Obese participants show greater visually-driven responses to food in reward-sensitive brain regions and, for obese individuals, greater responsiveness in these regions before weight-loss treatment predicts treatment outcome. Poor weight loss is also predicted by pre-treatment levels of activity to food stimuli in brain areas associated with visual attention and memory, consistent with the attentional effects of food being a predictor of weight loss success( Reference Hege, Stingl and Ketterer 42 ).

However, we have a poor understanding of how valuation and selection of food are encoded neuronally. The orbitofrontal cortex, dorsolateral prefrontal cortex and ventral striatum are all implicated, but we have limited knowledge of what neuronal mechanisms are subserved by these structures. If we are to use functional neuroimaging studies to inform policies that promote healthier food choices, we need a better understanding of how health interventions impact on the brain mechanisms that control food selection and valuation. We need to address how molecular and cellular events, initiated by the exposure to food, translate into changes at the neuronal circuit level and how these translate to food decisions.

Physiological mechanisms of appetite control

In all mammals, appetite and energy expenditure are regulated by conserved neuronal circuitry using common messengers. Ghrelin, secreted from the empty stomach, reaches high levels after a fast and activates neurons in the hypothalamus that make a potent orexigen, neuropeptide Y. Leptin, secreted by adipocytes, reports on the body's fat reserves; it inhibits neuropeptide Y neurons, while activating others that express anorexigenic factors, notably neurons that express pro-opiomelanocortin. Pro-opiomelanocortin neurons and neuropeptide Y neurons are reciprocally linked, and which population is dominant determines how much (on average) an animal will eat. As an animal eats, neural and endocrine signals from the gut report on the volume ingested and on its composition, including its complement of fat, carbohydrates and protein. These signals, relayed by satiety centres of the caudal brainstem, converge on the ghrelin and leptin sensing circuits of the hypothalamus( Reference Murphy and Bloom 43 ). These in turn project to other limbic sites, including the paraventricular nucleus, which is the primary regulator of the sympathetic nervous system, and which also regulates the hypothalamo–pituitary adrenal axis. These pathways are powerful moderators of energy intake. Despite huge variations in day-by-day food intake, in the long term, the body weight of most individuals is remarkably stable. However, ‘crash dieting’ is an example of an intervention that reduces body weight in the short term, but as a result of the disruption of normal homeostatic mechanisms it has counterproductive effects in the long term.

It seems that dietary decisions can be regulated by circulating metabolic hormones, including those that signal to brain areas involved in food intake and appetitive behaviours. One example is ghrelin, an orexigenic hormone that increases anticipatory( Reference Verhagen, Egecioglu and Luijendijk 44 ) and motivated behaviour for food, notably for fat( Reference Perello and Dickson 45 ) and sugar( Reference Skibicka, Hansson and Alvarez-Crespo 46 ). Ghrelin enhances the reward value of foods and hence increases their consumption( Reference Egecioglu, Jerlhag and Salomé 32 ). Recently, ghrelin has been shown to guide dietary choice, but not entirely as expected for a reward-promoting hormone. For example, rats offered a free choice of lard (100 % fat), sucrose and chow increased their lard consumption over 2 weeks; ghrelin administration changed this food choice and they started to consume chow. Interestingly, these effects of ghrelin diverge from those of fasting, after which the consumption of energy-dense foods is prioritised( Reference Schéle, Bake and Rabasa 47 ). The pathway from the ventral tegmental area to the nucleus accumbens appears to be engaged by ghrelin to change food choice( Reference Schéle, Bake and Rabasa 47 ) and reward-linked behaviour( Reference Skibicka, Shirazi and Rabasa-Papio 48 ). Several other gut- and fat-derived hormones also impact on food reward circuitry. Leptin, for instance, affects food reward encoding by dopamine neurons of the ventral tegmental area( Reference van der Plasse, van Zessen and Luijendijk 49 ).

While morbid obesity is characterised by dysfunctional gut–brain signalling, a key stage in the progression to obesity is the development of leptin resistance. As a consequence, dietary restriction has a limited effect on obesity; long term compliance is poor, and those who lose weight are likely to swiftly regain it and may even overshoot after the end of a diet. Normally, eating is most rewarding when there is energy deficiency, and least in an energy-replete state, but leptin resistance develops in both the appetite circuitry and in the reward circuitry, so food remains rewarding despite a state of energy excess. Imaging studies have confirmed the impact of hormones in the recruitment of both hypothalamic and reward circuits. For example, when subjects are infused with peptide YY (a postprandial gut-derived satiety factor) the changes in activity in the caudolateral orbital frontal cortex predict feeding, whereas when levels of peptide YY are low, hypothalamic activation predicts food intake( Reference Batterham, Ffytche and Rosenthal 50 ). Insulin, which is released in the periphery after food ingestion is also a potent modulator of brain activity. In recent years it has become clear that, just as peripheral insulin resistance develops in association with obesity, so does insulin resistance in the brain( Reference Kullmann, Heni and Hallschmid 51 ).

Thus, paradoxically, one of the strongest predictors of weight gain is weight loss dieting. One of the biggest studies to demonstrate this was the Growing Up Today Study, a prospective study of >16 000 adolescents( Reference Field, Austin and Taylor 52 ). At the 3-year follow-up, adolescents that were frequent or infrequent dieters had gained significantly more weight than non-dieters. The study controlled for BMI, age, physical development, physical activity, energy intake and height change over the period. The longest study that demonstrates this is Project EAT (Eating and Activity in Teens and Young Adults), a population-based study of middle and high school students( Reference Neumark-Sztainer, Wall and Story 53 ). This study, which controlled for socio-economic status and initial BMI, again showed that the strongest predictors of weight gain were dieting and unhealthy weight control behaviours. The behaviours associated with the largest increases in BMI over a 10-year period were skipping meals, eating very little, using food substitutes and taking ‘diet pills’.

This raises the concern that emphasising the health risks of obesity may lead to behaviours that exacerbate the problem. This worry is compounded when one looks at the media response in the UK to recent publicity, where concerns about the effects of excessive weight gain in pregnancy were translated as concern about obesity in pregnancy. These are very different; while excessive weight gain in pregnancy is detrimental, so is weight loss, even from a condition of obesity. Physiologically, dietary restriction during pregnancy can lead to starvation of the fetus, as homeostatic mechanisms defend maternal body weight at the expense of the fetus. Thus, how advice related to healthy eating and lifestyles is formulated and disseminated needs careful attention. There has been little work on food choice in children, and this is important to explore because of the weaker self-control capacity of children, which is coupled to the maturation of their prefrontal cortex( Reference van Meer, Charbonnier and Smeets 54 ). This has a bearing on in-store marketing (and legislation on that) and the development of interventions aimed at preventing childhood obesity.

The neuroimaging of food choice

Human associational and behavioural studies have many potential confounding factors, so interpreting them depends on inferences from our understanding of the neurobiology of appetite. However, there is a disconnect between our mechanistic understanding and our ‘softer’ knowledge of individual consumer behaviour, which makes these inferences unsafe. We need to create bridges in our understanding, enabling us to integrate behavioural and observational studies with neurobiological studies in a way that can be used to educate stakeholders and inform policy.

Human neuroimaging is an emerging technology that can be used to define the neural circuits involved in food valuation and selection. Food decision-making has been studied surprisingly little; most neuroimaging studies use passive viewing paradigms in which participants are exposed to food: they study food cue reactivity rather than the ensuing decision-making processes. Combining different imaging techniques can optimise the temporal and spatial description of the neuronal circuits underlying food valuation and selection during hunger and satiety. Recent developments in fMRI include (a) combining diffusion tensor imaging with resting state analysis to determine network structures and changes during different physiological states; (b) high-resolution anatomical MRI to improve investigation of hypothalamic and midbrain responses; and (c) arterial spin labelling techniques to establish a quantitative neural activity measure of hunger and satiety. In addition, developments in magnetoencephalography and electroencephalography include: extraction of resting state dynamics with high temporal resolution and combination with diffusion tensor imaging; and application of Bayesian-based source localisation to define the temporal and spatial network involved in food selection. Most fMRI studies that link a given brain circuit with cues associated with food or with the choice for a particular food are based on correlations between an event and a recorded brain activity. To determine causality, we need to be able to change brain activity and determine its impact on behaviour. In human subjects, defined neuronal structures can be manipulated using transcranial magnetic stimulation or direct current stimulation to either facilitate or attenuate cerebral activity.

Along with the rise in the number of neuroimaging studies there have been many neuroimaging data-sharing initiatives, and several databases contain resting fMRI data and anatomical MRI data from thousands of individuals. For functional imaging, things are more complicated but there are notable efforts of sharing fMRI datasets (openfmri.org), unthresholded statistical maps (neurovault.org) and coordinate-based data synthesis (neurosynth.org). However, the value of such databases depends on the available metadata, and existing databases lack most or all of the metadata necessary for research on food choice, such as weight( Reference van Meer, Charbonnier and Smeets 54 ), restraint eating status( Reference van der Laan, Charbonnier and Griffioen-Roose 55 ) and personality characteristics( Reference van der Laan and Smeets 56 ).

For policies to be built on robust evidence, it is essential that the evidence is developed in a way that facilitates meta-analysis. There is great variability in neuroimaging results, and this is especially true for fMRI tasks involving complex stimuli such as food stimuli( Reference Van Meer, van der Laan and Adan 40 , Reference van der Laan, de Ridder and Viergever 41 ). Bennett & Miller( Reference Bennett and Miller 57 ) showed that the reproducibility of fMRI results was only 50 %, even for the same task and stimuli in the same participants. This was confirmed by a meta-analysis of fMRI studies of responses to food pictures: measurements for the brain areas that were most consistently activated by looking at food v. non-food pictures were only reported in fewer than half of the studies included( Reference Van Meer, van der Laan and Adan 40 , Reference van der Laan, de Ridder and Viergever 41 ). Reproducibility can be improved by standardising measures, but there are no standardised fMRI protocols for assessing food responsivity and food choice for different food categories. To filter out effects due to subject characteristics rather than methodological difference, standardisation of instruments and measures is crucial for data sharing and pooling across studies( Reference Smeets, Charbonnier and van Meer 58 ). Recently, researchers have begun to share (standardised) food images for use in experimental paradigms (e.g.Reference Charbonnier, van Meer and van der Laan 59 , Reference Blechert, Meule and Busch 60 ) and tools for standardised collection of food-related subject characteristics( 61 ).

To connect data from human imaging studies with neurophysiological data from rats, we must improve and adapt high-field rodent fMRI technology in a setting that allows to map involvement of neural circuits in food valuation and selection. Small rodent resting state and pharmacological fMRI is an emerging technology that has not yet been applied to address how brain activity changes upon food restriction and food anticipation. Thus, it is not known, for example, how brain activity is changed upon food restriction in rodents or how gut peptides like leptin and ghrelin affect functional connectivity between brain regions. Small rodent fMRI bridges the gap between neuronal activity at the cellular level with fMRI measures in human subjects, making it possible to connect molecular and cellular data with fMRI measures.

Novel technologies to understand the brain mechanisms underlying food choice

There is a poor understanding of what underlies the responses quantified in neuroimaging studies. By combining in vivo electrophysiology with optogenetics or pharmacogenetics, it is now possible to record from and interfere with defined neurons involved in food valuation and choice, and this is key to unravelling what underlies the responses recorded by neuroimaging. Optogenetics takes advantage of genes that encode light-sensitive channels and these channels can be expressed conditionally in specific neurons. These neurons can then be either activated or inhibited by shining light on them. This technical approach requires that these neurons express the cre recombinase enzyme. Targeting cre for instance to tyrosine hydroxylase (the rate limiting enzyme for dopamine production) neurons such as in (germline) tyrosine hydroxylase-cre rats, allows these light-sensitive channels to be expressed only in midbrain dopamine neurons. To achieve this, light-sensitive channels are cloned into a recombinant viral vector such that, only upon expression of cre, the channels are expressed in dopamine neurons( Reference Witten, Steinberg and Lee 62 , Reference de Backer, Garner and Luijendijk 63 ). This makes it possible to activate precise populations of neurons in rodents, and to compare observations with brain responses observed by neuroimaging. Similarly, subpopulations of dopamine neurons can be targeted with viruses to express novel receptors that are not endogenously present; these can then be specifically activated (or inhibited) by systemically applied drugs that act on those novel receptors (e.g.Reference Boender, de Jong and Boekhoudt 64 ).

How the life-long learning process contributes to food selection and valuation

The sensory characteristics of food are important in food choice, but learning also has a major role( Reference Brunstrom 65 ). Associations are formed between the sensory characteristics of a food (the conditioned stimulus) and its post-ingestive effects (the unconditioned stimulus). Over time, these flavour-nutrient associations generate flavour preferences and they also control meal size. In human subjects, fundamental questions remain about the nature of the unconditioned stimulus and how this is combined with sensory signalling from the tongue to the brain.

In adult human subjects, flavour-nutrient learning is notoriously difficult to observe under controlled laboratory conditions, although in non-human animals this form of learning is extremely reliable. Several examples of flavour-nutrient learning have been reported in children, and this may be because most dietary learning occurs in early life. By adulthood, we have encountered so many foods and flavours that our capacity to learn new associations might be saturated. If so, this reinforces the importance of childhood as a critical period during which our dietary behaviours are established. A further consideration is the complexity of the modern Western dietary environment. Human subjects are now exposed to a wide range of foods, in numerous different brands and varieties. This may limit our opportunity to learn about individual foods, which has the potential to promote overconsumption( Reference Hardman, Ferriday and Kyle 66 ).

Learned beliefs impact our dietary choices directly. Typically, we decide how much we are going to eat before a meal( Reference Fay, Ferriday and Hinton 67 ). These decisions are often motivated by a concern to avoid hunger between meals, and the learned expected satiety of individual foods is important in this. Low energy-dense foods tend to have greater expected satiety, and such foods are often selected to avoid hunger between meals. Increasingly, portions are also determined by external agents such as restaurants or retailers. Recently, it has become clear that larger portions not only increase our food intake but also affect choice. This is because larger portions are likely to satisfy our appetite between meals and, in the absence of concerns about satiety, decisions tend to be motivated primarily by palatability.

A further possibility is that satiation or the absence of hunger between meals is itself valued( Reference Brunstrom and Shakeshaft 68 ). The results of human appetite studies suggest that both oral and gastric stimulation are needed for optimal satiety( Reference Wijlens, Erkner and Alexander 69 Reference Spetter, de Graaf and Mars 71 ). However, the underlying process also involves integration of explicit knowledge about the food and amount that has been consumed( Reference Brunstrom, Burn and Sell 72 , Reference Cassady, Considine and Mattes 73 ). Consistent with this, several studies show that satiety and satiation are reduced when eating occurs in the presence of cognitive distraction( Reference Brunstrom and Mitchell 74 ). Eating ‘attentively’ appears to have the opposite effect( Reference Higgs and Donohoe 75 ), and food properties like viscosity can increase perceived fullness for otherwise similar foods( Reference Camps, Mars and de Graaf 76 ). Despite its importance, the process by which interoceptive signals are integrated remains unclear. This merits attention because some studies indicate that differences in interoceptive awareness are a predictor of adiposity in human subjects( Reference Herbert, Blechert and Hautzinger 77 ).

How physiological, psychological and emotional factors predispose people to unhealthy eating

One key question in the effects of sensory, nutrient and satiety contributions to reward is whether the initial response to certain stimuli remains after repeated exposure. Does the response to a low-energy beverage with artificial sweeteners stay the same, or do people slowly learn that ‘diet’ products contain lower energy content? For this case, it is hard to demonstrate such dietary learning( Reference Griffioen-Roose, Smeets and Weijzen 78 ) although there is some evidence for detection of food energy content in the mouth( Reference Smeets, Weijzen and de Graaf 79 , Reference van Rijn, de Graaf and Smeets 80 ). Another important consideration is whether it makes a difference whether one goes from, for example, 836·8 → 209·2 kJ (200 → 50 kcal), or from 627·6 → 0 kJ (150 → 0 kcal) In both cases, there is a reduction of 627·6 kJ (150 kcal), but in the case of 836·8 → 209·2 kJ (200 → 50 kcal), there is still energy left in the stimulus, whereas in the case of 627·6 → 0 kJ (150 → 0 kcal), there is no energy left. It has been argued that the absence of any energy content will lead to a lower reward value after repeated exposure. Conversely, most ‘light'products still contain energy, albeit less than their regular counterparts, with soft drinks a notable exception.

In both human subjects and rodents, the motivation to choose one food over another is driven by the emotional, hedonic and metabolic properties of the foods. The dopamine system is critically involved in this, and is essential for associating rewards with environmental stimuli that predict these rewards. Activity of this system is affected by both metabolic information and emotional and cognitive information. The hypothalamus, amygdala and medial prefrontal cortex play important roles in, respectively, feeding behaviour, emotional processing and decision-making. Manipulation of the dopamine system can be achieved by nutritional interventions and reducing dopamine levels in lean and obese subjects leads to decreased activity in the reward system( Reference Frank, Veit and Sauer 81 ).

There is also evidence that incidental emotions can affect food choices. Sadness leads to greater willingness to pay for unnecessary consumer goods( Reference Lerner, Small and Loewenstein 82 , Reference Cryder, Lerner and Gross 83 ) and increased consumption of unhealthy food items( Reference Garg, Wansink and Inman 84 ). However, the biological mechanisms linking affective states to food choices are unknown. Recent work has begun to investigate the underlying neural mechanisms of dietary choice in human subjects using neuroimaging and brain stimulation techniques together with validated choice paradigms and behavioural trait measures (e.g.Reference Garg, Wansink and Inman 84 Reference van der Laan, Barendse and Viergever 88 ).

A natural assumption would be that the physiological and psychological reactions to an affective state use the same neural pathways to influence food choices. However, Maier et al. ( Reference Maier, Makwana and Hare 24 ) have recently shown using fMRI that experiencing an acute stressor leads to changes in two separate and dissociable neural pathways: one associated with the physiological reaction to stress, and the other with the conscious perception of being stressed. The physiological response was measured by sampling salivary cortisol, the psychological experience was recorded using a visual analog scale on which participants indicated how they felt right after the stress induction. Cortisol was associated with signals about the reward value of food: individuals with a higher cortisol response showed a higher representation of taste in the ventral striatum and amygdala, and amplified signalling between ventral striatum/amygdala and the ventromedial prefrontal cortex when a tastier food was chosen. Yet the subjective perception of being stressed did not correlate with the strength of this connection. Instead, the perceived stress level (but not the cortisol reaction) was associated with the connectivity strength between left dorsolateral prefrontal cortex and the ventromedial prefrontal cortex: the more stressed participants had felt, the weaker was the connectivity between these two regions when self-control was needed to overcome taste temptations in order to choose the healthier food. A series of studies have demonstrated that connectivity between dorsolateral prefrontal cortex and ventromedial prefrontal cortex relates to the degree to which individuals use self-control in dietary choice( Reference Hare, Camerer and Rangel 89 Reference Lim, Cherry and Davis 92 ). This connection in the prefrontal cortex may maintain a goal context that promotes focusing on long-term outcomes such as future health, whereas sensory and motivational signalling from subcortical areas may promote information about more immediate choice outcomes. Thus, self-control in dietary choice may depend on a balance of signalling and information exchange in value computation networks and disruptions to this balance during highly affective states may lead to impaired self-control.

Modeling the interactions between physiological, psychological and emotional factors related to feeding behaviour

An ultimate ambition must be to generate formal models that encapsulate scientific knowledge from diverse disciplines, and which embed understanding in a way that enables policy-relevant predictions to be made. Modelling is a natural way of working together to provide added value; it expresses intrinsically the need to make links between levels of understanding. Most importantly, it takes seriously the issue of how to generate policy guidelines that have a robust scientific basis, by providing a common framework of understanding across disciplines.

Modelling provides a logically coherent framework for a multi-level analysis of food choice, integrating measures of the neural components of the appetitive network with whole-system output (behavioural experiments) in a framework consistent with the neural homeostatic and hedonic mechanisms, and providing a test-bed for studies of behavioural interventions. The first phase in modelling is a scheme that embodies constructs that explain behavior by describing a causal chain of events. A computational model expresses these mathematically, usually as differential equations. Typically such differential equations are (a) coupled (expressing interdependence between factors) and (b) non-linear (expressing complex dependencies between variables). To be useful, a model must be developed at a level of detail appropriate for the data it is informed by, and the type of prediction that it is called upon to make. It must be complex enough to satisfy the former, but simple enough to satisfy the latter: making models over-complex is counterproductive, as such models are not predictive( Reference Leng and MacGregor 93 ).

For example, oxytocin neurons are well established as playing an important role in satiety( Reference Leng, Onaka and Caquineau 94 , Reference Blevins and Ho 95 ) and, according to recent studies, in food choice( Reference Olszewski, Klockars and Levine 96 , Reference Olszewski, Klockars and Olszewska 97 ). These neurons respond to signals from the gut that control meal size, and exactly how they respond has been analysed at the single-cell level. Their behaviour can be captured in detail by biophysical (Hodgkin–Huxley style) models, that can then be approximated by simpler models that capture the essential behaviour while being better suited for modelling networks of neurons( Reference Maícas Royo, Brown and Leng 98 ). Decision making at the level of the neuron networks that oxytocin engages can be modelled by biologically realistic ‘winner-takes-all’ networks, which provide predictive models of how continuous variables lead to categorical decision making, and such network models can be fit to human brain imaging data by mean field approximation( Reference Deco, Jirsa and Robinson 99 ). Such models can link brain imaging data with experimental behavioural data in a predictive way, as in the spiking search over time & space model that has been developed to analyse attentional processes( Reference Mavritsaki, Allen and Humphreys 100 ). Relatively simple mathematical models can capture important features of value-based decisions well, and in a similar way for food-based decisions as for social decisions, indicating that there is a common computational framework by which different types of value-based decisions are made( Reference Krajbich, Hare and Bartling 101 ). At a high level, the aim must be to generate agent-based models that describe by a set of explicit rules all the factors that influence food choice, validating each rule by a mechanistic understanding of the neurobiological and physiological mechanisms that implement these rules. It is a long goal, but working towards it provides a unified framework for multi-disciplinary research.

Conclusions

Clearly we need a more sophisticated understanding of the determinants of food choice, an understanding consistent with many different types of evidence. To translate this into policy recommendations will involve further challenges: we must be aware of the potential for unintended consequences, of the likely need for policies tailored to specific populations, and of the difficulties in achieving compliance and measuring outcomes. The nudge approach to behavioural change appears at present to be most likely to be fruitful; small interventions that can be trialled for effectiveness in controlled settings. To develop these policy tools we need to identify a set of specific proposed interventions that are aimed at particular target groups. We must identify the evidence that suggests that these will be effective, and identify the gaps in our knowledge that may make our predictions uncertain before deciding which interventions to trial, and exactly how to implement them.

Financial Support

The research leading to these results has received funding from the European Union's Seventh Framework programme for research, technological development and demonstration under grant agreement no 607310 (Nudge-it).

Conflict of Interest

Peter Rogers has received funding for research on food and behaviour from industry, including from Sugar Nutrition UK. He has also provided consultancy services for Coca-Cola Great Britain and received speaker's fees from the International Sweeteners Association.

Authorship

All authors participated in writing this review.

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The determinants of food choice
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