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Contextual Influences on Eating Behavior

A Brain-to-Society Model of Eating Behavior

Laurette Dubé James McGill Chair of Consumer and Lifestyle Psychology 

Founder & Scientific Director of McGill Center for Convergence of Health and Economics, McGill University 

Relationship between the Brain, Digestive System, and Behavior Institute of Medicine, July 9 2014

Agenda

• 4 context levels within which hypothalamus anddigestive systems below operate in impacting eatingbehavior1. Internal “higher” level brain systems and mental processes

(attention, cognitive schemata, free will)

2. Fetal environment/multi‐faceted lifelong programming

3. Parental/family/home environments

4. Broader social, commercial, and cultural environment

• The Brain‐to‐Society (BtS) model of eating behavior1. Conceptual framework

2. Computational models of behavioral and ecosystemtransformation

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CONTEXT 1Internal “higher” level brain systems and mental processes (attention, cognitive schemata, free will)

Brain Regions Activated during fMRI Studies of Food Cue Reactivity

(Dagher, Trends in Endocrinology and Metabolism, 2012)

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Neurobehavioral Correlates of Eating Behaviors and BMI

(Vanik et al, Neuroscience and Neurobehavioral Review, 2013)

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Context 1 A: Attention to Rewarding Cues and Experience

(71 young adults, LeBel, Lu, & Dubé, Physiology and Behavior, under review)

INDIVIDUAL PREDISPOSITION• External eating (sensory

focused/distraction; DEBQ, VanStrien, 1886)– An individual propensity

where eating is easilytriggered by attention toexternal hedonic rather thaninternal homeostatic cues(Rodin & Slochower 1976)

– External eating is associatedwith food overconsumptionand obesity (Burton, Smit, &Lightowler 2007).

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EPISODE‐LEVEL/SENSORYFOCUS‐DISTRACTION

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External Eating and Sensory focus change the course of Hunger

PRE‐ AND POST‐CONSUMPTION HUNGER INTENSITY

Low‐external Eating Participants

Context 1B: Mental Schemata(196 Women, LeBel, Lu, & Dubé, Physiology and Behavior, 2008)

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Context 1C: Free Will or Lack Thereof(Young Adults, Finkelstein & Fishbach, Journal of Consumer Research, 2010)

• Consumption of a same food framed as“healthy” compared to “tasty” under lack offree‐will leaves consumers hungrier

Your job is to taste our health bar/candy bar.

“Would you like to try our health bar/candy bar?”

CONTEXT 2 Fetal Environment

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Intrauterine growth restriction (IUGR)

• results from a failure to  achieve a highergrowth potential

• 7‐15% of all births

• Increased risk for NCDs

(overweight, type II diabetes, 

cardiovascular disease)

Barker et al., 1992

Adult life

IUGR =  overweight, metabolicsyndrome

Barker et al and many others

Pretermnewborns ‐27 weeksgestational

age

IUGR programs the

hedonicresponse to sweet food

3 years of age

IUGR girls are more impulsivetowards a sweetreward

Adulthood

Low birthweight

alters foodpreferences

Barbieri, Portella, 

Silveira et al., 2009

Silveira, Levitan & cols, 

2012

Ayres, Silveira& cols., 2012

4 years of age

Escobar, Levitan, Silveira & cols. 

2014

Quality of mother‐child interaction moderates emotional 

overeating in IUGR girls

1 year of age

IUGR altersfood

preferenceand feedingbehavior

Migraineet al., 2013; 

Crume etal., 2013

Lussana et al., 2008

Stein et al., 2009

Kaseva et al., 2013

Perala et al., 2012

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Eating Schematicityin children born with large BMI

(Extreme Group, 616 6-12 years old)

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DEBQ‐M 

Adjusted For:

• Age, BMI, Income, Gestational Age and Sex• Large BWR = BWR > 1 SD from mean• Controls = ‐1SD to +1 SD

**NSp=0.034p=0.013

26 32 186 165

CONTEXT 3 Parental/Familial/Home Environment

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Attachment Insecurity and Eating Behaviors

• Participants: 616 Canadian children

• Measures of attachment, 24‐hour recall, andhealthy/unhealthy eating‐related habits.

• Insecure attachment…– High eating schematicity on all 3 Dutch Schema (P<.05)

– Positively predicted salty snacks consumed in the last 24hours, p < .05; Negatively predicted amount of water andfruit consumed in the last 24 hours, ps < .05 (controlled forHH income and child age);

– Positively predicted skipping breakfast, eating out andeating in front of the TV during weekdays, p < .05.

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The effect of Food Rules and BAS on Children’s Eating Behavior

• Parental control food rules (Puhl & Schwartz, 2003): Usingfood as reward/punishment to encourage/ discouragegood/bad non‐food behavior– reinforcing properties other than taste of food, such as the sense of

achievement and parents’ approval, can be added to food;

– some “bad” behaviors may also trigger food craving because of theanticipated deprivation.

• BAS (Behavioral Activation System; Carver & White, 1994):high BAS individuals tend to be more sensitive to reward, andsuch sensitivity may enhance the reinforcement process

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The effect of Food Rules and BAS on Children’s dietary intake

• 208 Canadian Children (age 6‐12): parents reported FFQ, BASand food rules.

• Total energy intake (calories per day)

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Emotional Reinforcement Accounting for Higher Nutritional Quality of Home Meals

(Lu et al, AJCN, 2011)

• @ home (as compared to away‐from‐home):– Food consumption is typically healthier (Stroebele & De Castro 2004)

– Individuals typically experience more intense positive emotions (calmand peaceful) (Côté & Moskowitz 1998).

– Such superior affective states can be attached to home meals as theemotional reinforcing value, especially for foods low in sugar/far.

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Emotional Reinforcement Accounting for Higher Nutritional Quality of Home Meals

(Lu et al, AJCN, 2011)

• 160 participants’ food consumption (healthier / less healthy /baseline) and momentary emotions 6 times per day in 10 days

• Results: Only @ home, Reciprocal chain reactions betweenPositive Emotions (PE) and healthier meal (/baseline)

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CONTEXT 4 Broader social, commercial, and cultural environment

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Correlational Evidence Linking Change in Food Environment, Eating Behavior and BMI

• Correlational evidence has linked food advertising and various facets of productformulation, packaging, pricing ,and retailing to a shift in consumption patterns inan obesigenic direction (Buijzen, Schuurman, & Bomhof, 2008; Dhar & Baylis,2011; Goldberg, 1990; Kunkel et al., 2004; M. L. Scott, Nowlis, Mandel, & Morales, 2008). 

• The availability of supermarkets and chain stores, that usually carry healthieralternatives compared to smaller stores, has been linked to lower BMI levels (Powell et al, 2007; 2009). 

• Increasing shelf space available has been found to be an effective strategy inincreasing sales of both unhealthy and healthy foods  (Kelly, Flood, & Yeatman, 2011). 

• In a randomized trial, Foster and colleagues (2014) found that the way healthyproducts were stacked on the shelf and the aisle position where these products were placed could enhance purchase of the healthy products. Placing healthy items near the cashier increased their purchase likelihood (Foster et al., 2014). 

• Poor diet quality was linked to a lack of local outlets carrying fruits and vegetables(Franco et al., 2009). 21

Carbonated Soft Drinks and IncomeMontreal Metropolitan Area

(Buckridge et al, Annals NYAS 2013)

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MMA-Diabetes Prevalence

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Individuals and Their Biology, Positioned Within their Commercial, Physical, Social and Cultural Environment

Geographic Information System (GIS) as an analytical method

Disease mapping

Location analysis

Population factors

Spatial statistics

Modelling

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Interactive Effects of Fast Food Density and Reward Sensitivity on Eating Behavior

(Paquet et al, AJCN, 2010)

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The Brain‐to‐Society (BtS) model of eating behavior

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Eating as a Neurobehavior: Rational and Motivational Brain Processes in Response to Environment Cues that

Complement Processes at Hypothalamus Level and Below

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Eating: A Neurobehavior in Contexts that Operate on Different Sectoral, Temporal and Geographical Scales

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Test Beds for the Development of the BtS Model2005-2009 McGill Think Tanks on Childhood

Obesity

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Nobel LaureateD. Kahnman

Nobel LaureateP. Krugman

Towards a Brain-to-Society Systems Modelof Individual Choice

(Dube et al, Marketing Letters, 2008)

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Laurette Dubé & Antoine Bechara & Ulf Böckenholt & Asim Ansari & Alain Dagher & Mark Daniel & Wayne S. DeSarbo & Lesley K. Fellows & Ross A. Hammond & Terry T‐K Huang & Scott Huettel & Yan Kestens & Bärbel Knäuper & Peter Kooreman & Douglas Spencer Moore & Ale Smidts

Abstract:Canonical models of rational choice fail to account for many forms ofmotivated adaptive behaviors, specifically in domains such as food selections. Todescribe behavior in such emotion‐ and reward‐laden scenarios, researchers haveproposed dual‐process models that posit competition between a slower, analyticfaculty and a fast, impulsive, emotional faculty. In this paper, we examine theassumptions and limitations of these approaches to modeling motivated choice. Weargue that models of this form, though intuitively attractive, are biologicallyimplausible. We describe an approach to motivated choice based on sequentialsampling process models that can form a solid theoretical bridge between what isknown about brain function and environmental influences upon choice. We furthersuggest that the complex and dynamic relationships between biology, behavior, andenvironment affecting choice at the individual level must inform aggregate modelsof consumer choice. Models using agent‐based complex systems may further providea principled way to relate individual and aggregate consumer choices to the aggregatechoices made by businesses and social institutions. We coin the term “brain‐to‐society

systems” choice model for this broad integrative approach.

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Conceptual Framework of BtS

The Brain-to-Society Model of Eating Behavior

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Complexity and Systems Science Framework for Eating Behavior: Individual and Society

(Dube et al, PNAS 2012; Hammond & Dube, PNAS 2012)

Computational BtS Behavioral Model of Eating  Behavior and Obesity/Non‐Communicable Diseases Prevention

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Model

• Focuses specifically on elucidating the role of rewardlearning to capture individual heterogeneity inreward sensitivity and to  isolate the dynamic effect ofreward learning in the context of diverse and changingenvironmental reward exposure.

• Extension of the temporal difference learning (TDL)framework to explicitly model movement acrossdifferent exposure environments through time.– TDL signals are carried by dopamine neurons in the brain

(Montague et al., 1996)

• Simulation constructed using agent‐basedcomputational modeling (ABM) to capture dynamicsand local heterogeneity in environmental exposure.

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Individual Heterogeneity in Learning

• Differences in learning rates (or perceived reward valuation)could translate into non‐trivial calorie surpluses for high learningrate (α) or high responsivity (β) agents.

Heterogeneity and Dynamic Change in Environmental Exposure

Fig 9: Agents who begin in a more low‐palatable region take longer on average to learn the value of high‐palatable foods.

Fig 10: This demonstrates a persistent lock‐in, even when initial food environment contains a substantial proportion of both food types.

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Preparing for A More Sophisticated Brain-Based Model of Eating Behavior

The ACTIVInsights ™ Project

To arrive at a clearer picture of the experience we explore:• Beliefs

– Limiting– Permissible– Congruent – Incongruent – Conscious– Sub-conscious

• Values-social norms

• Attitudes

• The emotionalprogram thatunderlies theirmental state

• Anchors

• Triggers of- attention- decision to actor shut down

• Sensory Modalities- Feelings - Tactile - Visual - Auditory - Taste - Smell

• Neuro-LinguisticPatterns- HOW people communicate

- Verbal & non-verbal

• Archetypes

• Symbols & metaphors

“Looking where others have looked, and seeing what others have not seen.”

Project Overview

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Model: Know-Howto Influence

Healthy Behavior in Food Choices

Identify, validate and quantify the

Market Landscape

Decode: Language,

Beliefs, Sensory and Emotional

Responses

Identify and Measure Genetic Markers for Food

Intake*

Innovate & Sustain Healthy

Behavior*

• Survey based on Phase 1behavioral determinants.

• Quantitative Analysis (Matrix) of health factors.

• Customer/geographySpecific Segmentation.

• Model for behavior shifts• Build substitute reward

mechanisms.• Involve Neuro-imaging,

Genetics measures

Phase 1: ACTIVInsights™Understand , Decode + Model

Phase 2: Matrix™Validate the Opportunity

Phase 3: Innovate & Sustain Change

• Identify key determinants.• 3 cohorts of consumers over5 geographies.

• Identify Risk-Reward tradeofffor change.

• Develop communication toolbox

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A targeted approach to thebrain-based behavior model of eating

Overweight:– Currently have a BMI that is between 30 and 40 and a waist size

that is categorized as unhealthy. In this category, a mix of people will be included that :

I. Have not made efforts to rectify their weight issue and II. Are YoYo Strugglers: life has been a series of cycles between

normal and not normal weight and currently in a not normal weight state

Successful Strugglers: – Life has been a series of cycles between normal and not normal

weight but currently in extended success cycle (at least 3 years, preferably 5 years)

Normal Weight: – Always maintained a normal, ideal weight ; weight is not a

problem.

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Quantitative StageConsumer Typology

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We will surface a set of consumer mind-frame segments differentiated by lifestyles, behaviors, tensions and beliefs which predispose them towards different choices and behaviors and leverage to refine or expand our health state groups.

Healthy living is really not that hard but am I missing out on something? Ideal self and actual self well aligned.

Lucky Metabolizers18%

Health Committed18%

Strugglers37%

Uncommitted27%

I know what to do to be healthier, I want to do it, but it rarely turns out well and ideal self and actual self are not aligned.

I eat what I want and don’t have any health problems….. For now. Ideal self and actual self well aligned.

They know what they need to do to live a healthier lifestyle, but really don’t see the point. Ideal self and current self are in synch even though health status is not optimal

Consumer Health Mind-frames

May be easiest to

target

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•Candy Bars

I Can Enjoy Myself

I Can Control What Goes Into My Body

I Can Get Convenience Through Processed Solutions

I Can Make A Sensible Choice

•Carbonated SoftDrinks

•Granola Bars

•Product

•Product

•Homemade Baked Goods

•Fresh Fruit

•Congee

•Vitamins &Supplements

•Fresh Prepared Meals

•Porridge/Oatmeal

•Product

•Product

•Product

•Product

•Product

Each bubble represents a benefits bundle valued by a large number of consumers in a large number Example of consumption occasions. Each represents a manufacturer path to more sensible options consumers will accept.

Health Alibis27%

Energize & Reviatlize

19%

Growth & Development

14%

Painlessly Sensible via Ingredient

Control21%

Micro-Size Me

19%

ExampleConsumers utilizing strategies

that make themselves feelbetter about what they eat but

do nothing real to deal with weight gain (organic, sugar instead of corn syrup, etc.)

ExampleConsumers seek energy as a pseudo-health benefit and are willing to accept less healthy ingredient profiles in return

ExampleGive me the foods I love – just slightly smaller portions – that

I can do!

ExampleIn developing markets, more

size and weight is a measure of success so fat IS a desirable

ideal self.

Quantitative Stage (cont’d)Behavioral segmentation and

Tailored Intervention

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Conclusion: No Silver Bullet nor PanaceaBiology, Context and Complexity Matters for Both Healthy

and Unhealthy Eating Behavior

• Understand WHY peoplebehave the way they do.

• Know HOW TO influencea specific behavior

– Awareness

– Preference

– Choice

– Purchase

– Consumption

– Adoption/Loyalty

– Habit

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Healthy Choice Can Be Made Easy for Children and Adults Alike

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Foundational Handbook, ElsevierBrain-to-Society Model of Eating Behavior and

Obesity Prevention

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Core BtS team, Trainees and Financial support

• BtS Team– McGill University: Alain Dagher (Medicine); Lesley Fellows (Medicine);

Reut Gruber (NeuroPsychology); Barbel Knauper (Health Psychology);;Louise Thibeault (Nutrition); Thomas Shultz (ComputationalPsychology); Doina Precup (Computer Science); Derek Ruth (ComputerScience); Jeroen Struben (Operations Management)

– Collaborators from other universities, institutes and organizations:Ross Hammond (Economist/Complex Systems, Brookings Institute,USA); Robert Levitan (Medicine, Douglas Hospital, Canada), Yu Ma(Marketing; University of Alberta, Canada); André Portella (Medicine,Universidade Federal do Rio grande do Sul, Brazil); Patricia Silveira(Medicine, Universidade Federal do Rio grande do Sul, Brazil)

• Trainees– Derek Chan, Aida Faber, Hajar Fatemi‐Shariatpanahi, Alice Labban,

Jordan LeBel, Ji Lu, Catherine Paquet, Uku Vanik

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Financial Support

• Peer reviewed research

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• 2005‐2009 Think Tanks

• ActivInsights project

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