Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Why People Drink Shampoo? Food Imitating Products Are Fooling Brains and Endangering Consumers for Marketing Purposes

  • Frédéric Basso ,

    Affiliations: Psychology@LSE, London School of Economics and Political Science, St Clements Building, London, United Kingdom, Graduate School of Management, University Rennes 1 & Center for Research in Economics and Management, UMR CNRS 6211, Rennes, France, Aix Marseille Université, CNRS, LPC UMR 7290, Cognitive Psychology Laboratory, Fédération de Recherche 3C, FR 3512, Case D, Marseille, France

  • Philippe Robert-Demontrond,

    Affiliation: Graduate School of Management, University Rennes 1 & Center for Research in Economics and Management, UMR CNRS 6211, Rennes, France

  • Maryvonne Hayek,

    Affiliation: Poison Control Centre, Hôpital Salvator, Marseille, France

  • Jean-Luc Anton,

    Affiliation: Centre d’IRM Fonctionnelle Cérébrale, Institut de Neurosciences de la Timone, UMR 7289, Aix-Marseille Université, CNRS, Marseille, France

  • Bruno Nazarian,

    Affiliation: Centre d’IRM Fonctionnelle Cérébrale, Institut de Neurosciences de la Timone, UMR 7289, Aix-Marseille Université, CNRS, Marseille, France

  • Muriel Roth,

    Affiliation: Centre d’IRM Fonctionnelle Cérébrale, Institut de Neurosciences de la Timone, UMR 7289, Aix-Marseille Université, CNRS, Marseille, France

  • Olivier Oullier

    Affiliation: Aix Marseille Université, CNRS, LPC UMR 7290, Cognitive Psychology Laboratory, Fédération de Recherche 3C, FR 3512, Case D, Marseille, France

Why People Drink Shampoo? Food Imitating Products Are Fooling Brains and Endangering Consumers for Marketing Purposes

  • Frédéric Basso, 
  • Philippe Robert-Demontrond, 
  • Maryvonne Hayek, 
  • Jean-Luc Anton, 
  • Bruno Nazarian, 
  • Muriel Roth, 
  • Olivier Oullier


A Food Imitating Product (FIP) is a household cleaner or a personal care product that exhibits food attributes in order to enrich consumption experience. As revealed by many cases worldwide, such a marketing strategy led to unintentional self-poisonings and deaths. FIPs therefore constitute a very serious health and public policy issue. To understand why FIPs are a threat, we first conducted a qualitative analysis on real-life cases of household cleaners and personal care products-related phone calls at a poison control center followed by a behavioral experiment. Unintentional self-poisoning in the home following the accidental ingestion of a hygiene product by a healthy adult is very likely to result from these products being packaged like foodstuffs. Our hypothesis is that FIPs are non-verbal food metaphors that could fool the brain of consumers. We therefore conducted a subsequent functional neuroimaging (fMRI) experiment that revealed how visual processing of FIPs leads to cortical taste inferences. Considered in the grounded cognition perspective, the results of our studies reveal that healthy adults can unintentionally categorize a personal care product as something edible when a food-like package is employed to market nonedible and/or dangerous products. Our methodology combining field (qualitative) and laboratory (behavioral and functional neuroimaging) findings could be of particular relevance for policy makers, as it can help screening products prior to their market release – e.g. the way they are packaged and how they can potentially confuse the mind of consumers – and therefore save lives.


In 2006, – a website edited by Consumers Union, an organization based in the United States – exposed that “some cleaners look like beverages” and led to numerous cases of poisoning in the home [1]. This was revealed by a study conducted at the Texas Poison Control Centre [2]. From January to April of 2006, a surprisingly high volume of calls reported an unusual series of accidents related to a specific household cleaner known as Fabuloso. As many as 94 unintentional ingestions occurred, 39 of which implied healthy adults. Unfortunately, these cases were not specific to the US market. In Europe, accidental ingestions (some with lethal outcomes) were also reported in 2011 [3]. The repeated cases led for this public health issue to be named and now to be referred to as Food Imitating Products (FIPs). FIPs are defined by article #1 of a legislative act of the European Union – European Council Directive 87/357/EEC [4] – as products that “[…] although not foodstuffs, possess a form, odour, colour, appearance, packaging, labeling, volume or size, such that it is likely that consumers, especially children, will confuse them with foodstuffs and in consequence place them in their mouths, or suck or ingest them, which might be dangerous and cause, for example, suffocation, poisoning, or the perforation or obstruction of the digestive tract[4].

Subsequently, in 2001, the General Product Safety Directive (2001/95/EC), a key legal framework for the European common market, launched the RAPEX, a rapid alert system for non-food consumer products [5]. The RAPEX enforces the withdrawal of all FIPs from the European market and to “restrict the marketing or use of consumer products posing a serious risk to the health and safety of consumers” ([6], p.12). Marketing, especially product marketing, seems to be a key factor in confusing consumers and leading not only for children to be poisoned [7] but to fooling healthy adults as well. Regarding Fabuloso – the floor cleaning product mentioned earlier that caused so many poisoning cases in Texas – physicians from the State of New York pointed that: “ultra-modern designs of Liquid Cleaner Agents [LCA] in order to make them more attractive […] may be a source of unintentional exposures in children and adults” ([8], p.745).

Product package is well known to contribute to the experiential aspects of consumption (fantasies, feelings and fun). The act of consuming is no longer to be reduced to a utilitarian perspective: consumption has become hedonic [9]. Regardless the risks for the health of the consumers, some product managers openly disclose in the press that people should no longer be ashamed to display hygiene products in their homes [10]. Anchoring product package in “food imaginary” is a differentiation strategy that intends to minimize the lack of commercial appeal of some products, the burden they represent (cleaning the house) and their potential danger [11]. This is everything but surprising in light of findings supporting strong links between food and subjective (hedonic) experience of consumption at the neural [12], conceptual [13], [14] and social levels [15].

In the present series of studies – conducted on the field and in the lab –, we propose to consider the food imaginary associated with the package of FIPs in the framework provided by the conceptual metaphor theory [16]. Indeed, “marketers contribute to the stock of marketplace metaphors through both packaging and advertising” ([17], p.232). The conceptual metaphor theory therefore seems to be a good candidate for the investigation of the rationale behind the marketing of FIPs. Moreover, this theory has proven useful to analyze and better understand public health related issues such as tobacco (e.g., [18]), medicalization [19], physician/patient relationship [20], epidemics [21], how packages are designed [22] and even current marketing strategies (e.g., [23][36]).

The essence of a metaphor is to experience one kind of thing in the terms of another [16]. In the case of FIPs, a (novel and non-verbal) metaphor such as: hygiene products are food [37], draws the attention of people purchasing household cleaners to an appealing and somewhat positive aspect of the product while masking their more negative and sometimes dangerous features. The experience of the source domain (food) is mapped onto the target domain (hygiene products) [38]. Thanks to the implicit (gustatory) inference at the core of a (food) metaphor [39], such a strategy intends to further the brain of the consumer from some attributes of the product that could not be commercially appealing. The concept of hygiene products, and the not so entertaining and hedonic baggage that comes with it at the aesthetics and behavioral levels (sweeping, cleaning, etc.), is intentionally masked by the manufacturers’ use of metaphors associated with food, the latter being way more appealing and pleasurable than hygiene products. Hence, in the case of FIPs, the consumer will “forget” how boring and unattractive the imaginary of a hygiene product can be as (s)he is manipulated to non-consciously focus on more exciting attributes associated with the pleasure of eating.

We therefore entertain the idea that metaphors – and their sensorimotor origin – guide behaviors: they could influence our experiences and our actions [16], leading to accidental poisoning in the home in the case of FIPs. Furthermore, one might be able to identify (the effects of) food metaphors at the cerebral level [39][41] because of the possible increased activity in gustatory cortices of the brain they might elicit. However, according to the epidemiologic host-agent-environment triad [42], [43], consumers’ self-poisonings could be related to individual (host) or contextual (environment) influences rather than solely to the appearance of the product per se (agent) and its marketing strategies.

Therefore, many questions regarding FIPs are still pending as they are under investigated worldwide, in spite of the European Council’s Directive 87/357/EEC [4] and cases like those reported in Texas [2] and in the State of New York [8]. The European Commission’s (EC) Scientific Committee on Consumer Safety (SCCS) points at the lack of (scientific) studies on FIPs in both lab and field settings recommending to utilize “the information collected by poison centers [that] are required in order to identify product groups associated with the highest risk of poisoning and to describe the circumstances that lead to oral intake” ([44], p.2).

This is why, to understand the rationale behind the dangerous marketing strategy that FIPs represent and the related risks for the health of consumers [7], we decided to start with a field study on real life cases of home poisoning. The goal of this first step was to identify some dangerous products that are available on the market. This was followed by laboratory experiments to test at the behavioral and brain levels the effects of some of the FIPs identified during the qualitative field study. To our knowledge this is the first time in consumer behavior studies that intra- and inter-individual multilevel research is conducted articulating qualitative (physician-patients conversations) and lab (behavioral and neuroimaging) studies and findings. By considering real products involved in home poisoning cases with real people, we differ clearly from most lab studies in consumer neuroscience and neuromarketing that very often test highly artificial products that often do not exist in real retail settings [45] but are always considered outside the host-agent-environment interactions [42], [43].

Our work introduces a new way to investigate public health issues by combining real life information sourced on a large-scale sample together with behavioral experiments and neuroscientific insights collected in the laboratory context. Our goal is to shed new light on the behavioral and neural underpinning of the confusion operating in the minds of consumers because of the marketing strategy behind FIPs. This research could help develop a new procedure to test FIPs prior to their launch on the market but could be used in many other domains, beyond the FIP context. In light of our previous work with national and supra-national governing bodies advocating for using behavioral and brain insights to inform policy [37], [46], our results and our procedure could therefore be of particular relevance in the public health policy sphere. For instance, it could be considered in the General Product Safety Directive [5] – currently under revision and expected to come into effect in 2015 [47] – and/or by the call for measure to protect consumers against FIPs expressed by a European online consumer education platform [48] and by associations for consumers’ protection (e.g. US Consumer Union in 2006 [1] and 2012 [49]).

Overview of the Studies

In order to test for potential gustatory inferences triggered by FIPs, we first conducted a qualitative field study on more than 30,000 phone calls made to the Marseille Poison Control Center over a period of 14 months [37]. It allowed to select a product (FIP) that used a food metaphor in its package and had been accidentally ingested by healthy individuals in real life, rather than picking up one independent of its purchase and/or consumption context as it is too often the case in consumer neuroscience and neuromarketing studies. The appetitive dimension of this product was first evaluated against other FIPs and non-FIPs at the behavioral level. Our main hypothesis was that implicit gustatory inferences – i.e. neural activations in gustatory cortices – will be observed when consumers look at metaphorical hygiene products (compared to non metaphorical ones). An experiment using functional magnetic resonance imaging (fMRI) was then conducted. Comparisons of gustatory inferences at the cortical level were made between a FIP, a non metaphorical accidentally ingested hygiene product, a fruit juice and a ‘classically shaped’ bottle of bleach (the last two serving as controls). As we were working on the neural correlates of processing visual food cues, prior to scanning brains, we made specific controls on participants (Body Mass Index (BMI), fasted time, food preferences), stimuli (valence and arousal) and neuroimaging data acquisition settings.

All together, these qualitative and laboratory studies are meant to provide new information regarding the non-conscious neural processes elicited by the FIP and how they could participate in the confusion occurring in the mind of the consumer, possibly leading to poisoning in the home.

Study 1: Qualitative Analysis of the Data Collected on the Field

The need for running our own qualitative study on medical cases is related to the lack of homogeneity and accuracy of existing medical data on FIPs. The qualitative analysis allowed to better investigate and understand the circumstances under which poisonings in the home by accidental ingestion of hygiene products occurred.

Funded by the European Chemical Industry Council (CEFIC), a large scale study was conducted by the DeNaMiC project (Description of the Nature of Accidental Misuse of Chemicals and Chemical Products) to determine the availability of information from poison control centers and other sources and to characterize the nature of accidental exposures to these products to inform and improve risk management [50]. This study revealed the lack of homogeneity of the data on poisonings related to household chemical products. As highlighted by a UK Health Protection Agency publication: “Published statistical data and literature analysis on the nature and frequency of incidents and events related to accidental exposures of chemical products could not be analyzed statistically due to the heterogeneity of the data. The information commonly reported varied dramatically due to a lack of a standard reporting format” ([51], p.43). Similarly, another study by the DeNaMiC project concluded that publicly available data on accidental exposures to chemical products provided little on the circumstances of exposure and could only be compared qualitatively [52]. It seems that location, use and storage of household chemical products by the consumers are not documented routinely in all poison control center databases [53].

Moreover, because of the turn over and renewal of products on the marketplace, and the extent to which the name of various products vary from one country to another, poison control center databases collect information on product categories (e.g., bleach, disinfectants, cosmetics, detergents, etc. [50]) rather than on products’ brands and names. However, this information is crucial when one wants to check whether a hygiene product accidentally ingested is a FIP, at the national and/or international levels, and, when possible, take the appropriate measures to withdraw it from the market to protect the lives of the consumers.

In order to improve both the homogeneity and the accuracy of the data on FIPs, we therefore conducted a qualitative study on naturally occurring talks [54] (physician-patient phone calls) selected from a poison control center database. The COREQ guidelines were followed for reporting this study [55].


Ethics statement.

All participants were informed that the phone call they placed to report the accident of interest was recorded. The written transcripts of the physician-patient phone calls were anonymized and their use in the study received the approval of local (Aix-Marseille Université Ethics Committee), regional (Comité de Protection des Personnes Sud Méditerranée 1) and national ethics and regulatory agencies (French Agency for the Safety of Health Products/Agence Française de Sécurité Sanitaire des Produits de Santé) – this was the case for all the studies reported in this article.

Participants and setting.

To select cases of poisoning related to our topic of interest (FIPs), we examined medical records received at the Marseille Poison Control Center (MPCC).

The MPCC is part of French Public Hospital System (Assistance Publique – Hôpitaux de Marseille, France). Phone calls placed to the MPCC therefore constitute medical cases per se for investigative epidemiology [56]. We were interested in cases that report accidental poisoning in the home when a healthy adult would ingest a hygiene product that was mistaken for a food beverage. For information purposes, when a call was placed by someone else than the patient – due to the fact that (s)he was unable to talk given that (s)he drank a household cleaner or a body care product – we only kept the case for further analysis if the caller were physically present with the patient (additional details are provided in the following sections on data collection and analyses).

Data collection.

Due to legal constraints, all phone calls placed to the MPCC 24-hour emergency phone unit were recorded and kept in a medical file. Data was then stored in the national database of products and composition (BNPC) thanks to the poison control center information system (SICAP). When searching this database, it is possible to find all the medical files related to one kind of poisoning and/or product. The medical files detail the patient’s name and surname, gender, age, weight, home address, phone number, medical history and medical treatment as well as the date, time, place and circumstances under which the poisoning occurred in addition to the incriminated product name and medical monitoring.

To select the poisoning cases that will undergo our qualitative analysis, a specific item – named “confusion between hygiene product and food” – was created and added to the MPCC checklist and database. The local medical staff was therefore asked to rate this item for each poisoning case meeting this criterion. Because of interpersonal differences among medical staff members in compliance with item rating requirement, a careful and detailed examination of each of the 31,283 medical records collected over the 14-month period at MPPC was also performed.

Data was first coded as a medical case by MPCC physicians and, subsequently, a detailed examination of each of the medical records was performed including listening again to the phone call recordings and re-coding the data (e.g. audio-to-text transcription), when necessary.

Given the call volume and our qualitative methodology, we decided to limit our selection of poisoning cases to (1) ingestion of (2) products that can be identified (brand and/or name) (3) occurring at home by (4) healthy (5) adults.

We first selected 515 medical cases in which adult patients ingested a hygiene product. Among these 515 medical cases, we kept the 477 related to healthy individuals. Among these 477 medical cases, 465 accidental ingestions took place at home. Finally, only 44 of these 465 medical records provided clear information of the ingested product (brand and/or name).

Our choices limit dramatically the number of calls to undergo the qualitative analysis but are justified by our willingness to avoid cases of misuse (e.g. spatter instead of ingestion or even cases where the content of a product is put in another package), occupational injuries (most of professional products are not aestheticized and are not of particular relevance to the usual consumer in his/her house), intentional poisoning (murder attempts, psychiatric or suicide cases, the latter being the first cause of caustic ingestions in adults [57]) and ingestions by children.

We concur with Kopjar and Wickizer who emphasized the interest of paying attention to “the problem of unintentional home injuries among adults and to the development of appropriate prevention strategies” ([58], p.403).

Notwithstanding, our decision to exclude cases involving children deserves a little more explanation, especially since they constitute a significant call volume and an important area of research [59], [60]. First, we were interested in naturally occurring talk about the reasons and circumstances why poisoning occurred. This is why we never phoned the callers and/or the patients back (physician-patient interactions were spontaneous, one-shot and no transcripts had been returned to patients for comments or correction). Second, given the Haddon matrix [61] applied to risk factors for children injuries [62], we know that these cases often take place in the absence of parental supervision and that children are unable -or unwilling- to communicate accurately about the accident (for example, some cannot even talk or some might lie to avoid upsetting their parents). Hence, even without considering the natural curiosity of children [63], the real circumstances why and how the poisoning occurred generally would be very likely to remain unknown. Then, if we bear in mind the specificity of any research involving children [64], we would like to suggest that if a metaphorized product can lead to adult unintentional poisonings, ipso facto it can lead to children poisonings as well. This is why, even if the ingestion of hygiene products by adults remains a small part of the call volume to poison control centers, it enables to understand what most exposed and poisoned people themselves, i.e. children, cannot describe.

Data analysis.

Our qualitative study is a content analysis of the naturally occurring talks collected both by FB (PhD) as a social scientist who was not affiliated with the poison control center and its staff and MH (MD) as the head of the MPCC. These two researchers had no relationship established with the participants (patients and callers). These naturally occurring talks have been transcribed by FB.

On the basis of the content analysis of these naturally occurring talks, we looked for 8 attributes listed in the first article of the European Council’s Directive 87/357/EEC [4] related to FIPs: form, odor, color, appearance, packaging, labeling, volume or size. Indeed, these criteria are very close to the specific elements that clearly contribute to the metaphoric meaning of a hygiene product that is available on the market (verbal prose, visual images, shape, color and scent) and were also identified by Hirschman [17]. Metaphors induce a specific way of thinking and acting towards a product when used as a marketing strategy. Applied to hygiene products, a food metaphor can lead to an action that is not cleaning or sweeping per se but ingesting a cleaner or a body care product. Something that, from a health (policy) perspective, is not acceptable.


The naturally occurring talks reported are the 20 explicit cases illustrating the effects of non-verbal food metaphor in accidental poisoning of healthy adults (see Table 1 and Table 2).

Table 1. General information about the poisoning cases analysed in the qualitative study.

Table 2. Excerpts of 20 illustrative poisoning cases recorded at the Marseille Poison Control Center (transcript from the MPCC audio recordings and French-to-English translation by the authors).

As reported in Table 3, all of these hygiene products met at least one of FIP criteria – to the notable exception of one call in which no information that could relate to these criteria whatsoever regarding the product was available (Soupline lavender #E).

Table 3. FIP criteria (as proposed by the European Commission) met by each of the products cited in the MPCC calls reported in previous tables.

Three main conclusions can be drawn from this qualitative analysis.

First, there are more liquid hygiene products confused with food (beverages) than solid ones. However, we have to bear in mind that this could be related to the choices we made by selecting only branded products, given that solid hygiene products (such as dishwasher or washing machine tablets) are not easy to name once they are out of their initial package.

Second, labeling, packaging and color are the visual dimensions that are cited the most by patients (or callers). But even if form, volume and/or size are rarely reported as confusing factors, they should not be neglected. Poisonings happen because products can be manipulated. For instance, a 5-liter bottle is not likely to be confused with a strawberry syrup bottle that usually has a capacity of 0.75 to 1 liter. In addition, there is little mention of odor in phone calls. If the smell of a product were unpleasant, consumers would not have drunk these hygiene products. By essence, a hygiene product is meant to smell good – but may be not ‘too good’ to avoid its ingestion.

Third, the attributes of products are probably related to these accidental poisonings as stated by patients (or callers). But, as emphasized in many public health studies, the role of contextual and personal factors should not be underestimated in an accidental exposure to a toxic agent [42], [43]. Studies in (consumer) psychology clearly show how contextual and personal goals can modify the way people categorize products [65]. This means that hygiene products could have been miscategorized as food or beverages not only because of their attributes but also because of the context and/or patients’ goals. Regarding the latter, findings from qualitative study reveal that hygiene products had been ingested while patients were looking for a drink or wanted to take some medicine. Hence, one cannot rule out the possibility that, in poisoning cases, FIPs neither prevented confusion, nor did they create it. Regarding the contextual factor, results remain somewhat unclear. Some of the FIPs listed in Tables 1, 2 and 3 have been ingested while they were stored out of their context of use (e.g., Tropic force #G, Skip Actigel #O) but some others have not (e.g., Comptoir de Famille tomato leaf soap #H, Apta berry vinegar #Q).

Taken together, these findings do not allow us to conclude that each of the FIPs selected are dangerous on their own. However, as raised by Norman: “If an error is possible, someone will make it” ([66], p.36). Or, as he also put it: “Most accidents are attributed to human error, but almost in all cases the human error was the direct result of poor design” ([66], p.viii). In this article we therefore adopt a system approach rather than a person approach of human error [67]. In order to show that the accidental ingestions of FIPs are not only related to the context and the goals of consumers, we propose to test whether some of the products identified in our qualitative study and involved in accidental poisoning could lead to a confusion in an experimental setting where these two factors (context and goals) are controlled.

Study 2: Behavioral and Functional Neuroimaging Experiments Conducted in the Laboratory

In light of the evidence and the limits of the qualitative study, we decided to test whether the food metaphor applied to a hygiene product could lead to implicit and automatic gustatory inferences when consumers are looking at such an item. Neuroimaging (functional magnetic resonance imaging; fMRI) was therefore used to estimate brain activity in this particular experimental context. fMRI turns to be of particular relevance when exploring non-conscious (implicit and automatic) processes in consumer research (or else) [68]. For instance, several recent studies have hinted at how neuroscientific insights can provide additional information that classical investigation techniques based on verbalization (surveys, questionnaires, focus group, etc.) cannot [69].

Given that 7 out of 8 EC criteria defining FIPs are visual in nature, we assumed the vision of the product in an experimental setting should be quite informative. A metaphor only makes sense because of the implicit and automatic embodied simulation it affords – also referred to as an embodied reenactement [70][72]. Grounded in the consumers’ bodily experience of food, these enacted inferences are embodied and have a sensorimotor component with neural counterparts [39][41], [73], [74]. In the context of FIPs, this means that we want to know whether a food metaphor applied to a hygiene product leads to increased activity in gustatory cortices, which, of course, is not what a hygiene product should do.

Our hypothesis is that consumers make implicit gustatory inferences (that they should not be making) when looking at hygiene products packaged like foodstuffs and that this should be revealed at the cerebral level in a network of brain activity repeatedly identified when people look at food.

Gustatory inferences are mental simulations. In grounded cognition, mental simulations are generally an automatic and implicit (non-conscious) perceptual reenactment [75] contrary to the simulation already explored in mental imagery (e.g., [76][79]). Hence, in the fMRI experiment, we used an experimental paradigm (a 1-back task) where food perceptual simulations (gustatory inferences) that might arise from the metaphorical hygiene product perception were not suggested by the task itself [80]. Moreover, that task ensured that participants allocated their attention to looking closely at the products displayed (e.g., [81]).

A landmark study, conducted in an embodied cognition perspective, showed that food knowledge is distributed across (a network of) modality-specific areas of the brain that participants rely upon when viewing pictures of foodstuffs [80]. The brain areas exhibiting the most significant increase in activity are the primary and the secondary gustatory cortices – the (right) insula/operculum and the (left) orbitofrontal cortex (OFC) – and the fusiform gyrus. Interestingly, this finding is confirmed by an Activation Likelihood Estimation (ALE) meta-analysis on the neural correlates of processing visual food cues [82]. We therefore decided to test whether activity in this brain network significantly increases when people look at a FIP but not when they look at a hygiene product that is not packaged like foodstuffs.

Over the past decade, how metaphors are processed in the brain has been addressed through the combined lens of cognitive linguistics and neuroscience (e.g., [83], [84]). It has been argued, that, given that metaphors can be considered as extra-linguistic knowledge, the cerebral activity associated with their processing and understanding should be localized in the right hemisphere of the brain (e.g. [85], [86]). Yet, many findings from studies on patients and healthy participants (e.g., [87][89]), using repetitive transcranial magnetic stimulation (rTMS) [90] or fMRI [91], [92], challenge what appears to be a simplistic view on how the brain would process metaphors (for discussions and experimental demonstrations of the limits of the left-brain versus right-brain hypotheses, see [93], [94]). In light of the inconsistent findings regarding the lateralization of metaphor processing in the brain (see [95]), recent neuroimaging studies have argued for a dependence on context [96] or task demands [97] not only in hemispheric recruitment when the human brain processes metaphors, but also in the cerebral networks underlying this task, as illustrated by a study revealing that textural metaphors activate the somatosensory cortex [98].

However, the key issue in the conceptual metaphor theory is not a matter of lateralization of brain activity but to suggest that any conceptual knowledge is a function of our embodied experience [40], [41]. This is supported by the claim, that there is no longer a theoretical clear-cut distinction between the sensorimotor system and conceptual knowledge [74]. This is why recent neuroimaging embodied cognition studies have shifted their focus towards the relations between perception, action and cognition (e.g., [80], [99][101]).

By hypothesizing FIPs are food metaphors, our neuroimaging study focuses on the brain processes at play when the concept of hygiene product is somewhat replaced by the one of food. This is why, in the case of FIPs being understood as non-verbal food metaphors, we decided to focus on studying the automatic and implicit reenactments of embodied food experience resulting from the mapping of one conceptual knowledge (food) onto another one (hygiene product).

Materials and Methods

Ethics statement.

All volunteers underwent a mandatory medical exam and gave their written informed consent prior to participating in the neuroimaging exam. The behavioral and functional neuroimaging research protocols received the approval of local (Aix-Marseille Université Ethics Committee), regional (Comité de Protection des Personnes Sud Méditerranée 1) and national (French Agency for the Safety of Health Products/Agence Française de Sécurité Sanitaire des Produits de Santé) ethics and regulatory agencies – as required by the French Bioethics Law following the established guidelines and procedure for running experiments on human participants in this country.


Fourteen healthy adult individuals (8 females and 6 males, age ranging from 20 to 46 years (M = 27.4±8.47; BMI = [18.81–24.48]) volunteered in the experiment which took place at the Marseille Functional MRI Center located in La Timone Hospital. They received a €50 monetary compensation for participating in the experiment. All but one were right-handed, had normal or corrected-to-normal visual acuity and no significant history of medical, psychiatric or neurological illness. Before entering the fMRI scanner, each participant confirmed (s)he fasted for at least 4 hours as required by the experimental protocol, then rated how (s)he liked different drinks (orange juice, apple juice, etc.) on a 7-item Likert scale and performed a brief pre-fMRI scanning task familiarization session. After the fMRI session (i.e. outside the scanner), given the limits of declarative hunger ratings (e.g., [81], [102], [103]) each participant was offered to eat as a behavioral control for hunger. Participants were then debriefed to ensure they identified and categorized visually each of the stimuli properly [104] and that the metaphorical FIP and foodstuff were judged as more appealing than the two other hygiene products.

Stimuli selection and preparation.

Among the hygiene products selected from our qualitative study, 2 FIPs were of particular interest: a shower gel, the Cottage Happy Shower Tequila Sunrise (#F), and a dish liquid, the Visior sweet almond (#A).

The first one, the Cottage Happy Shower, is interesting on several accounts. First, this shower gel was accidentally ingested at home by a 41-year old healthy woman because of food attributes that meet 6 out of the 8 European Council Directive FIP criteria [4]. Its metaphorical content fits with the elements listed by Hirschman [17]. Visual images are oranges, the shape looks like a bottle of orange juice, the background color of the package is green and it smells like an orange flavor. The verbal prose that can be read on the Cottage Happy Shower bottle is explicit: “Stimulating orange to recharge your batteries… An amazing tequila sunrise scent to get you in the mood. When you wake up in the morning, or before you go in the evening, let the good vibrations take over with Happy Shower and… Feel Good !!”. Second, the Cottage Happy Shower bottle cap is a push-pull one. As the DeNaMiC research project revealed, through the analysis of 457 accidentally ingested products in 5 different European countries: similar push-pull-closures that are used for sports drinks and for liquid chemical products seem to be responsible for many accidents [105]. Third, under the RAPEX system, the authorities ordered the withdrawal of the first version of the Cottage Happy Shower from the European market seven months before this French case occurred [106].

The second product, the Visior (sweet almond), was accidentally ingested by a 69 year-old healthy woman. Instead of taking a bottle of mint syrup, she took the dish liquid bottle. Except for the color and the labeling (and, to some extent, the odor), the consumer gave no FIP Directive [4] criteria or metaphoric content element. The accident is principally due to the context of the hygiene product: the dish liquid and the mint syrup bottles were next to each other under the sink.

We selected these two hygiene products not only because of the circumstances under which they had been accidentally ingested but also because of the way they were rated on valence and arousal in an ancillary behavioral experiment conducted prior to the neuroimaging one, as recommended for a visual food perception fMRI protocol (e.g., [81]) (see Text S1 for details). Indeed valence and arousal are considered as “motivational vectors that indicate the degree to which stimuli engage the brain’s motive systems, appetitive and defensive” ([107], p.277).

The results of this ancillary behavioral experiment revealed that Cottage Happy Shower was rated as the more appetitive of the 8 FIPs used as stimuli (MPleasure = 5.92, SD = 1.75; MArousal = 5.97, SD = 2.19) and Visior the more neutral of the FIPs (MPleasure = 4.00, SD = 1.44; MArousal = 3.14, SD = 1.50). These two hygiene products were therefore selected for the fMRI experiment. We also selected a Tetra Pak fruit juice, the Joker, rated as appetitive (MPleasure = 6.50, SD = 1.36; MArousal = 5.93, SD = 1.98) and a La Croix bottle of bleach, referred to as Bleach, rated neutral (MPleasure = 3.03, SD = 1.66; MArousal = 2.63, SD = 1.51) as controls for the neuroimaging experiment.

Each one of these 4 products – like all other stimuli – was correctly categorized by the 52 participants during the debriefing of the ancillary experiment. Cottage Happy Shower belongs in the body care product category, Visior in the dish liquid one, Joker in the soft drinks one and bleach in the household cleaners one.

Our main hypothesis in the laboratory part of our work is that FIPs elicit non-conscious gustatory inferences in the brain of consumers. One of our regions of interest (ROIs) was the orbitofrontal cortex (OFC) known to be – among other things – the secondary gustatory cortex [12]. Given this region is very close to brain areas found to be participating in brand preferences on drinks (e.g., [108]), we standardized brand name on our stimuli using the term “Fabuloso”, after the product accidentally ingested in Texas and in the State of New York mentioned in the introduction of this article, given this brand name is unknown in France where our studies were conducted. We also removed information about the volume and the composition of the products to control for additional features that could have distracted the consumer but were not crucial for the purpose of this study (see Figure 1).

Figure 1. Visual stimuli in the fMRI experiment. (a) Standardized Cottage Happy Shower Tequila Sunrise (b) Standardized Joker fruit juice (c) Standardized Visior (d) Standardized bleach.

(a–d) Stimuli were presented branded with the name Fabuloso but cannot be depicted as such in the article here due to the creative common license of PLOS ONE. Please follow this link for additional details on our stimuli and FIPs:

Four versions of each product were made by a horizontal inversion of half or the totality of the product label. All 16 stimuli were presented against a black background to minimize environmental cues [109] and improving visual contrast. Different product shapes were matched for proportionality [110] and pictures were modified for consistent size and resolution and to match the highest resolution (800×600 pixels) provided by the MRI compatible display setting available at the Marseille fMRI Center.

Task and fMRI block design.

This experimental paradigm is a 1-back task in which participants were asked to monitor the identity of two consecutive stimuli and to indicate whether the currently presented stimulus is the same (or not) as the one presented during the previous trial. The attention of the participants is therefore focused on the stimuli. Hence possible brain activations related to gustatory inferences would be implicit to the visual discrimination task as it happens to be repeated in all conditions. The cerebral activity related to such a task is therefore theoretically controlled when contrasting our experimental conditions.

Four experimental conditions represent different product categories: a FIP that has been accidentally ingested mainly because of its package (Cottage Happy Shower), a FIP that has been accidentally ingested mainly because of its context (Visior), a soft drink (Joker) and a household cleaner (Bleach). Each condition is composed by the four different standardized visual versions of each product: e.g., the Cottage Happy Shower condition is composed of four versions of the Cottage Happy Shower product made by a horizontal inversion of half or the totality of the Cottage Happy Shower label. Participants performed the 1-back task on these versions of the Cottage Happy Shower product as stimuli within the Cottage Happy Shower condition.

The whole functional session was divided into 3 functional runs randomized and counterbalanced across participants. Within each functional run, there was a total of 36 blocks of stimuli (9 blocks per condition, 8 stimuli per block). The interblock interval was 2000 ms (Figure 2). Within each block, stimulus presentation time was 2000 ms (with an interstimulus interval (ISI) of 200 ms) during which participants performed the 1-back task. Within each functional run, 63 responses were expected in each condition and, ideally, participants had to find 16 identical cases given that 25% of consecutive images within each functional run were the same.

Figure 2. Block design of the fMRI experiment.

A functional neuromaging session was divided in three functional runs randomized and counterbalanced across participants. Within each functional run, there was a total of 36 blocks of stimuli (9 blocks per condition, each of which contained 8 images). The interblock interval was 2000 ms. Within each block, stimulus presentation time was 2000 ms (with an interstimulus interval (ISI) of 200 ms) during which participants performed the 1-back task.

Data acquisition and preprocessing.

Functional neuroimaging data was acquired on a 3-Tesla BRUKER MEDSPEC 30/80 functional MRI scanner equipped with a circular polarized head coil. Whole brain anatomical MRI data was acquired using high-resolution structural T1-weighted image (MPRAGE sequence, resolution 1×1×1 mm) in the sagittal plane prior to the functional runs. A fieldmap acquisition (3D FLASH sequence inter-echo time 4.552 ms) was collected in order to estimate and correct the B0 inhomogeneity.

For functional neuroimaging, OFC being one of our region of interest, functional slices acquisition was axial oblique, angled −30° relative to AC-PC plane (e.g., [111]), in order to limit frontal distortions and to cover cortical and subcortical areas. This setting prevented us from collecting data at the cerebellar level. A T2*-weighted echo planar sequence was used with 30 interleaved 3 mm-thick/1 mm-gap slices (repetition time = 2000 ms, echo time = 30 ms, flip angle = 78,4°, field of view = 192 mm, 64×64 matrix of 3×3×3 mm voxels). Each participant performed 3 functional MRI runs. For each run, 357 functional volumes were acquired. The whole “in-scanner” part of the experiment lasted for about one hour (anatomical MRI and fieldmap acquisition included).

Six dummy scans in each run were discarded in order to ensure that the longitudinal relaxation time equilibration was achieved. Data was pre-processed and analyzed using SPM8 (Wellcome Department of Cognitive Neurology, London, UK). First, processing consisted in including the voxel displacement map computed using the fieldmap toolbox during the realign and unwarp procedure for distortion and motion correction. Second, the high-resolution structural T1-weighted image was coregistered to the mean EPI image. Third, all MRI volumes were processed with SPM8's New Segment option to generate grey matter (GM) and white matter (WM) images of the participants. Fourth, a DARTEL template was generated and (affine-only) spatial normalized to MNI space. Fifth, that DARTEL template was used to normalize functional data of each participant. Last, each participant’s normalized functional data was spatially smoothed using a 8 mm full-width at half isotropic Gaussian kernel.

Four regressors (one for each experimental condition: Cottage Happy Shower, Joker, Visior and Bleach) were modeled using a 2-second box-car waveform convolved with the canonical hemodynamic response function (HRF). To account for inter-subject variability in the group analysis, the contrast images obtained during the first level analysis were included in a second level t-test in order to conduct a random effects analysis at group level. A statistical significance threshold of p<.001 (uncorrected for multiple comparisons) and a spatial extent threshold of at least 3 contiguous voxels was used in the random effects analyses. Images resulting from these analyses were inclusively masked using a priori regions of interest defined by the WFU Pickatlas software [112] and chosen after the functional neuroimaging literature on visual food processing [82]. These regions included the fusiform gyrus, the rolandic operculum, the insula and the frontal lobe’s lateral (dorsolateral superior frontal gyrus, middle frontal gyrus, opercular part inferior frontal gyrus, triangular part inferior frontal gyrus) and orbital surfaces (orbital part superior frontal gyrus, medial orbital superior frontal gyrus, orbital part middle frontal gyrus, orbital part inferior frontal gyrus). Regions of interest were anatomically defined using the automated anatomical labeling (AAL) software [113] and have been 1-voxel dilated by using the 2D dilation function. The anatomical localization of the activations was provided by the MNI stereotaxic space and the neuroanatomical labeling of significant foci performed using AAL.

Given that the Cottage Happy Shower had been ingested because of a confusion with orange juice and that we are looking for implicit gustatory inferences triggered by this product, behavioral data on orange juice preferences for each participant collected before the fMRI experiment were used in the SPM8 random-effects multiple regression analysis on the contrast fMRI images in the processing of the Cottage Happy Shower condition. A statistical significance threshold of p<.001 (uncorrected for multiple comparisons) and a spatial extent threshold of at least 3 contiguous voxels was used in this multiple regression analysis within the defined ROIs. We controlled that any activity in the gustatory cortices in response to seeing Cottage Happy Shower was not related to explicit orange juice preferences by statistically regressing the participants’ orange juice preferences on BOLD activity. Indeed, as the hypothesized gustatory inferences are automatic and implicit when participants perform the 1-back task, it seems to us that these are mainly related to a categorization process rather than to participants’ preferences.


Behavioral results.

The analysis of the behavioral dataset of the fMRI experiment (answers and reaction time inside scanner) was performed using SPSS (Version 17, SPSS Inc., Chicago, Illinois, USA). Given that we were interested in automatic and implicit gustatory inferences at the cerebral level [80], we tested that the 1-back task was performed similarly throughout the experimental session. The performances of participants in the 1-back task were therefore rated as percentages of inaccurate answer given in each condition. An inaccurate answer was defined as an unseen difference between two consecutive stimuli. Reaction times were relative to inaccurate answers. Comparisons were performed using the non-parametric Friedman test. When a significant statistical difference was found, the Wilcoxon signed-rank test (Bonferroni corrected for multiple comparisons) was employed.

For reaction times, there was no significant difference between the conditions across the sessions (N = 10; Chi-square = 1.971; d.f. = 3; p = .578; N<14 is due to the fact that 4 participants made 0 errors and therefore there were no reaction times whatsoever in their performance). A significant difference in accuracy of the answers across conditions was found in the 1-back task (N = 14; Chi-square = 12.429; d.f. = 3; p<.05). Given the Wilcoxon signed-rank test Bonferroni corrected for multiple comparisons (p<.0083), results indicated there was no significant statistical difference between the Cottage Happy Shower, Joker and Visior conditions – Bleach vs Cottage Happy Shower (Z = −2.731; p = .006) and Bleach vs Joker (Z = −2.668; p = .008). We will therefore only comment the neuroimaging results relative to these conditions.

The behavioral results following the fMRI session (i.e. outside the scanner) reveal that each participant had well identified and categorized the different stimuli (Joker as a fruit juice, Cottage Happy Shower as a shower gel, Visior and Bleach as household cleaners) and ate (therefore, confirming (s)he was hungry). Overall, it was found that Joker and Cottage Happy Shower were judged as more appealing than Visior and Bleach.

Neuroimaging results.

When comparing Cottage Happy Shower to Visior, we found a significant increase in activity in the insular cortex, the OFC and the fusiform gyrus (Figure 3). However, the Joker vs Visior contrasts revealed a significant increase in the activity of the the fusiform gyrus only at the chosen statistical threshold. The Visior related contrasts (Visior vs Cottage Happy Shower and Visior vs Joker) revealed no significant difference in brain activity (no suprathreshold clusters).

Figure 3. Main neuroimaging results.

(a) Left orbitofrontal cortex activations (x = −24, y = 51, z = −3). (b) Left insular activations (x = −30, y = 18, z = −3). Statistical parametric maps of the Cottage Happy Shower vs Visior contrast (sagittal (x), coronal (y) and axial (z) views) (p<.001, uncorrected, cluster size >3 contiguous voxels) displaying the brain activity that is significantly higher when participants look at the Cottage Happy Shower compared to when they look at the Visior.

Detailed brain regions obtained by a random effect model showing significant activations (p<.001, uncorrected, cluster size >3 contiguous voxels) and labeled using AAL for the Cottage Happy Shower vs Visior, Joker vs Visior, Visior vs Cottage Happy Shower and Visior vs Joker contrasts are reported in Table 4 (see also Table S1 and Text S2 reporting the results of a whole brain analysis, i.e. significant activations (cluster size >3 contiguous voxels) surpassing a threshold of p<.001, uncorrected; the settings of acquisition of fMRI data did not allow to collect data in the cerebellum).

Table 4. Brain regions obtained by a random effect model showing significant activations (p<.001, uncorrected, cluster size>3 contiguous voxels) and labeled using AAL for the Cottage Happy Shower vs Visior, Joker vs Visior, Visior vs Cottage Happy Shower and Visior vs Joker contrasts (x, y and z refer to spatial coordinates in the MNI space).

The multiple regression analyses conducted on the contrast images of the Cottage Happy Shower vs Visior comparison revealed a significant relation between ratings of orange juice and BOLD activity only in the fusiform gyrus. Orange juice preferences were positively associated with increased activity in the bilateral fusiform gyrus (x = 42, y = −15, z = −24; T = 4.95, p<.001; x = −36, y = −6, z = −30, T = 4.31, p<.001) in the Cottage Happy Shower vs Visior contrast. No significant activation (no suprathreshold clusters) is negatively correlated on this contrast image.


The home poisoning and death cases reported earlier illustrate that FIPs have been found to be a specific category of mass consumption goods bearing potential danger for the health of the consumers [1], [49]. When asked, people explicitly mention that they would not drink a FIP intentionally, hence, cases of poisoning in the home as accidental poisonings are likely to be behavioral errors [37]. However, one cannot solely blame the consumer for making a mistake when the packaging of the product is the result of a highly elaborated and deliberate strategy aiming at using the visual codes of one category of product (foodstuffs) to enhance the consumer experience in another: the not so appealing hygiene and cleaning products. This does not mean at all that other factors but FIPs per se are not playing a role when people drink them. For instance, even if the danger FIPs represent is now undisputed in the public health sphere, in spite of most consumers not being aware of it because of little media coverage, several other factors could participate in the poisoning in the home, such as the state of mind of the consumer (e.g., distraction, stress, etc.) or the physical properties of the environment in which the poisoning is taking place. In other words, human and contextual factors are also at play and, to date, there is an obvious lack of scientific evidence that would help public authorities take firm(er) measures against FIPs. This is where our methodology and design, involving a combination of qualitative field study and quantitative laboratory experiment at several levels of analysis ranging from behavior to neurobiology can help. In a setting where the context and what the person does is controlled, and even when people are informed and aware that what they are going to see is not to be drunk, non-conscious taste inferences occur in their brains when they see a FIP. People would never explicitly say they would drink FIPs yet, implicitly, their brains are somewhat facilitating the drinking process as a confusion occurs because of a dangerous marketing strategy: packaging hygiene and cleansers as food products.

Following Loken et al. [114], we tested, in a consumer psychology perspective, the idea that FIP representations reside in modality-specific systems of the brain. We also considered real products involved in real life poisoning cases of real people. In doing so, we are in line with the grounded cognition view that “laboratory paradigms could be more oriented toward explaining real-world phenomena” ([115], p.324). Regarding the latter, our fMRI experiment was therefore designed to test data originating from both a field and a laboratory study. This is a clear departure from neuromarketing studies where, to date, products are, at best, tested before being launched on markets or, as it is the case in many studies, tested as such with little if no connection whatsoever to what people do with the product in real life (e.g., [116][119]).

As previously reported in several studies (e.g., [120], study 4, [121]), the design of our fMRI experiment aimed at a better understanding of cognitive mechanisms behind a specific consumer behavior. We appreciate whether a metaphor transfers experientially remote manipulations of bodily source concepts [122]. We suggest that food inferences elicited by the metaphor conveyed by the FIPs are mapped onto the target domain of hygiene products. In other words, the consequence of a food metaphor applied to a hygiene product is that implicit gustatory inferences can be found in the brain of the consumers. Such inferences most certainly participate in the accidental ingestion of a hygiene product.

As expected from the conceptual metaphor theory and the grounded cognition perspective, the Cottage Happy Shower versus Visior contrast revealed increased neural activations when looking at the former in all of our three regions of interest – insular cortex, OFC and fusiform gyrus. These areas have been repeatedly identified in the literature on visual food processing – reinforcing our belief that participants did implicit gustatory inferences while they were viewing this FIP, and the non-verbal metaphor it conveys. Moreover, except for the increased bilateral activity found in the fusiform gyrus, probably related to the emotional salience of the Cottage Happy Shower [123], we think these activations are not linked to the explicit preference participants exhibited for orange juice – as revealed in the post-experiment debriefing during which they identified the former product as a shower gel.

Because of a difference in the complexity of the Bleach stimulus revealed by pre-fMRI testing and given that we work on real world stimuli, in line with our behavioral responses inside the fMRI scanner, we only studied the Joker fruit juice versus Visior (FIP) contrast. The Joker fruit juice, when contrasted with the Visior revealed no significant increase of activity in the cerebral regions of interest we focused on, probably because of the lack of graspability of a product under that looks like a Tetra Pak package (see Text S2 for a discussion on the whole brain fMRI analyses).

Such a result leads us to the methodological concern that can be raised about our study, starting with the reverse inferences employed in interpreting the Cottage Happy Shower versus Visior contrast. There are reverse inferences when the engagement of a particular cognitive process is inferred from the activation of a particular brain region [124]. In our study, without a similar pattern of activations for the Cottage Happy Shower and the Joker on the same contrast, we inferred the existence of gustatory inferences from the co-activation of the insular cortex, OFC and fusiform gyrus. With no gustatory inferences when looking at the Joker, it is difficult to ensure that, in the sample population that participated in our fMRI experiment, the neural correlates of visual food cue processing are identical to the ones found in other studies relying on the same functional neuroimaging technique [80][82].

Moreover, even if the results of the Activation Likelihood Estimation (ALE) meta-analysis on the neural correlates of processing visual food cues [82] when contrasting food versus non food stimuli are very close to the brain's modality specific areas that Simmons et al. [80] found for food knowledge in the same kind of contrast images, only about 33% of the experiments included in the meta-analysis led to activations within the insular cortex, left lateral OFC and fusiform gyrus.

We nevertheless kept a bilateral activation hypothesis on these three regions of interest. First, because the meta-analysis is not only focused on studies with an implicit task such as Santel et al. [123] and Uher et al.’s [125] which could explain some activation differences. Second, in the Simmons et al. [80] study, lowering the cluster size threshold in their random effects analysis revealed a significant bilateral activity in the frontal operculum and in the OFC. Third, in the food versus non-food contrast image in Frank et al.’s ([81], supplemental table 3), 1-back task design revealed a significant bilateral activity in both the insular cortex and the lateral OFC. Regarding the latter, we do not follow the idea of a left lateralization of pleasantness in the OFC that was previously reported. For instance, in the Beaver et al. ([104], supplemental table 1) study, neural responses to images of appetizing food are localized in the left lateral OFC whereas the ones to images of disgusting stimuli are in the right lateral OFC. However, in the Schienle et al. ([126], table 2, supplemental table 2) study, there were bilateral OFC activations for pleasantness of high-caloric food as for disgust-inducing pictures.

We argue that the reverse inference methodological limitation is mitigated for the two following reasons.

First, even if a small sample size undermines the reliability of neuroimaging studies [127], our experimental design is similar to two other fMRI studies [80], [81], in which a random effects analysis can be found and allows for inferences to be made about such a population. Second, the fact the Cottage Happy Shower has been accidentally ingested by healthy adult consumers, withdrawn from the European market under the RAPEX procedure and rated on valence and arousal with an appetitive motivational dimension in our behavioral experiment, allows us to assume this product can lead to activations in gustatory cortices.

This calls for one more comment. The Cottage Happy Shower was a new product when it was accidentally ingested. And new products outweigh the categorizing difficulties [128], [129] that could explain poisoning (as miscategorization). Notwithstanding, the novelty of a hygiene product does not necessarily imply for it to look like food! Moreover, to our knowledge, all new hygiene products launched on the market are not ingested. Any new product can increase the risk of ingestion but its sole novelty is not sufficient to provoke the poisoning, the use of food metaphors is the key factor leading to such a consequence.


The use of food metaphor in marketing strategies to improve the sales of hygiene product constitutes a serious health problem. One of the goals of the metaphoric content of a product package is to suggest the experience of product consumption (hygiene) in light of another experience (food). In this study, we built on a field study together with behavioral insights to design a final neuroimaging experiment aiming at investigating how (metaphoric) marketing strategies that led to FIPs induce confusion in the mind of the consumers. Here it happens to be the voluntarily evoked experience of food consumption for marketing purposes that leads people to drink shampoo. In other cases, it can be games [130], [131] such as relying on the so-called child-appealing products [3] inherited from toy packaging and metaphors (hygiene product is a toy) meant to associate washing dishes as something easy and fun (e.g., dishwashing is a piece of cake). Similarly to our study on FIPs, we strongly encourage public authorities to support scientific studies on this kind of novel metaphors.

Over the past 15 years, we have witnessed an increasing interest and use of neuroimaging techniques in the private sector to better understand consumer behavior [116]. More recently, several governments and governing bodies have been considering how behavioral and brain insights could inform policy making, when possible, particularly in health-related issues (e.g., [132]) and corporation-induced diseases [133] such as tobacco [134], obesity [135] and, in the following case, poisonings [136].

Our combination of a qualitative field study together with behavioral and neuroimaging data collected in a laboratory setting constitutes a potential useful benchmark for psychology and neuroscience to be used at the experimental level to inform health and many other domains of policy making [46]. We therefore believe that this work illustrates how “ideas emerging in neuroscience [that] could potentially be a richer language for talking about cases like accidental child poisoning and, more broadly, about welfare and paternalism in some limited cases” ([137], p.87). But, in our view, this implies to start from real cases in people’s real lives, to use multiple perspectives and levels of analysis to have a first qualitative analysis of these behaviors, then to design behavioral experiments that will allow to assess particular features. Finally, if some information cannot be revealed by the behavioral testing, relying on functional neuroimaging could lead to useful insights – without the use of brain sciences being mandatory as many private neuromarketing practices tend to promote [138].

Although we think our methodology constitutes an interesting benchmark for the use of behavioral and brain insights to inform policy making [46], we are well aware that it only covers one half of the process. For instance, providing qualitative data from real life health cases to narrow down the issue and identifying information only a functional MRI experiment can provide is without a doubt useful. But the other half of the process will require to use the ensemble of information and findings our results revealed in order to develop innovative and durable solutions to improve people’s health and well-being by implementing them. Then only would behaviorally and neuroscientifically evidence-informed policy make sense.

Supporting Information

Figure S1.

Pictures of FIPs.



Table S1.

Additional fMRI results – Spatial localization of significant brain activity (whole brain analysis).



Text S1.

Pre-selection of FIPs – Ancillary behavioral experiment conducted prior to functional neuroimaging.



Text S2.

Additional fMRI results - Whole brain analysis.




The authors are grateful to Luc de Haro, Rym Djouri, Pascal Huguet, Olivia Petit and Hilke Plassmann for their helpful comments and active support of the studies reported in this article. This research is part FB's PhD research under the supervision of PRD and OO. Funding was provided by the French Ministry of Research (FB’s full fellowship), Aix-Marseille University and the Centre National de la Recherche Scientifique (OO) and the Center for Research in Economics and Management, UMR CNRS 6211 (PRD).

Author Contributions

Conceived and designed the experiments: FB JLA BN OO. Performed the experiments: FB MH BN OO. Analyzed the data: FB JLA MR. Contributed reagents/materials/analysis tools: FB PRD MH OO. Wrote the paper: FB PRD OO.


  1. 1. (2006) Consumer reports on safety, Fabuloso’s better bottle - thanks to you. Available:
  2. 2. Miller M, Levsky M, Masneri D, Borys D (2006) FABULOSO: A cleaning product that tastes and smells good enough to drink. Ann Emerg Med 48: 81–81. doi: 10.1542/peds.2006-1299
  3. 3. Scientific Committee on Consumer Safety (2011) Opinion on the potential health risks posed by chemical consumer products resembling food and/or having child-appealing properties. European Union: Directorate General for Health and Consumers.
  4. 4. Council Directive 87/357/EEC of 25 June 1987 (1987). Off J 192 11071987.
  5. 5. Directive 2001/95/EC of the European Parliament and the Council of 3 December 2001 on general product safety (2001). Off J L11 15012002.
  6. 6. Directorate General for Health and Consumers (2008). Keeping European consumers safe - Annual report on the operation of the rapid alert system for non-food consumer products RAPEX 2007. Luxembourg: Office for Official Publications of the European Communities.
  7. 7. Schneider KC (1977) Prevention of accidental poisoning through package and label design. J Consum Res 4: 67–74. doi: 10.1086/208681
  8. 8. Caraccio T, Carbain A, Daly A, Dahm D, Iovino B, et al. (2006) Designs of Liquid Cleaning Agents (LCA) resembling Food Supplement Drinks (FSD) may lead to unintentional exposures. Clin Toxicol 44: 745.
  9. 9. Holbrook MB, Hirschman EC (1982) The experiential aspects of consumption: Consumer fantasies, feelings, and fun. J Consum Res 9: 132–140. doi: 10.1086/208906
  10. 10. Cristofari J-F (2008) Packaging et produits d’entretien font bon ménage. Mark Mag: 61–66.
  11. 11. Martens L, Scott S (2006) Under the kitchen surface: Domestic products and conflicting constructions of home. Home Cult 3: 39–62. doi: 10.2752/174063106778053255
  12. 12. Kringelbach ML (2005) The human orbitofrontal cortex: Linking reward to hedonic experience. Nat Rev Neurosci 6: 691–702. doi: 10.1038/nrn1747
  13. 13. Fischler C (1988) Food, self and identity. Soc Sci Inf 27: 275–292. doi: 10.1177/053901888027002005
  14. 14. Ross BH, Murphy GL (1999) Food for thought: Cross-classification and category organization in a complex real-world domain. Cognit Psychol 38: 495–553. doi: 10.1006/cogp.1998.0712
  15. 15. Douglas M (2002) Purity and danger: an analysis of concept of pollution and taboo. New York: Routledge.
  16. 16. Lakoff G, Johnson M (1980) Metaphors we live by. Chicago: University of Chicago Press.
  17. 17. Hirschman EC (2007) Metaphor in the marketplace. Mark Theory 7: 227–248. doi: 10.1177/1470593107080343
  18. 18. Basso F, Oullier O (2011) Smokers are suckers: Should incongruous metaphors be used in public health prevention? Am J Public Health 101: 203–204. doi: 10.2105/ajph.2010.197996
  19. 19. Coveney CM, Nerlich B, Martin P (2009) Modafinil in the media: metaphors, medicalisation and the body. Soc Sci Med 68: 487–495. doi: 10.1016/j.socscimed.2008.11.016
  20. 20. Rees CE, Knight LV, Wilkinson CE (2007) Doctors being up there and we being down here: A metaphorical analysis of talk about student/doctor-patient relationships. Soc Sci Med 65: 725–737. doi: 10.1016/j.socscimed.2007.03.044
  21. 21. Wallis P, Nerlich B (2005) Disease metaphors in new epidemics: The UK media framing of the 2003 SARS epidemic. Soc Sci Med 60: 2629–2639. doi: 10.1016/j.socscimed.2004.11.031
  22. 22. Rompay T van, Hekkert P, Saakes D, Russo B (2005) Grounding abstract object characteristics in embodied interactions. Acta Psychol 119: 315–351. doi: 10.1016/j.actpsy.2005.02.001
  23. 23. Bremer K, Lee M (1997) Metaphors in marketing: Review and implications for marketers. Adv Consum Res 24: 419–424.
  24. 24. Cornelissen JP (2003) Metaphor as a method in the domain of marketing. Psychol Mark 20: 209–225. doi: 10.1002/mar.10068
  25. 25. Cotte J, Ratneshwar S, Mick DG (2004) The times of their lives: Phenomenological and metaphorical characteristics of consumer timestyles. J Consum Res 31: 333–345. doi: 10.1086/422112
  26. 26. Coulter RA, Chowdhury TG (2006) Sensory metaphor and meanings: development of a cross-sensory heterogeneity index. Adv Consum Res 33: 453–454.
  27. 27. Forceville C (2007) Multimodal metaphor in ten Dutch TV commercials. Public J Semiot 1: 15–34.
  28. 28. Joy A, Sherry Jr JF (2003) Speaking of art as embodied imagination: A multisensory approach to understanding aesthetic experience. J Consum Res 30: 259–282. doi: 10.1086/376802
  29. 29. Joy A, Sherry Jr J, Venkatesh A, Deschenes J (2009) Perceiving images and telling tales: A visual and verbal analysis of the meaning of the internet. J Consum Psychol 19: 556–566. doi: 10.1016/j.jcps.2009.05.013
  30. 30. Joy A, Sherry JF, Deschenes J (2009) Conceptual blending in advertising. J Bus Res 62: 39–49. doi: 10.1016/j.jbusres.2007.11.015
  31. 31. Phillips BJ, McQuarrie EF (2009) Impact of advertising metaphor on consumer belief. J Advert 38: 46–61. doi: 10.2753/joa0091-3367380104
  32. 32. Spiggle S (1994) Analysis and interpretation of qualitative data in consumer research. J Consum Res 21: 491–503. doi: 10.1086/209413
  33. 33. Thompson CJ, Pollio HR, Locander WB (1994) The spoken and the unspoken: A hermeneutic approach to understanding the cultural viewpoints that underlie consumers’ expressed meanings. J Consum Res 21: 432–452. doi: 10.1086/209409
  34. 34. Thompson CJ (1997) Interpreting consumers: A hermeneutical framework for deriving marketing insights from the texts of consumers’ consumption stories. J Mark Res 34: 438–455. doi: 10.2307/3151963
  35. 35. Zaltman G, Coulter RH (1995) Seeing the voice of the customer: Metaphor-based advertising research. J Advert Res 35: 35–51.
  36. 36. Zaltman G, Zaltman LH (2008) Marketing metaphoria: what deep metaphors reveal about the minds of consumers. Boston: Harvard Business School Press.
  37. 37. Basso F, Oullier O, Hayek-Lanthois M, Robert-Demontrond P (2010) From marketing differentiation to household poisoning: are commercial practices on cleansing products a public health issue? In: Oullier O, Sauneron S, editors. Improving public health prevention with behavioural, cognitive and neuroscience. Paris: La Documentation Française. 94–103.
  38. 38. Lakoff G (1987) Women, fire, and dangerous things: What categories reveal about the mind. Chicago: University of Chicago Press.
  39. 39. Lakoff G, Johnson M (2003) Afterword, 2003. In: Lakoff G, Johnson M (2003) Metaphors we live by. Chicago: University of Chicago Press. 243–276.
  40. 40. Lakoff G (2008). The neural theory of metaphor. In: Gibbs Jr RW, editor. Cambridge handbook of metaphor and thought. New York: Cambridge University Press. 17–38.
  41. 41. Lakoff G, Johnson M (1999) Philosophy in the flesh. New York: Basic Books.
  42. 42. McFarland RA (1955) Epidemiologic principles applicable to the study and prevention of child accidents. Am J Public Health Nations Health 45: 1302–1308. doi: 10.2105/ajph.45.10.1302
  43. 43. Krieger N (2008) Proximal, distal, and the politics of causation: What’s level got to do with it? Am J Public Health 98: 221–230. doi: 10.2105/ajph.2007.111278
  44. 44. Scientific Committee on Consumer Safety (2011b) Explanatory note on the modifications of the SCCS opinion on the potential health risks posed by chemical consumer products resembling food and/or having child-appealing properties following the public consultation on the pre-consultation opinion. European Union: Directorate General for Health and Consumers.
  45. 45. Holbrook MB (1983) On the importance of using real products in research on merchandising strategy. J Retail 59: 4.
  46. 46. Oullier O (2013) Behavioural insights are vital to policy-making. Nature 501: 463–463. doi: 10.1038/501463a
  47. 47. European Commission (2013) Press release – Safer products and a level playing field in the internal market. Available:
  48. 48. (2011) Online consumer education – Dangerous imitations – Products resembling food. Available:
  49. 49. (2012) Detergent pods shouldn't look like candy! Available:
  50. 50. Wyke S, Tempowski J, Duarte-Davidson R, Wagner R, Desel H, et al. (2009) The DeNaMiC project: Description of the nature of accidental misuse of chemicals and chemical products. Toxicol Lett 189: S47. doi: 10.1016/j.toxlet.2009.06.113
  51. 51. McParland M, Kennedy K, Sutton N, Tizzard Z, Edwards JN, et al.. (2008) Description of the nature of the accidental misuse of chemicals and chemical products (DeNaMiC). Chem Hazards Poisons Rep Health Prot Agency - HPA - WHO Collab Cent: 42–43.
  52. 52. Tempowski J, Wyke S, Duarte-Davidson R, Wagner R, Desel H, et al. (2009) The DeNaMiC project: Description of the nature of accidental misuse of chemicals and chemical products. Clin Toxicol 47: 445. doi: 10.1016/j.toxlet.2009.06.113
  53. 53. Heiland A, Heinemeyer G, Wyke S, Tempowski J, Desel H, et al. (2009) Product information and case documentation systems in poison centres - Results from a European-wide questionnaire survey (DeNaMiC). Clin Toxicol 47: 478.
  54. 54. Silverman D (2011) Interpreting qualitative data. SAGE.
  55. 55. Tong A, Sainsbury P, Craig J (2007) Consolidated criteria for reporting qualitative research (COREQ): A 32-item checklist for interviews and focus groups. Int J Qual Health Care 19: 349–357. doi: 10.1093/intqhc/mzm042
  56. 56. Iskrant AP (1962) The epidemiologic approach to accident causation. Am J Public Health Nations Health 52: 1708–1711. doi: 10.2105/ajph.52.10.1708
  57. 57. Christesen HB (1994) Caustic ingestion in adults-epidemiology and prevention. J Toxicol Clin Toxicol 32: 557–568. doi: 10.3109/15563659409011060
  58. 58. Kopjar B, Wickizer TM (1996) Home injuries among adults in Stavanger, Norway. Am J Public Health 86: 400–404. doi: 10.2105/ajph.86.3.400
  59. 59. Kay M, Wyllie R (2009) Caustic ingestions in children. Curr Opin Pediatr 21: 651–654. doi: 10.1097/mop.0b013e32832e2764
  60. 60. McKenzie LB, Ahir N, Stolz U, Nelson NG (2010) Household cleaning product-related injuries treated in US emergency departments in 1990–2006. Pediatrics: peds. 2009–3392.
  61. 61. Haddon W (1970) On the escape of tigers: an ecologic note. Am J Public Health Nations Health 60: 2229–2234. doi: 10.2105/ajph.60.12.2229-b
  62. 62. World Health Organization (2008) World report on child injury prevention. World Health Organization. Switzerland: Geneva.
  63. 63. Wheatley GM (1956) The public health problem of accidental poisoning: A condensation of a symposium. Am J Public Health Nations Health 46: 951–958. doi: 10.2105/ajph.46.8.951
  64. 64. Smyth RL (2001) Research with children. BMJ 322: 1377–1378. doi: 10.1136/bmj.322.7299.1377
  65. 65. Ratneshwar S, Barsalou LW, Pechmann C, Moore M (2001) Goal-derived categories: the role of personal and situational goals in category representations. J Consum Psychol 10: 147–157. doi: 10.1207/153276601750132713
  66. 66. Norman D (2002) The design of everyday things. Reprint. New York: Basic Books.
  67. 67. Reason J (2000) Human error: models and management. BMJ 320: 768–770. doi: 10.1136/bmj.320.7237.768
  68. 68. Reimann M, Schilke O, Weber B, Neuhaus C, Zaichkowsky J (2011) Functional magnetic resonance imaging in consumer research: A review and application. Psychol Mark 28: 608–637. doi: 10.1002/mar.20403
  69. 69. Falk EB, Berkman ET, Mann T, Harrison B, Lieberman MD (2010) Predicting persuasion-induced behavior change from the brain. J Neurosci 30: 8421–8424. doi: 10.1523/jneurosci.0063-10.2010
  70. 70. Gibbs RW (2006) Metaphor interpretation as embodied simulation. Mind Lang 21: 434–458. doi: 10.1111/j.1468-0017.2006.00285.x
  71. 71. Gibbs Jr RW, Matlock T. (2008) Metaphor, imagination, and simulation: psycholinguistic evidence. In: Gibbs Jr RW, editor. Cambridge handbook of metaphor and thought. New York: Cambridge University Press. 161–176.
  72. 72. Gibbs Jr RW, Perlman M (2010) Language understanding is grounded in experiential simulations: a response to Weiskopf. Stud Hist Philos Sci Part A 41: 305–308. doi: 10.1016/j.shpsa.2010.07.004
  73. 73. Coulson S (2008) Metaphor comprehension and the brain. In: Gibbs Jr RW, editor. Cambridge handbook of metaphor and thought. New York: Cambridge University Press. 177–194.
  74. 74. Gallese V, Lakoff G (2005) The brain’s concepts: the role of the sensory-motor system in conceptual knowledge. Cogn Neuropsych 22: 455–479. doi: 10.1080/02643290442000310
  75. 75. Barsalou LW (2008) Grounded cognition. Annu Rev Psychol 59: 617–645. doi: 10.1146/annurev.psych.59.103006.093639
  76. 76. Kosslyn SM (1980) Image and mind. Cambridge: Harvard University Press.
  77. 77. Kosslyn SM (1994) Image and brain. Cambridge: MIT Press.
  78. 78. Paivio A (1986) Mental representations. New York: Oxford University Press.
  79. 79. MacInnis DJ, Price LL (1987) The role of imagery in information processing: Review and extensions. J Consum Res 13: 473–491. doi: 10.1086/209082
  80. 80. Simmons WK, Martin A, Barsalou LW (2005) Pictures of appetizing foods activate gustatory cortices for taste and reward. Cereb Cortex 15: 1602–1608. doi: 10.1093/cercor/bhi038
  81. 81. Frank S, Laharnar N, Kullmann S, Veit R, Canova C, et al. (2010) Processing of food pictures: Influence of hunger, gender and calorie content. Brain Res 1350: 159–166. doi: 10.1016/j.brainres.2010.04.030
  82. 82. Van der Laan LN, de Ridder DTD, Viergever MA, Smeets PAM (2011) The first taste is always with the eyes: A meta-analysis on the neural correlates of processing visual food cues. NeuroImage 55: 296–303. doi: 10.1016/j.neuroimage.2010.11.055
  83. 83. Bambini V, Gentili C, Ricciardi E, Bertinetto PM, Pietrini P (2011) Decomposing metaphor processing at the cognitive and neural level through functional magnetic resonance imaging. Brain Res Bull 86: 203–216. doi: 10.1016/j.brainresbull.2011.07.015
  84. 84. Lakoff G (2012) Explaining embodied cognition results. Top Cogn Sci 4: 773–785. doi: 10.1111/j.1756-8765.2012.01222.x
  85. 85. Cardillo ER, Watson CE, Schmidt GL, Kranjec A, Chatterjee A (2012) From novel to familiar: tuning the brain for metaphors. NeuroImage 59: 3212–3221. doi: 10.1016/j.neuroimage.2011.11.079
  86. 86. Gold R, Faust M, Ben-Artzi E (2012) Metaphors and verbal creativity: the role of the right hemisphere. Laterality 17: 602–614. doi: 10.1080/1357650x.2011.599936
  87. 87. Gagnon L, Goulet P, Giroux F, Joanette Y (2003) Processing of metaphoric and non-metaphoric alternative meanings of words after right- and left-hemispheric lesion. Brain Lang 87: 217–226. doi: 10.1016/s0093-934x(03)00057-9
  88. 88. Giora R, Zaidel E, Soroker N, Batori G, Kasher A (2000) Differential effects of right- and left-hemisphere damage on understanding sarcasm and metaphor. Metaphor Symb 15: 63–83. doi: 10.1080/10926488.2000.9678865
  89. 89. Papagno C, Tabossi P, Colombo MR, Zampetti P (2004) Idiom comprehension in aphasic patients. Brain Lang 89: 226–234. doi: 10.1016/s0093-934x(03)00398-5
  90. 90. Oliveri M, Romero L, Papagno C (2004) Left but not right temporal involvement in opaque idiom comprehension: a repetitive transcranial magnetic stimulation study. J Cogn Neurosci.
  91. 91. Rapp AM, Leube DT, Erb M, Grodd W, Kircher TTJ (2004) Neural correlates of metaphor processing. Brain Res Cogn Brain Res 20: 395–402. doi: 10.1016/j.cogbrainres.2004.03.017
  92. 92. Subramaniam K, Faust M, Beeman M, Mashal N (2012) The repetition paradigm: enhancement of novel metaphors and suppression of conventional metaphors in the left inferior parietal lobe. Neuropsychologia 50: 2705–2719. doi: 10.1016/j.neuropsychologia.2012.07.020
  93. 93. Corballis MC (2014) Left Brain, Right Brain: Facts and Fantasies. PLoS Biol 12: e1001767. doi: 10.1371/journal.pbio.1001767
  94. 94. Nielsen JA, Zielinski BA, Ferguson MA, Lainhart JE, Anderson JS (2013) An evaluation of the left-brain vs. right-brain hypothesis with resting state functional connectivity magnetic resonance imaging. PLoS ONE 8: e71275. doi: 10.1371/journal.pone.0071275
  95. 95. Mashal N, Faust M, Hendler T, Jung-Beeman M (2009) An fMRI study of processing novel metaphoric sentences. Laterality 14: 30–54. doi: 10.1080/13576500802049433
  96. 96. Diaz MT, Hogstrom LJ (2011) The influence of context on hemispheric recruitment during metaphor processing. J Cogn Neurosci 23: 3586–3597. doi: 10.1162/jocn_a_00053
  97. 97. Yang FG, Edens J, Simpson C, Krawczyk DC (2009) Differences in task demands influence the hemispheric lateralization and neural correlates of metaphor. Brain Lang 111: 114–124. doi: 10.1016/j.bandl.2009.08.006
  98. 98. Lacey S, Stilla R, Sathian K (2012) Metaphorically feeling: comprehending textural metaphors activates somatosensory cortex. Brain Lang 120: 416–421. doi: 10.1016/j.bandl.2011.12.016
  99. 99. González J, Barros-Loscertales A, Pulvermüller F, Meseguer V, Sanjuán A, et al. (2006) Reading cinnamon activates olfactory brain regions. NeuroImage 32: 906–912. doi: 10.1016/j.neuroimage.2006.03.037
  100. 100. Hauk O, Johnsrude I, Pulvermüller F (2004) Somatotopic representation of action words in human motor and premotor cortex. Neuron 41: 301–307. doi: 10.1016/s0896-6273(03)00838-9
  101. 101. Martin A, Haxby JV, Lalonde FM, Wiggs CL, Ungerleider LG (1995) Discrete cortical regions associated with knowledge of color and knowledge of action. Science 270: 102–105. doi: 10.1126/science.270.5233.102
  102. 102. Passamonti L, Rowe JB, Schwarzbauer C, Ewbank MP, von dem Hagen E, et al. (2009) Personality predicts the brain’s response to viewing appetizing foods: the neural basis of a risk factor for overeating. J Neurosci 29: 43–51. doi: 10.1523/jneurosci.4966-08.2009
  103. 103. Wallner-Liebmann S, Koschutnig K, Reishofer G, Sorantin E, Blaschitz B, et al. (2010) Insulin and hippocampus activation in response to images of high-calorie food in normal weight and obese adolescents. Obesity 18: 1552–1557. doi: 10.1038/oby.2010.26
  104. 104. Beaver JD, Lawrence AD, van Ditzhuijzen J, Davis MH, Woods A, et al. (2006) Individual differences in reward drive predict neural responses to images of food. J Neurosci 26: 5160–5166. doi: 10.1523/jneurosci.0350-06.2006
  105. 105. Wagner R, Bauer K, Desel H, Tempowski J, Mathieu-Nolf M, et al. (2009) Poisons centres can provide substantial information on circumstances of poisonings needed for regulatory risk assessment - Results from a prospective study (DeNaMiC/S 5). Clin Toxicol 47: 475.
  106. 106. Société Générale de Surveillance (2008) Product recalls – Consumer testing services (CTS). Geneva: SGS Publication 0802/1, 1st to 15th February.
  107. 107. Bradley MM, Codispoti M, Cuthbert BN, Lang PJ (2001) Emotion and motivation I: defensive and appetitive reactions in picture processing. Emot 1: 276–298. doi: 10.1037//1528-3542.1.3.276
  108. 108. Paulus MP, Frank LR (2003) Ventromedial prefrontal cortex activation is critical for preference judgments. Neuroreport 14: 1311–1315. doi: 10.1097/00001756-200307180-00005
  109. 109. Rainer G, Augath M, Trinath T, Logothetis NK (2001) Nonmonotonic noise tuning of BOLD fMRI signal to natural images in the visual cortex of the anesthetized monkey. Curr Biol 11: 846–854. doi: 10.1016/s0960-9822(01)00242-1
  110. 110. Führer D, Zysset S, Stumvoll M (2008) Brain activity in hunger and satiety: an exploratory visually stimulated FMRI study. Obesity 16: 945–950. doi: 10.1038/oby.2008.33
  111. 111. Plassmann H, O’Doherty J, Rangel A (2007) Orbitofrontal cortex encodes willingness to pay in everyday economic transactions. J Neurosci 27: 9984–9988. doi: 10.1523/jneurosci.2131-07.2007
  112. 112. Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH (2003) An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. NeuroImage 19: 1233–1239. doi: 10.1016/s1053-8119(03)00169-1
  113. 113. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, et al. (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15: 273–289. doi: 10.1006/nimg.2001.0978
  114. 114. Loken B, Barsalou LW, Joiner C (2008) Categorization theory and research in consumer psychology: category representation and category-based inference. In: Haugtvedt CP, Kardes F, Herr P, editors. Handbook of consumer psychology. Mahwah, NJ: Lawrence Erlbaum Associates. 133–163.
  115. 115. Barsalou LW (2010) Introduction to 30th Anniversary perspectives on cognitive science: Past, present, and future. Top Cogn Sci 2: 322–327. doi: 10.1111/j.1756-8765.2010.01104.x
  116. 116. Ariely D, Berns GS (2010) Neuromarketing: the hope and hype of neuroimaging in business. Nat Rev Neurosci 11: 284–292. doi: 10.1038/nrn2795
  117. 117. Plassmann H, O’Doherty J, Shiv B, Rangel A (2008) Marketing actions can modulate neural representations of experienced pleasantness. Proc Natl Acad Sci U S A 105: 1050–1054. doi: 10.1073/pnas.0706929105
  118. 118. Reimann M, Zaichkowsky J, Neuhaus C, Bender T, Weber B (2010) Aesthetic package design: A behavioral, neural, and psychological investigation. J Consum Psychol 20: 431–441. doi: 10.1016/j.jcps.2010.06.009
  119. 119. Van der Laan LN, De Ridder DTD, Viergever MA, Smeets PAM (2012) Appearance matters: Neural correlates of food choice and packaging aesthetics. PLoS ONE 7: e41738. doi: 10.1371/journal.pone.0041738
  120. 120. Dietvorst RC, Verbeke WJM, Bagozzi RP, Yoon C, Smits M, et al. (2009) A sales force–specific theory-of-mind scale: Tests of its validity by classical methods and functional magnetic resonance imaging. J Mark Res 46: 653–668. doi: 10.1509/jmkr.46.5.653
  121. 121. Yoon C, Gutchess AH, Feinberg F, Polk TA (2006) A Functional magnetic resonance imaging study of neural dissociations between brand and person judgments. J Consum Res 33: 31–40. doi: 10.1086/504132
  122. 122. Landau MJ, Meier BP, Keefer LA (2010) A metaphor-enriched social cognition. Psychol Bull 136: 1045–1067. doi: 10.1037/a0020970
  123. 123. Santel S, Baving L, Krauel K, Münte TF, Rotte M (2006) Hunger and satiety in anorexia nervosa: fMRI during cognitive processing of food pictures. Brain Res 1114: 138–148. doi: 10.1016/j.brainres.2006.07.045
  124. 124. Poldrack RA (2006) Can cognitive processes be inferred from neuroimaging data? Trends Cogn Sci 10: 59–63. doi: 10.1016/j.tics.2005.12.004
  125. 125. Uher R, Treasure J, Heining M, Brammer MJ, Campbell IC (2006) Cerebral processing of food-related stimuli: Effects of fasting and gender. Behav Brain Res 169: 111–119. doi: 10.1016/j.bbr.2005.12.008
  126. 126. Schienle A, Schäfer A, Hermann A, Vaitl D (2009) Binge-eating disorder: Reward sensitivity and brain activation to images of food. Biol Psychiatry 65: 654–661. doi: 10.1016/j.biopsych.2008.09.028
  127. 127. Button KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J, et al. (2013) Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14: 365–376. doi: 10.1038/nrn3475
  128. 128. Moreau CP, Markman AB, Lehmann DR (2001) “What is it?” Categorization Flexibility and consumers’ responses to really new products. J Consum Res 27: 489–498. doi: 10.1086/319623
  129. 129. Rajagopal P, Burnkrant RE (2009) Consumer evaluations of hybrid products. J Consum Res 36: 232–241. doi: 10.1086/596721
  130. 130. Ger G, Yenicioglu B (2004) Clean and dirty: Playing with boundaries of consumer’s safe havens. Adv Consum Res 31: 462–467.
  131. 131. Holbrook MB, Chestnut RW, Oliva TA, Greenleaf EA (1984) Play as a consumption experience: The roles of emotions, performance, and personality in the enjoyment of games. J Consum Res 11: 728–739. doi: 10.1086/209009
  132. 132. Oullier O, Sauneron S (2010) Improving public health prevention with behavioural, cognitive and neuroscience. Paris: La Documentation Française. Free download:
  133. 133. Jahiel RI (2008) Corporation-induced diseases, upstream epidemiologic surveillance, and urban health. J Urban Health 85: 517–531. doi: 10.1007/s11524-008-9283-x
  134. 134. Soriano A, Rieu D, Oullier O (2013) Combined behavioural and neuroscientific insights can improve anti-tobacco strategies. Médecine Sci MS 29: 1042–1050.
  135. 135. Petit O, Basso F, Huguet P, Plassmann H, Oullier O (2011) Food behaviour and obesity: insights from decision neuroscience. Médecine Sci MS 27: 1000–1008. doi: 10.1051/medsci/201127111000
  136. 136. Bouille J, Robert-Demontrond P, Basso F (2014) Measuring the persuasive power of consumerist activism: An experimental study on the Polity model applied to food imitating products. Rech Appl En Mark Engl Ed 29: 72–102. doi: 10.1177/2051570714526960
  137. 137. Camerer CF (2006) Wanting, liking, and learning: Neuroscience and paternalism. Univ Chic Law Rev 73: 87–110.
  138. 138. Oullier O (2012) Clear up this fuzzy thinking on brain scans. Nature 483: 7. doi: 10.1038/483007a
  139. 139. Creusen MEH, Schoormans JPL (2005) The Different Roles of Product Appearance in Consumer Choice. J Prod Innov Manag 22: 63–81. doi: 10.1111/j.0737-6782.2005.00103.x
  140. 140. Mehrabian A, Russell JA (1974) An approach to environmental psychology. MIT Press.
  141. 141. Lang P (1980) Behavioral treatment and bio-behavioral assessment: computer applications. In: Sidowski J, Johnson J, Williams T, editors. Technology in mental health care delivery systems. Norwood, NJ: Ablex. 119–l37.
  142. 142. Kobayashi M, Takeda M, Hattori N, Fukunaga M, Sasabe T, et al. (2004) Functional imaging of gustatory perception and imagery: “top-down” processing of gustatory signals. NeuroImage 23: 1271–1282. doi: 10.1016/j.neuroimage.2004.08.002
  143. 143. Cavanna AE, Trimble MR (2006) The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129: 564–583. doi: 10.1093/brain/awl004
  144. 144. Cornier M-A, Von Kaenel SS, Bessesen DH, Tregellas JR (2007) Effects of overfeeding on the neuronal response to visual food cues. Am J Clin Nutr 86: 965–971.
  145. 145. Cornier M-A (2009) The effects of overfeeding and propensity to weight gain on the neuronal responses to visual food cues. Physiol Behav 97: 525–530. doi: 10.1016/j.physbeh.2009.03.019
  146. 146. Davids S, Lauffer H, Thoms K, Jagdhuhn M, Hirschfeld H, et al. (2009) Increased dorsolateral prefrontal cortex activation in obese children during observation of food stimuli. Int J Obes 34: 94–104. doi: 10.1038/ijo.2009.193
  147. 147. Goldstone AP, Prechtl de Hernandez CG, Beaver JD, Muhammed K, Croese C, et al. (2009) Fasting biases brain reward systems towards high-calorie foods. Eur J Neurosci 30: 1625–1635. doi: 10.1111/j.1460-9568.2009.06949.x
  148. 148. Killgore WDS, Yurgelun-Todd DA (2005) Developmental changes in the functional brain responses of adolescents to images of high and low-calorie foods. Dev Psychobiol 47: 377–397. doi: 10.1002/dev.20099
  149. 149. Malik S, McGlone F, Bedrossian D, Dagher A (2008) Ghrelin modulates brain activity in areas that control appetitive behavior. Cell Metab 7: 400–409. doi: 10.1016/j.cmet.2008.03.007
  150. 150. St-Onge M-P, Sy M, Heymsfield SB, Hirsch J (2005) Human cortical specialization for food: a functional magnetic resonance imaging investigation. J Nutr 135: 1014–1018.
  151. 151. Stoeckel LE, Weller RE, Cook III EW, Twieg DB, Knowlton RC, et al. (2008) Widespread reward-system activation in obese women in response to pictures of high-calorie foods. NeuroImage 41: 636–647. doi: 10.1016/j.neuroimage.2008.02.031
  152. 152. Tataranni PA, Gautier JF, Chen K, Uecker A, Bandy D, et al. (1999) Neuroanatomical correlates of hunger and satiation in humans using positron emission tomography. Proc Natl Acad Sci U S A 96: 4569–4574. doi: 10.1073/pnas.96.8.4569
  153. 153. Pelchat ML, Johnson A, Chan R, Valdez J, Ragland JD (2004) Images of desire: food-craving activation during fMRI. NeuroImage 23: 1486–1493. doi: 10.1016/j.neuroimage.2004.08.023
  154. 154. LaBar KS, Gitelman DR, Parrish TB, Kim YH, Nobre AC, et al. (2001) Hunger selectively modulates corticolimbic activation to food stimuli in humans. Behav Neurosci 115: 493–500. doi: 10.1037//0735-7044.115.2.493