ORIGINAL RESEARCH ARTICLE

published: 24 June 2014

doi: 10.3389/fpsyg.2014.00617

Food-pics: an image database for experimental research on

eating and appetite
Jens Blechert 1*, Adrian Meule 2,3 , Niko A. Busch 4,5 and Kathrin Ohla 6
1
Division of Clinical Psychology, Psychotherapy and Health Psychology, University of Salzburg, Salzburg, Austria
2
Institute of Psychology, University of Würzburg, Würzburg, Germany
3
Hospital for Child and Adolescent Psychiatry, LWL University Hospital of the Ruhr University Bochum, Hamm, Germany
4
Institute of Medical Psychology, Charité–Universitätsmedizin, Berlin, Germany
5
Berlin School of Mind and Brain, Humboldt Universität zu Berlin, Berlin, Germany
6
Section Psychophysiology, Department of Molecular Genetics, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany

Edited by: Our current environment is characterized by the omnipresence of food cues. The
Boris C. Rodríguez-Martín, Central sight and smell of real foods, but also graphically depictions of appetizing foods, can
University “Marta Abreu” of Las
Villas, Cuba
guide our eating behavior, for example, by eliciting food craving and influencing food
choice. The relevance of visual food cues on human information processing has been
Reviewed by:
Martin Yeomans, University of demonstrated by a growing body of studies employing food images across the disciplines
Sussex, UK of psychology, medicine, and neuroscience. However, currently used food image sets
Amy Claire Reichelt, University of vary considerably across laboratories and image characteristics (contrast, brightness, etc.)
New South Wales, Australia
Michelle Dalton, University of
and food composition (calories, macronutrients, etc.) are often unspecified. These factors
Leeds, UK might have contributed to some of the inconsistencies of this research. To remedy
*Correspondence: this, we developed food-pics, a picture database comprising 568 food images and 315
Jens Blechert, Division of Clinical non-food images along with detailed meta-data. A total of N = 1988 individuals with
Psychology, Psychotherapy and large variance in age and weight from German speaking countries and North America
Health Psychology, University of
Salzburg, Hellbrunner Str. 34,
provided normative ratings of valence, arousal, palatability, desire to eat, recognizability
5020 Salzburg, Austria and visual complexity. Furthermore, data on macronutrients (g), energy density (kcal), and
e-mail: jens.blechert@sbg.ac.at physical image characteristics (color composition, contrast, brightness, size, complexity)
are provided. The food-pics image database is freely available under the creative commons
license with the hope that the set will facilitate standardization and comparability across
studies and advance experimental research on the determinants of eating behavior.
Keywords: standardized food images, food pictures, food-cues, image properties, ERP, fMRI, eating behavior,
obesity

INTRODUCTION food evaluation (e.g., Seibt et al., 2007; Hoefling and Strack,
Our current environment is characterized by frequent cues for 2008), salivation (e.g., Wooley and Wooley, 1981), autonomic
highly palatable foods. Many researchers partially attribute rising responding (e.g., Rodriguez et al., 2005), visual attentional pro-
obesity rates and problems in eating-related self-regulation to this cessing (e.g., Stockburger et al., 2009b) and neural reward system
factor (Meule and Vögele, 2013). To examine the factors underly- activity (e.g., Labar et al., 2001; Uher et al., 2006; Castellanos
ing appetitive responses to foods, research is increasingly using et al., 2009; Goldstone et al., 2009). Second, food image responses
food images (Van Der Laan et al., 2011). Visual food cues consti- reliably differentiate individuals with abnormal eating behav-
tute, like odors, a primary sensory input that allows predictions ior from healthy controls: altered food cue processing has been
about the edibility and palatability of a food object. Thus, visual reported in individuals with restrained (Blechert et al., 2010;
food cues can be regarded conditioned stimuli that are associated Burger and Stice, 2011), external (Nijs et al., 2009) or emotional
with the hedonic and homeostatic effects of ingestion and are eating (Bohon et al., 2009), as well as in patients with eating dis-
therefore themselves rewarding (Dagher, 2012). Also, overt eat- orders (Blechert et al., 2011; Nikendei et al., 2012) or obesity
ing behaviors are under strong conscious control and therefore (Nijs and Franken, 2012; Martens et al., 2013). Third, food pic-
do not always reveal underlying response tendencies. Using food ture viewing tasks have been adapted to train eating control, for
images, neurocognitive and indirect measures have been partic- example, through food-specific inhibition tasks (e.g., stop-signal
ularly successful in the study of subtle appetitive and regulatory task; Van Koningsbruggen et al., 2013) or attentional retraining
determinants of overt eating behavior. (Werthmann et al., 2013; Kakoschke et al., 2014; Kemps et al.,
The “picture viewing approach” is validated by several lines 2014) with measurable effects on actual food intake, supporting
of evidence. First, food deprivation/hunger affects the response the notion that responding to food images is causally involved
to food images on several levels. Short term food deprivation in eating behavior. In sum, there is good evidence that the food
affects responses to food pictures as demonstrated for implicit picture viewing approach is a useful tool for the study of eating

www.frontiersin.org June 2014 | Volume 5 | Article 617 | 1

Blechert et al. Food-pics database

behavior and appetitive/motivational brain systems. However, a picture viewing (Bradley et al., 2007; Delplanque et al., 2007;
number of factors need to be taken into account during selec- Wiens et al., 2011). Thus, similar standards must apply to stud-
tion of images to be able to draw firm conclusions. These factors ies using food pictures rendering standardized stimulus sets and
broadly fall into the categories (1) food types, (2) individual associated meta-data essential. To this end, Foroni et al. (2013)
differences, and (3) image characteristics. recently presented an image database featuring food (natural,
Regarding food types, cultures around the world have brought transformed), rotten food, non-food objects (natural, artificial),
about a vast variety of foods that researchers need to consider animals, and scenes along with normative ratings by 73 healthy
when designing experiments. What might be the dimensions that participants and physical image properties (size, brightness and
need to be considered during image selection? First, foods differ spatial frequency). They focused on the natural (food, non-
in caloric content, which has been shown to affect early elec- food) and artificial distinction in their data analyses. While their
trocortical responses (Toepel et al., 2009; Meule et al., 2013). database (Foodcast Research Image Database, FRIDa) represents
However, caloric density often goes along with the degree of food an important step forward in the field of food picture research and
processing: processed foods are often more energy-dense than nat- their variety of images is broad, the number of edible food items
ural, unprocessed foods. Processed foods furthermore differ in is relatively restricted and, in part, specific to the Mediterranean
their colors from whole foods like fruits and vegetables, which cuisine. Moreover, their normative data stem from a small sam-
utilize the entire color spectrum. Thus, image selection accord- ple with little demographic diversity, resulting in a relatively low
ing to caloric density should simultaneously consider level of number of ratings per image.
processing and colors. Besides caloric density, macronutrients, In the present study, we present food-pics, a stimulus set of 568
that are proteins, fats, and carbohydrates, should be taken into food and 315 non-food images. In our study design (normative
account, if craving for certain types of food is a construct of inter- ratings, image characteristics), we aimed to be complementary
est (e.g., craving for carbohydrates, Corsica and Spring, 2008). to FRIDa and at the same time address some of its limitations.
Furthermore, there are distinguishable food classes such as vegeta- Food-pics was aggregated to represent a wider range of foods
bles, meat-containing dishes, fruits, and snacks which each differ to allow applicability in western countries. Our normative par-
in their (seasonal) availability, readiness to eat, flavor, nutritional ticipant samples (N = 1988) were selected to represent typical
composition, healthiness, color, and familiarity. Obviously, the university student samples but, in total, span a comparatively
categorization of foods into some classes is dependent not only wide range of age (11–77 years), BMI (12–67 kg/m2 ), and cultural
on individual experiences and availability of certain foods but also background (German-speaking countries and the USA), to pro-
on the research questions asked. It is for that reason, that food-pics vide robust and generalizable normative data on commonly used
provides a variety of food images that cover many food classes and perceptual and psychological parameters like palatability, desire
that, most importantly, can be classified as needed by the user. to eat, recognizability, familiarity, valence and arousal. Physical
Not only is there a wide variety of food types to choose from image characteristics, that is, color, size, contrast, brightness, and
but researchers need to consider the targeted population and complexity, were computed to complement the dataset and allow
therefore individual differences for image selection. For example, the selection of physically matching groups of images. Our anal-
if vegetarians or vegans are part of the sample, meat containing yses explore several example dimensions relevant to study design:
images should probably be avoided as these trigger altered neu- (1) image type (e.g., food vs. non-food images) and food type (e.g.,
ral and behavioral responses in vegetarians compared to omni- vegetables vs. meat vs. fruits, high- vs. low-calorie dense food,
vores (Stockburger et al., 2009a). Similar considerations apply to sweet vs. savory food, whole vs. processed food), (2) individual
food preferences based on cultural, religious or health grounds differences (e.g., demographics such as age, gender, and BMI, but
(Hoffman et al., 2013). Individual preferences affect brain also cultural background and vegetarianism) and (3) state vari-
responses, which is why some studies individualize stimuli to ables (e.g., hunger and current weight reduction diet) on image
match each participant’s preferences (e.g., Hollmann et al., 2012; ratings. We also explored the relationship of (4) image characteris-
Giuliani et al., 2013). Further individual differences in age and tics (e.g., contrast, brightness, complexity) with subjective ratings
gender, educational status, and body mass index (BMI) should be and nutritional content.
considered for images selection (Caine-Bish and Scheule, 2009;
Raffensperger et al., 2010; Berthoud and Zheng, 2012). METHODS
A third class of factors are image characteristics. Unfortunately, STIMULI
dimensions such as brightness, contrast, or spatial frequencies The database comprises 568 food images including sweet (e.g., ice
have not received much attention in studies using food-related cream, chocolate), savory (e.g., pistachios, sandwiches), processed
images. However, effects of such image features on visual per- (e.g., hamburger, French fries, potato chips, chocolate bars) and
ception and stimulus-evoked neuronal responses are well known. whole foods (e.g., vegetables and fruits) and beverages (e.g., cof-
Consequently, it is recommended to carefully control the phys- fee, orange juice). Images of single items (e.g., one apple), several
ical properties of visual stimulus material (Knebel et al., 2008; items (e.g., three apples) as well as full meals (e.g., roast beef with
Willenbockel et al., 2010; Kovalenko et al., 2012; Ball et al., 2013). vegetables), were included. The food images are complemented
For example, the role of image complexity and spatial frequencies by 315 non-food images comprising animals (n = 37, e.g., but-
for neural responses are heavily debated in the field of face pro- terflies, dogs), flowers and leaves (n = 42), common household
cessing (Vuilleumier et al., 2003; Thierry et al., 2007; Rossion and objects (n = 89, e.g., bucket, flat iron), office supply (n = 20, e.g.,
Jacques, 2008) and are increasingly considered during affective paper clip, ball pen), kitchen accessories (n = 46, e.g., toaster,

Frontiers in Psychology | Eating Behavior June 2014 | Volume 5 | Article 617 | 2

Blechert et al. Food-pics database

pan), as well as tools (n = 23, e.g., pliers, screws), food packag- USA). Scripts can be downloaded from the food-pics website
ing (n = 33, e.g., pizza box; no food visible on packaging), and (www.food-pics.sbg.ac.at). With the exception of the RGB chan-
other objects (n = 25). Images were selected from a commercially nel contribution, all properties were computed after converting
available database (Hemera Photo Objects, Vols. I-III), collected the colored image to gray values by forming a weighted sum of
from non-copyrighted sources on the internet, or taken in our the red, green, and blue color channels: 0.2989 × red + 0.5870 ×
lab using an Olympus SZ-31MR digital camera (OlympusCorp., green + 0.1140 × blue. This procedure converts RGB images
Tokyo, Japan). All images are color photographs with a resolu- to gray-scale by eliminating the hue and saturation information
tion of 600 × 450 pixels (96 dpi, sRGB color format). Images were while retaining image luminance (Poynton, 2012). The following
standardized on background color (white) and selected/edited image properties were analyzed:
to be relatively homogeneous with regard to, viewing distance Color, quantified as the proportional contribution of the red,
(≈80 cm), angle and simple figure-ground composition. The green, and blue channel, averaged across all non-white pixels. For
background was adapted to meet eating conditions: some foods example, a tomato is characterized by a strong contribution of the
can be presented without dishware (e.g., fruits or hamburger), red channel (see Figure 1).
while others naturally require a plate or bowl (e.g., soup or fruit Size, quantified as the proportion of non-white pixels relative
salad). to total number of pixels (identical as in Foroni et al., 2013).
Brightness, quantified as the difference between the mean
IMAGE CHARACTERISTICS luminance of all non-white pixels of the gray scale image and the
For each image, we computed relevant image properties that white background (Foroni et al., 2013). Thus, the most salient
characterize the images’ physical appearance using customized objects (i.e., very dark objects on white background) yielded the
scripts written in Matlab R2011b (The Mathworks, Inc. Natick, highest brightness values.

FIGURE 1 | Example pictures illustrating image characteristics from low (left) to high parameter value (right).

www.frontiersin.org June 2014 | Volume 5 | Article 617 | 3

Blechert et al. Food-pics database

Within-object contrast, quantified as the standard deviation of PARTICIPANTS

luminance across all non-white pixels of the gray scaled image. Four samples completed an anonymous online survey (see
For example, an image of a black chocolate bar on a white plate Table 1 for sample descriptions) to provide normative data
contains pixels with luminance values ranging from very dark for food-pics. Only participants who completed all ratings for
to white. Thus, this image is characterized by a high standard at least 3 food images were included (see “Online Survey”
deviation of luminance values. By contrast, an image of whipped below). The first sample (“UniHagen sample,” n = 638) com-
cream on a white plate comprises very few dark pixels, and so is prised undergraduates of the University of Hagen, a German dis-
characterized by a small standard deviation. tance teaching university, who completed the survey in exchange
for course credit and the option of participating in a raf-
Spatial frequencies fle for 3 × 50 Euro upon completion. The second sample was
Median power of each object was analyzed by computing a two- recruited through mailing lists of several universities in Germany,
dimensional fast Fourier transform on the gray-scale images. Switzerland and Austria (“German-speaking sample,” n = 831).
One-dimensional power spectra were obtained by computing a The third sample addressed US-participants (“US sample,” n =
radial average of the two-dimensional power spectra. This proce- 496), recruited though the online work marketplace “Mechanical
dure yields a measure of the image’s spatial frequencies, reflecting Turk” at Amazon, where registered users work on online tasks
variations in pixel luminance, independent of their location in
the image. To represent spectral power in a single value for
each image, we computed the median power across all spatial Table 1 | Demographic characteristics by sample.
frequencies.
German- US- UniHagen Austrian
speaking American children
Complexity sample Sample and youth
While some images display a single homogenous object (e.g.,
a slice of cheese), other images display multiple objects (e.g., AGE
an assortment of different fruits) or objects consisting of mul- Mean (SD) 24.7 (5.46) 35.9 32.8 13.9 (1.56)
tiple components (e.g., a pizza). Images that are complex in (13.41) (10.07)
this sense are characterized by multiple object outlines. Thus, Median (Min, Max) 23 (18–65) 32 (18–77) 30 (17–73) 14 (11–18)
we analyzed the images for outlines using a Canny edge detec- GENDER
tion algorithm (Canny, 1986) and quantified image complexity Male (%) 16.7 36.3 17.2 60.9
by computing the proportion of outline-related pixels within the NATIONALITY (%)
image. However, the number of outline-pixels is also determined Germany 93.0 0 93.1 4.35
by the object’s size—a magnified version of the identical object Austria 3.01 0 2.35 91.3
would have larger outlines and would yield a higher complexity Switzerland 0.12 0 1.1 0
value. Therefore, we also computed a normalized complexity mea- Other European country 2.17 0 2.19 4.35
sure that is independent of object size, by additionally dividing the Non-European country 1.68 0 1.25 0
proportion of outline-related pixels by the total number of non- USA 0 98.7 0 0
white pixels in the image. Size and brightness were computed in Canadian 0 0.4 0 0
the same way as reported by Foroni et al. (2013). Other 0 0.80 0 0
BODY MASS INDEX (kg/m2 )
MACRONUTRIENTS Mean(SD) 22.5 (3.70) 27.3 (7.29) 23.4 (4.68) 18.7 (2.77)
Number of kcal and macronutrient composition (proteins, car- Median (Min, Max) 21.7 25.7 22.4 18.6
bohydrates, fat) of a depicted food were estimated for each food (14.2–45.3) (15.5–67.4) (12.1–60.5) (14.6–24.34)
image by a trained research assistant (psychology master level stu- EATING STYLE (%)
dent) using food databases on the internet and food packaging Omnivore 75.9 92.3 77.7 95.7
information. Kcal and macronutrients are provided as kcal/100g Vegetarian 20.2 5.4 19.6 4.3
and grams/100g as well as total kcal and grams, respectively, Vegan 3.9 2.2 2.7 0.0
for the depicted portion. Whenever multiple food items were CURRENTLY DIETING
displayed (e.g., grapes) counts were provided to facilitate analy- (%) 10.3 23.8 9.9 4.3
ses of experimental test meals. To cross-validate the accuracy of EMPLOYMENT (%)
these data, a second research assistant (also a psychology master High school 1.2 10.7 0.0 100
level student) estimated these data a second time for a randomly College/University 86.8 11.3 100 0.0
selected subsample of 38 food items1. Agreement between the two Apprenticeship 5.1 0.6 0.0 0.0
coders was excellent; Pearson correlations ranged from r = 0.84 Self-employed 1.1 21.6 0.0 0.0
to r = 0.99 with a mean of r = 0.95. Unemployed 1.2 17.1 0.0 0.0
Other 4.6 38.7 0.0 0.0
1 Image numbers were 4, 9, 10, 15, 26, 41, 46, 64, 85, 95, 101, 110, 116, 134, PROGRESS IN SURVEY
148, 152, 153, 159, 185, 189, 192, 193, 194, 198, 199, 205, 206, 211, 244, 248, % Completed 77.6 69.6 89.8 78.3
249, 262, 264, 265, 282, 298, 308, 309. % Partial completion 22.4 30.4 10.2 21.7

Frontiers in Psychology | Eating Behavior June 2014 | Volume 5 | Article 617 | 4

Blechert et al. Food-pics database

in exchange for payment. The fourth sample addressed children (2) Individual differences and demographics: Effects of gender,
and youth at an Austrian high school (“Children/youth sample,” age, and BMI, as well as diet (omnivore vs. vegetarian),
n = 23) to extend the age range. The German-speaking and the and culture (German speaking vs. North American) were
Children/youth samples were also offered participation in a raffle explored with regard to palatability and desire to eat ratings.
for 3 × 50 Euros. All surveys were completed between May and (3) State variables: Hunger ratings were correlated with palata-
August 2013. The ethics board of the University of Salzburg had bility and desire to eat ratings. Likewise, dieters (“current
approved the study. weight reduction diet”) were compared with non-dieters on
palatability and desire to eat ratings.
ONLINE SURVEY (4) Image characteristics, ratings, and macronutrients:
As participants could not be expected to reliably rate all 882 Correlational analyses explored relationships between
images, each participant rated a random subset of images, sep- subjective ratings, image characteristics, and nutrients.
arately drawn from non-food and food images. Due to dif-
ferent modes of compensation (course credit, payment, raffle) Generally, due to the high statistical power in the present sam-
the samples differed in the number of images rated by each ple, we only report effects with at least medium (η2 > 0.06,
participant: UniHagen sample 40 non-foods/80 foods, German- Cohen’s d > 0.3) effect sizes unless otherwise noted. Within each
speaking sample 25/40, US sample 17/35, and Children/Youth subgroup of comparisons we used paired sample Student t-test
sample 5/35. On average, each image was rated by 48.8 to compare subgroups of images or display 95% confidence
(SD = 22.9) participants. intervals.
The survey commenced with an assessment of demographics
(age, gender, height, occupation, nationality) and eating habits RESULTS
(weight, diet: omnivore/vegetarian/vegan, weight-loss dieting) IMAGE TYPE
before displaying a detailed explanation as well as an example rat- To provide an example characterization food and non-food
ing for all scales. During the survey, one image was displayed at a objects were classified into several specific categories. Food
time and ratings were required for the dichotomous item famil- objects were categorized, based on the dominant food in the
iarity (yes or no) and recognizability (easy or difficult). Visual image, into fruits (13.3% of all food images), vegetables (20.7%),
analog scales (VAS, approximately 8 cm long) were displayed to chocolates (11.4%), meat (11.1%), fish (2.28%), nuts (1.76%),
rate complexity (only the extremes were labeled, scale ranged from beverages (1.58%) and 38% other foods without clear domi-
“very little” to “very high”), valence (from “very negative” to nance of one food type. Non-food images were categorized into
“very positive”), and arousal (from “not at all” to “extremely”). flowers & leaves (13.4%), animals (10.1%), tools (7.32%), house-
Food items were additionally rated on palatability (from “not hold items (non-kitchen, 28.3%), kitchen utensils (14.6%), office
at all” to “extremely”) and desire to eat (from “not at all” to supply (6.37%), food packaging (10.5%) and other items (1%).
“extremely”). General instructions read “how palatable is this Figure 2 displays valence, arousal, palatability, and desire to eat
food for you in general?” and “how much would you like to ratings for these categories along with 95% confidence intervals.
eat this food right now if it was in front of you.” Anchors on Objects, flowers & leaves and animals were rated more positively
each visual analog scale for each image read “Palatability” (in on valence compared to tools, household and kitchen utensils
German “Schmackhaftigkeit”): “not at all” to “extremely”; and as revealed by non-overlapping confidence intervals. Flowers &
“Desire to eat” (German “Verlangen”): “not at all” to “extremely.” leaves and animals were also rated more positive on valence than
Complexity (German “Komplexität”): “very low” to “very high”; most of the foods, except for fruit. Within foods, fruits were
was explained as being characterized by “many components, most popular, both in terms of valence and palatability and in
details and subobjects” as well as by “many edges and borders.” terms of desire to eat. Interestingly, meat was rated lowest on
The VAS was displayed as a solid bar along which a cursor was palatability and desire to eat (closely followed by nuts for desire
to be moved; the rating was logged upon mouse click. The scale to eat).
represented, invisible to the participants, 100 points (from 1 to In addition, as previous research has contrasted foods accord-
100). ing to caloric density, degree of processing, and gustatory qualities,
we classified our food pictures into high vs. low caloric den-
DATA ANALYSES sity (median split regarding caloric density = kcal/100 g) as well
To describe and explore the food-pics normative database and to as into processed (32.0% of all foods) vs. whole (66.7% of all
highlight some variables that might guide users during image foods, 1.3% not classifiable) and sweet (42.8%) vs. savory foods
selection and study design we performed the following analyses: (38.8%, 18.4% not classifiable; see Table 2 for means and stan-
dard deviations of all ratings of the different food types) and
(1) Image type: Descriptive data are given on stimulus valence determined palatability and desire to eat ratings for each category.
and arousal across different stimulus classes (including non- High vs. low calorie-dense foods received lower ratings in terms of
food images) in the database. For foods (and most remaining palatability, t(1942) = 13.0, p < 0.001, d = 0.46, and desire to eat,
analyses), palatability and desire to eat ratings are of prime t(1942) = 9.3, p < 0.001, d = 0.42. Sweet vs. savory foods received
importance and are reported as a function of caloric con- higher ratings in terms of palatability, t(1960) = 20.3, p < 0.001,
tent (high- vs. low-calorie foods), sweetness (sweet vs. savory d = 0.46, and desire to eat, t(1960) = 18.8, p < 0.001, d = 0.42.
foods) and degree of processing (whole vs. processed foods). Whole vs. processed foods received higher ratings in terms of

www.frontiersin.org June 2014 | Volume 5 | Article 617 | 5

Blechert et al. Food-pics database

FIGURE 2 | (A) Means and 95% confidence intervals for valence (“very negative” to “very positive”) and arousal (“very little” to “very high”) across all image
categories. (B) Means and 95% confidence intervals for palatability and desire to eat (both “not at all” to “extremely”) across food types.

palatability, [t(1858) = 15.1, p < 0.001, d = 0.35] and desire to calorie/processed vs. non-processed foods, meat vs. non-meat)
eat, [t(1858) = 9.86, p < 0.001, d = 0.23]. In brief, valence and were significant but of small effect size (η2p < 0.06) when con-
arousal ratings largely mirrored these differences and familiarity sidering age and gender differences between the samples as
and recognizability was consistently high (>93.2% of all foods covariates.
were rated as recognizable and 94.6% of all foods were rated as Women gave lower desire to eat ratings for all foods com-
familiar). pared to men [M = 32.2, SD = 19.6 vs. M = 40.5, SD = 20.5,
t(1963) = 7.70, p < 0.001, d = 0.42] whereas no gender differ-
INFLUENCE OF DEMOGRAPHICS AND INDIVIDUAL DIFFERENCE ences were found for palatability [M = 58.8, SD = 14.5 vs.
VARIABLES: CULTURE, GENDER AND VEGETARIANISM, BMI AND AGE M = 59.1, SD = 15.6, t(1963) < 1.00].
In brief, effects of culture (North America vs. German speak- Vegetarians rated meat containing images lower than omni-
ing) on all food ratings (all foods, high calorie vs. low vores on palatability [M = 19.6, SD = 21.2, vs. M = 56.1, SD =

Frontiers in Psychology | Eating Behavior June 2014 | Volume 5 | Article 617 | 6

Blechert et al. Food-pics database

Table 2 | Subjective ratings as a function if different food types (mean, standard deviations).

High calorie Low calorie Processed Whole Sweet Savory

Palatability 56.8 (16.8) 60.9 (15.4) 57 (16.1) 62.8 (17) 62.2 (16.6) 55.5 (16.4)
Desire to eat 32 (21.5) 35.1 (20.7) 32.4 (21) 36.1 (21.7) 37.5 (22.5) 30.4 (21)
Valence 52 (16.7) 58.1 (16.2) 52.3 (16.1) 61.2 (18.3) 56.9 (16.8) 52.2 (15.7)
Arousal 33.4 (20.9) 34.7 (21.4) 33.9 (20.6) 34.6 (22.6) 37 (22.3) 32.2 (20.4)
Recognizability (%) 94.6 (8.72) 96.1 (9.30) 93.2 (9.25) 96.4 (7.3) 94.4 (9.27) 93.4 (10.4)
Familiarity (%) 94.6 (8.73) 96.2 (9.35) 95.1 (8.42) 97.6 (6.9) 95.2 (9.58) 96.1 (8.66)

Recognizability and Familiarity were dichotomous yes/no decisions.

21.2, t(1879) = 29.6, p < 0.001, d = 1.72] and desire to eat color channel went along with lower concentrations of pro-
[M = 7.46, SD = 13.2, vs. M = 31.3, SD = 26.3, t(1879) = 16.8, tein, fat and carbohydrates as well as with lower number of
p < 0.001, d = 1.21]. calories (r = −0.251, r = −0.209, r = −0.257, and r = −0.313,
BMI was not associated with palatability [r(1916) = 0.029, respectively)
n.s.] and positively but weakly correlated with desire to eat
[r(1961) = 0.117, p < 0.001, for high-calorie foods, r(1961) = DISCUSSION
0.146, p < 0.001, for low calorie foods r(1961) = 0.059, p < The present study presents food-pics, a database of images of
0.001]. Correlations of age with palatability and desire to eat were foods for experimental research on food perception and eating
very weak (rs < 0.1). behavior. Previous studies are limited considerably in stimulus
selection and/or characterization of stimulus material and food
INFLUENCE OF STATE VARIABLES: HUNGER AND CURRENT DIETING contents hampering the comparability of findings across labora-
Interestingly, being currently on a weight reduction diet (13.6% tories. Food-pics comprises a large variety of foods and non-foods
answered this question with yes) did influence ratings only along with detailed data on image characteristics, food contents,
to a minor degree. Dieters did not differ from non-dieters and normative ratings. We presented example analyses of food
on palatability ratings [M = 59.0, SD = 14.9, vs. M = 58.8, types, individual differences, state effects, and image character-
SD = 14.8, t(1963) < 1.00] and gave slightly elevated desire to istics to explore key variables relevant for experimental design of
eat ratings [M = 37.1, SD = 19.9, vs. M = 33.5, SD = 20.1, food viewing studies.
t(1963) = 2.71, p = 0.007, d = 0.21]. Hunger (averaged across Regarding food types, our results confirm that calorie content
pre- and post-questionnaire ratings) was weakly positively corre- is a relevant determinant of subjective responses, in line with a
lated with palatability, r(1965) = 0.120, p < 0.001 (r = 0.04 and several studies showing distinct neural responses for high- vs.
r = 0.141 for low- and high-calorie food images, respectively), low-calorie images (e.g., Killgore et al., 2003; Cornier et al., 2007;
but showed a medium sized positive correlation with desire to eat, Toepel et al., 2009; Frank et al., 2010). Interestingly, our nor-
r(1965) = 0.528, p < 0.001 (r = 0.473 and r = 0.524 for low- and mative data suggest slightly lower palatability and desire to eat
high-calorie food images, respectively). ratings for high-calorie images (small to medium effect size), pos-
sibly reflecting the rising awareness of the unhealthy nature of
IMAGE CHARACTERISTICS, RATINGS AND MACRONUTRIENTS these foods in the populations studied here or self-presentation
The main purpose of including image characteristics was to allow biases. Other self-report studies show the opposite (Richter et al.,
for matching of different stimulus sets in studies using neurocog- under review), as do implicit measures (Houben et al., 2012). It
nitive measures (e.g., set of high and low calories, i.e., Toepel is possible that food restrictions prior testing played a role here
et al., 2009). Since we had no neurocognitive measures in this because food deprivation renders particularly high-calorie foods
database, we explored how image characteristics were related to more attractive (Goldstone et al., 2009). Our data indicate that
(a) the subjective ratings and (b) macronutrients of the displayed participants were not very hungry [M = 28.5, SD = 25.4, on a
foods. Such data could serve to raise awareness of the importance 1 (not hungry) to 100 (very hungry) scale] but hunger corre-
to control for such characteristics by an appropriate selection of lated slightly stronger with palatability/desire to eat ratings of
images in future research. To do so, we computed Pearson cor- high- compared to low-calorie images. Sweet compared to savory
relations (images on rows) between picture characteristics and foods were rated more palatable and with stronger desire to eat, as
subjective ratings (averaged across all participants) as well as with were whole vs. processed foods. One has to keep in mind that we
macronutrients. used all images of the respective type of the database so it might
The only correlation of close to medium size indicated that well be that certain subcategories with a high number of images
image with stronger contribution of the red color channel contributed more than others (e.g., 76 images displayed fruit in
were rated as more arousing, r(883) = 0.279, p < 0.001. In addi- the whole and sweet categories). Together these results suggest
tion, image complexity (edge detection), as well as normalized that image selection will substantially influence (rating) results,
image complexity (complexity relative to image size) correlated depending on the proportion of high-calorie, sweet and whole
with subjectively rated complexity [r(883) = 0.349, p < 0.001 and foods in a specific category. Processed foods are often higher in
r(883) = 0.248, p < 0.001]. A higher contribution of the green caloric density, however, researchers could still match the total

www.frontiersin.org June 2014 | Volume 5 | Article 617 | 7

Blechert et al. Food-pics database

amount of calories displayed in the images between whole and macronutrients. Red color went along with higher arousal ratings
processed foods by selecting pictures with larger amounts of whereas green color was indicative of lower calories and lower
whole foods (e.g., wild berry mix, 53,75 kcal, image #214) and concentrations of protein, fat, and carbohydrates. Colors should
pictures with smaller amounts of processed foods (e.g., 4 pretzels, therefore be considered in the study design. Expectedly, our
44 kcal, #494). Although recognizability and familiarity of the objective index of complexity (reflecting the number of object-
objects were relatively high, it should be noted that participants components displayed in the image) correlated positively with
performed a yes/no task and did not name the objects. rated complexity. However, the low to medium sized correlation
Individual differences such as restraint, external or emotional indicates that subjective and objective measures of complexity
eating, eating disorders, or obesity are central independent vari- are partially independent constructs and studies need to make
ables in the study of eating behaviors. However, sampling error their pick of which index to use depending on study aims. Future
can induce group differences on other individual difference vari- studies might further measure image aesthetics which was not
ables unless carefully stratified. Age and BMI differences are tol- measured here but might be related to expected palatability.
erable to some degree because they showed only minor influence Further research should also employ neurocognitive measures
on ratings in our analyses (rs < 0.117). Gender and vegetari- to determine which objective and subjective image character-
anism are more relevant for sampling/matching because lower istics influence neural responses. In the lack of such evidence,
ratings for palatability and desire to eat were found for women researchers could use food-pics metadata to match image sets on
in general and for vegetarians specifically for meat-containing factors unrelated to their independent variable, particularly when
foods. These results reflect in part also inconsistencies in the lit- comparing different food types against each other. For example,
erature with regard to gender: women are sometimes reported to if the influence of caloric density is to be examined, high- and
experience cravings more frequently (Cepeda-Benito et al., 2003) low-caloric density image sets could be matched for total amount
but also restrain and worry about their eating more than men of calories in the image, sweet/savory and processed/whole food
(Dinkel et al., 2005). The present data suggest that in a large uns- proportion, and green color contribution to increase the speci-
elected sample and across a wide range of foods, women give ficity of the comparisons. If matching is not possible or not
lower palatability/desire to eat ratings. Thus, normative ratings desired, researchers should still describe their images in more
provided along with the images are reported separately for veg- detail using the metadata provided with food-pics or list the image
etarians and omnivores and for males and females to facilitate numbers in a footnote or supplementary material.
selection of suitable images. In conclusion, we hope that food-pics will facilitate experimen-
State variables like hunger are obviously important in the food tal research on food perception, eating behavior and appetitive
context. Hunger influenced desire to eat to a higher degree than responses. Databases such as food-pics will increase the compa-
palatability, which is in line with findings that specific state crav- rability of study results and therefore facilitate research commu-
ings correlate with food deprivation (Cepeda-Benito et al., 2003; nication as it is the case in object recognition, face processing or
Meule et al., 2012) and interesting in the context of the discussion emotional picture viewing. Food-pics as well as normative rating
whether “wanting” (∼desire to eat) and “liking” (∼palatability) data can be downloaded free of charge from the first author’s web-
are dissociable in humans (Finlayson et al., 2007; Havermans site at www.food-pics.sbg.ac.at upon completion of appropriate
et al., 2009; Finlayson and Dalton, 2011; Havermans, 2011). license agreements.
Hunger might further interact with caloric density as discussed
above. Interestingly, current dieting did not influence results ACKNOWLEDGMENT
much: only a small increase in desire to eat was found for dieters The authors thank Caroline Hoff, Ina Lanz, Désirée Baumbusch,
as compared to non-dieters. The literature on dieting effects is Tina Hermann, Silvya Korn, Melanie Schwarz, Tobias Burkhard
mixed: some studies have found dieting to decrease food cravings and Prof. Dr. Wolfgang Mack, University of Hagen, for their help
(reviewed in Martin et al., 2011) other studies found the opposite with data collection.
(Massey and Hill, 2012). On the other hand, weight reduction
has been reported to lead to long term weight gain (so called REFERENCES
“yoyo effects”) although the mechanisms are not clear (Ochner Ball, F., Elzemann, A., and Busch, N. A. (2013). The scene and the unseen: manip-
ulating photographs for experiments on change blindness and scene memory.
et al., 2013). These findings underscore the necessity to assess the
Behav. Res. Methods. doi: 10.3758/s13428-013-0414-2. [Epub ahead of print].
short and long-term dieting status, the diet success (i.e., weight Berthoud, H.-R., and Zheng, H. (2012). Modulation of taste responsiveness and
reductions) as well as the current hunger levels of the participants. food preference by obesity and weight loss. Physiol. Behav. 107, 527–532. doi:
Just as the appearance of foods influences their acceptability 10.1016/j.physbeh.2012.04.004
for consumption (Wadhera and Capaldi-Phillips, 2014), image Blechert, J., Feige, B., Hajcak, G., and Tuschen-Caffier, B. (2010). To eat or not to
eat? Availability of food modulates the electrocortical response to food pictures
characteristics have been shown to affect neurocognitive measures in restrained eaters. Appetite 54, 262–268. doi: 10.1016/j.appet.2009.11.007
(Vuilleumier et al., 2003; Bradley et al., 2007; Delplanque et al., Blechert, J., Feige, B., Joos, A., Zeeck, A., and Tuschen-Caffier, B. (2011).
2007; Thierry et al., 2007; Rossion and Jacques, 2008) which is Electrocortical processing of food and emotional pictures in Anorexia nervosa
why we calculated the key measures that have been established in and Bulimia nervosa. Psychosom. Med. 73, 415–421. doi: 10.1097/PSY.0b013
the field of visual perception (Knebel et al., 2008; Willenbockel e318211b871
Bohon, C., Stice, E., and Spoor, S. (2009). Female emotional eaters show abnor-
et al., 2010; Wiens et al., 2011; Kovalenko et al., 2012; Foroni malities in consummatory and anticipatory food reward: a functional mag-
et al., 2013). Lacking neurocognitive measures in the present netic resonance imaging study. Int. J. Eat. Disord. 42, 210–221. doi: 10.1002/
study, we explored their relationship with normative ratings and eat.20615

Frontiers in Psychology | Eating Behavior June 2014 | Volume 5 | Article 617 | 8

Blechert et al. Food-pics database

Bradley, M. M., Hamby, S., Low, A., and Lang, P. J. (2007). Brain potentials in Hollmann, M., Hellrung, L., Pleger, B., Schlogl, H., Kabisch, S., Stumvoll, M., et al.
perception: picture complexity and emotional arousal. Psychophysiology 44, (2012). Neural correlates of the volitional regulation of the desire for food. Int.
364–373. doi: 10.1111/j.1469-8986.2007.00520.x J. Obes. 36, 648–655. doi: 10.1038/ijo.2011.125
Burger, K. S., and Stice, E. (2011). Relation of dietary restraint scores to activation Houben, K., Roefs, A., and Jansen, A. (2012). Guilty pleasures II: restrained eaters’
of reward-related brain regions in response to food intake, anticipated intake, implicit preferences for high, moderate and low-caloric food. Eat. Behav. 13,
and food pictures. Neuroimage 55, 233–239. doi: 10.1016/j.neuroimage.2010. 275–277. doi: 10.1016/j.eatbeh.2012.03.007
12.009 Kakoschke, N., Kemps, E., and Tiggemann, M. (2014). Attentional bias
Caine-Bish, N. L., and Scheule, B. (2009). Gender differences in food prefer- modification encourages healthy eating. Eat. Behav. 15, 120–124. doi:
ences of school−aged children and adolescents. J. Sch. Health 79, 532–540. doi: 10.1016/j.eatbeh.2013.11.001
10.1111/j.1746-1561.2009.00445.x Kemps, E., Tiggemann, M., Orr, J., and Grear, J. (2014). Attentional retrain-
Canny, J. (1986). A computational approach to edge detection. Patt. Anal. ing can reduce chocolate consumption. J. Exp. Psychol. Appl. 20, 94–102. doi:
Mach. Intell. IEEE Trans. Anal. Mach. Intell. PAMI-8, 679–698. doi: 10.1037/xap0000005
10.1109/TPAMI.1986.4767851 Killgore, W. D., Young, A. D., Femia, L. A., Bogorodzki, P., Rogowska, J., and
Castellanos, E. H., Charboneau, E., Dietrich, M. S., Park, S., Bradley, B. P., Mogg, Yurgelun-Todd, D. A. (2003). Cortical and limbic activation during viewing of
K., et al. (2009). Obese adults have visual attention bias for food cue images: high- versus low-calorie foods. Neuroimage 19, 1381–1394. doi: 10.1016/S1053-
evidence for altered reward system function. Int. J. Obes. (Lond). 33, 1063–1073. 8119(03)00191-5
doi: 10.1038/ijo.2009.138 Knebel, J. F., Toepel, U., Hudry, J., Le Coutre, J., and Murray, M. M. (2008).
Cepeda-Benito, A., Fernandez, M. C., and Moreno, S. (2003). Relationship of gen- Generating controlled image sets in cognitive neuroscience research. Brain
der and eating disorder symptoms to reported cravings for food: construct Topogr. 20, 284–289. doi: 10.1007/s10548-008-0046-5
validation of state and trait craving questionnaires in Spanish. Appetite 40, Kovalenko, L. Y., Chaumon, M., and Busch, N. A. (2012). A pool of pairs of related
47–54. doi: 10.1016/s0195-6663(02)00145-9 objects (POPORO) for investigating visual semantic integration: behavioral and
Cornier, M. A., Von Kaenel, S. S., Bessesen, D. H., and Tregellas, J. R. (2007). Effects electrophysiological validation. Brain Topogr. 25, 272–284. doi: 10.1007/s10548-
of overfeeding on the neuronal response to visual food cues. Am. J. Clin. Nutr. 011-0216-8
86, 965–971. Labar, K. S., Gitelman, D. R., Parrish, T. B., Kim, Y. H., Nobre, A. C., and
Corsica, J. A., and Spring, B. J. (2008). Carbohydrate craving: a double-blind, Mesulam, M. M. (2001). Hunger selectively modulates corticolimbic activation
placebo-controlled test of the self-medication hypothesis. Eat. Behav. 9, to food stimuli in humans. Behav. Neurosci. 115, 493–500. doi: 10.1037/0735-
447–454. doi: 10.1016/j.eatbeh.2008.07.004 7044.115.2.493
Dagher, A. (2012). Functional brain imaging of appetite. Trends in Endocrinol. Martens, M. J. I., Born, J. M., Lemmens, S. G. T., Karhunen, L., Heinecke, A.,
Metab. 23, 250–260. doi: 10.1016/j.tem.2012.02.009 Goebel, R., et al. (2013). Increased sensitivity to food cues in the fasted state
Delplanque, S., N’Diaye, K., Scherer, K., and Grandjean, D. (2007). Spatial frequen- and decreased inhibitory control in the satiated state in the overweight. Am. J.
cies or emotional effects? A systematic measure of spatial frequencies for IAPS Clin. Nutr. 97, 471–479. doi: 10.3945/ajcn.112.044024
pictures by a discrete wavelet analysis. J. Neurosci. Methods 165, 144–150. doi: Martin, C. K., McClernon, F. J., Chellino, A., and Correa, J. B. (2011).
10.1016/j.jneumeth.2007.05.030 “Food cravings: a central construct in food intake behavior, weight
Dinkel, A., Berth, H., Exner, C., Rief, W., and Balck, F. (2005). German version loss, and the neurobiology of appetitive behavior,” in Handbook of
of the Restraint Scale for the assessment of restrained eating. Diagnostica 51, Behavior, Food and Nutrition, eds V. R. Preedy, R. R. Watson, and C.
67–74. doi: 10.1026/0012-1924.51.2.67 R. Martin (New York, NY: Springer), 741–755. doi: 10.1007/978-0-387-
Finlayson, G., and Dalton, M. (2011). Current progress in the assessment of ‘liking’ 92271-3_49
vs. ‘wanting’ food in human appetite. Response to RC Havermans [“You Say it’s Massey, A., and Hill, A. J. (2012). Dieting and food craving. A descriptive, quasi-
Liking, I Say it’s Wanting....” On the difficulty of disentangling food reward in prospective study. Appetite 58, 781–785. doi: 10.1016/j.appet.2012.01.020
man. Appetite 57, 286–294. doi: 10.1016/j.appet.2011.05.310 Meule, A., Kübler, A., and Blechert, J. (2013). Time course of electrocortical food-
Finlayson, G., King, N., and Blundell, J. E. (2007). Is it possible to dissociate ‘liking’ cue responses during cognitive regulation of craving. Front. Psychol. 4:669. doi:
and ‘wanting’ for foods in humans? A novel experimental procedure. Physiol. 10.3389/fpsyg.2013.00669
Behav. 90, 36–42. doi: 10.1016/j.physbeh.2006.08.020 Meule, A., Lutz, A., Vögele, C., and Kübler, A. (2012). Food cravings discrimi-
Foroni, F., Pergola, G., Argiris, G., and Rumiati, R. I. (2013). The FoodCast nate differentially between successful and unsuccessful dieters and non-dieters.
research image database (FRIDa). Front. Hum. Neurosci. 7:51. doi: Validation of the Food Cravings Questionnaires in German. Appetite 58, 88–97.
10.3389/fnhum.2013.00051 doi: 10.1016/j.appet.2011.09.010
Frank, S., Laharnar, N., Kullmann, S., Veit, R., Canova, C., Hegner, Y. L., et al. Meule, A., and Vögele, C. (2013). The psychology of eating. Front. Psychol. 4:215.
(2010). Processing of food pictures: influence of hunger, gender and calorie doi: 10.3389/fpsyg.2013.00215
content. Brain Res. 1350, 159–166. doi: 10.1016/j.brainres.2010.04.030 Nijs, I. M. T., and Franken, I. H. (2012). Attentional processing of food cues in over-
Giuliani, N. R., Calcott, R. D., and Berkman, E. T. (2013). Piece of cake. Cognitive weight and obese individuals. Curr. Obes. Rep. 1, 106–113. doi: 10.1007/s13679-
reappraisal of food craving. Appetite 64, 56–61. doi: 10.1016/j.appet.2012. 012-0011-1
12.020 Nijs, I. M. T., Franken, I. H. A., and Muris, P. (2009). Enhanced processing
Goldstone, A. P., Prechtl De Hernandez, C. G., Beaver, J. D., Muhammed, of food-related pictures in female external eaters. Appetite 53, 376–383. doi:
K., Croese, C., Bell, G., et al. (2009). Fasting biases brain reward systems 10.1016/j.appet.2009.07.022
towards high-calorie foods. Eur. J. Neurosci. 30, 1625–1635. doi: 10.1111/j.1460- Nikendei, C., Friederich, H. C., Weisbrod, M., Walther, S., Sharma, A., Herzog,
9568.2009.06949.x W., et al. (2012). Event-related potentials during recognition of semantic and
Havermans, R. C. (2011). “You Say it’s Liking, I Say it’s Wanting. . . .” On the pictorial food stimuli in patients with anorexia nervosa and healthy controls
difficulty of disentangling food reward in man. Appetite 57, 286–294. doi: with varying internal States of hunger. Psychosom. Med. 74, 136–145. doi:
10.1016/j.appet.2011.05.310 10.1097/PSY.0b013e318242496a
Havermans, R. C., Janssen, T., Giesen, J. C., Roefs, A., and Jansen, A. (2009). Food Ochner, C. N., Barrios, D. M., Lee, C. D., and Pi-Sunyer, F. X. (2013). Biological
liking, food wanting, and sensory-specific satiety. Appetite 52, 222–225. doi: mechanisms that promote weight regain following weight loss in obese humans.
10.1016/j.appet.2008.09.020 Physiol. Behav. 120, 106–113. doi: 10.1016/j.physbeh.2013.07.009
Hoefling, A., and Strack, F. (2008). The tempting effect of forbidden foods. High Poynton, C. (2012). Digital Video and HD: Algorithms and Interfaces. Waltham, MA:
calorie content evokes conflicting implicit and explicit evaluations in restrained Morgan Kaufmann Publishers.
eaters. Appetite 51, 681–689. doi: 10.1016/j.appet.2008.06.004 Raffensperger, S., Kuczmarski, M. F., Hotchkiss, L., Cotugna, N., Evans, M. K.,
Hoffman, S. R., Stallings, S. F., Bessinger, R. C., and Brooks, G. T. (2013). and Zonderman, A. B. (2010). The effect of race and predictors of socioe-
Differences between health and ethical vegetarians. Strength of conviction, conomic status on diet quality in the Healthy Aging in Neighborhoods of
nutrition knowledge, dietary restriction, and duration of adherence. Appetite Diversity across the Life Span (HANDLS) study sample. J. Natl. Med. Assoc. 102,
65, 139–144. doi: 10.1016/j.appet.2013.02.009 923–930.

www.frontiersin.org June 2014 | Volume 5 | Article 617 | 9

Blechert et al. Food-pics database

Rodriguez, S., Fernandez, M. C., Cepeda-Benito, A., and Vila, J. (2005). Vuilleumier, P., Armony, J. L., Driver, J., and Dolan, R. J. (2003). Distinct spa-
Subjective and physiological reactivity to chocolate images in high and tial frequency sensitivities for processing faces and emotional expressions. Nat.
low chocolate cravers. Biol. Psychol. 70, 9–18. doi: 10.1016/j.biopsycho.2004. Neurosci. 6, 624–631. doi: 10.1038/nn1057
10.001 Wadhera, D., and Capaldi-Phillips, E. D. (2014). A review of visual cues associated
Rossion, B., and Jacques, C. (2008). Does physical interstimulus vari- with food on food acceptance and consumption. Eat. Behav. 15, 132–143. doi:
ance account for early electrophysiological face sensitive responses in the 10.1016/j.eatbeh.2013.11.003
human brain? Ten lessons on the N170. Neuroimage 39, 1959–1979. doi: Werthmann, J., Field, M., Roefs, A., Nederkoorn, C., and Jansen, A. (2013).
10.1016/j.neuroimage.2007.10.011 Attention bias for chocolate increases chocolate consumption - an attention
Seibt, B., Häfner, M., and Deutsch, R. (2007). Prepared to eat: how imme- bias modification study. J. Behav. Ther. Exp. Psychiatry 45, 136–143. doi:
diate affective and motivational responses to food cues are influenced 10.1016/j.jbtep.2013.09.009
by food deprivation. Eur. J. Soc. Psychol. 37, 359–379. doi: 10.1002/ Wiens, S., Sand, A., and Olofsson, J. K. (2011). Nonemotional features suppress
ejsp.365 early and enhance late emotional electrocortical responses to negative pictures.
Stockburger, J., Renner, B., Weike, A. I., Hamm, A. O., and Schupp, H. T. (2009a). Biol. Psychol. 86, 83–89. doi: 10.1016/j.biopsycho.2010.11.001
Vegetarianism and food perception. Selective visual attention to meat pictures. Willenbockel, V., Sadr, J., Fiset, D., Horne, G. O., Gosselin, F., and Tanaka, J. W.
Appetite 52, 513–516. doi: 10.1016/j.appet.2008.10.001 (2010). Controlling low-level image properties: the SHINE toolbox. Behav. Res.
Stockburger, J., Schmalzle, R., Flaisch, T., Bublatzky, F., and Schupp, H. T. Methods 42, 671–684. doi: 10.3758/BRM.42.3.671
(2009b). The impact of hunger on food cue processing: an event-related Wooley, O., and Wooley, S. (1981). Relationships of salivation in humans to
brain potential study. Neuroimage 47, 1819–1829. doi: 10.1016/j.neuroimage. deprivation, inhibition, and the encephalization of hunger. Appetite 2, 331. doi:
2009.04.071 10.1016/S0195-6663(81)80019-0
Thierry, G., Martin, C. D., Downing, P., and Pegna, A. J. (2007). Controlling for
interstimulus perceptual variance abolishes N170 face selectivity. Nat. Neurosci. Conflict of Interest Statement: The authors declare that the research was con-
10, 505–511. doi: 10.1038/nn1864 ducted in the absence of any commercial or financial relationships that could be
Toepel, U., Knebel, J. F., Hudry, J., Le Coutre, J., and Murray, M. M. (2009). The construed as a potential conflict of interest.
brain tracks the energetic value in food images. Neuroimage 44, 967–974. doi:
10.1016/j.neuroimage.2008.10.005 Received: 19 March 2014; accepted: 31 May 2014; published online: 24 June 2014.
Uher, R., Treasure, J., Heining, M., Brammer, M. J., and Campbell, I. C. (2006). Citation: Blechert J, Meule A, Busch NA and Ohla K (2014) Food-pics: an image
Cerebral processing of food-related stimuli: effects of fasting and gender. Behav. database for experimental research on eating and appetite. Front. Psychol. 5:617. doi:
Brain Res. 169, 111–119. doi: 10.1016/j.bbr.2005.12.008 10.3389/fpsyg.2014.00617
Van Der Laan, L. N., De Ridder, D. T. D., Viergever, M. A., and Smeets, P. A. This article was submitted to Eating Behavior, a section of the journal Frontiers in
M. (2011). The first taste is always with the eyes: a meta-analysis on the neu- Psychology.
ral correlates of processing visual food cues. Neuroimage 55, 296–303. doi: Copyright © 2014 Blechert, Meule, Busch and Ohla. This is an open-access article
10.1016/j.neuroimage.2010.11.055 distributed under the terms of the Creative Commons Attribution License (CC BY).
Van Koningsbruggen, G. M., Veling, H., Stroebe, W., and Aarts, H. (2013). The use, distribution or reproduction in other forums is permitted, provided the
Comparing two psychological interventions in reducing impulsive processes of original author(s) or licensor are credited and that the original publication in this
eating behaviour: effects on self-selected portion size. Br. J. Health Psychol. doi: journal is cited, in accordance with accepted academic practice. No use, distribution or
10.1111/bjhp.12075. [Epub ahead of print]. reproduction is permitted which does not comply with these terms.

Frontiers in Psychology | Eating Behavior June 2014 | Volume 5 | Article 617 | 10