Food Research International 97 (2017) 199–208

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Food Research International
journal homepage: www.elsevier.com/locate/foodres

Visual attention to food cues is diﬀerentially modulated by gustatoryhedonic and post-ingestive attributes

MARKDavid Garcia-Burgos⁎, Junpeng Lao, Simone Munsch1, Roberto Caldara1
Department of Psychology, University of Fribourg, Fribourg, Switzerland

A R T I C L E I N F O

A B S T R A C T

Keywords:
Antisaccade
Attentional bias
Food avoidance
Food related-cue
Food reward
Free viewing

Although attentional biases towards food cues may play a critical role in food choices and eating behaviours, itremains largely unexplored which speciﬁc food attribute governs visual attentional deployment. The allocationof visual attention might be modulated by anticipatory postingestive consequences, from taste sensations derived
from eating itself, or both. Therefore, in order to obtain a comprehensive understanding of the attentional
mechanisms involved in the processing of food-related cues, we recorded the eye movements to ﬁve categories ofwell-standardised pictures: neutral non-food, high-calorie, good taste, distaste and dangerous food. In particular,
forty-four healthy adults of both sexes were assessed with an antisaccade paradigm (which requires thegeneration of a voluntary saccade and the suppression of a reﬂex one) and a free viewing paradigm (which
implies the free visual exploration of two images). The results showed that observers directed their initial
ﬁxations more often and faster on items with high survival relevance such as nutrient and possible dangers;
although an increase in antisaccade error rates was only detected for high-calorie items. We also found longer
prosaccade ﬁxation duration and initial ﬁxation duration bias score related to maintained attention towards
high-calorie, good taste and danger categories; while shorter reaction times to correct an incorrect prosaccade
related to less diﬃculties in inhibiting distasteful images. Altogether, these ﬁndings suggest that visual attention
is diﬀerentially modulated by both the accepted and rejected food attributes, but also that normal-weight, noneating disordered individuals exhibit enhanced approach to food's postingestive eﬀects and avoidance ofdistasteful items (such as bitter vegetables or pungent products).

1. Introduction
Under strong survival pressure, organisms face the problem ofidentifying food sources, avoiding lethal toxins and eating a nutritionally balanced diet. In humans, this activity basically relies on vision
when it comes to locate, identify and select potential food in theenvironment. While searching nutritious sources of food, visual scenes
typically contain many objects that compete for the control of visual
attention and eye movements, which cannot all be processed simultaneously because of our inherently limited information-processing
resources. To overcome such limitations, the allocation of visual
attention is determined by bottom-up factors like physical salience ofthe objects (e.g., its colour, orientation or velocity; Wolfe, 2007) and by
top-down factors like incentive salience (constructed via its learned
rewarding value given by the brain; Horstmann, 2015). The interactionof bottom-up sensory information and top-down inﬂuences creates anintegrated saliency map of the visual environment that ﬂags regions ofinterest in the retinal image; this map appears to be distributed across⁎1

areas of the visual cortex and is closely linked to the oculomotor systemthat controls eye movements and orients the gaze to locations in thevisual scene characterized by a high salience (Treue, 2003).
Among the visual stimuli with high incentive salience, food-related
visual cues are particularly eﬀective at grabbing attention. Indeed,viewing pictures of food as opposed to non-food appears to enhanceattention in both normal-weight and overweight/obese individuals
when measuring the activation of brain regions related to attention
via functional magnetic resonance imaging (Yokum, Ng, & Stice, 2011)and event-related potentials (Nijs, Muris, Euser, & Franken, 2010). It is
worth noting that food items diﬀer in their properties such as tastehedonic attribute (e.g., via the sensory pleasure of tasting non-caloric
sweeteners) and nutritional value (e.g., calorie delivery), which bothinﬂuence attention processing. For instance, using indirect methods forassessing attention to food-related visual stimuli, visual evoked potentials and response latencies during a spatial attention paradigm indicatethat limited attentional resources are preferentially directed towards
the processing of high-energy compared to low-energy food-related

Corresponding author at: University of Fribourg, Department of Psychology, Rue P.-A. de Faucigny 2, CH-1700 Fribourg, Switzerland.
E-mail address: david.garcia-burgos@unifr.ch (D. Garcia-Burgos).
Shared last authorship.

http://dx.doi.org/10.1016/j.foodres.2017.04.011
Received 13 December 2016; Received in revised form 8 April 2017; Accepted 10 April 2017
Available online 13 April 20170963-9969/ © 2017 Elsevier Ltd. All rights reserved.

Food Research International 97 (2017) 199–208

D. Garcia-Burgos et al.

and/or orosensory attributes of the well-standardised food-related
pictures, which were individually matched as closely as possible forsize, brightness, contrast, complexity and spatial frequencies. In addition, the symptoms of disordered eating and diﬀerences in individual
eating style (e.g., levels of restrained and external eating) wereconsidered because they may inﬂuence attentional bias to food-related
images either in combination with weight status or as a factor in itself(Doolan et al., 2015). Furthermore, reward responses and impulsivity
were also controlled as individuals with high sensitivity to reward havebeen shown to be more prone to detect signals of reward in theirenvironment and to approach them (Beaver et al., 2006). We thus used
an eye-tracking approach due to its critical advantages over indirect
methods such as the continuous measures of the orientation, maintenance and disengagement of visual attention to stimuli with acquired
incentive salience; the unique and easily accessible window to examine
how semantic knowledge modulates attentional responses to foodbecause the rapid visual system's ability to access a wealth of knowledge about food attributes (Stevenson, 2009); and the same visualsensory modality for the input and output processes, allowing for tightcontrol and interpretation of the temporal components (early/involuntary or later/voluntary attentional processes) and the direction of
attention (approach or increasing attention versus avoidance or reducing attention) (Holmqvist et al., 2011). To measure diﬃculties ininhibition and attentional disengagement towards rewarding andaversive food cues, the well-established eye tracking antisaccade
paradigm was selected, in which people are instructed to look awayfrom a newly appearing neutral stimulus; while automatic attention
orienting and conscious maintenance of attention towards food versus
non-food pictures were simultaneously examined using another wellcharacterized eye tracking paradigm: free viewing (both previously
used for studies in visual attention to food cues; e.g., Schag et al., 2013).
The guidance of our picture selection followed the Rozin andFallon's (1986) psychological categories: high-calorie, good taste,
distaste, danger and neutral non-food; which are based on knowledge(perceptual and semantic) about sensory properties of food, anticipatedconsequences of ingestion, and ideas about the nature or origin of thefoodstuﬀs (see Appendix, Table A1). The high-calorie category referredto things people expect to have postingestive consequences such as
feeling of fullness and satiation after eating high calorie food (e.g.,
cakes, potato chips) and whose tastes usually become liked; while thegood taste category consisted of items with pleasant sensory eﬀects inthe mouth such as sweet sensations. In opposite to good taste, distaste
category was included and referred to items with commonly unpleasanttaste qualities including bitter, sour or irritants sensations (e.g.,cabbage, broccoli or spinach). The danger category referred to foodbelieved to have harmful consequences if ingested (e.g., nauseas,stomach discomfort) and considered as both distasteful and dangerous(e.g., rotten food). We hypothesized that items with higher relevance tosurvival (such as high-calorie and danger) should aﬀect not only early/
involuntary but also later/voluntary attentional processes with atendency to initially and longer ﬁxate on the food pictures. In addition,
given the modulating eﬀect of the sensory-hedonic properties on visual
attention (Brignell, Griﬃths, Bradley, & Mogg, 2009; Graham et al.,2011), approach/increasing attention by pleasant taste and high-calorie
food as well as avoidance/reducing attention by unpleasant distastefuland danger content are expected.

cues in weight loss maintainers and obese individuals (Harrar, Toepel,
Murray, & Spence, 2011; Phelan et al., 2011; Toepel, Knebel, Hudry, leCoutre, & Murray, 2009). Considering the impact of the sensory-hedonic value, studies applying temporary devaluations in the pleasantnessof the taste of high-energy foods via sensory-speciﬁc satiety andviewing the same as stimuli in a visual probe task revealed that theallocation of visual attention is rapidly adjusted to food images that areperceived to be more pleasant (di Pellegrino, Magarelli, & Mengarelli,2011). However, spatial attention and visual probe paradigms only
provide an simpliﬁed view of selective attention and tend to ignore
relevant distinctions between the mechanisms involved in the initialorienting versus maintenance of attention as it is not possible tomeasure shifts in attention between stimuli presented side by side orgauge subject disengagement from pictures presented during the task
(Doolan, Breslin, Hanna, & Gallagher, 2015).
More accurate and insightful measures for attention provided bydirect and ecologically valid eye-tracking techniques also support thepower of food pictures to capture visual attention in obese samples,eating-disorders patients and restrained eaters. In particular, an increased visual attention bias for high-energy foods (deﬁned as thetendency to selectively attend to [orientation towards] and/or holdattention on [slowed disengagement from] high-energy food cues) havebeen revealed in those samples (Doolan et al., 2015; Kim, Kim, & Lee,
2016; Popien, Frayn, von Ranson, & Sears, 2015; Werthmann,Jansen, & Roefs, 2015; Werthmann et al., 2011, 2013). Surprisingly,
preliminary evidence suggests that normal-weight, non-clinical andunrestrained individuals exhibit similar food-related attention biases.
Thus, it could be assumed that biases consisting in selective attention
towards high energy food are shared by everyone (Werthmann et al.,2015). Nevertheless, studies in subjects with normal eating and weight
regulation habits remain scarce. In addition, available evidence iscontradictive and generalization is limited, as so far only women wereenrolled. For instance, Castellanos et al. (2009) and Nijs et al. (2010)
reported enhanced initial gaze direction and gaze duration to the highcaloric food pictures in normal-weight females, especially while they
were fasting. By contrast, Nummenmaa, Hietanen, Calvo, and Hyona(2011, Experiment 2) found that such attentional orienting biases
towards food items disappear when physical salience of the picturesets was strictly matched. Therefore, in order to obtain a comprehensive
understanding of the attentional mechanisms involved in the processingof food-related cues, further studies with food compared to non-foodpictures better controlled for visual saliency are needed in order to test
whether healthy participants do have attentional biases.
Another crucial issue is the type of food stimuli used to measure
visual attention. Although it has been suggested that the high-calorie
content (Castellanos et al., 2009) rather the hedonic value of the taste
might be responsible for the attentional biases, preferences for energydense foods diﬀer depending on the taste value of speciﬁc foodproducts. For instance, normal-weight subjects exhibit attentionalbiases as indexed by a larger proportion of initial ﬁxations to highcalorie sweet (e.g., ice cream, chocolate cake) but not savory (e.g.,bacon cheese burger, fried chicken meal) food pictures relative to low
calorie images (e.g., sliced fruit, veggie wrap) when participants freely
viewed pairs of images (Graham, Hoover, Ceballos, & Komogortsev,
2011). Consequently, it has been argued that the taste of food products
inﬂuences attentional responses towards diﬀerent kinds of fattening
food. Unfortunately, the diﬀerential impact of taste and post-ingestive
properties is diﬃcult to determine from the current studies as the lowenergy food pictures used as control are not satisfactory. In fact, sincethese low-energy control conditions are not usually matched with highenergy conditions for taste pleasantness or even include unpleasant
bitter vegetables, they cannot rule out the possibility that heightened
attention towards high-energy food pictures may be partly due to
avoidance of the distasteful control pictures.
For these reasons, the present study is the ﬁrst to examine theattentional responses of normal-weight subjects to the post-ingestive

2. Material and methods
2.1. Participants
A total of 44 students (7 men and 37 women) of the University of
Fribourg (Switzerland) participated in our study and were compensatedwith course credits. All participants self-reported normal or correctedto-normal visual acuity. The participants reported sociodemographic
characteristics. Additionally, eating disorder pathology using the Eating
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Internet sources. Physical image characteristics were computed using acustomized script written in Matlab R2011b to account for the potential
confounds of low-level visual properties (https://goo.gl/T1BzDw;
Blechert et al., 2014) and matched for physical properties (Table A2),
which did not diﬀer in size, brightness, within-object contrast, complexity (edge detection) and spatial frequencies (Kruskal-Wallis tests; highest c2 = 9.4, df = 4, p > 0.05). For a deﬁnition of the physical image
properties size, brightness, within-object contrast, complexity andspatial frequencies, see Blechert et al.’s (2014). All pictures had thesame resolution and colour depth (600 × 450 pixels, 96 dpi, 24 bpp)
and were homogenous with regard to neutral (white) background.
The stimulus material consisted of 126 coloured pictures (36 for theantisaccade task and 90 for the free exploration task). BE pictures rated
high in palatability, craving (i.e., desire-to-eat), valence (i.e., the feelingof pleasantness/unpleasantness towards a stimulus) and in calorie
content; while GT pictures were related to high ratings for palatability,
craving and valence but to low calories. By contrast, DA pictures rated
low in palatability, craving and valence; while DI pictures rated low inpalatability, craving and calories. Analyses of the database ratings andmacronutrients showed signiﬁcant diﬀerences accordingly (KruskalWallis tests; lowest c2 = 9.4, df = 4, p < 0.05; Appendix, Table A2).
In order to establish the maximum separation possible (and according
to kcal limits for foods sold outside of the middle and high schools;
Robles, Wood, Kimmons, & Kuo, 2013), the criterion for high calorie
content was ﬁxed at > 250 kcal/100g, while low calories values were
determined by foods with < 70 kcal/100g. Scores of palatability >60, craving > 37 and valence > 55 were respectively considered ashigh palatable, high craving and pleasant food pictures; while scores ofpalatability < 45, craving < 26 and valence < 45 as low palatable,
less desirable and unpleasant food pictures.

Table 1
Characteristics of male and female participants.
N = 44

Male

FemaleP

Age (years)
Body mass index (kg/m2)
Eating Disorder Examination—Questionnaire(EDE-Q)
Shape concern
Weight concern
Restraint
Eating concern
GlobalDutch Eating Behaviour Questionnaire(DEBQ)
Restrained eating
Emotional eating
External eating
Behavioural Inhibition System and
Behavioural Activation System Scale(BIS/BAS)BAS Drive
BAS Fun seeking
BAS Reward
BIS

23.29(1.38)
22.28(1.65)

21.68(2.4)
21.61(1.71)

0.09
0.34

1.09(1.24)
0.74(0.99)
0.43(0.37)
0.20(0.38)
0.97(1.13)

1.50(1.20)
1.09(1.02)
0.70(0.78)
0.36(0.54)
1.77(1.07)

0.45
0.40
0.38
0.46
0.34

2.47(0.77)
2.56(0.64)
1.17(0.68)

2.97(0.59)
2.37(0.51)
1.25(0.63)

0.05
0.40
0.75

10.28(1.60)
8.14 (1.77)
16.71(2.88)
17.71(3.87)

11.27(1.97)
9.27(1.69)
16.30(2.89)
15.22(3.65)

0.22
0.12
0.60
0.03

Note. Mean and standard deviation (in parentheses). Clinical cut-oﬀ value of EDEQGlobal ≥ 4. Internal consistency of the scales (gender data collapsed): α = 0.90 for EDEQShape concern, α = 0.83 for EDE-QWeight concern, α = 0.72 for EDE-QRestraint, α = 0.89 for
EDE-QEating concern, α = 0.92 for EDE-QGlobal; α = 0.84 for DEBQRestrained eating, α = 0.84
for DEBQEmotional eating, α = 0.80 for DEBQExternal eating; α = 0.62 for BASDrive, α = 0.65
for BASFun seeking, α = 0.61 for BASReward, α = 0.69 for BIS.

Disorder Examination Questionnaire (EDE-Q4; Fairburn & Beglin,1994), eating styles using the Dutch Eating Behaviour Questionnaire(DEBQ; Lluch et al., 1996), and sensitivity of reward and impulsivity
using the Behavioural Inhibition System and Behavioural Activation
System Scale (BIS/BAS; Caci, Deschaux, & Baylé, 2007) were assessed.
Exclusion criteria were diseases/medication inﬂuencing weight/eating
behaviour, eating disorders, diabetes, allergies or aversions to any of
the food pictures used, 30 > age > 18 years, vegetarians, people
reporting religious food prohibition and a body mass index, BMI, of
below 18.5 or over 25.0 kg/m2. The participant characteristics are
depicted in Table 1. No signiﬁcant diﬀerences were found in a multivariate generalized linear model analysis including age, BMI, EDE-Q4,
BEDQ and BIS/Bas scores related to gender, except for DEBQ–Restrained eating and BIS scores. While mean scores of DEBQ, EDE-Q, BAS
Drive and BAS Fun seeking were comparable to scores in other nonclinical samples (e.g., (Castellanos et al., 2009; Hilbert, TuschenCaﬃer, Karwautz, Niederhofer, & Munsch, 2007) and none of theparticipants scored above the cut oﬀ for clinical eating disorders (i.e.,a global score of at least 4 on the EDE-Q; Mond, Hay, Rodgers, & Owen,
2006), BAS reward and BIS scores were lower compared with healthy
aged-matched samples (Castellanos et al., 2009). The participants were
informed of the general procedure and gave their written consent. The
study was approved by the ethics committee of the Department of
Psychology of Fribourg and the Ethical Committee of the Canton of
Vaud (Switzerland).

2.2.2. Subjective ratings of the images
Additionally, the participants also rated the pictures in terms ofarousal (i.e., the degree to which one feels excited or activated by astimulus), valence, palatability, craving, and negative post-ingestive
consequences before completing the tasks. The analyses conﬁrmed theeﬀectiveness of the picture selection in our sample (see Table 3)
showing signiﬁcant diﬀerences in arousal (BE,GT,DI,DA > NE), valence (BE,GT > DI, NE > DA), palatability (BE,GT > DI > DA) andcraving (BE,GT > DI > DA) as expected according to Blechert et al.
(2014), as well as negative consequences (BA > BE > DI > GT)(ANOVAs with Greenhouse-Geisser correction and post-hoc comparisons with Bonferroni corrections; the lowest F[3.19,137.16] = 48.46,
p < 0.001; η2 = 0.53).
2.3. Experimental paradigms and eye-movement metrics
We used two eye movement paradigms to investigate: 1) the initial
engagement of visual attention towards food/non-food pictures (antisaccade paradigm), and 2) the general visual attention towards
diﬀerent pictures in competition (free viewing paradigm).
In the antisaccade paradigm, pictures of each category appear
randomly on the left or right side of the screen. Participants were
instructed to look in the direction of the stimulus (prosaccade) or in the
opposite direction of the stimulus (antisaccade) depending on thecolour (red/green) of a previously presented dot, as fast as accurately
they could (see Fig. 2A) according to the following instruction: “If agreen dot appears on the screen, please look at the picture as fast as
possible. If a red dot appears on the screen, please don't to look at the
picture but to look in the opposite direction”. The antisaccade paradigm
was composed by 720 randomized trials; each image was presented 20
times after the red/green cue (12 blocks, each with 60 trials). The
percentage of directional errors (incorrect pro/antisaccades over the
total number of pro/antisaccades) and the latency of the ﬁrst correct/
incorrect saccade (saccadic reaction times from the onset of the image
for the ﬁrst correct/incorrect pro/antisaccade) represented automatic

2.2. Food images
2.2.1. Parameters of the image database
Except for one category, images were selected from a food picture
database featuring food images with simple ﬁgure ground compositions
for experimental research (Blechert, Meule, Busch, & Ohla, 2014;
Table 2) and comprised non-food pictures and arousing food pictures
according to Rozin and Fallon's (1986) classiﬁcation: neutral (NE),high-calorie (HC), good taste (GT), distaste (DI) and danger (DA) foods(Fig. 1). The exception was the DA food pictures, which were notavailable in the Blechert et al.’s (2014) database and were selected from
201

202

358, 365, 407, 442, 446, 452, 453, 460,
461, 467, 468, 482, 526, 528, 530

Strawberry, half peaches, plate of salad,
mixed salad, peach, raspberry, blackberry,
tomato, peach, watermelon, tomato, sliced
red bell pepper, blackberries, orange, glassof orange juice

Chocolate ﬁlled with milk cream,croissant with butter and jam, pralines,
pistachios, croissant, brownie with nuts,
KitKat, Toblerone, chocolate cookies,
white chocolate, chocolate truﬄe, bar of
chocolate, chocolate (candy) bar, marble
cake, chocolate pieces

Salad plate, tomatoes bell peppers,
raspberries, watermelon, salad plate

Chocolate cookie, Croissants, bag ofchips, ﬁlled chocolates, bar of chocolatewith nuts, bar of chocolate

173, 174, 179, 286, 287, 289, 290, 291,
293, 295, 344, 351, 416, 465, 474,

196, 197, 198, 199, 201, 531

GT4, 7, 27, 83, 111, 112

HC

List of pictures

1004, 1007, 1008, 1009, 1010, 1011,
1012, 1013, 1014, 1015 1016, 1018
Shoe, brush, hair brush, red bucket, yellow
bucket, light bulb, hammer, towel,
cushion, clothes hanger, ladder, nails,
paintbrushes
1022, 1128, 1232, 1135, 1137, 1031,
1032, 1033, 1035, 1036, 1038, 1058,
1059, 1080, 1092, 1095, 1098, 1129,
1130, 1131, 1132, 1198, 1200, 1202,
1208, 1145, 1218, 1219, 1222, 1223
Scissors, brown plant starter set, candle,
bucket, lamb, sockets, telephone, clock,
clock, books, sponge, hair brush, wastebin, sofa, shopping bag, paper bag, brownwallet, paper bag, screw and nuts, screw
and nuts, tape, tape, toilet paper, wateringcan, suitcase, broom, books, closed chair,chair, garbage bin, wooden caseNE–

228, 247, 251, 252, 254, 257, 260, 266,
276, 333, 360, 367, 405, 434, 449

Rotten salami, burnt nuggets, burnt
chocolate cookies, rotten hotdog, rotten
bread, rotten bread, cheese, chocolatebar, sausages, rotten chicken, toast,rotten salami, burnt cookies

Rotten bread, burnt slice of toast, rotten
sausage, rotten salami, rotten meatﬁlets, rotten chicken

Celery, endives, artichoke, red chilli
pepper, green chilli pepper, Brussels
sprouts

Artichoke, zucchini, lemons, green
lettuce, green bell peppers, spring
onions, Chinese cabbage, lettuce(iceberg), ﬁgs, bowl of salad, leek,
asparagus, lettuce (lollo rosso) garden
radish green onion (shallot)–

DA

262, 342, 368, 401, 520, 550

DINote. Number in the food-pics database (Blechert et al., 2014). DA pictures were not available in the food-pics database and were elaborated by our group. HC = sweet/savory high-calorie food; GT = sweet low-calorie foods; NE = non-kitchenhousehold items; DI = low calorie items with unpleasant taste; DA = potentially noxious substance.

Free exploration

Antisaccade

Paradigm

Table 2
List of food pictures (code and food description) used in the current study.

D. Garcia-Burgos et al.

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Food Research International 97 (2017) 199–208

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Fig. 1. A subset of the types of images used for high-calorie (HC), good taste (GT), neutral non-food (NE), distaste (DI) and danger (DA) categories.

attention orienting. In addition, the ﬁxation duration after the ﬁrst
correct saccade and the time to correct directional errors in the ﬁrst
saccade represented conscious and maintained attention.
In the free viewing paradigm, participants were instructed to freely
look at the two diﬀerent images presented at the same time on thescreen for 2000 ms (as previous studies using eye tracking; e.g., Priebe,
Messingschlager, & Lautenbacher, 2015) (Fig. 2B) according to thefollowing instruction: “Please look freely at the images”. It wascomposed by 90 randomized trials, meaning 90 picture comparisons(each picture appears 2 times: once on the left and once on the right).
According to Mogg, Bradley, Field, and De Houwer (2003), attentional
orienting is represented by the ﬁxation direction bias score, calculated
as the percentage of trials when the ﬁrst ﬁxation was directed to thefood pictures as a proportion of the total number of trials (scores >50% reﬂect a bias in orienting towards food pictures relative to nonfood pictures; 50% indicates no bias). Attentional engagement wasmeasured by the ﬁxation duration bias score, calculated by subtracting
the mean duration of initial ﬁxations on non-food pictures from themean duration of initial ﬁxations on food-related pictures (positivescores reﬂected more time spent looking at food-related pictures); andgaze duration bias score (Giel et al., 2011), calculated by subtracting
the total gaze duration (sum of all duration ﬁxations in an image pertrial) on the food pictures from the total gaze duration on the non-foodpictures (positive scores reﬂect the tendency to continuously pay
attention to the food picture). It should be noted that the eﬀects offood pictures were always compared using the neutral non-food (NE) as
reference category in order to rule out the possibility that attentional
responses, e.g., towards rewarding food pictures are rather due to
avoidance of the aversive food pictures or that contrast categoryinﬂuences the context in which the relevant images are automaticallyevaluated.

infrared camera and an infrared reﬂective mirror. The eye tracker had aspatial resolution of 0.01° of the visual angle, an average gaze position
error of about 0.25°, and the signal was sampled and stored at a rate of1000 Hz. Although the viewing was binocular, the recording was
monocular (a standard procedure in eye-tracking studies). Calibrationand validation of the measurements were performed before eachexperimental session using a nine-point ﬁxation to adapt the eye
tracker to the individual properties of the participants. If results weren't
satisfying (drift correction > 1°), the procedure was performed again.
The eye tracking tower with the chin and forehead pad was arranged
80 cm in front of a 60.9 cm computer screen with a resolution of1920 × 1080 pixels (Dell U2412 M). Both antisaccade and free-exploration tasks were running using Matlab 7.10.0 (R2010a), EyeLink
Toolbox and Psychophysics extensions.
2.5. Procedure
Before coming to the laboratory, participants completed online
versions of the questionnaires. The additional subjective computerbased rating of all pictures was conducted. After a calibration andvalidation, the antisaccade task was performed ﬁrst as it required more
eﬀort. The whole experiment was organized in two sessions of about
1.5 h each over two days, the ﬁrst one serving to ﬁll out thequestionnaires and the second one involving the eye movement tasks.
In order to control the motivational states, participants were asked not
to eat or drink anything for 3 h prior to each test session (as used inprevious studies on visual saliency bias in consumer choices; e.g.,
Milosavljevic, Navalpakkam, Koch, & Rangel, 2012), except water
which was allowed up to 1 h before the lab session.
2.6. Data preprocessing and analysis

2.4. Apparatus

Artefact-contaminated trials and trials without saccades (e.g., failures to detect the pupil and eyeblinks) were subtracted from the total
number of valid trials. Trials with latencies below 80 ms or above
700 ms after target onset were also excluded as they are considered as
anticipatory or delayed (Fischer, Gezeck, & Hartnegg, 1997). The

Using the EyeLink® 1000 Desktop Mount system (SR Research Ltd.,
Ontario, Canada), eye position and eye movements were determined bymeasuring the corneal reﬂection and dark pupil with a video-based
Table 3
Additional subjective ratings of the food pictures.
Paradigm

Attribute

Antisaccade

Rating data

Free exploration

Rating data

Category

Palatability
Craving
Valence
Arousal
Negative consequences
Palatability
Craving
Valence
Arousal
Negative consequences

HC

GT

NE

DI

DA

64.64(11.77) a
44.01(11.07) a
61.19(7.75) a
35.39(17.70) a
50.38(8.70) a
62.25(15.98) a
47.07(13.32) a
54.70(16.04) a
44.75(15.01) a
26.99(10.37) a

59.58(11.98) a
47.21(8.63) a
60.53(6.59) a
35.96(12.94) a
10.36(5.34) b
64.55(8.15) a
52.85(12.67) a
60.49(18.90) a
40.41(11.42) a
15.02(8.60) b––
39.51(12.24) c
10.35(6.72) c–––
42.08(10.20) c
15.52(7.92) c–

45.82(14.8) d
20.10(8.56) d
44.23(7.63) d
20.95(11.60) d
18.18(8.50) d
42.01(14.99) d
34.58(5.18) d
42.06(18.32) c, d29.50(10.31) d
15.37(13.63) b, d4.92(2.78) e
5.00 (2.78) e
10.90(6.53) e
33.66(15.05) a, e
68.00(13.64) e
4.78(2.53) e
6.50(3.62) e
9.92(6.53) e
43.35(16.01) a, e
63.76(14.94) e

Note. Mean and standard deviation (in parentheses). Palatability (from “not at all” to “extremely”), craving (from “not at all” to “extremely”), valence (from “very negative” to “verypositive”), arousal (from “not at all” to “extremely”) and negative consequences (from “not at all” to “extremely”; considered as anticipated postingestive consequences such as the risk ofsickness in eating the food in the picture) were rated using visual analogue scales (from 1 to 100). Values with diﬀerent superscripts represent means that are statistically diﬀerent;p < 0.001.

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Fig. 2. Schematic representation of the antisaccade paradigm (A) and the free viewing paradigm (B). (For interpretation of the references to colour in this ﬁgure, the reader is referred to
the web version of this article.)

the tests of ﬁxed eﬀects did not have F distributions, the degrees of
freedoms were adjusted by a Satterthwaite approximation. Analyses
were performed using the MIXED procedure in SPSS (IBM SPSS
Statistics 22).

percentage of invalid trials amounted up to 33.81 ± 20.01 (mean ±SD; average percentage anticipatory/delayed trials = 8.62) for theantisaccade and to 6.67 ± 7.84 (average percentage anticipatory trials= 4.93) for free exploration tasks. Although the percentage of anticipatory/early responses was similar to previous studies with participants
aged 20–35 years (e.g., Klein, Fischer, Hartnegg, Heiss, & Roth, 2000), a
large number of invalid trial was observed for antisaccade possibly as aresult of fatigue and the diﬃculty of our task. On the basis of thenumber of valid trials, whether a picture category signiﬁcantly aﬀected
eye-movement metrics of the antisaccade (percentage of directional
errors, latency of the ﬁrst correct saccade, ﬁxation duration after theﬁrst correct saccade and time to correct directional errors in the ﬁrst
saccade) and free exploration (direction bias score, ﬁxation duration
bias score and gaze duration bias score) paradigms was tested using
Linear Mixed-Eﬀects Modelling (LMM) (Heck, Thomas, & Tabata, 2013;
Maxwell & Delaney, 2004).
Hierarchical modelling was performed using LMM. The need for
multilevel modelling of dependent variables was indicated by the
intraclass correlation coeﬃcient (ICC > 0.05) and the design eﬀect(deﬀ > 2) both showing dependency in the data (Hox & Balderjahn,1998). LMM was ﬁt to the data with random intercepts for participantand questionnaire scores. Picture category and gender are considered asﬁxed eﬀects. All models were ﬁtted using Maximum Likelihood with afull variance component structure. In the basic model, we applied arandom intercept model with only picture category as ﬁxed eﬀect andparticipant as random eﬀect. A step-wise procedure was then applied toconstruct the more detailed model. The following terms are added: 1)
the gender of the participant as the ﬁxed eﬀect; 2) the interaction
between gender and picture category as ﬁxed eﬀects; and 3) thequestionnaires scores (i.e., EDE-Q, DEBQ, and BIS/BAS) as random
eﬀects. Model comparison was performed on the model log-likelihood.
Concerning the ﬁxed eﬀects, the model predicting antisaccade metrics
was signiﬁcantly improved when gender was included (direction error;
χ2Change = 4.113, dfChange = 1, p < 0.05), but not for free exploration
metrics (higher χ2Change = 1.01, dfChange = 1, p = 0.3). On the other
hand, no model was signiﬁcantly improved when picture category x
gender was added and neither model produced a signiﬁcant picture
category x gender interaction (Cohen's d < 0.2; following the Taylor(2014)’s approach); thus, the interaction was dropped from all subsequent analyses. Concerning the questionnaire scores as random
eﬀects, the model predicting antisaccade metrics was found to have asigniﬁcantly better ﬁt adding only EDEQ–Eating Concern and BASReward scores (ps < 0.05). The questionnaires scores however did not
substantially alter the model for free exploration (higher
χ2Change = 0.99, dfChange = 1, p = 0.32). We thus reported the modelﬁtting for antisaccade using Eq. 1 and free exploration metrics using Eq.
2. In all analysis, a signiﬁcance level of α = 0.05 was adopted. Post-hoc
factor level comparisons were calculated using a pairwise comparison
method and the Bonferroni correction for multiple comparisons. WhenYij = b00 + b10 Picture Categoryij + b01 Genderj + u1j EDEQ –Eating Concernj+ u2j BAS –Rewardj + u 0j + εij(1)

Yij = b00 + b10 Picture Categoryij + u 0j + εij(2)

3. Results
3.1. Antisaccade paradigm
The model only yielded a signiﬁcant main eﬀect for gender onantisaccade directional error rate (F(1,43.35) = 5.11, p < 0.05),
showing higher rates in females (32.9%) compared to males (20.5%)(b = − 12.4, t(43.36) = 2.26, p < 0.05). Concerning the eﬀect for
picture category, the means for both pro- and antisaccades metrics arepresented in Table 4 and the results from the model's estimate aredisplayed in Fig. 3 (see also Appendix, Table A3). The analysis showed asigniﬁcant main eﬀect of picture category on the percentage ofdirectional errors for antisaccade (F(4176) = 2.92, p < 0.05), latencyof the ﬁrst correct prosaccade (F(4176) = 8.27, p < 0.001), ﬁxation
duration after the ﬁrst correct prosaccade (F(4176) = 8.12,
p < 0.001) and time to correct directional errors in the ﬁrst antisaccade (F(4176) = 135.20, p < 0.001). No signiﬁcant eﬀects were
found on the percentage of directional errors for prosaccade, latency of
the ﬁrst correct antisaccade, ﬁxation duration after the ﬁrst correct
antisaccade and time to correct directional errors in the ﬁrst prosaccade
Table 4
Metrics for pro- and antisaccade as a function of the picture category.
Trial

Picturecategory

Direction
errors (%)

Latency of
the ﬁrst
correctsaccade(msec)

Fixation
durationafter the ﬁrst
correctsaccade(msec)

Time tocorrect
directional
errors in theﬁrst saccade(msec)

Antisaccade

HC
GT
DI
DA
NE
HC
GT
DI
DA
NE

32.3(16.4)
31.3(17.0)
29.6(15.1)
32.1(16.5)
30.7(14.4)
5.4(4.9)
5.4(4.7)
7.2(6.3)
5.6(4.1)
5.8(4.1)

172.9(39.0)
173.0(42.5)
178.4(37.6)
175.7(42.5)
177.7(40.0)
137.8(25.4)
136.8(26.1)
143.3(25.7)
141.3(24.9)
144.3(26.1)

759.5(39.9)
759.3(43.1)
754.9(38.9)
756.7(43.2)
755.8(39.4)
804.9(25.7)
805.5(26.3)
799.4(26.2)
801.2(25.9)
797.5(26.6)

80.4(14.2)
83.3(12.9)
58.4(10.5)
84.5(14.8)
83.9(13.7)
66.3(10.7)
70.1(10.3)
66.0(9.9)
67.9(9.5)
68.1(9.9)

Prosaccade

Note. Mean and standard deviation (in parentheses).

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Fig. 3. Antisaccade-paradigm. The model's estimates for percentage of directional errors, saccadic reaction time of the ﬁrst correct saccade, ﬁxation duration after the ﬁrst correct
saccade, and time to correct directional errors in the ﬁrst saccade obtained for both (left) pro-saccades and (right) anti-saccades as a function of picture type (high-calorie [HC], good taste[GT], distaste [DI] and danger [DA] categories compared with neutral non-food [NE]). Bars indicate standard errors. *p < 0.05, ***p < 0.001.

prosaccade (ps > 1). Finally, it should be noted that, even after
controlling for between participant variability via EDEQ-Eating Concern and BAS-Reward scores, the relationship between picture category
and the time to correct directional errors in the ﬁrst antisaccade showedsigniﬁcant variance in intercepts across participants (var[u0j] = 0.11,
Wald Z = 4.12, p < 0.001).

(the highest F(4176) = 2.16, p = 0.075). Compared to the control
pictures (NE), the analyses of the signiﬁcant eﬀects showed that
directional error rates for antisaccade were higher in BE (b = 2.72, t(176) = 2.24, p < 0.05); the latency or reaction time of the ﬁrst
correct prosaccade was shorter in BE (b = −6.20, t(176) = − 4.26,
p < 0.001), GT (b = − 6.47, t(176) = −4.26, p < 0.001) andDA(b = −2.98, t(176) = −2.01, p < 0.05); the ﬁxation duration
after the ﬁrst correct prosaccade was longer in BE (b = 6.03, t(176)= 4.16, p < 0.001), GT (b = 6.95, t(176) = 4.83, p < 0.001) andDA(b = 3.44, t(176) = 2.39, p < 0.05); whereas time to correct
directional errors in the ﬁrst antisaccade was longer in DI(b = − 25.72, t(176) = − 18.19, p < 0.001). Post-hoc analyses did
not show any diﬀerence among BE, GT and DA in reaction time of theﬁrst correct prosaccade or ﬁxation duration after the ﬁrst correct

3.2. Free exploration paradigm
Mean ﬁxation direction bias score, ﬁxation duration bias score, andgaze duration bias score as a function of the food pictures comparedwith neutral non-food (NE) as reference category are presented inFig. 4. According to the model (see Appendix, Table A4), the picture
category showed a signiﬁcant eﬀect on the ﬁxation direction bias score
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D. Garcia-Burgos et al.

(b = 0.06, t(132) = 5.76, p < 0.001 and b = 0.16, t(129.2) = 4.70,
p < 0.01, respectively). Pairwise post-hoc comparisons did not reveal
any diﬀerence between pictures categories with post-ingestive consequences (BE and DA) across the diﬀerent types of bias (p > 0.30), but
did between DA and DI in ﬁxation duration and total gaze durationbiases (p < 0.05). Concerning the random eﬀect, the relationshipbetween picture comparison and each dependent measure showedsigniﬁcant variance in intercepts across participants (the lowest var.
[u0j] = 0.02, Wald Z = 1.88, p = 0.06). Additionally, t-tests wereseparately performed in order to determine whether these bias scores
diﬀered signiﬁcantly from the “no bias” value. Participants showed asigniﬁcant tendency in direction bias towards BE and DA (the lowest t(43) = 2.65, p < 0.05) but not towards GT or DI (the higher t(43)= 1.75, p = 0.085). In ﬁxation duration bias, the tendency was
towards BE and DA (the lowest t(43) = 5.68, p < 0.001); and towards
all food-related picture categories in total gaze duration bias (the lowest
t(43) = 2.42, p < 0.05).
4. Discussion
We examined whether pictures associated with the positive/negative hedonic evaluation of taste and post-ingestive consequences aﬀect
visual attention processes in normal-weight people, by investigating
how rewarding and aversive food attributes diﬀerentially modulate eye
movements. This question was addressed by using an antisaccade and afree viewing paradigm, in which the incentive salience of the four foodpicture data sets elicited signiﬁcant diﬀerences in gaze bias scorescompared with neutral non-food categories. Congruent with our ﬁrst
hypothesis, we observed early and later attentional tendencies to itemswith post-ingestive consequences regardless of whether there are highcalories or dangerous content. Indeed, prosaccade latency and ﬁxation
direction bias score conﬁrmed a higher initial attentional orientation
towards HC (high energy sources) and DA (possible dangers) items.
Also, the results in prosaccade ﬁxation duration and initial ﬁxation
duration bias score reﬂected a maintained attention to HC and DA.
These ﬁndings should come as no surprise considering that the primaryfunctions of the brain include identifying biologically relevant stimuli
such as nutritious food and poisonous/harmful substance and assigningthese stimuli a processing priority, in which the sense of vision plays acentral role (Stevenson, 2009).
According to the second hypothesis, the modulatory eﬀect of thesensory-hedonic properties on visual attention, our results partially
supported the impact of the aﬀective taste valence of the pictures onvisual attention. When strong post-ingestive components were absent,
participants showed attentional approach to pleasant GT items withshorter reaction times and longer ﬁxation duration for prosaccade. The
motivational power of good taste was however inconsistent across thetasks showing no diﬀerences (antisaccade) or lower scores (freeexploration) in comparison with highly palatable and calorically dense
foods. Another interesting ﬁnding relies on the avoidance shown tounpleasant DI in terms of shorter time reactions (≈58 msec) to correct
an incorrect prosaccade under antisaccade instructions. This avoidancefor distasteful stimuli such as bitter vegetables or pungent chilli
products was also suggested by the reduced antisaccade error rates(< 30%) and the score < 50 in ﬁxation direction bias, although both
trends were not signiﬁcant. On the other hand, when post-ingestive
attributes were present, the expected down-modulating eﬀect of theunpleasant taste could not be observed on DA items compared with HC
in early or later attentional measures; but only inferred indirectly via
antisaccade directional errors: the participants were more able todisengage attention from DA than HC pictures. Taken together, theorosensory-hedonic value of the taste seems to play a relative minor
role with respect to visual attention to cues with survival relevance.
These aﬀective taste aspects might rather operate during the subsequent processing of food objects associated with decision-making(starting at ∼ 300 ms post-stimulus) (Toepel et al., 2009).

Fig. 4. Free exploration paradigm. Mean scores of ﬁxation direction bias, ﬁxation
duration bias and gaze duration bias as a function of the food pictures compared withneutral non-food (NE) as reference category. Bars indicate standard errors. Values next tothe horizontal lines represent no bias relative to neutral non-food controls.*p < 0.05,**p < 0.01, ***p < 0.001, indicating that bias scores diﬀered signiﬁcantly from the “nobias” value.

(F(3176) = 2.78, p < 0.05), initial ﬁxation duration bias score (F(3132) = 14.46, p < 0.001) and total gaze duration bias score (F(3129.38) = 8.92, p < 0.001). Compared with the bias towards GT
pictures, the analyses only showed signiﬁcant lower ﬁxation direction
bias to distasteful DI pictures (b = − 4.97, t(176) = − 2.05,
p < 0.05), as well as a longer initial ﬁxation duration and a longer
gaze duration to BE (b = 0.04, t(132) = 3.78, p < 0.001 andb = 0.10, t(129.2) = 3.00, p < 0.01, respectively) and DA pictures
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2011), it remains completely unexplored how much attention is
directed and allocated when it comes to unpalatable high-fat food. In
this sense, eye movements-based studies on distasteful substances with
post-ingestive physiological eﬀects such as alcohol products (e.g., beeror wine) may provide important information. For instance, heavy(Miller & Fillmore, 2010) and light drinkers (e.g., when beer is expected
imminently; Field et al., 2011) maintain their gaze longer on alcoholrelated compared to on control picture. From these results, it appears
that rewarding post-ingestive determinants such as psychoactive andmetabolic eﬀects may overcome the unpleasant oral component. In any
case, future research is needed in order to clarify the role of calories
alone in attentional biases to food cues.
Finally, some limitations should also be taken into account. Firstly,
while factors that have been recognised as having the potential toimpact on attentional processing of visual food cues such as individual
eating style trait, weight status, level of food deprivation and the energy
density of food-related stimuli were considered, additional hunger level
during the experiment was not reported. As the participants wererefrained from eating for 3 h prior the experiment, one possible reason
for the increased visual attention towards high-calorie foods might bethat the participants were hungry. However, previous studies on theimpact of hunger on food cue processing did not reveal an eﬀect ofhunger with up to four hours of fasting prior to the experiment (cf. vander Laan, De Ridder, Viergever, & Smeets, 2011). Secondly, the ideational dimension related to cultural reasons, and predominant in foodrejections (e.g., disgust to pork for religious reasons and meat forvegetarians) (Rozin & Fallon, 1986), was controlled avoiding pictures ofmeat and excluding participants with moralization/religious foodprohibitions. Nevertheless, it would be helpful for future research tofurther examine how symbolic and ideational cultural meanings impact
on attentional biases towards food pictures. Finally, although the model
predicting antisaccade metrics was signiﬁcantly improved when gender
was included and the composition of the sample did neither lead toincreased Type 1 errors nor did it inﬂuence estimates (one thousandsample bootstrapping; P = 0.001, 95% IC 0.10–0.14), the number ofmale subjects was too small to draw reliable and representative
conclusions about gender diﬀerences.

With regard to HC items, the higher attention may result from theinteraction of the two diﬀerent aspects of food reward that have beenshown to activate the brain's reward system: pleasant taste and caloric
content (e.g., Avena, 2015; Rolls, 2007). Indeed, when the visual foodcues are made more salient through the use of images of foods of highhedonic value (e.g., chocolate, bacon) compared with hedonicallyneutral foods (e.g., cereal, bread) in thin individuals, robust and lasting
activation of visual processing and attention-related cortical regions isobserved (Cornier, Von Kaenel, Bessesen, & Tregellas, 2007). Anyhow,
studies that have used either indirect methods (Gearhardt, Treat,
Hollingworth, & Corbin, 2012; Phelan et al., 2011) or direct methods
such as eye-tracking (Castellanos et al., 2009) agree that caloric content
and/or metabolic eﬀects of nutrients elicit stronger involuntary andattentional engagement responses than other visual stimuli, reporting
increased initial and maintained attention to palatable energy-dense ascompared with low energy density food (e.g., fruit and vegetables) innormal weight groups. However, unlike these studies which included
aversive tasting foods (e.g., bitter vegetables) as a misleading control
condition, the present work with more appropriate neutral non-foodcontrols did allow to rule out the alternative explanation that computation of the attentional bias score towards high-energy food might rather
be explained by attentional avoidance of DI items. Methodologically, it
should be noted that food can be categorized on multiple dimensions(e.g., organoleptic attributes, food safety, nutritional value, functionality, healthiness or psychological factors); and diﬀerent choices inpicture sets could have created diﬃculties in terms of comparability ofﬁndings and contributed to mixed ﬁndings. In fact, it is possible that thecontrast category inﬂuences the context in which food is automaticallyevaluated (Werthmann et al., 2015). For instance, presenting highenergy food together with low-energy food, might prime participants
with beliefs/attitudes about the unhealthy value of fattening food,whereas comparisons between high-energy and neutral non-foodstimuli should minimize this eﬀect (Roefs et al., 2006; Werthmann
et al., 2015). To the best of our knowledge, there has been just one
previous study assessing pairs of images containing high-energy-density
foods vs. non-food matches and low-energy-density foods vs. non-foodmatches, conﬁrming that high-energy-density foods attract more attention (Doolan, Breslin, Hanna, Murphy, & Gallagher, 2014).
Despite previous work reporting that higher attentional responses topleasant food images (Brignell et al., 2009; di Pellegrino et al., 2011),
the present study is the ﬁrst using eye movement to monitor attentional
processing to examine the role of the hedonics of taste besides thenutritional and energetic content. Furthermore, the design of thepresent study allowed to explore the impact of the positive (e.g., sweet
fruits) and negative (e.g., bitter vegetables) hedonic value for foodregardless of the post-ingestive content, ﬁlling this gap in the literature.
In this sense, it is somewhat paradoxical that most published studies
examining gaze behaviour in consumer choice context with healthy
samples have a special focus on package design and food labels (e.g.,
Ares et al., 2013; Bialkova & van Trijp, 2011) while the principalfeatures motivating acceptance or rejection such as the taste (sensory)
properties of a substance are neglected. Up to now, no study has either
attempted to understand the attentional impact of our present danger
category condition or the anticipated harmful consequences of ingestion in consumer choice. This applies for many relevant items such as
potential allergens or carcinogens that may be also listed in the danger
category. Investigating these circumstances is especially pertinent tocomplete our understanding of the (dis)like for foods and thus
consumer choice due to impact of visual attention, e.g., in decision
making (Orquin & Loose, 2013), of the strong correlations between
gazing behaviour and food choice (Danner et al., 2016) and of therelationship between the total ﬁxation duration and preference formation (van der Laan, Hooge, De Ridder, Viergever, & Smeets, 2015).
Nevertheless, more questions remain unanswered. Although it has
been stated that the taste of high calorie food products may increase
attentional biases to diﬀerent kinds of fattening foods (Graham et al.,

5. Conclusion
Our ﬁndings indicate that eye movements are modulated by boththe accepted and rejected food attributes. Even more important it is theresult that normal-weight, non-eating disordered and non-food-deprived participants exhibit food-related attention biases especially forpalatable high-fat food cues, as well as an avoidance of distastefulitems. Since people who detect more rapidly and increase attention toenergy dense food-related cues and avoid healthy unpleasant food suchas bitter vegetables are thought of as being prone to overeating,understanding how food images interact with the brain's reward system
to direct visual attention is of practical, ecological and environmental
importance in determining the role of food cues in eating behaviour.
Furthermore, the importance of ﬁlling this gap becomes more and morecrucial as attentional biases towards food cues may inﬂuence cravings,
food choice and food consumption and therefore be implicated as a risk
factor for eating disorder and/or weight-related problems in the foodcue-rich environment of today.
Conﬂict of interest
The authors have no conﬂict of interest to declare.
Acknowledgments
We thank Nicole Bianchi for her assistance with participant
recruitment and data collection for this study. This work was partly
supported by the Swiss Government (awarded to D. Garcia-Burgos)
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under the program “Swiss Government Excellence Scholarships for
Foreign Scholars” (Ref. Nº 2013.0404, 2013–2014).

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Appendix A. Supplementary data
Supplementary data to this article can be found online at http://dx.
doi.org/10.1016/j.foodres.2017.04.011.
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