Don`t get distracted! Involuntary attention towards rewarding stimuli and its
relationship with eating related factors
Viket Benzesin University of Amsterdam
Author Note
This study was a part of 24 credits internship supervised by Prof. Dr. Reinout Wiers and Dr. Bram Van Bockstaele, University of Amsterdam, Department of Psychology (Developmental Psychology). The ethics committee reference code for this study is 2017-DP-7852. For more information please email the author v.benzesin@gmail.com or the supervisors
Abstract
The attentional processing of different rewarding stimuli is rarely studied simultaneously. In this study, we examined the influence of different categories of high reward stimuli (attractive nudes, appetizing unhealthy food and neutral stimuli associated with monetary gain) on attention. Furthermore, we investigated the relationship between attention towards unhealthy food stimuli and eating related factors (binge eating, restricted eating, weight, hunger, and hip-waist ratio) in sixty one participates aged between 18 and 28. Participants completed an involuntary attentional breadth task, two versions of an attentional capture task and
questionnaires about their eating behaviours. We anticipated that high reward stimuli would enhance attentional processes more than low reward stimuli, and eating related factors would be positively correlated with attention towards unhealthy food stimuli but not with other stimuli. However, our results failed to support our hypotheses. Naturally high reward stimuli did not significantly enhance attentional processes compared to low reward stimuli. In the attentional capture task using stimuli with monetary gain, participants were faster in trials without a distractor but reaction times were not significantly different for high or low reward trials. Finally, there were no significant correlations between attentional processes and eating related factors, suggesting that it may be important to systematically examine gender
differences in attentional processes. Overall, this study stressed the importance of examining gender differences in involuntary attention toward reward.
Don`t get distracted! Involuntary attention towards rewarding stimuli and its relationship with eating related factors.
Signs of an appetizing food, money and an attractive naked person are all desirable stimuli that could increase people`s chance to survive and reproduce (Panksepp, 1998). So, when these stimuli are present in our environment, detecting them as quickly as possible would have an adaptive value. Cognitive and neuroimaging studies support the notion that people have an enhanced involuntary (automatic) attention towards rewarding stimuli (Sescousse et al., 2013; Pool et al, 2015; Sennwald, et al., 2016). However, involuntary attention can also be counterproductive and distract us from goal-related behaviours. For instance, in most modern societies, we are surrounded by unhealthy food cues through social media and advertisement. Attending to unhealthy food cues can result in a desire to eat, increasing the risk to consume more than we biologically need. In healthy people, exposure to food cues can change people`s cephalic phase responses (CHR; e.g. heart rate, salivation, blood pressure, skin conductance and gastric activity) which influence the amount of food intake. Furthermore, CHR can increase more if the people are hungry during the stimuli exposure (Nederkoorn, Smulders, & Jansen, 2000). Understanding the way we attend to these cues is especially important, as excessive consumption of unhealthy food in long term could cause various health problems such as obesity, diabetics and heart diseases (Chopra,
Galbraith, & Darnton-Hill, 2002).
Compared to healthy-weight participants, obese and overweight participants appear to have an enhanced involuntary attention towards appetizing food stimuli (Castellanos, et al., 2009) which could be related to their weight gain. Similar claims have also been made for restricted eaters (Hollitt, Kemps, Tiggemann, Smeets, &Mills, 2010), and people with binge eating disorder (BED; Shafran, Lee, Cooper, Palmer, & Fairburn, 2007). However,
conflicting findings exist (e.g., weight: Graham, Hoover, Ceballos, & Komogortsev, 2011; restricted eating: Meule, Vogele, & Kubler, 2012; BED: Smeets, Roefs, van Furth, & Jansen, 2008). Therefore, the relationships between involuntary attention towards food and, weight, binge eating and restricted eating are still unclear. Many studies that investigated the
processing of any stimuli have considered involuntary attention as a single component, and did not differentiate between various involuntary attentional processes, such as attentional
breadth and attentional capture. Thus, little is known about how different involuntary attentional processes are affected by rewarding stimuli, such as appetizing food, and how eating related factors (such as hunger, weight, binge eating and restricted eating) may mediate these associations.
Attentional narrowing is an attentional process that occurs when attention is narrowed at the expense of encoding other environmental details (Rowe, Hirsh, & Anderson, 2007). Attention is likely to be narrowed when people attend to naturally high value stimuli which either convey desire or signal fear, indicating enhanced attentional focus towards that stimuli (Juergensen & Demaree, 2015). Although it is suggested that food stimuli would narrow the attentional focus (Wallis & Hetherington, 2004), empirical evidence supporting this idea is limited. Juergensen and Demaree studied the influence of food stimuli on attentional focus in healthy participants, who were first exposed to images of appetizing desserts, then completed an adapted version of Navon`s (1977) letters task. The stimuli in this task consisted of large (global) letters made up of smaller (local) letters. In each trial, the global or the local letters were either “T”s or “H”s. Participants were asked to indicate whether image contained “T”s or and “H”s as fast as possible. Faster correct responses to the global letters were interpreted as a wider focus, whereas faster correct responses to the local letters were interpreted as a more narrow focus. Juergensen and Demaree found that participants with high desire ratings for the food had a more narrow attentional focus during the task. However, the study did not provide information about the immediate impact of food stimuli on involuntary attention and on whether this effect is specific to food stimuli or expands to other high reward stimuli. Moreover, they did not consider other important factors that may influence attention to food stimuli, such as binge eating, weight and hunger.
Bosmans, Braet, Koster and de Raedt (2009) designed a task that would allow investigating involuntary attentional narrowing in response to different stimuli. They
investigated the relationship between secure attachment and attentional breadth to the mother. During the task children saw a central image of their mother or an unfamiliar female, and grey dots that formed two, differently sized, imaginary circles around the centre. A black circle was presented inside one of the grey dotes. After the stimuli presentation, participants were asked to identify the central image and the location of the black circle. Participants` accuracy
in identifying the location of the black circle, as a function of its distance to the centre of the screen, indicated the extent of their attentional narrowing. Moreover, the trial duration varied to examine both voluntary and involuntary processes. The authors found that in fastest trials (34 ms), which was designed to examine involuntary attentional breadth, less secured children had a narrower attentional field towards their mothers` images compared to more secured children. Moreover, the task has been successfully adapted to examine psychological resilience (Grol & De Readt, 2014) and social anxiety (Yoon, Vidaurri, Joormann & De Raedt, 2015).
Another important attentional process is attentional capture, which occurs when a stimulus, among other surrounding stimuli, is processed involuntarily (Anderson, Laurent & Yantis, 2011). Physically salient stimuli (Anderson, Laurent, & Yantis, 2011) and naturally rewarding stimuli such as attractive nudes (Bradley, Costa & Lang, 2015), tend to capture attention. Furthermore, there is evidence that hunger (Piech, Pastorino & Zald, 2010; Siep, et al., 2008), restricted eating (Papies, Stroebe & Aarts, 2009) and obesity (Braet & Crombez, 2003) are related to attentional capture towards unhealthy food stimuli. Compared to healthy people, binge eaters, who often display compulsive eating behaviours (Deal, Wirth, Gasior, Herman & McElroy, 2015), appear to react faster to food stimuli (Finlayson, Arlotti, Dalton, King, & Blundell, 2011), and find it more difficult to disengage their attention away from food stimuli (Schag, Schönleber, Teufel, Zipfel & Giel, 2013).
Task-irrelevant stimuli can also capture attention if they have been previously associated with monetary gain (e.g., Anderson, et al. 2011; Le Pelley, Pearson, Griffiths, & Beesley, 2015). In a visual search task, Le Pelley et al. asked participants to respond to a target that was presented within a diamond-shaped stimulus in the presence of a coloured circular stimulus (distractor) that was associated with monetary gain. They found that participants were slower to respond to the target when it was presented in the presence of a distractor that signalled high reward (compared to a distractor that signalled low reward). This was the case even when participants knew that slow response would decrease their total earning. Therefore, the authors concluded that learned high reward distractor captured attention more than learned low reward distractors.
In the present study, we set out to investigate the effects of different categories of rewarding stimuli on attentional capture and attentional narrowing. In the attentional capture task, in addition to investigating the influence of naturally high reward stimuli (food and nude pictures), we also included a conceptual replication of Le Pelley et al.`s (2015) study using pictures of neutral stimuli (blue or red furniture) that were consistently associated with high versus low monetary gains. In the attentional breadth task, we used an adapted version of Bosmans et al. (2008)`s task, but instead of images of mothers and strangers, participants saw various naturally rewarding stimuli in the centre of the screen.By using different categories of rewarding stimuli, we wanted to examine how attention is affected by each type of stimuli. In addition, we wanted to investigate how the attentional processing of food stimuli is
associated with different eating-related outcomes.Better understanding the relationship between food cues and eating related factors could help us clarify unhealthy eating patterns and enhance healthy eating.
In line with previous studies, narrowed attention in the attentional breadth task and slower reaction times to targets in the attentional capture task were interpreted as enhanced attentional processes. First, we hypothesized that high reward stimuli would enhance attentional processes more than low reward stimuli (H1). Second, we expected that people with high scores on eating related factors, namely binge eating, restricted eating, body mass index (BMI), waist-hip ratio (WHR) and hunger, would have an enhanced attentional
processing of unhealthy food stimuli specifically. Thus, we hypothesized that unhealthy food stimuli would enhance the attentional processes more in people with relatively high scores on eating related factors compared to people with low scores food related factors (H2). If the influence of unhealthy food stimuli on attentional processes is not specific to eating related factors but reflects a more general attentional sensitivity for any type of reward, we would expect the same correlations to be there for nude stimuli as well (H3).
Method
Participants
Based on previous studies, with the effect size of 0.25, power of 0.95 and assumed correlation among repeated measures of .2, we estimated that we would need 57 participants in total. In case of drop outs, we aimed recruit 60 participants aged 18 to 28, who received course credits or monetary compensation for their participation (20 €). Moreover, all participants received additional money depending on their performance in the attentional capture task (M= 10.77€, SD= 0.57).We recruited a total of 61 participants (21 males) aged between 18 and 28 (M= 22.10, SD= 2.61). 56 of those participants took part for monetary compensation. The binge eating and restricted eating scores of one participant were missing (computer error).
Operationalisation
We used a within-subjects repeated measures designs for both attention tasks. To investigate the influence of high reward stimuli, the picture type and the level of reward (both categorical variables; see Table 1, 2 and 3), were used as the independent variables. The dependent variables of each task are described in Materials. To predict the impact of unhealthy food stimuli on attentional processes, the predictors were eating related factors (continuous variables) described in Materials.
Materials
All materials were administrated either in English or in Dutch based on participants` preferences (see Appendices).
Attentional breadth task (Figure 1)
A variation of Bosmans et al.`s (2009) task was used to test involuntary attentional narrowing. During the task, a chin rest was located 27 cm away from the 23” screen to keep the distance between participants` eyes and the screen consistent and to ensure participants were looking directly at the centre of the screen.
On each trial, participants were presented with a central target picture (2.5 x 2.5 cm) of various categories (see Table 1) and 16 grey dots (ø 2.0 cm) forming two imaginary circles around the target. Half of the dots were 4.5 cm and the other half were 11.2 cm away from the centre. A smaller black circle (ø 1.3 cm) appeared randomly in one of the grey dots.
Following the stimuli exposure, participants were asked whether the target was edible or not (picture identification) and in which axis the black circle appeared (location identification).
After visual and written instructions, the task started with 16 training trials with gradually decreasing presentation times. The picture and target were presented for 500 ms in the first 4 trials, 250 ms in the following 6 trials and 70 ms in the last 8 trials. Stimuli used in the practice trials were different from those used during the experimental blocks. Participants were then presented with 5 experimental blocks of 48 trials, in which the picture and target were always presented for 70 ms. In each block, 12 pictures of each category were presented in random order. Participants had no time restriction to answer the questions, and they were allowed to take a small break after each block.
Table 1
Type of stimuli in attentional breadth task
Type of stimuli Categories Answer
Food stimuli Savoury or Sweet* Edible
Alcohol stimuli (Part of another project)
Beer or wine* Edible
General high natural reward Male nudes or Female nudes* Non-Edible
General low natural reward control Dressed pictures (other sex than high reward) Non-Edible
Note. *Participants were asked for their preference before the task.
Attentional capture task (Figure 2)
Two picture versions of the visual search task used by Le Pelley et al. (2015) were designed to examine attentional capture. The first version examined the distracting effect of naturally rewarding stimuli (see Table 2). The
second version was a conceptual replication of Le Pelley et al. (2015)`s task, in which imagines of red and blue furniture (see Table 3) were associated with different monetary gains (+1c or +10 c).
On each trial of both versions, participants saw a screen with a fixation cross surrounded by a diamond-shaped target and 5 grey circles (2.3 x 2.3 dva) positioned at equal intervals. One of the grey circles was overlaid with a distracting stimulus (see Table 2 and Table 3). The diamond-shaped target contained a horizontal or vertical white line segment (length 0.76 dva). The remanding 4 circles contained a white line segment tilted 45° randomly to the right or left.
Figure 2: First version of the attentional capture
Participants were asked to indicate whether the line inside the diamond-shaped target was horizontal or vertical by pressing M or Z on the keyboard as fast as possible. The key buttons that corresponded to horizontal or vertical were counterbalanced and blinded to the experimenter. The direction of the line inside the diamond-shaped target and the four circles, the location of the target and the type of distractor presented were randomized within each block.
Feedback was provided after each response. In the first version, the feedback was either “Correct +1c!”, “Incorrect” or “Too slow!”. In the second version, after a correct response participants gained either +1c or +10c depending on the colour of the furniture that was presented as a distractor and their current gain was displayed on the screen. On both versions, feedback lasted 700 ms for correct and 1000 ms for incorrect or slow (>750ms) responses. In some trials, no circle was overlaid with a distractor. In those trials, correct responses were not followed by +1c gain.
The task included 10 practice trials (7 with yellow circle as a distractor and 3 without a distractor). The practice was repeated until participants had at least 70% performance
accuracy. The first version of the task had 10 experimental blocks of 32 trials (6 trials of each stimulus type and 2 distractor free trials). Participants had a break after the second, fifth, seventh and tenth blocks. All participants were led to believe that they had earned 1.03 cents and 2.36 cents after the fifth and tenth blocks respectively. The total gain was standardized to avoid its influence on the rest of the task. The second version of the task had 6 blocks of 32 trials (14 trials of each stimuli type and 4 distractor free trials), with a break after every two blocks. The task ended with a subjective awareness question about monetary gain associated with blue and red furniture stimuli.
Table 2
Type of stimuli in attentional capture task (First version)
Type of stimuli* Categories
High reward food stimuli Unhealthy savoury or sweet food**
Alcohol stimuli (Part of another project) Beer or wine**
Low reward food and drink stimuli Healthy food and drinks
General high natural reward Male nude or female nude**
General low natural reward control Dressed pictures (other sex than high reward)
Notes. *In each block participants saw 6 different pictures of each stimulus type in a random order. **Participants were be asked for their preference before the task.
Table 3
Type of stimuli in attentional capture task (Second version)
Type of stimuli* Categories
High learned reward (+10 cents) Furniture (blue or red)**
Low learned reward (+1 cent) Furniture (blue or red)**
Notes. *In each block participants saw 7 different pictures of each stimulus type twice in a random order. ** The colour association with high or low learned reward was
counterbalanced, and the experimenters were blinded to the association.
Eating related factors:
Following measurements were administered to examine various eating related factors. In the current study participants` scores were regarded as continuous variables, as we are interested in the extent to which these scores are correlated with involuntary attention. However, in previous studies, some of these materials were also analysed categorially with cut-off scores. Although, we are not using these cut-off scores to sub-group our sample, these scores are stated below as they are informative about the characteristics of our sample. Both English and Dutch versions of the questionnaires can be found in Appendices.
Binge eating. The commonly used 16 item multiple choice Binge Eating Scale (BES; Gormally, Black, Daston, & Rardin, 1982) was used to measure binge eating tendencies. Although it was originally designed to test bingeing in obese people, it also has high validity among general population (Duarte, Pinto-Gouveia, Ferreira, 2015).
The score range is between 0 and 46 with higher scores indicating more severe binge eating symptoms. Previous studies often categorised scores below or equal to 17 as not binge eating, scores of 18-26 as mild to moderate binge eating and scores equal to or above 27 as severe binging (e.g., Marcus, Wing, Hopkins, 1988; Grupski, et al., 2012).
Restrained eating. The Concern for Dieting subscale of Revised Restraint Scale (Herman & Polivy, 1980), which includes 6 Likert-scaled questions, was administered to assess
participants` chronic motivation to control their weight by dieting. This scale has been used (Papies et al., 2008) and assessed for its validity (van Strien, Herman, Engels, Larsen, & Leeuwe, 2007). The total score range is between 0 and 20 with higher scores indicating more severe restricted eating tendencies.
Hunger. Current hunger was assessed with a 7-point Likert-scale question, 1 being not hungry at all and 7 being extremely hungry.
Body Mass Index and the waist-hip ratio. Height, weight, waist and hip measurements were used to calculate Body Mass Index (BMI) and the waist-hip ratio (WHR). BMI was calculated as weight in kilograms divided by the square of height in meters (kg/𝑚2). In adults BMI scores of 18.5-24.9 are considered as normal weight statues. Scores below or above are considered underweight or overweight respectively (Centers for Disease Control and
Prevention, 2013).
However, weight may not be an informative eating related factor for participants who regularly do a muscle building workout (e.g. weightlifters). Their weight would be highly influenced by their exercise routine. Thus, we also measured participants` waist-hip ratio, which has been found to be positively correlated with type 2 diabetes (Harding et al., 2004) and cardiovascular diseases (Dalton et al., 2003). Healthy waits-hip ratio can vary depending
on age, ethnicity and gender (Consultation, 2008). However, a WHR of below 0.8 for women and below 0.9 for men is often considered healthy (Bigaard, et al., 2004).
Procedure
Participants filled in the informed consent and hunger rating followed by a short working memory task which was a part of another project. Participants then completed the visual attentional breadth task. As it was a challenging task without feedback, participants were instructed to trust their gut feeling when they were unsure of their answer. This instruction was stated to help participants stay motivated throughout the task. After the attentional breadth task, participants were introduced to the attentional capture task, and were told that they could earn additional money depending on their performance. After the practice trials, participants first completed the task with naturally rewarding stimuli, followed by the version that included stimuli associated with monetary gain. The order of the tasks was fixed to ensure the optimal motivation thought out the experiment.
Once both tasks were completed, participants filled in questionnaires about their binge eating and restricted eating behaviours. Participants also filled in questionnaires about impulsivity and their alcohol use for another project. Their height, weight, waist and hip circumferences were measured and they were debriefed. The entire session took
approximately 2 hours.
Data analysis
All the ANOVAs and correlations were run in JASP 0.8.1.2. Reliability and the normality assumptions were run using SPSS.
H1: High reward stimuli would enhance attentional more than low reward stimuli. To investigate attentional narrowing, the difference scores of accuracy between close and far trials (close-far) of nude, dressed people and unhealthy food stimuli were calculated and used in one-way repeated measures ANOVA. We expected to find a significant main effect of picture, caused by lower attentional narrowing in dressed pictures.
high & low) x 2 (Picture Category; human & food) repeated measures ANOVA. We expected a main effect of Reward (slower reaction time on trials with high reward stimuli). To examine if the reaction time differed for trials without a distractor, one-way repeated measures
ANOVA was ran including all picture categories and trials with no distractor. We expected to find a main effect of distractor driven by faster reaction time in no distractor trials.
Attentional capture for stimuli associated with monetary gain was analysed using 6 (Block) x 3 (Reward: high, low & absent) repeated measures ANOVA. We expected to find a main effect of Reward (slowest reaction to high reward and fastest to no distractor) and a Block - by Reward interaction (the reward effect would increase as participants learned the association between furniture colour and rewards value). Furthermore, to examine if subjective awareness influenced the outcome, we ran an exploratory one-way repeated measures ANOVA with awareness accuracy as a between subject variable.
To test which type of stimuli had the strongest influence Post Hoc comparisons were ran using Bonferroni correction for multiple testing. In both tasks, gender was added to the analysis as an exploratory between subject variable.
H2 & H3: People with high scores on eating related factors would have enhanced attentional processing of unhealthy food stimuli specifically.
For attentional narrowing, the close- far difference scores of different picture types and eating related factors were entered into a correlation matrix. Similarly, for attentional capture we calculated difference scores of reaction times for unhealthy food – minus healthy food, unhealthy food – minus no distractor, healthy food – minus no distractor, nude – minus dressed, nude – minus no distractor. These difference scores were entered into a correlation matrix with eating related factors. In both attentional processes, we expected to find positive correlations between BMI, WHR, BES and restricted eating with the difference scores related to unhealthy food stimuli but not with healthy food, nude or dressed people stimuli.
Results
H 1: High reward stimuli would enhance attentional more than low reward stimuli. 1. Attentional breadth
Like Bosmans et al. (2009), if the picture identification was answered incorrectly, the trial was discarded as it indicates that participants did not attend to the target. Additionally, participants performed below 3 standard deviations from the mean in picture identification (n=1) and participants who performed under 15 % accuracy in the location identification were regared as outliers and were discarded (n=11). From the reminding data, the accuracy of location identification was used as the dependent variable in the analysis. In total, we had 49 (18 male) participants for this analysis.
Assumptions. The average rate of identification accuracy for each picture varied between
0.94 and 1, suggesting that there were no ambiguous pictures in the task. The Shapiro-Wilk`s normality test revealed that the difference scores of each picture category were normally distributed (all p`s > .05). Moreover, the assumption of sphericity was met for each factors.
Hypothesis testing. The one-way repeated measures ANOVA investigating the difference in
attentional narrowing in trials with nudes (M = .483, SD = .189), dressed people (M= .471, SD = .171) or unhealthy food (M= .483, SD = .142) revealed no significant effect of picture type (F < 1).
Table 4
Attentional narrowing towards each picture category
Unhealthy food Nude people Dressed people
0.483 (0.142) 0.483 (0.189) 0.471 (0.171)
Notes. The values in brackets are the standard deviations. Attentional narrowing is defined by the
Figure 3. Attentional narrowing towards each picture category. Attentional narrowing is defined by the difference between accuracy in close and far trials. Error bars represent the standard error.
2. Attentional capture
Like Le Pelley et al., reaction time on the accurate responses was used as the dependent variable in our analyses. The first two trials, the first two trails after each break and the responses under 150 ms were discarded. Furthermore, participants with average accuracy score or reaction time below or above 3 standard deviations from the mean were regarded as outliers and were discarded.
Table 5:
The mean reaction times for each reward category in the attentional capture task (both versions combined).
High reward Low reward Distractor free trials
Human Nudes:
612.1 (48.54)
Dressed people:
612.0 (49.88) 608.5 (50.53) Food Unhealthy food:
608.5 (48.12)
Healthy food: 612.1 (44.30) Monetary gain +10c furniture:
609.8 (64.00)
+1c furniture:
609.2 (65.78) 598.2 (71.12)
Notes. The values in brackets are the standard deviations. Distractor free trials were the same for both
Naturally rewarding stimuli.
One participant had an accuracy rate below 3 standard deviations from the group mean and was discarded from the analyses. In total, we had 60 (20 male) participants for these analysis.
Assumptions. All picture categories had high reliability as the lowest Cronbach`s alpha for a
category was above .86. The Shapiro-Wilk`s normality test revealed that the reaction time for each picture category were normally distributed (all p`s > .05). Moreover, the assumption of sphericitywas met for each factor.
Hypothesis testing. 2 (Reward) x 2 (Picture Category) repeated measures ANOVA revealed
no significant main effect of neither Reward nor Picture Category (F`s <1, p`s >.05). Moreover, there was no significant interaction effect between Reward and Picture Category (F <1, p >.05) (for all relevant means and SDs, see Table 5).
To understand if there was any distractor effect caused by the presence of a picture, a one-way repeated measures ANOVA including trials with no distractor was run. The analysis revealed no significant effect of distractor (F<1, p >.05).
Figure 4: Right figure: Reaction time to stimuli with different levels of reward and picture category. High & Human = nudes, Low & Human = dressed people, High & Food= unhealthy food, Low & Food = healthy food. Error bars represent standard error. Left figure: reaction time to different stimuli compared to trials without a distractor (absent). Error bars represent standard error.
Stimuli associated with monetary gain.
Based on the data cleaning procedure, no participant was discarded.
Assumptions. All picture categories had high reliability as the lowest Cronbach`s alpha for a
category was above .95. The test for normality, the Shapiro-Wilks test, indicated the data were statistically normal (all p`s > .05). The assumption of shericity was not met for reward (W= .829, p = .004) and the block - by reward interaction (W= .059, p < .001). Thus,
Greenhouse-Geisser corrected values were reported on these factors.
Hypothesis testing. 6 (Block) x3 (Reward) repeated measures ANOVA revealed a significant
main effect of Reward (F (1.708, 102.485) = 12.100, p < .001, ŋ𝑝2 = .168). However, there was neither a significant main effect of Block (F (5, 300) = 1.364, p = .238, ŋ𝑝2 = .022), nor an interaction effect (F < 1, p >.05). As the direction of the reward effect was hypothesised, a planned comparison was run on Reward. There was a significant difference between no reward and high/low reward trials (t(60)= - 4.918, p < .001). However, there was no significant difference between low and high reward trials (t(60)= .095, p = .925).
Figure 5. Reaction time to stimuli with different monetary gains per block. Significant difference
between distractor absent and high/low reward stimuli. Error bars represent standard error.
As there was no block effect, we ran a one-way repeated measures ANOVA using subjective Awareness as a between subject variable. Out of 61 participants, 31 of them
answered the subjective awareness question correctly. As before, there was a significant effect of Reward (F (2,118) = 6.012, p= .003, ŋ𝑝2 = .092). There was no significant interaction between Subjective Awareness and Reward (F(2, 118)= 32.43, p = .074, ŋ𝑝2 = .001), but the between subject effect approached significance (F(1,59) = 3.743, p= .058), as aware
participants performed faster in all reward categories (no distractor: 582.7 (56.58), low: 593.0(47.56), high: 595.1 (52.56)) than not aware participants (no distractor: 614.1 (81.44), low: 625.9 (77.72), high: 625.1 (71.72)).
H2 & H3: People with high scores on eating related factors would have enhanced attentional processing specifically towards unhealthy food stimuli.
The average WHR of the total sample was 0.76 (0.06), BMI was 22.36 (2.82), hunger rating was 3.33 (1.38), BES score was 11.82 (5.54) and restricted eating scale was 6.50 (3.90). Based on the standard interpretations of these measurements, the average scores of our sample were considered normal. One participant had a BMI marginally above 3 standard deviation (3 SD = 30.87, participants` score = 31.60). There were no other outliers. The scores of all measurements were normally distributed. Binge eating and restricted eating measurements both had high reliability Cronbach`s alpha for both measurements were above .82.
Table 6
Participant details divided by gender
Age WHR BMI Hunger BES Restricted eating
Female 22.57 (2.64) .81( .05) 23.04 (3.3) 3.71 (1.52) 9.14 (4.26) 4.33 (3.23) Male 21.85 (2.59) .73( .04) 22.00 (2.50) 3.13 (1.26) 13.26 (5.66) 7.67 (3.77)
Note. The values in brackets are the standard deviations. The calculations were done on 60 participants
as the BES and restricted eating scores of one participant was missing.
1. Attentional breadth
In addition to the twelve participants discarded during data cleaning, BES and
Restricted eating scores of one participant was missing. Thus, in total there were 48 (18 male) participants for these analyses.
As anticipated restricted eating was positively correlated with BES (r = .635, p < .001) and negatively with WHR (r = - .309, p = .033). However, there were no significant
correlations between attentional narrowing towards food, nude or dressed people stimuli and eating related factors, all r`s < .22, all p`s > .15.
2. Attentional capture
Two people from the initial sample were discarded due to data cleaning procedure (n=1) and missing data (n =1). In total, we had 59 (20 male) participants for these analyses.
Restricted eating was, again, positively correlated with BES (r = .723, p < .001) and negatively with WHR (r = - .267, p = .041). Moreover, BMI was positively correlated with WHR (r = .281, p = .031). There were no significant correlations between attentional capture towards unhealthy food stimuli and eating related factors.
Exploratory analysis: gender effect
The analyses were repeated with Gender as between subject variable. There was no significant Gender –by Reward interaction in attentional breadth task (F <1, p >.05).
However, gender had an influential impact on the attentional capture. In an one-way repeated measures ANOVA with all naturally reward categories and trials without a distractor, we found a significant interaction between Trial Type and Gender (F(3.378, 195.946) = 4.427, p = .002, ŋ𝑝2 = .071, sphericity corrected). To further explore if there was an effect of distractor, the analyses was repeated on both genders separately. Indeed, in male participants only, there was a distractor effect (F(4,80) = 5.256, p < .001, ŋ𝑝2 = .208) caused by faster reaction time to no distractor trials. However, there was no significant difference among trials with different natural reward categories (all t-test statistics < 2.30, all p`s > .35). The analysis on female revealed no significant distractor effect (F< 1, p >.05).
A significant Reward – by Gender interaction (F(2, 118) = 4.952) p = .009) was also apparent in attentional capture towards stimuli associated with monetary gain. As with the previous analyses, only in the males, post hoc tests revealed a significant difference between no distractor trials and high (mean difference= -27.227, SE= 7.008, t(20) = 3.885 , p = .003) and low (mean difference= -21.088, SE = 6.193, t(20) = 3.405 , p = .008) reward stimuli
trials, but no significant difference between high or low reward stimuli trials (mean difference= -6.139, SE= 4.938, t(20) = - 1.243, p = .685, all Bonferroni corrected).
Similarly, in males-only analyses, BES, BMI and restricted eating were all positively correlated with attentional capture towards unhealthy food compared to healthy food (all Pearson`s r`s ranged between .55 and .44, all p`s <.05), but not with any other stimuli. There were no significant correlations between attentional capture towards unhealthy food stimuli and eating related factors in female only analysis (all r`s< .2, all p`s > .24).
A different pattern of correlations was observed between the attentional breadth task after splitting the data based on gender. In males-only analyses, there were no significant correlations of interest (all r`s < .42, all p`s > .08). However, when the analysis was run on females, restricted eating was positively correlated with accuracy in unhealthy food (r=.469, p=.009) and nude (r=.435, p =.018) stimuli trials. These strong correlations did not appear in the combined gender analyses as males showed a non-significant trend towards a negative correlation (food: r = -0.413, p = 0.088; nude: r= -.384, p = .116). A regression model was fitted with gender - attentional narrowing towards unhealthy food (Food C-F), and the interaction between gender ad attentional narrowing towards unhealthy food to predict restricted eating. Overall, the model significantly predicted restricted eating (𝑅2= 0.338, F (3,47) = 7.495, p < .001). As expected Gender (t(47)= 3.275, p < .01), and the Food C-F - by Gender interaction (t(47) = 3.02, p < .01) were a significant predictor of restricted eating. Figure 6 illustrates that, when participants saw an unhealthy food stimulus, high attentional narrowing was associated with less restricted eating scores for males, whereas in females high attentional narrowing was associated with more restricted eating.
Figure 6. The regression model with gender and attentional narrowing towards unhealthy food stimuli to predict restricted eating tendencies. Error bars represent 95 % confidence intervals.
Discussion
In the present study, we set out to investigate the effects of different categories of rewarding stimuli on attentional capture and attentional narrowing, and how the attentional processing of unhealthy food stimuli is associated with hunger, weight, restricted eating and binge eating tendencies. Overall, our results failed to support our hypotheses. Naturally rewarding stimuli did not significantly differ on how much they influenced attentional
narrowing and attentional capture. In the attentional capture task using stimuli with monetary gain, participants were faster in no distractor trials but reaction times were not significantly different for high or low reward trials. Subjective awareness did not influence the finding. In the correlational analyses, there were no significant relationships between eating related factors and attentional processing of unhealthy food stimuli. However, our exploratory analysis revealed interesting gender differences. For attentional capture, males showed sensitivity to the presence of a distractor. Moreover, in line with our hypothesis, high BMI, BES and restricted eating scores in males were positive correlated with enhanced attentional capture towards unhealthy food stimuli only. Whereas the gender based analysis showed a different pattern for attentional narrowing. There was a strong interaction between gender and attentional narrowing towards unhealthy food stimuli. Attentional narrowing towards food stimuli and restricted eating scores were positively correlated in females, but approached to a significantly negative correlation in males.
In the current study, attentional capture was enhanced when there was a stimulus associated with monetary gain compared to when there was no distractor. Nevertheless, unlike hypothesised, we found no evidence for different processing of stimuli with high versus low monetary gain. Thus, it is plausible that the observed effect was due to the salience of the stimuli (e.g., Theeuwes, 1992; Parkhurst, Law & Niebur, 2002) rather than the
monetary gain associated with them. Our results may suggest that stimuli associated with different monetary gains are not processed differently. However, although not captured in our study, the difference in attentional capture towards stimuli with high and low monetary gains was found and replicated in previous studies (e.g., Anderson, et al. 2011; Anderson & Yantis, 2013; Le Pelley et al., 2015). Alternatively, fix blocks (a total of 192 trials) may not have been enough for participants to learn the monetary association. This statement is challenged
as the subjective awareness of the participants did not influence the findings. Nevertheless, their confidence of their awareness was not asked, and some participants stated that they had guessed the association. Thus, we cannot completely rule out the chance that the association between the colour of the furniture and the size of the reward was not yet learnt. Although Le Pelley et al. found an enhanced attention of high compared to low reward stimuli with less trials, we used blue/red furniture pictures instead of coloured circles. Thus, our stimuli were more complex and may have required more trials to learn the correct associations.
Contrary to what we hypothesized, we found no evidence for differences in attentional processing of naturally high (nudes and unhealthy food) and low (dressed people and healthy food) reward stimuli. A number of experimental limitations may be responsible for this. Firstly, the stimuli may have been too small or too complex to be processed sensitively. Indeed, in the attentional capture task there was no significant difference in reaction time to trials with or without a distractor, suggesting that the stimuli had no distractive influence. Therefore, it makes sense that there were no reaction time variations among different picture categories. To our knowledge, this study was to first to conceptually replicate Le Pelley et al. (2015)`s attentional capture task using pictures and the first to replicate Bosmans et al. (2009)`s attentional breadth task with non-facial stimuli. In this study, the size of the pictures was matched to the stimuli in the original tasks that were manipulated, but the stimuli were more complex than the original versions. This issue could have been overcome by using bigger/less complex images. Alternatively, in attentional capture task, we could have used coloured circles like Le Pelley et al, and consistently associated them with different categories of stimuli. For example a trial with a red circle could have always been followed by an image of a cheesecake (unhealthy food condition) and a trial with a blue circle could have always been followed by an image of celery (healthy food condition). Secondly, the division of high and low reward stimuli may have been inaccurate. Although participants were asked about their preference of male or female nudes and sweet or savoury unhealthy food, the
participants` subjective desire of each picture was not reported. Thus, it is possible that some pictures of the healthy foods (e.g., carrots and hummus) or the dressed people were more desirable for the participants than we anticipated.
In the attentional breadth task, it is also possible that the picture identification question (whether the picture was edible or not) was not specific enough for participants to
differentiate between stimuli. Participants could have focused on the edibility of the pictures instead of humans being nude or dressed and edibles being food or drink. If that was the case, it would make sense that there were no significant differences in attentional narrowing
towards different stimuli, as participants could have processed the pictures in more general terms (edible or not) instead of the pictures being differently rewarding stimuli. Furthermore, to keep participants motivated throughout the study, the tasks were completed in a fixed order. Participants always started with the attentional breadth task as it was a challenging task without any feedback and with the chin rest restriction, and finished with the attentional capture task with additional monetary gain. However, in both tasks the same pictures were presented. Therefore, it is plausible that the data from the attentional capture task was influenced by the habituation effect as participants became less sensitive to the stimuli (Balkenius, 2000). Finally, a possible gender difference in attentional processes towards rewarding stimuli may have influenced the non-significant finding in the gender combined analyses. The gender differences in attention towards rewarding stimuli has been previously documented (Rubia, Hyde, Halari, Giampietro, & Smith, 2010; Lykins, Meana & Strauss, 2008) and is also illustrated by our exploratory findings. However, other researchers such as Le Pelley et al. (2015) found a difference in attentional processing of rewarding stimuli regardless of the participants` gender. Thus, it is unlikely that gender difference alone confounded the current findings. Nevertheless, along with the study limitations, gender differences in attentional processing may have influenced our findings.
The exploratory analyses revealed significant gender differences in attentional
processing of rewarding stimuli and their correlations with eating related factors.1 There were significant gender differences in attentional capture towards rewarding stimuli and in
correlations among eating related factors and attentional processing of unhealthy food stimuli. The observed gender -by attention interaction could be related to the gender differences in reward sensitivity and the desire for the stimuli. Both reward sensitivity (Avila, & Parcet,
1 Although these findings could have valuable implications, these analyses were exploratory and were not considered in the initial power calculations. Thus, we had a limited number of participants for these analysis and the findings should be interpreted with caution.
2002) and motivation (Engelmann, Damaraju, Padmala, & Pessoa, 2009; Li, Sescousse, Amiez & Dreher, 2015) have been previously associated with enhanced involuntary attention. Although there are contradictory findings regarding which gender is more reward sensitive, there are numerous studies that reported a relationship between gender and reward sensitivity (e.g., Li, Huang, Lin, & Sun, 2007). Similarly, it is possible that there were gender differences in the desire for each stimulus. As the general reward sensitivity and the desire for the stimuli were not recorded in the current study, we could not further explore these possibilities. However, future studies on rewarding stimuli would benefit from including desire ratings of each stimulus and a general reward sensitivity questionnaire such as the Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ; Torrubia, Avila, Molte, & Grande, 1995).
Interestingly, the gender differences displayed different patterns for the attentional
narrowing and the attentional capture. Males` attentional processing of unhealthy food stimuli were positively correlated with their scores on restricted eating, binge eating and BMI in attentional capture task. However, they displayed no significant correlations between eating related factors and attentional narrowing towards the food stimuli. Females had no significant correlations of interest in attentional capture task, but their restricted eating scores were positively correlated with their attentional narrowing towards unhealthy food stimuli. It could be that gender influences different attentional processes differently. However, the direct comparisons of involuntary attentional processes were beyond the aim of this study and the current literature on such comparison is highly limited to support or to reject this possibility. Alternatively, the difference in task outcomes could have confounded the gender effect. Attentional capture was measure with a reaction time task whereas attentional breadth was measured using an accuracy task. Males tend to respond faster in reaction time tasks (Der & Deary, 2006), which was also the case in our attentional capture task. Their fast response could have caused them to make more errors than females. Thus, there may have been a gender difference in speed-accuracy trade-off. This possibility could have been avoided if both tasks had the same outcome measure (either accuracy or reaction time).
Although the hypotheses were not supported, the current study explored alternative uses of two practical involuntary attention tasks that have relatively simple designs and require no expensive equipment. We suggest that less complex or bigger images would be more suitable for both tasks. Alternatively, the influence of naturally rewarding stimuli on attentional capture could be investigated by conditioning different coloured circles with different categories of stimuli. Moreover, in the attentional breadth task the picture identification question should be specific enough for participants to differentiate between picture categories. We also highly recommend for future studies that use rewarding stimuli to measure
participants` subjective desire for each stimulus and their general reward sensitivity.
Furthermore, our findings stress the importance of examining gender differences in involuntary attentional processing of reward, and how they interact with restricted eating, binge eating and BMI (factors that were most strongly correlated with attention to food stimuli) scores. Many previous studies did not account for gender differences, which may have influenced their data. Thus, future investigations on gender differences would not only help understanding how male and female minds differ, but they could clarify how gender impacts the findings in empirical studies.
References
Anderson, B. A., Laurent, P. A., & Yantis, S. (2011). Learned value magnifies salience-based attentional capture. PLoS One, 6(11), e27926. doi:10.1371/journal.pone.0027926
Anderson, B. A. (2013). A value-driven mechanism of attentional selection. Journal of Vision, 13(3), 7-7. doi:10.1167/13.3.7
Anderson, B. A., & Yantis, S. (2013). Persistence of value-driven attentional capture. Journal of Experimental Psychology: Human Perception and Performance, 39(1), 6.
doi:10.1037/a0030860
Ávila, C., & Parcet, M. A. (2002). Individual differences in reward sensitivity and attentional focus. Personality and Individual Differences, 33(6), 979-996. doi: 10.1016/S0191-8869(01)00207-0
Balkenius, C. (2000). Attention, habituation and conditioning: Toward a computational model. Cognitive Science Quarterly, 1(2), 171-214.
https://pdfs.semanticscholar.org/ec4e/a372ec57ec949b8aa3334d08421595d1fe4a.pdf
Bigaard, J., Frederiksen, K., Tjønneland, A., Thomsen, B. L., Overvad, K., Heitmann, B. L., & Sørensen, T. I. (2004). Body fat and fat‐free mass and all‐cause mortality. Obesity, 12(7), 1042-1049.doi: 10.1038/oby.2004.131
Bosmans, G., Braet, C., Koster, E., & Raedt, R. D. (2009). Attachment security and attentional breadth toward the attachment figure in middle childhood. Journal of Clinical Child & Adolescent Psychology, 38(6), 872-882.
doi:10.1080/15374410903258926
Bradley, M. M., Costa, V. D., & Lang, P. J. (2015). Selective looking at natural scenes: Hedonic content and gender. International Journal of Psychophysiology, 98(1), 54-58. doi:10.1016/j.ijpsycho.2015.06.008
Braet, C., & Crombez, G. (2003). Cognitive interference due to food cues in childhood obesity. Journal of Clinical Child and Adolescent Psychology, 32(1), 32-39.
http://www.tandfonline.com/doi/abs/10.1207/S15374424JCCP3201_04
Castellanos, E. H., Charboneau, E., Dietrich, M. S., Park, S., Bradley, B. P., Mogg, K., & Cowan, R. L. (2009). Obese adults have visual attention bias for food cue images: evidence for altered reward system function. International Journal of Obesity, 33(9), 1063-1073. doi:10.1038/ijo.2009.138
Centers for Disease Control and Prevention. (2013). Body mass index: considerations for practitioners. Department of Health and Human Services.
Chopra, M., Galbraith, S., & Darnton-Hill, I. (2002). A global response to a global problem: the epidemic of overnutrition. Bulletin of the World Health Organization, 80(12), 952-958. doi:10.1590/S0042-96862002001200009
Consultation, W. E. (2008). Waist circumference and waist-hip ratio. Report of a WHO Expert Consultation Geneva: World Health Organization, 8-11.
Dalton, M., Cameron, A. J., Zimmet, P. Z., Shaw, J. E., Jolley, D., Dunstan, D. W., & Welborn, T. A. (2003). Waist circumference, waist–hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults. Journal of Internal Medicine, 254(6), 555-563. doi:10.1111/j.1365-2796.2003.01229.x
Deal, L. S., Wirth, R. J., Gasior, M., Herman, B. K., & McElroy, S. L. (2015). Validation of the yale‐brown obsessive compulsive scale modified for binge eating. International Journal of Eating Disorders, 48(7), 994-1004. doi: 10.1002/eat.22407
Der, G., & Deary, I. J. (2006). Age and sex differences in reaction time in adulthood: results from the United Kingdom Health and Lifestyle Survey. Psychology and Aging, 21(1), 62-73. DOI: 10.1037/0882-7974.21.1.62
Duarte, C., Pinto-Gouveia, J., & Ferreira, C. (2015). Expanding binge eating assessment: Validity and screening value of the Binge Eating Scale in women from the general population. Eating Behaviors, 18, 41-47. doi.org/10.1016/j.eatbeh.2015.03.007 Engelmann, J. B., Damaraju, E., Padmala, S., & Pessoa, L. (2009). Combined effects of
attention and motivation on visual task performance: transient and sustained motivational effects. Frontiers in Human Neuroscience, 3. doi:
10.3389/neuro.09.004.2009
Finlayson, G., Arlotti, A., Dalton, M., King, N., & Blundell, J. E. (2011). Implicit wanting and explicit liking are markers for trait binge eating. A susceptible phenotype for overeating. Appetite, 57(3), 722-728. doi:10.1016/j.appet.2011.08.012
Gormally, J., Black, S., Daston, S., & Rardin, D. (1982). The assessment of binge eating severity among obese persons. Addictive Behaviors, 7(1), 47-55. doi:10.1016/0306-4603(82)90024-7.
Graham, R., Hoover, A., Ceballos, N. A., & Komogortsev, O. (2011). Body mass index moderates gaze orienting biases and pupil diameter to high and low calorie food images. Appetite, 56(3), 577-586. doi:10.1016/j.appet.2011.01.029
Grol, M., & De Raedt, R. (2014). Effects of positive mood on attentional breadth for emotional stimuli. Frontiers in Psychology, 5. doi: 10.3389/fpsyg.2014.01277 Grupski, A. E., Hood, M. M., Hall, B. J., Azarbad, L., Fitzpatrick, S. L., & Corsica, J. A.
(2012). Examining the Binge Eating Scale in screening for binge eating disorder in bariatric surgery candidates. Obesity Surgery, 1-6. doi:10.1007/s11695-011-0537-4 Harding, A. H., Day, N. E., Khaw, K. T., Bingham, S., Luben, R., Welsh, A., & Wareham, N.
J. (2004). Dietary Fat and the Risk of Clinical Type 2 Diabetes The European Prospective Investigation of Cancer-Norfolk Study. American Journal of Epidemiology, 159(1), 73-82. doi:10.1093/aje/kwh004
Herman, C. P., & Polivy, J. (1980). Restrained eating. In A. J. Stunkard (Ed.), Obesity (pp. 208-225). Philadelphia: Saunders.
Hollitt, S., Kemps, E., Tiggemann, M., Smeets, E., & Mills, J. S. (2010). Components of attentional bias for food cues among restrained eaters. Appetite, 54(2), 309-313. doi:10.1016/j.appet.2009.12.005
Juergensen, J., & Demaree, H. A. (2015). Approach-motivated positive affect and emotion regulation alter global–local focus and food choice. Motivation and Emotion, 39(4), 580-588. doi:10.1007/s11031-015-9472-3
Le Pelley, M. E., Pearson, D., Griffiths, O., & Beesley, T. (2015). When goals conflict with values: Counterproductive attentional and oculomotor capture by reward-related stimuli. Journal of Experimental Psychology: General, 144(1), 158-171.
doi:10.1037/xge0000037
Li, Y., Sescousse, G., Amiez, C., & Dreher, J. C. (2015). Local morphology predicts
functional organization of experienced value signals in the human orbitofrontal cortex. Journal of Neuroscience, 35(4), 1648-1658. doi:10.1523/JNEUROSCI.3058-14.2015 Li, C. S. R., Huang, C. Y., Lin, W. Y., & Sun, C. W. V. (2007). Gender differences in
punishment and reward sensitivity in a sample of Taiwanese college students. Personality and Individual Differences, 43(3), 475-483.
Lykins, A. D., Meana, M., & Strauss, G. P. (2008). Sex differences in visual attention to erotic and non-erotic stimuli. Archives of Sexual Behavior, 37(2), 219-228. doi:10.1007/s10508-007-9208-x
Marcus, M. D., Wing, R. R., & Hopkins, J. (1988). Obese binge eaters: Affect, cognitions, and response to behavioral weight control. Journal of Consulting and Clinical Psychology, 56(3), 433-439.
http://psycnet.apa.org/index.cfm?fa=buy.optionToBuy&id=1988-33492-001
Nederkoorn, C., Smulders, F. T. Y., & Jansen, A. (2000). Cephalic phase responses, craving and food intake in normal subjects. Appetite, 35(1), 45-55.
doi:10.1006/appe.2000.0328
Papies, E. K., Stroebe, W., & Aarts, H. (2008). The allure of forbidden food: On the role of attention in self-regulation. Journal of Experimental Social Psychology, 44(5), 1283-1292. doi:10.1016/j.jesp.2008.04.008
Parkhurst, D., Law, K., & Niebur, E. (2002). Modeling the role of salience in the allocation of overt visual attention. Vision Research, 42(1), 107-123.
doi:10.1016/S0042-6989(01)00250-4
Piech, R. M., Pastorino, M. T., & Zald, D. H. (2010). All I saw was the cake. Hunger effects on attentional capture by visual food cues. Appetite, 54(3), 579-582.
doi:10.1016/j.appet.2009.11.003
Rowe, G., Hirsh, J. B., & Anderson, A. K. (2007). Positive affect increases the breadth of attentional selection. Proceedings of the National Academy of Sciences, 104(1), 383-388. doi: 10.1073/pnas.0605198104
Rubia, K., Hyde, Z., Halari, R., Giampietro, V., & Smith, A. (2010). Effects of age and sex on developmental neural networks of visual–spatial attention allocation. Neuroimage, 51(2), 817-827. doi.org/10.1016/j.neuroimage.2010.02.058
Shafran, R., Lee, M., Cooper, Z., Palmer, R. L., & Fairburn, C. G. (2007). Attentional bias in eating disorders. International Journal of Eating Disorders, 40(4), 369-380. doi: 10.1002/eat.20375
Schag, K., Schönleber, J., Teufel, M., Zipfel, S., & Giel, K. E. (2013). Food‐related impulsivity in obesity and Binge Eating Disorder–a systematic review. Obesity Reviews, 14(6), 477-495. doi: 10.1111/obr.12017
(2016). Emotional attention for erotic stimuli: Cognitive and brain mechanisms. Journal of Comparative Neurology, 524(8), 1668-1675. doi: 10.1002/cne.23859
Siep, N., Roefs, A., Roebroeck, A., Havermans, R., Bonte, M. L., & Jansen, A. (2009). Hunger is the best spice: an fMRI study of the effects of attention, hunger and calorie content on food reward processing in the amygdala and orbitofrontal cortex.
Behavioural Brain Research, 198(1), 149-158. doi:10.1016/j.bbr.2008.10.035
Smeets, E., Roefs, A., van Furth, E., & Jansen, A. (2008). Attentional bias for body and food in eating disorders: Increased distraction, speeded detection, or both?. Behaviour Research and Therapy, 46(2), 229-238. doi: 10.1016/j.brat.2007.12.003
Theeuwes, J. (1992). Perceptual selectivity for color and form. Attention, Perception, & Psychophysics, 51(6), 599-606. doi:10.3758/BF03211656
Torrubia, R., Avila, C., Molto, J., & Grande, I. (1995). Testing for stress and happiness: The role of the behavioral inhibition system. Stress and Emotion: Anxiety, Anger, and Curiosity, 15, 189-211.
van Strien, T., Herman, C. P., Engels, R. C., Larsen, J. K., & van Leeuwe, J. F. (2007). Construct validation of the Restraint Scale in normal-weight and overweight females. Appetite, 49(1), 109-121. doi:10.1016/j.appet.2007.01.003
Yoon, K. L., Vidaurri, D. N., Joormann, J., & De Raedt, R. (2015). Social anxiety and narrowed attentional breadth toward faces. Emotion, 15(6), 682.
https://biblio.ugent.be/publication/5855870/file/5855872
Wallis, D. J., & Hetherington, M. M. (2004). Stress and eating: the effects of ego-threat and cognitive demand on food intake in restrained and emotional eaters. Appetite, 43(1), 39-46. doi:10.1016/j.appet.2004.02.001
Appendices
Hunger rating
English version.
How hungry are you at the moment, from a scale of 1(not hungry) to 7(extremely hungry) ?
Dutch version.
Hoe veel honger/trek heeft u op een schaal van 1 (helemaal geen honger) tot 7 (extreem veel honger)?
The Concern for Dieting subscale of Revised Restraint Scale (Herman & Polivy, 1980)
English version.
1. How often are you dieting?
1. Not at all 2.Rarely 3. Sometimes 4. Often 5.Always 2. Do you have feelings of guilt after overeating?
1. Not at all 2. Sometimes 3. Often 4.Always 3. Do you eat sensibly in front of others and splurge alone?
1. Not at all 2. Sometimes 3. Often 4.Always 4. Do you give too much time and thought to food?
1. Not at all 2. Sometimes 3. Often 4.Always 5. Would a weight fluctuation of 5 lb affect the way you live your life?
1. Not at all 2. A little 3. Somewhat 4.Srongly 6. How conscious are you of what you are eating?
1. Not at all aware 2. A little aware 3. Quite aware 4.Extremely aware
Dutch version.
Maak h et geode antwoord bold: 1. ik "lijn"
1. nooit 2. Zelden 3. Soms 4. Vaak 5. Altijd 2. Ik voel me schuldig nadat ik teveel gegeten heb
1. nooit 2. Soms 3. Vaak 4. Altijd
3. Ik eet beheerst in het bijzijn van anderen, maar ga me te buiten aan eten zodra ik alleen ben
1. nooit 2. Soms 3. Vaak 4. Altijd
4. Ik besteed meer aandacht en tijd aan voedsel dan goed voor me is 1. nooit 2. Soms 3. Vaak 4. Altijd
5. Zou een gewichtsschommeling van 2,5 kilo uw manier van leven beinvloeden? 1. Helemaal niet 2. Een beetje 3. Enigszins 4. Sterk
6. Hoe bewust bent u zich van wat u eet?
1. helemaal niet bewust 2. een beetje bewust 3. redelijk bewust 4. extreem bewust
Binge eating scale (Gormally, Black, Daston, & Rardin, 1982)
English version.
INSTRUCTIONS: Below are 16 groups of numbered statements. Please read all the statements by each group. Choose the phrase that best describes how you feel about your eating behavior. Circle the number that is for your chosen statement.
1.
1. I do not think about my weight or size when I’m around other people. 2. I worry about my appearance, but it does not make me unhappy.
3. I think about my appearance or weight and I feel disappointed in myself. 4. I frequently think about my weight and feel great shame and disgust. 2.
1.I have no difficulty eating slowly.
2. I may eat quickly, but I never feel too full. 3. Sometimes after I eat fast I feel too full.
4. Usually I swallow my food almost without chewing, then feel as if I ate too much. 3.
2. I think I have less control over food than the average person. 3. I feel totally unable to control my impulses toward food.
4.I feel totally unable to control my relationship with food and I try desperately to fight my impulses toward food.
4.
1. I do not have a habit of eating when I am bored.
2. Sometimes I eat when I am bored, but I can often distract myself and not think about food. 3. I often eat when I am bored, but I can sometimes distract myself and not think about food. 4. I have a habit of eating when I am bored and nothing can stop me.
5.
1.Usually when I eat it is because I am hungry.
2. Sometimes I eat on impulse without really being hungry.
3. I often eat to satisfy hunger even when I know I’ve already eaten enough. On these occasions I can’t even enjoy what I eat.
4. Although I have not physically hungry, I feel the need to put something in my mouth and I feel satisfied or only when I can fill my mouth (for example with a piece of bread).
6.After eating too much:
1. I do not feel guilty or regretful at all. 2. I sometimes feel guilty or regretful.
3. I almost always feel a strong sense of guilt or regret. 7.
1.When I’m on a diet, I never completely lose control of food, even in times when I eat too much. 2. When I eat a forbidden food on a diet, I think I’ve failed and eat even more.
3. When I’m on a diet and I eat to much, I think I’ve failed and eat even more. 4. I am always either binge eating or fasting.
8.
2. About once a month I eat so much that I felt uncomfortably full.
3. There are regular periods during the month when I eat large amounts of food at meals or between meals.
4. I eat so much that usually, after eating, I feel pretty bad and I have nausea. 9.
1. The amount of calories that I consume is fairly constant over time.
2. Sometimes after I eat too much, I try to consume few calories to make up for the previous meal. 3. I have a habit of eating too much at night. Usually I’m not hungry in the morning and at night I eat too much.
4. I have periods of about a week in which I imposed starvation diets, following periods of when I ate too much. My life is made of binges and fasts.
10.
1. I can usually stop eating when I decide I’ve had enough. 2. Sometimes I feel an urge to eat that I cannot control.
3. I often feel impulses to eat so strong that I cannot win, but sometimes I can control myself. 4. I feel totally unable to control my impulses to eat.
11.
1. I have no problems stopping eating when I am full.
2.I can usually stop eating when I feel full, but sometimes I eat so much it feels unpleasant. 3. It is hard for me to stop eating once I start, I usually end up feeling too full.
4. It is a real problem for me to stop eating and sometimes I vomit because I feel so full. 12.
1. I eat the same around friends and family as I do when I am alone.
2. Sometimes I do not eat what I want around others because I am aware of my problems with food. 3. I often eat little around other people because I feel embarrassed.
4. I’m so ashamed of overeating, I only eat at times when no one sees me. I eat in secret. 13.
1. Ik eet drie maaltijden per dag met heel af en toe een tussendoortje. 2. Ik eet drie maaltijden per dag met vaste tussendoortjes.
3. Wanneer ik veel tussendoortjes heb gehad, ben ik geneigd om vaste maaltijden te laten vervallen. 4. Er zijn regelmatig periodes waarin ik het gevoel heb continu te eten, zonder vaste maaltijden.
14.
1.I eat three meals a day and occasionally a snack. 2. I eat three meals a day and I usually snack as well. 3. I eat many meals, or skip meals regularly.
4. There are times when I seem to eat continuously without regular meals. 15.
1.I don’t think about food any more than most people. 2. I have strong desires for food, but only for short periods. 3. There are some days when I think of nothing but food.
4. Most of my days is filled with thoughts of food. I feel like I live to eat. 16.
1.I usually know if I am hungry or or not. I know what portion sizes are appropriate.
2. Sometimes I do not know if I am physically hungry or not. In these moments, I can hardly understand how much food is appropriate.
3. Even if I knew how many calories should I eat, I would not have a clear idea of what is, for me, a normal amount of food.
Dutch version. INSTRUKTIE:
Hieronder staan 16 groepen van genummerde uitspraken. Leest u bij elke groep alle uitspraken goed door. Kies per groep dié uitspraak die het beste weergeeft hoe u zich voelt over uw eetgedrag. Omcirkel het cijfer dat voor de door u gekozen uitspraak staat.
1.
2. Ik ben me bewust van mijn gewicht of lichaam als ik bij anderen ben, maar voel me hierdoor niet teleurgesteld in mezelf.
3. Ik ben me bewust van mijn gewicht of lichaam en voel me hierdoor teleurgesteld in mezelf. 4. Ik ben me erg bewust van mijn gewicht en vaak walg ik van mezelf en schaam me voor mijn
uiterlijk. 2.
1. Ik vind het niet moeilijk om langzaam te eten.
2. Ondanks dat het lijkt of ik mijn eten opslok, voel ik me daarna niet onprettig vol zitten. 3. Soms eet ik erg snel en veel en voel me daarna onprettig vol zitten.
4. Ik heb de gewoonte om mijn eten op te slokken zonder goed te kauwen en voel me dan onprettig vol zitten.
3.
1. Ik kan mijn eetgewoonten controleren.
2. Ik kan mijn eetgewoonten minder goed controleren dan andere mensen. 3. Ik voel me machteloos over de controle die ik over mijn eetgewoonten heb. 4. Ik voel me totaal machteloos over de controle die ik over mijn eetgewoonten heb. 4.
1. Ik eet bijna nooit uit verveling. 2. Ik eet soms uit verveling. 3. Ik eet regelmatig uit verveling.
4. Ik eet vaak uit verveling en niets kan mij dan doen stoppen met eten. 5.
1. Ik heb bijna altijd honger als ik iets eet.
2. Soms eet ik zomaar terwijl ik niet echt honger heb.
3. Ik eet vaak iets terwijl ik lichamelijk gezien het eten niet echt nodig heb. 4. Ondanks dat ik geen honger heb moét ik toch iets eten om mijn mond te vullen. 6.
1. Ik voel me totaal niet schuldig als ik teveel heb gegeten. 2. Ik voel me soms schuldig als ik teveel heb gegeten. 3. Ik voel me vaak schuldig als ik teveel heb gegeten. 7.
