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Attentional bias to food-related visual cues: is there a role in obesity?

Published online by Cambridge University Press:  19 September 2014

K. J. Doolan
Affiliation:
Northern Ireland Centre for Food and Health, University of Ulster, Coleraine BT52 1SA, UK
G. Breslin
Affiliation:
Sport and Exercise Science Research Institute, University of Ulster, Jordanstown BT37 OQB, UK
D. Hanna
Affiliation:
School of Psychology, Queen's University Belfast, Belfast BT7 1NN, UK
A. M. Gallagher
Affiliation:
Northern Ireland Centre for Food and Health, University of Ulster, Coleraine BT52 1SA, UK
Corresponding
E-mail address:
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Abstract

The incentive sensitisation model of obesity suggests that modification of the dopaminergic associated reward systems in the brain may result in increased awareness of food-related visual cues present in the current food environment. Having a heightened awareness of these visual food cues may impact on food choices and eating behaviours with those being most aware of or demonstrating greater attention to food-related stimuli potentially being at greater risk of overeating and subsequent weight gain. To date, research related to attentional responses to visual food cues has been both limited and conflicting. Such inconsistent findings may in part be explained by the use of different methodological approaches to measure attentional bias and the impact of other factors such as hunger levels, energy density of visual food cues and individual eating style traits that may influence visual attention to food-related cues outside of weight status alone. This review examines the various methodologies employed to measure attentional bias with a particular focus on the role that attentional processing of food-related visual cues may have in obesity. Based on the findings of this review, it appears that it may be too early to clarify the role visual attention to food-related cues may have in obesity. Results however highlight the importance of considering the most appropriate methodology to use when measuring attentional bias and the characteristics of the study populations targeted while interpreting results to date and in designing future studies.

Type
Irish Postgraduate Winners
Copyright
Copyright © The Authors 2014 

Prevalence of overweight and obesity has now reached epidemic proportions with more than 1·4 billion adults considered as overweight and of these, over 500 million being obese worldwide( 1 ). On a fundamental level the cause of obesity is linked to an imbalance of energy intake v. energy expenditure; however, it is now evident that obesity is a multifactorial condition that arises from interactions between genetic and environmental factors( Reference Vandenbroeck, Goossens and Clemens 2 ). One such factor is the present ‘obesogenic’ environment that is characterised by a wealth of highly palatable, energy-dense foods( Reference Swinburn, Sacks and Hall 3 ). These foods are heavily represented in the visual environment for example, through advertising and it has been suggested that frequent exposure to these forms of food-related cues may impact on eating behaviours and food choices( Reference Kemps, Tiggemann and Orr 4 Reference Brignell, Griffiths and Bradley 6 ).

According to the theory of incentive sensitisation, ‘addictive substances’ modify reward-related pathways in the brain leading to hypersensitisation of reward-related stimuli( Reference Robinson and Berridge 7 ). This results in increased salience to reward-related stimuli resulting in these stimuli becoming more ‘attention grabbing’. It has been proposed that this heightened awareness is the consequence of repeated exposure to a particular stimulus and the association between the stimulus and a rewarding experience, which results in related cues becoming salient. The stimulus related to the rewarding experience may then attract greater attention and in turn trigger feelings of cravings that may potentially impact on behaviours and choices. This process is often referred to as attentional bias and is based on the theory of incentive sensitisation put forth by Robinson and Berridge( Reference Robinson and Berridge 7 ). This theory has been applied to drugs of addiction( Reference Marhe, Luijten and van de Wetering 8 ), smoking( Reference Chanon, Sours and Boettiger 9 ) and alcohol research( Reference Weafer and Fillmore 10 ). More recently, it has been suggested that food-related stimuli may also have the ability to capture attention and activate reward pathways in the brain thus making visual food cues more salient to the observer( Reference Volkow and Wise 11 , Reference Polivy, Herman and Coelho 12 ). The processing of reward and pleasure is mediated by the mesocorticolimbic dopamine system, in particular the ventral tegmental area, the ventral striatum and nucleus accumbens. Activation of these reward pathways occurs on exposure to drugs of addiction; however, recent neuroimaging studies have indicated that these pathways may also be activated on exposure to foods high in fat and/or sugar (see Stice et al. ( Reference Stice, Figlewicz and Gosnell 13 ) for a review).

Previously, detection and awareness of food within the visual environment would have been considered as a necessary evolutionary adaption to survive( Reference Denton 14 ); however, in today's Western environment, such a resourcefulness could be contributing to the current obesity epidemic( Reference Castellanos, Charboneau and Dietrich 15 ). Research has suggested that some individuals may have greater attentional bias to food-related visual cues( Reference Leland and Pineda 16 Reference Mogg, Bradley, Hyare and Lee 18 ) within their environment (for example, being more ‘tuned in’ or aware of the sight of a food or a related food image) and it is possible that they may be at greater risk of overeating and subsequent weight gain. It is therefore of interest to further investigate whether some individuals are more susceptible to increased attention to food-related stimuli, in particular, potential weight group differences considering the current rates of overweight and obesity.

Several methods exist for the assessment of attentional bias and are often referred to as indirect and direct methods( Reference Field, Munafo and Franken 19 ). These methods have previously been used to examine attention to food-related stimuli in normal weight populations in both a fasted and satiated condition( Reference Hepworth, Mogg, Brignell and Bradley 20 Reference Meule, Lutz and Krawietz 22 ). More recently research has begun to focus on visual attention to food-related stimuli in individuals who are overweight or obese( Reference Castellanos, Charboneau and Dietrich 15 , Reference Nijs, Franken and Muris 23 Reference Graham, Hoover and Ceballos 29 ). Results from these studies however have been inconsistent and this may be due in part to the methodology used to measure attention. In this review, both indirect and direct behavioural methods of measuring attentional bias to food-related stimuli in normal weight and overweight/obese adult populations are discussed and questions are raised as to the most appropriate methodology to use when assessing attentional bias to visual food cues.

Methodologies used to measure attention: indirect v. direct methods

Attentional bias can be measured using behavioural methods, consisting of indirect and direct measures. Studies investigating attentional bias to food-related stimuli have employed a variety of these methods either as a sole measure of attention or in combination and results appear to depend on the methodology used, the type of stimuli employed for the attentional task, stimuli presentation time and/or the study cohort targeted. The most common indirect and direct behavioural methods used to date in food-related attentional bias research will now be described.

Indirect methods for assessing attention to food-related visual stimuli

The most common indirect methods used to measure attentional bias to visual food cues in normal weight v. overweight/obese adults have been the food-modified Stroop task (presented in Table 1) and the visual probe task (presented in Table 2).

Table 1. A summary of food-modified Strop task measures of attention to food-related stimuli in overweight/obese v. normal weight populations

OB, obese; F, female; M, male; NW, normal weight; HED, high energy dense; LED, low energy dense.

Table 2. A summary of visual probe task and eye-tracking measures of attention to food-related stimuli in overweight/obese v. normal weight populations

OW, overweight; OB, obese; NW, normal weight; VPT, visual probe task; ET, eye-tracking; HED, high energy dense; LED, low energy dense.

Food-modified Stroop task

The classic Stroop task( Reference Stroop 30 ) has been adapted from cognitive psychology to examine human attention and information processes. This task involves the presentation of words printed in different colours of ink consisting of control words and stimuli of interest-related words. A delay in correctly naming the colour of ink in which the word is printed is considered as an interference effect and is traditionally considered as an example of cognitive competition.

More recently, the classic Stroop task has been modified to measure attention to food-related stimuli. In a food-related Stroop task, participants are exposed to food-related words and control words (non-food-related words matched for word length, complexity and familiarity) and instructed to name as quickly and accurately as possible, the colour in which each word is printed while attempting to ignore the content of the word. Attentional bias is then determined from the length of time it takes to colour name a food-related word as compared with a non-food-related word. A slower reaction time in successfully naming the colour of ink in which a food-related word is printed is usually considered as having a greater attentional bias towards the content of the food-related word; however, it has been suggested from other areas of attentional bias research (e.g. substance abuse( Reference Field and Cox 31 )) that some individuals may attempt to divert attention away from a stimulus as a means to control feelings of cravings which may also result in a slower colour naming time.

This measure of attentional bias has been used in two studies investigating potential weight group differences in attentional processing of food stimuli in an adult population. The first conducted by Nijs et al. ( Reference Nijs, Franken and Muris 23 ) used high energy-dense food-related words and observed no differences in attentional processing of food stimuli between normal weight and obese participants. Similarly in 2011, Phelan et al. ( Reference Phelan, Hassenstab and McCaffery 24 ) found no weight group differences in attentional processing of food words between normal weight and obese participants but did observe slower reaction times to high energy-dense food words in a group of long-term weight loss maintainers with a previous history of being overweight/obese. Although well recognised in the field of attentional bias research, some questions have been raised in regards to the index of information processing measured by the food-modified Stroop task. Slower reaction times may reflect an increase in attention to the content of the word or diversion of attention from food-related stimuli as a means to control food cravings and desire to eat. This was demonstrated by Phelan et al. ( Reference Phelan, Hassenstab and McCaffery 24 ) who although as previously discussed did not identify weight group differences in attentional processing of food words between normal weight and obese women, did report slower reaction times in colour naming of high energy-dense food-related words in weight loss maintainers. This slower colour naming of food-words could be interpreted as a possible deliberate effort to restrict (i.e. shift attention away from) attentional responses to food stimuli as a means of controlling food cravings. Finally, it has been suggested that as individuals tend to be exposed to reward-related images through the visual environment, the use of reward-related words normally employed in Stroop tasks may not be considered as ecologically valid and representative of visual cues as reward-related images( Reference Bruce and Jones 32 , Reference Johansson, Gharderi and Andersson 33 ).

The classic Stroop task is one of the most widely used tests of substance-related attentional bias; however, attentional research has continued to evolve in an attempt to measure how participants’ visual attention is allocated to particular stimuli in a more ecologically valid manner by using simultaneously competing reward-related images or words. One such method is the visual probe task( Reference MacLeod, Mathews and Tata 34 ).

Visual probe task

In a food-related visual probe task, participants are exposed to a food-related word or image matched to a control word or image simultaneously presented side by side on a computer screen. The paired food words or images then disappear after a set period of time (usually 50–2000 ms) and a dot probe replaces one of the previous stimuli. Participants are instructed to respond as quickly as possible to the location of the probe and attentional bias is measured from the length of time it takes to successfully identify the location of a probe that replaces a food or non-food stimuli. In general, individuals react faster to probes that appear in visual areas that they have already expressed interest in, responding more quickly to a probe that appears in a visual display to which attention has already been directed compared with a probe that appears in an area to which individuals have not attended( Reference Posner, Snyder and Davidson 35 ).

Visual probe task data appears to provide information on both initial orientation and maintenance of attention by varying the length of the stimulus presentation time (i.e. how long the image is presented on the screen). For example, Nijs et al. ( Reference Nijs, Muris and Euser 27 ) using food stimuli presented for 100 ms (which they considered as an indication of initial attention allocation) found a main effect for image type with all participants, regardless of weight status (normal weight v. overweight/obese) having a greater attentional bias to food images as compared with control images. In comparison, using the same visual probe task and stimuli presented for 500 ms (which they considered to be an indication of maintained attention) in the same cohort of participants, a main effect for weight group was observed with normal weight females having significantly quicker reaction times to probes that replaced food images as compared with their overweight/obese counterparts. Castellanos et al. ( Reference Castellanos, Charboneau and Dietrich 15 ) also using a visual probe task, found no effects of weight status or hunger condition in attentional processing of food images in a group of normal weight and obese women with a stimulus presentation time of 2000 ms.

Similar to the Stroop task, it has been suggested that it can be difficult to interpret results obtained from visual probe tasks, particularly those with a longer stimulus presentation time (e.g. >500 ms) as it is not possible to measure shifts in attention between stimuli presented side by side or gauge participant disengagement from stimuli presented during the task( Reference Field, Mogg and Zetteler 36 , Reference Bradley, Field and Mogg 37 ). This may in part explain the conflicting findings that have been observed in studies that have attempted to measure maintained attention to food-related stimuli using a visual probe task with longer stimulus presentation times( Reference Castellanos, Charboneau and Dietrich 15 , Reference Nijs, Muris and Euser 27 ) and indicates that this particular measure of attention may be more suitable for providing an indication of initial attention to visual food cues.

Direct methods and combination studies for assessing attention to food-related visual stimuli

The most common direct method used to measure attentional bias to visual food cues in normal weight v. overweight/obese adults has been the use of eye-tracking technology (presented in Table 2). A limited number of studies have used this method in combination with indirect methods or as a sole measure of attentional bias to food-related stimuli.

Eye-tracking

Recently, the use of novel eye-tracking technologies has been recognised as a direct method of measuring visual attention, overcoming some of the methodological issues encountered using the Stroop or visual probe task( Reference Field and Cox 31 , Reference Field, Mogg and Zetteler 36 ). These eye-tracking systems record participants’ eye-movements and interspersed visual fixations as they complete an attentional task. The duration of each visual fixation is presumed to represent the degree of cognitive processing whereas the point of visual gaze indicates the initial area of expressed visual interest. This method of assessing visual gaze allows for eye-movements to be monitored in a non-invasive manner while individuals are exposed to visual stimuli, providing a measure of attentional shifts that cannot be monitored using indirect methods( Reference Field and Cox 31 ). It appears that different cognitive mechanisms may underpin the initial direction of allocated attention, the maintenance of visual attention to a particular stimuli and the disengagement of attention( Reference Allport and Posner 38 , Reference LaBerge 39 ). As previously discussed, shorter stimuli presentation times while using an indirect method are usually employed to measure initial attention. Longer presentation times are considered as a measure of maintained attention( Reference Field and Cox 31 ). According to the work of Duncan et al. ( Reference Duncan, Ward and Shapiro 40 ) 50 ms is the required time needed for an individual to shift their attention towards a particular stimulus of interest. Furthermore, Theeuwes( Reference Theeuwes, Itti, Rees and Tsotsos 41 ) suggested that approximately 150 ms are required to disengage attention from a stimulus and shift attention to a competing stimulus of interest. From this, it would appear that when stimuli are presented side by side, for example in a visual probe task for a duration of 200 ms, any attentional bias that occurs, could be considered as an indication of initial attentional allocation alone since stimuli presentation time is not sufficient to allow for a measure of any further shifts in visual attention. To investigate maintained attention, many researchers have therefore used stimuli presentation times of 1000 ms or longer; however, interpretation of results from these studies have been conflicting. For example, a study conducted by Bradley et al. ( Reference Bradley, Mogg and Wright 42 ) investigating attentional bias to cigarette-related visual cues in smokers considered a stimuli presentation time of 500 ms to be a measure of initial directed attention. In comparison, Koster et al. ( Reference Koster, Verschuere and Crombez 43 ) investigating attention to threat-associated stimuli, considered a stimuli presentation time of 500 ms as an indication of maintained attention. More recently, a review by Field et al. ( Reference Field and Cox 31 ) examining attentional bias in addictive behaviours agreed with the suggestions of Koster et al.( Reference Koster, Verschuere and Crombez 43 ) that stimuli presentation times of 500 ms or greater could be interpreted as maintained attention and presentation times of 200 ms or less, considered a measure of initial attentional allocation. It is evident that there is some contradiction not only in food-related attentional bias research, but also attentional bias research as a whole of the most appropriate length of stimuli presentation time to successfully measure the difference stages of attentional allocation. This has led to some researchers using eye-tracking methodologies as a more comprehensive tool to allow for the continuous assessment of eye-movements as a measure of attentional allocation (both initial and maintained) and attentional disengagement.

Three studies have used eye-tracking in combination with a visual probe task (an indirect measure of attention) to investigate both initial and maintained visual attention to food-related stimuli in normal weight v. overweight/obese populations( Reference Castellanos, Charboneau and Dietrich 15 , Reference Nijs, Muris and Euser 27 , Reference Werthmann, Roefs and Nederkoorn 28 ). Interestingly, data obtained from eye-tracking and visual probe tasks used in these studies appear to yield different results despite both methods being used in the same participant cohort. For example, Castellanos et al. ( Reference Castellanos, Charboneau and Dietrich 15 ) reported enhanced gaze direction and gaze duration obtained from eye-tracking data in both normal weight and obese females to food images while participants were in a fasted condition and this was maintained in the obese group even following satiation from consumption of a liquid-based meal, but found no weight group differences in reaction time data obtained from the visual probe task. Werthmann et al. ( Reference Werthmann, Roefs and Nederkoorn 28 ) used eye-tracking coupled with a visual probe task and established increased gaze direction to food images in overweight/obese females as compared with normal weight females but results from reaction time data results did not yield any between weight group differences. Similarly Nijs et al. ( Reference Nijs, Muris and Euser 27 ) also used a visual probe task plus recording of eye-movements and observed faster reaction times to a visual probe that replaced food-related stimuli, in normal weight females as compared with their overweight/obese counterparts but observed no differences between the two weight groups in eye-tracking data despite both measures being conducted in the same sample. Finally one study has used the monitoring of eye-movements as a sole measure of attentional processing of food-related images in normal weight v. overweight/obese females. Graham et al. ( Reference Graham, Hoover and Ceballos 29 ) used eye-tracking as a measure of visual attention without the use of an indirect method and reported no differences in maintained visual gaze to food images as a function of BMI group but did observe greater initial attentional orientation to visual food cues in normal weight women as compared with their overweight/obese counterparts.

These conflicting results demonstrate that the choice of methodology, the length of stimuli presentation time and the type of stimuli used to measure visual attention may influence the results obtained even in studies that have employed two methods (an indirect and a direct method) to measure what could be considered as the same aspect of attentional processing. This indicates that future studies are necessary to clarify the most suitable measure of visual attention required to accurately measure potential weight group differences in the attentional processing of food-related stimuli.

Other methodological considerations

It appears that no consensus has been reached with regards to the most appropriate method to use in measuring attentional bias to food-related stimuli; however, as this novel area of obesity research grows, other factors have been recognised as having the potential to impact on attentional processing of visual food cues and as such, these factors may need to be taken into consideration when designing and subsequently interpreting results of studies conducted in the field. These factors include the levels of participant satiation, energy density of food-related stimuli, individual eating style traits and the weight status of the study population targeted. The proposed interactions between these factors are summarised in Figure 1. The potential of each will now be considered in relation to their possible impact on the attentional processing of visual food cues.

Fig. 1. The potential role of attentional bias to food-related stimuli in the development and maintenance of obesity (adapted from Franken et al.( Reference Franken 63 ))

Effects of satiation

Several studies have investigated potential associations between self-reported levels of hunger and/or food cravings and attentional bias to food-related stimuli. From an evolutionary perspective, it would be expected that individuals may be more aware of their food-related environment when they are hungry as compared with when they are satiated( Reference Denton 14 ). For example, Geardhardt et al. ( Reference Gearhardt, Treat and Hollingworth 26 ) using a visual probe task found that self-reported hunger levels were a significant predictor of attention to food images in overweight/obese women. Similarly, Nijs et al. ( Reference Nijs, Muris and Euser 27 ) reported that participants, regardless of weight status (i.e. normal weight v. overweight/obese) in a condition of hunger had faster reaction times to food-related stimuli presented for 100 ms than participants provided with a liquid-based study meal although it should be noted that the present study employed a between-subject rather than a within-subject design. In comparison, Loeber et al. ( Reference Loeber, Grosshans and Korucuoglu 25 ) reported no association between levels of hunger and reaction times to food stimuli in a group of obese and normal weight women. Within these studies, although attentional bias was measured consistently using a visual probe task, there were significant variations in how hunger levels were assessed and/or whether hunger levels were manipulated as part of the study protocol. For example, Geardhardt et al. ( Reference Gearhardt, Treat and Hollingworth 26 ) recorded participant self-reported hunger levels using a 100 mm visual analogue scale; however, participants did not receive any instructions in regards to fasting prior to the study session and no study meal was provided. In comparison, Nijs et al.( Reference Nijs, Muris and Euser 27 ) using a between-subject design, recorded self-reported hunger levels using a visual analogue scale in participants following a 17 h fast and in participants provided with a liquid-based study meal. Loeber et al. ( Reference Loeber, Grosshans and Korucuoglu 25 ) also assessed self-reported hunger levels using a visual analogue scale and participants were instructed to refrain from eating at least 3 h before the testing session.

Only one study has investigated potential weight group differences in attentional processing of food-related stimuli using a direct method of assessing attentional bias while taking into consideration the potential effects of hunger condition using a within-between subject design. Castellanos et al. ( Reference Castellanos, Charboneau and Dietrich 15 ) used a visual probe task and recording of eye-movements to assess attentional bias to food-related visual cues. Results demonstrated that following consumption of a liquid-based study meal, normal weight participants had lower levels of self-reported hunger and reduced attention to food cues as compared with obese females who maintained increased attentional bias to visual food cues despite also reporting lower levels of self-reported hunger. This suggests that a future work is needed to identify the most suitable method for conducting studies assessing and/or controlling for the potential impact hunger condition may have on the attentional processing of visual food cues, in particular employing the use of a within-between subject design using a validated method to assess self-reported hunger such as a visual analogue scale( Reference Hill, Blundell, Hill and Blundell 44 ).

Effects of energy density of food-related cues

It has been suggested that attentional processing of visual food cues may be influenced by the energy-density content of foods, with several studies demonstrating increased attention to high energy-dense foods (those high in fat and/or sugar) as compared with food with a low energy density (those with a high water and/or fibre content)( 45 ). For example, Phelan et al. ( Reference Phelan, Hassenstab and McCaffery 24 ) reported greater attention to high energy-dense food images as compared with low energy-dense food images in a group of normal weight, recent weight loss maintainers and obese women. Similarly, Geardhardt et al.( Reference Gearhardt, Treat and Hollingworth 26 ) also suggested that foods high in fat and/or sugar appear to grab attention more readily in a group of normal weight and obese women. Both of these studies however used an indirect method of measuring attentional bias by means of a visual probe task, which as previously discussed may present some issues with regards to the interpretation of results obtained.

Two studies however have used a more direct method using eye-tracking to assess the potential impact that the energy density of food images may have on attentional processing of food-related visual cues in normal weight v. overweight/obese populations. Castellanos et al. ( Reference Castellanos, Charboneau and Dietrich 15 ) demonstrated increased gaze duration and gaze direction towards high energy-dense foods (e.g. chocolate and pizza) as compared with low energy-dense foods (e.g. fruit and vegetables) regardless of participants’ weight status or hunger condition. This is in contrast to the work of Graham et al. ( Reference Graham, Hoover and Ceballos 29 ) who observed enhanced initial attentional orientation to high energy-dense sweet food images in females with a low BMI (ranging from 18·1 to 24·9 kg/m2) as compared with those with a high BMI (ranging from 25 to >30 kg/m2).

From the few studies that have investigated the potential role that energy density of food images may have in attentional processing of visual food cues in normal weight v. over/weight/obese populations; it appears that high energy-dense foods may receive greater selective attention than low energy-dense foods. This may be of particular concern in relation to the development and maintenance of obesity given the association between consumption of high energy-dense foods and subsequent weight gain( Reference Miller, Lindeman, Wallace and Niederpruem 46 Reference Schlundt, Hill and Sbrocco 48 ). These findings also reflect results from recent neuroimaging studies( Reference Martin, Hosen and Chambers 49 Reference Stice, Yokum and Bohon 53 ) that demonstrated greater activation of reward pathways in the brain on exposure to high energy-dense food images. Coupled with attentional bias studies, these neuroimaging studies provide a further insight into how eating behaviours may be affected by potential dysregulation in areas of the brain involved in processing the rewarding properties of foods. As high energy-dense foods tend to be heavily represented within our visual environment( Reference Blundell, Stubbs and Golding 54 ) further research is required to clarify the potential link between increased visual attention to food-related visual stimuli and the energy density of visual food cues, in particular those high in fat and/or sugar and whether this differs in normal weight as compared with an overweight and obese population.

Effects of individual eating style traits

As previously discussed, several studies have considered the impact of weight status on attentional processing of food-related visual stimuli( Reference Castellanos, Charboneau and Dietrich 15 , Reference Nijs, Franken and Muris 23 Reference Graham, Hoover and Ceballos 29 ); however, there has been considerable variation in the methodologies used to conduct these studies and the study populations targeted resulting in varying conclusions being drawn. It has been suggested that although attentional bias to food images may be greater in an overweight/obese population( Reference Castellanos, Charboneau and Dietrich 15 , Reference Werthmann, Roefs and Nederkoorn 28 ) there may be subgroups of individuals that have greater selective attention to their food-related environment due to particular eating style traits. For example, previous studies have demonstrated enhanced food-related attentional processing in individuals with higher levels of self-reported external eating( Reference Brignell, Griffiths and Bradley 6 , Reference Hou, Mogg and Bradley 55 , Reference Newman, O'Connor and Conner 56 ) and higher levels of restraint( Reference Polivy, Coleman and Herman 57 , Reference Tapper, Pothos and Fadardi 58 ). External eating is considered as eating in response to food-related stimuli, for example the sight or smell of food regardless of the internal state of hunger or satiety( Reference Rodin 59 , Reference Van Strien, Schippers and Cox 60 ). Restrained eating is considered as a tendency for an individual to restrict food intake to achieve weight loss or to prevent weight gain; however, constant excessive restriction may have the opposite effect resulting in overconsumption of foods and subsequent weight gain( Reference Herman and Mack 61 ).

Some recent studies investigating potential weight group differences in attentional processing of visual food stimuli have taken these particular eating style traits into consideration. For example, Nijs et al.( Reference Nijs, Franken and Muris 23 ) examined potential weight group differences (normal weight v. obese) in the attentional processing of high energy-dense food words using a modified Stroop task and found a significant positive correlation between reaction times and external eating; however, this was only observed in obese participants. Nijs et al. ( Reference Nijs, Muris and Euser 27 ) also took into consideration eating style traits while investigating attentional processing of food images in a cohort of normal weight and overweight/obese women using a visual probe task, reporting a positive correlation between initial attention to food images and self-reported levels of dietary restraint. Interestingly, Graham et al.( Reference Graham, Hoover and Ceballos 29 ) reported a significant negative correlation between restrained eating scores and initial gaze fixation to food images considered to be high in sugar in a group of overweight/obese females. Castellanos et al. ( Reference Castellanos, Charboneau and Dietrich 15 ) found a significant positive correlation between initial attention to food images and both external and restrained eating scores using eye-tracking as a measure of attentional bias in a group of normal weight and obese women. These results indicate that it may be differences in individual eating style traits within a study cohort that may be influencing attentional bias to food-related images either in combination with increasing weight status or as a factor in itself. It may therefore be useful for future studies to employ eating style trait scales such as the Dutch Eating Behaviour Questionnaire( Reference Van Strien, Frijters, Bergers and Defares 62 ) to measure levels of restrained and external eating within study cohorts to allow for useful comparison with previously conducted studies.

The study population targeted

Studies assessing visual attention to food-related stimuli as highlighted within this review have targeted both normal weight, overweight and obese individuals. It has been suggested however that enhanced attention to foods through activation of reward-related pathways in the brain may be specific to obese individuals as compared with those who are overweight( Reference Nijs, Muris and Euser 27 , Reference Graham, Hoover and Ceballos 29 ) and indicates future studies are required perhaps specifically targeting obese individuals to establish whether a linear relationship could be observed between increasing attentional bias to visual food cues and increasing BMI.

Finally, it is also evident that in the majority of studies, males have been largely underrepresented and research has failed to take into consideration potential weight group differences in the attentional processing of food-related visual cues within a male population. Within this review, only three studies have included male participants( Reference Nijs, Franken and Muris 23 Reference Loeber, Grosshans and Korucuoglu 25 ), in which attention to food stimuli was measured using an indirect method. It may therefore be of interest for future studies to include male participants and in particular, studies are required using a direct method of assessing attentional bias such as eye-tracking to provide data on visual attention to food stimuli within an overweight/obese male population.

Conclusions

Overall it appears that results of investigating potential weight group differences in the attentional processing of food-related stimuli are both varied and contradictory. This may be explained by different methodologies used to measure attention, methods used to measure hunger levels and/or induce satiety, the energy density of food-related stimuli used and stimuli presentation time and also the weight status of the study population targeted. It may be too early to identify definitive patterns in increased attentional bias to foods in overweight and/or obese individuals and furthermore a potential approach-avoidance pattern may also need further exploration with the suggestion that with increasing BMI, comes an attempt to reduce attentional allocation to food-related stimuli as a means to control feelings of cravings. Although attentional bias research with regards to food-related stimuli presents challenges in relation to the most effective study design to successfully assess weight group differences, it is of importance to further understand the complex cognitive processes and attentional mechanisms, in particular the interplay between weight status, state of satiation, eating style traits and energy density of visual food cues that may be impacting on food behaviours and choices related to the development and maintenance of obesity.

Financial Support

This work was supported by funding from the Northern Ireland Department for Employment and Learning which funded the PhD studentship for K. J. D.

Conflicts of Interest

None.

Authorship

K. J. D. drafted the outline of the review, conducted the literature search and drafted the manuscript. A. M. G., G. D. and D. H. were responsible for critically reviewing and approving the final version of the manuscript.

References

1. World Health Organisation. Obesity and overweight factsheet (2013) http://www.who.int/mediacentre/factsheets/fs311/en/ Google Scholar
2. Vandenbroeck, IP, Goossens, J & Clemens, M (2007) Foresight. Tackling Obesities: Future Choices – Building the Obesity System Map. London: Government Office for Science.Google Scholar
3. Swinburn, BA, Sacks, G, Hall, KD et al. (2011) The Global obesity pandemic: shaped by global drivers and local environments. Lancet 378, 804814.CrossRefGoogle ScholarPubMed
4. Kemps, E, Tiggemann, M, Orr, J et al. (2014) Attentional retraining can reduce chocolate consumption. J Exp Psychol 20, 94102.Google ScholarPubMed
5. Ferriday, D & Brunstrom, JM (2011) ‘I just can't help myself’: effects of food-cue exposure in overweight and lean individuals. Int J Obes 35, 142149.CrossRefGoogle ScholarPubMed
6. Brignell, C, Griffiths, T, Bradley, BP et al. (2009) Attentional and approach biases for pictorial food cues. Influence of external eating. Appetite 52, 299306.CrossRefGoogle ScholarPubMed
7. Robinson, T & Berridge, K (1993) The neural basis of drug craving – an incentive-sensitization theory of addiction. Brain Res Rev 18, 247291.CrossRefGoogle Scholar
8. Marhe, R, Luijten, M, van de Wetering, BJM et al. (2013) Individual differences in anterior cingulate activation associated with attentional bias predict cocaine use after treatment. Neuropsychopharmacology 38, 10851093.CrossRefGoogle ScholarPubMed
9. Chanon, VW, Sours, CR & Boettiger, CA (2010) Attentional bias toward cigarette cues in active smokers. Psychopharmacology 212, 309320.CrossRefGoogle ScholarPubMed
10. Weafer, J & Fillmore, MT (2013) Acute alcohol effects on attentional bias in heavy and moderate drinkers. Psychol Addict Behav 27, 3241.CrossRefGoogle ScholarPubMed
11. Volkow, ND & Wise, RA (2005) How can drug addiction help us understand obesity? Nat Neurosci 8, 555560.CrossRefGoogle ScholarPubMed
12. Polivy, J, Herman, CP & Coelho, J (2008) Caloric restriction in the presence of attractive food cues: external cues, eating and weight. Physiol Behav 94, 729733.CrossRefGoogle Scholar
13. Stice, E, Figlewicz, DP, Gosnell, BA et al. (2013) The contribution of brain reward circuits to the obesity epidemic. Neurosci Biobehav Rev 37, 20472058.CrossRefGoogle ScholarPubMed
14. Denton, D (2006) The Primordial Emotions: The Dawning of Consciousness. Oxford University Press: New York, NY.CrossRefGoogle Scholar
15. Castellanos, EH, Charboneau, E, Dietrich, MS et al. (2009) Obese adults have visual attention bias for food cue images: evidence for altered reward system function. Int J Obes 33, 10631073.CrossRefGoogle ScholarPubMed
16. Leland, D & Pineda, J (2006) Effects of food stimuli on visual spatial attention in fasting and nonfasting normal subjects: behavior and electrophysiology. Clin Neurophysiol 117, 6784.CrossRefGoogle ScholarPubMed
17. Placanica, JL, Faunce, GJ & Soames Job, RF (2002) The effect of fasting on attentional biases for food and body shape/weight words in high and low Eating Disorder Inventory scorers. Int J Eat Disord 32, 7990.CrossRefGoogle ScholarPubMed
18. Mogg, K, Bradley, B, Hyare, H & Lee, S (1998) Selective attention to food stimuli in hunger: are attentional biases specific to emotional and psychopathological states, or are they also found in normal drive states? Behav Res Ther 36, 227237.CrossRefGoogle ScholarPubMed
19. Field, M, Munafo, MR & Franken, IHA (2009) A meta-analytic investigation of the relationship between attentional bias and subjective craving in substance abuse. Psychol Bull 135, 589607.CrossRefGoogle ScholarPubMed
20. Hepworth, R, Mogg, K, Brignell, C & Bradley, BP (2010) Negative mood increases selective attention to food cues and subjective appetite. Appetite 54, 134142.CrossRefGoogle ScholarPubMed
21. Loeber, S, Grosshans, M, Herpertz, et al. (2013) Hunger modulates behavioral disinhibition and attention allocation to food-associated cues in normal-weight controls. Appetite 71, 3239.CrossRefGoogle ScholarPubMed
22. Meule, A, Lutz, APC, Krawietz, V et al. (2014) Food-cue affected motor response inhibition and self-reported dieting success: a pictorial affective shifting task. Front Psychol 5:216 CrossRefGoogle ScholarPubMed
23. Nijs, IMT, Franken, IHA & Muris, P (2010) Food-related Stroop interference in obese and normal-weight individuals: behavioural and electrophysiological indices. Eat Behav 11, 258265.CrossRefGoogle Scholar
24. Phelan, S, Hassenstab, J, McCaffery, JM et al. (2011) Cognitive interference from food cues in weight loss maintainers, normal weight and obese individuals. Obesity 19, 6973.CrossRefGoogle ScholarPubMed
25. Loeber, S, Grosshans, M, Korucuoglu, O et al. (2012) Impairment of inhibitory control in response to food-associated cues and attentional bias of obese participants and normal-weight controls. Int J Obes 36, 13341339.CrossRefGoogle ScholarPubMed
26. Gearhardt, AN, Treat, TA, Hollingworth, A et al. (2012) The relationship between eating-related individual differences and visual attention to foods high in added fat and sugar. Eat Behav 13, 371374.CrossRefGoogle ScholarPubMed
27. Nijs, IMT, Muris, P, Euser, AS et al. (2010) Differences in attention to food and food intake between overweight/obese and normal-weight females under conditions of hunger and satiety. Appetite 54, 243254.CrossRefGoogle ScholarPubMed
28. Werthmann, J, Roefs, A & Nederkoorn, C (2011) Can(not) take my eyes off it: attentional bias for food in overweight participants. Health Psychol 30, 561569.CrossRefGoogle Scholar
29. Graham, R, Hoover, A, Ceballos, NA et al. (2011) Body mass index moderates gaze orienting biases and pupil diameter to high and low calorie food images. Appetite 56, 577586.CrossRefGoogle ScholarPubMed
30. Stroop, JR (1935) Studies of interference in serial verbal reaction. J Exp Psychol 18, 643662.CrossRefGoogle Scholar
31. Field, M & Cox, WM (2008) Attentional bias in addictive behaviors: a review of its development, causes and consequences. Drug Alcohol Depend 97, 120.CrossRefGoogle ScholarPubMed
32. Bruce, G & Jones, BT (2004) A pictorial Stroop paradigm revels an alcohol attentional bias in heavier compared to lighter social drinkers. J Psychopharmacol 18, 527533.CrossRefGoogle Scholar
33. Johansson, L, Gharderi, A & Andersson, G (2005) Stroop interference for food and body-related words: a meta-analysis. Eat Behav 6, 271281.CrossRefGoogle ScholarPubMed
34. MacLeod, C, Mathews, AM & Tata, P (1986) Attentional bias in emotional disorders. J Abnorm Psychol 95, 1520.CrossRefGoogle ScholarPubMed
35. Posner, MI, Snyder, CR & Davidson, BJ (1980) Attention and the detection of signals. J Exp Psychol 109, 160174.CrossRefGoogle ScholarPubMed
36. Field, M, Mogg, K, Zetteler, J et al. (2004) Attentional biases for alcohol cues in heavy and light social drinkers: the roles of initial orienting and maintained attention. Psychopharmacology 176, 8893.CrossRefGoogle ScholarPubMed
37. Bradley, B, Field, M, Mogg, K et al. (2004) Attentional and evaluative biases for smoking cues in nicotine dependence: component processes of biases in visual orienting. Behav Pharmacol 15, 2936.CrossRefGoogle ScholarPubMed
38. Allport, A (1989) Visual attention. In Foundations of Cognitive Science, pp. 631682 [Posner, MI editor]. Cambridge, MA: MIT Press.Google Scholar
39. LaBerge, D (1995) Attentional Processing, Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
40. Duncan, J, Ward, R & Shapiro, K (1994) Direct measurement of attentional dwell time in human vision. Nature 369, 313315.CrossRefGoogle ScholarPubMed
41. Theeuwes, J (2005) Irrelevant singletons capture attention. In Neurobiology of Attention, pp. 418424 [Itti, L, Rees, G and Tsotsos, JK, editors]. London: Elsevier Academic Press.CrossRefGoogle Scholar
42. Bradley, BP, Mogg, K & Wright, T (2003) Attentional bias in drug dependence: vigilance for cigarette-related cues in smokers. Psychol Addict Behav 17, 6672.CrossRefGoogle ScholarPubMed
43. Koster, EHW, Verschuere, B, Crombez, G et al. (2005) Time-course of attention for threatening pictures in high and low trait anxiety. Behav Res Ther 43, 10871098.CrossRefGoogle ScholarPubMed
44. Hill, AJ, Blundell, JE, Hill, AJ & Blundell, JE (1983) Nutrients and behaviour: research strategies for the investigation of taste characteristics, food preferences, hunger sensations and eating patterns in man. J Psychiatr Res 17, 203212.CrossRefGoogle Scholar
45. British Nutrition Foundation (2009) What is energy density? http://www.nutrition.org.uk/healthyliving/fuller/what-is-energy-density Google Scholar
46. Miller, WC, Lindeman, AK, Wallace, J & Niederpruem, M (1990) Diet composition, energy intake and exercise in relation to body fat in men and women. Am J Clin Nutr 52, 426–30.CrossRefGoogle ScholarPubMed
47. Romieu, I, Willett, WC, Stampfer, MJ et al. (1988) Energy intake and other determinants of relative weight. Am J Clin Nutr 47, 406–12.CrossRefGoogle ScholarPubMed
48. Schlundt, DG, Hill, JO, Sbrocco, T et al. (1990) Obesity: a biogenetic or biobehavioral problem. Int J Obes 14, 815–28.Google ScholarPubMed
49. Martin, LE, Hosen, LM, Chambers, RJ et al. (2009) Neural mechanisms associated with food motivation in obese and healthy weight adults. Obesity 18, 254260.CrossRefGoogle ScholarPubMed
50. Nummenmaa, L, Hirvonen, J, Hannukainen, J et al. (2012) Dorsal striatum and its limbic connectivity mediate abnormal anticipatory reward processing in obesity. PLoS ONE 7, e31089.CrossRefGoogle ScholarPubMed
51. Rothemund, Y, Preuschhof, C, Bohner, G et al. (2007) Differential activation of the dorsal striatum by high-calorie visual food stimuli in obese individuals. Neuroimage 37, 410421.CrossRefGoogle ScholarPubMed
52. Stoeckel, LE, Weller, RE & Cook, EW (2008) Widespread reward-system activation in obese women in response to pictures of high-calorie foods. Neuroimage 41, 636647.CrossRefGoogle ScholarPubMed
53. Stice, E, Yokum, S, Bohon, C et al. (2010) Reward circuitry responsivity to food predicts future increases in body mass: moderating effects of DRD2 and DRD4. Neuroimage 50, 16181625.CrossRefGoogle ScholarPubMed
54. Blundell, JE, Stubbs, RJ, Golding, C et al. (2005) Resistance and susceptibility to weight gain: individual variability in response to a high-fat diet. Physiol Behav 86, 614622.CrossRefGoogle ScholarPubMed
55. Hou, R, Mogg, K, Bradley, BP et al. (2011) External eating, impulsivity and attentional bias to food cues. Appetite 56, 424427.CrossRefGoogle ScholarPubMed
56. Newman, E, O'Connor, DB & Conner, M (2008) Attentional biases for food stimuli in external eaters: possible mechanism for stress-induced eating? Appetite 51, 339342.CrossRefGoogle ScholarPubMed
57. Polivy, J, Coleman, J & Herman, CP (2005) The effect of deprivation on food cravings and eating behaviour in restrained and unrestrained eaters. Int J Eat Disord 38, 301309.CrossRefGoogle ScholarPubMed
58. Tapper, K, Pothos, EM, Fadardi, JS et al. (2008) Restraint, disinhibition and food-related processing bias. Appetite 51, 335338.CrossRefGoogle ScholarPubMed
59. Rodin, J. (1980). Social and immediate environmental influences on food selection. Int J Obes 4, 364370.Google ScholarPubMed
60. Van Strien, T, Schippers, GM & Cox, WM. (1995). On the relationship between emotional and external eating behavior. Addict Behav 20, 585594.CrossRefGoogle ScholarPubMed
61. Herman, CP & Mack, D (1975) Restrained and unrestrained eating. J Pers 43, 647–60.CrossRefGoogle ScholarPubMed
62. Van Strien, T, Frijters, JE, Bergers, GPA, & Defares, PB (1986). The Dutch Eating Behavior Questionnaire (DEBQ) for assessment of restrained, emotional and external eating behavior. Int J Eat Disord 5, 295315.3.0.CO;2-T>CrossRefGoogle Scholar
63. Franken, IHA (2003) Drug craving and addiction: integrating psychological and neuropsychopharmacological approaches. Prog Neuropsychopharmacol, 27, 563579.CrossRefGoogle ScholarPubMed
Figure 0

Table 1. A summary of food-modified Strop task measures of attention to food-related stimuli in overweight/obese v. normal weight populations

Figure 1

Table 2. A summary of visual probe task and eye-tracking measures of attention to food-related stimuli in overweight/obese v. normal weight populations

Figure 2

Fig. 1. The potential role of attentional bias to food-related stimuli in the development and maintenance of obesity (adapted from Franken et al.( 63 ))

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