Elsevier

Appetite

Volume 157, 1 February 2021, 104986
Appetite

Research review
Food-related attentional bias and its associations with appetitive motivation and body weight: A systematic review and meta-analysis

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Abstract

Theoretical models suggest that food-related visual attentional bias (AB) may be related to appetitive motivational states and individual differences in body weight; however, findings in this area are equivocal. We conducted a systematic review and series of meta-analyses to determine if there is a positive association between food-related AB and: (1.) body mass index (BMI) (number of effect sizes (k) = 110), (2.) hunger (k = 98), (3.) subjective craving for food (k = 35), and (4.) food intake (k = 44). Food-related AB was robustly associated with craving (r = 0.134 (95% CI 0.061, 0.208); p < .001), food intake (r = 0.085 (95% CI 0.038, 0.132); p < .001), and hunger (r = 0.048 (95% CI 0.016, 0.079); p = .003), but these correlations were small. Food-related AB was unrelated to BMI (r = 0.008 (95% CI -0.020, 0.035); p = .583) and this result was not moderated by type of food stimuli, method of AB assessment, or the subcomponent of AB that was examined. Furthermore, in a between-groups analysis (k = 22) which directly compared participants with overweight/obesity to healthy-weight control groups, there was no evidence for an effect of weight status on food-related AB (Hedge's g = 0.104, (95% CI -0.050, 0.258); p = .186). Taken together, these findings suggest that food-related AB is sensitive to changes in the motivational value of food, but is unrelated to individual differences in body weight. Our findings question the traditional view of AB as a trait-like index of preoccupation with food and have implications for novel theoretical perspectives on the role of food AB in appetite control and obesity.

Introduction

The number of individuals with overweight and obesity worldwide has continuously increased in most countries since 1980 (Afshin et al., 2017). These rising levels contribute to the increased global incidence of non-communicable disease and have created an unprecedented social and economic burden (Di Angelantonio et al., 2016; Wang, McPherson, Marsh, Gortmaker, & Brown, 2011). Understanding the key drivers of obesity is therefore paramount to developing effective preventive and treatment approaches. The causes of obesity are multi-faceted and impairments within the physiological processes that regulate hunger and satiety are known to be important (MacLean, Blundell, Mennella, & Batterham, 2017). However, it is becoming increasingly apparent that cognitive processes, such as attention and memory, play a critical role in controlling eating and weight-related behaviour (Field et al., 2016; Higgs, 2016; Higgs et al., 2017; Werthmann, Jansen, & Roefs, 2015). One such cognitive process is the tendency to pay attention to stimuli associated with food (e.g., food-related pictures and words).

An attentional bias (AB) to food occurs when food cues selectively capture and hold visual attention (Field et al., 2016). Numerous experimental paradigms are used to assess AB for food cues, most commonly the emotional Stroop task with food words, the visual probe task (or dot probe task), and the visual search task. A brief overview of these paradigms is provided in Table 1. AB can be assessed indirectly in these paradigms based on the measure of response latencies to food cues versus control cues during the task. However, the response latency measures of AB derived from the Stroop and visual probe tasks have poor reliability (Ataya et al., 2012; Rodebaugh et al., 2016). More reliable measures of AB may be obtained by directly monitoring participants’ eye movements as they complete the tasks (Christiansen, Mansfield, Duckworth, Field, & Jones, 2015; van Ens, Schmidt, Campbell, Roefs, & Werthmann, 2019). Electroencephalography (EEG) can also be used to record event-related potentials (ERPs) as an index of attentional processing of food-related stimuli during passive viewing or oddball tasks (see Table 1).

In an early study, AB for food words was increased in participants who had fasted for 24 h compared to participants who were non-fasted (Lavy & van den Hout, 1993). This finding is consistent with evidence indicating that appetitive motivational states are associated with biases in selective attention for motivationally-relevant stimuli (for review see Field, Munafò, & Franken, 2009). Relatedly, it is well-established that aversive motivational states, such as anxiety, are associated with attentional bias for threat-related cues (Bar-Haim, Lamy, Pergamin, Bakermans-Kranenburg, & van IJzendoorn, 2007). This “motivated attention” is believed to represent automatic attentional capture by stimuli that reflect basic drive states (both appetitive and aversive) that are necessary for an individual to survive (Lang, Bradley, & Cuthbert, 2013). Food-related AB may have been particularly adaptive in our evolutionary past when food sources were scarce and famine was a very real threat (Berthoud, 2004), therefore the ability to rapidly detect and attend to potential food sources is likely to have been highly advantageous (Nijs, Muris, Euser, & Franken, 2010). However, modern westernised environments have been termed “obesogenic” because they are characterized by easy access to energy-dense, palatable foods which are constantly available and extensively marketed (Hall, 2018). There is concern that certain individuals may be particularly responsive to these food cues resulting in increased food cravings, food intake and ultimately weight gain and obesity (Nijs & Franken, 2012). Indeed, in the last decade, the relationship between food AB and hunger, food craving, food intake and weight status in populations with obesity and disordered eating has attracted increasing research interest. A central premise of this research is that food AB could be implicated in the maintenance of problematic eating behaviour and its consequences, including overweight and obesity (Appelhans, French, Pagoto, & Sherwood, 2016; Brooks, Prince, Stahl, Campbell, & Treasure, 2011; Nijs & Franken, 2012; Stojek et al., 2018).

Numerous theoretical models have been put forward to explain the occurrence of food-related AB. One of the most influential is incentive sensitization theory, which was originally proposed to account for neurobiological adaptations that arise in response to addictive drugs (Robinson & Berridge, 1993, 2008). According to this model, the repeated administration of a drug leads to the development of a sensitized dopaminergic response in brain reward areas (e.g., the nucleus accumbens) and this causes the drug to become highly desired and “wanted”. Through classical conditioning, a cue that is related to the drug also becomes highly salient, so that it grabs attention (i.e., attentional bias) and guides behaviour towards obtaining the incentive. Recent developments of the theory posit that a similar process occurs in obesity whereby brain reward systems become sensitized to food-related cues which, in turn, leads to increased attention for these cues in the environment (Castellanos et al., 2009; Nijs & Franken, 2012). Moreover, the relationship between attentional bias and substance craving is believed to be mutually excitatory whereby an increase in one produces a corresponding increase in the other (Franken, 2003).

A key prediction from these theoretical accounts is that food AB causally contributes to craving and consummatory behaviour. A further prediction is that AB for substance/food cues develops as a consequence of associative learning and once established, it should be an enduring characteristic. Because eating is a universal behaviour and essential for survival, AB for food should be present in almost everybody to some degree (Werthmann, Jansen, & Roefs, 2015). However, because obesity is strongly characterized by overeating (Rosenheck, 2008), food-related AB should be most pronounced in people who have obesity relative to individuals with healthy body weights (Appelhans et al., 2016; Nijs & Franken, 2012). For example, Appelhans et al. (2016) argue that “for obese individuals participating in lifestyle interventions, palatable food may act as a “motivational magnet” that monopolizes attention and triggers lapses in diet adherence” (p.270). These ideas appear intuitive. However, despite intensive research into this subject, empirical evidence for the precise role of AB in craving, food intake and obesity has remained equivocal to date.

Narrative reviews of the literature on food-related AB in participants with obesity and healthy body weights have highlighted conflicting findings (Doolan, Breslin, Hanna, & Gallagher, 2015; Nijs & Franken, 2012; Werthmann, Jansen, & Roefs, 2015). For example, Werthmann, Jansen, and Roefs (2015) reported that, of 11 published studies, some found that AB was positively associated with obesity and overweight, others found the opposite (smaller AB in participants with overweight/obesity relative to participants of healthy body weight), and others found no difference. The observed contradictions were attributed to differences between studies in terms of the assessment of AB (direct assessment via eye-tracking vs. indirect assessment using response latencies), the temporal components of AB (early vs. later attention processes), the food stimuli presented (high-calorie vs. low-calorie) and specific characteristics of heterogeneous samples.

Interestingly, Nijs and Franken (2012) found some evidence for an approach-avoid pattern of responding, whereby individuals with overweight/obesity showed enhanced initial attention to food stimuli (particularly high-calorie foods), but reduced maintenance of attention to those stimuli. This was interpreted as reflecting a conflict between an appetitive response (i.e., the desire to eat) which results in strong initial orientation toward food, and an aversive response (i.e., trying to ignore food cues in order to stick to a diet) which results in a subsequent shift in attention away from food. The review by Doolan et al. (2015) similarly suggested that individuals with higher body mass index (BMI) show attentional avoidance of food cues which may represent a cognitive strategy to control food cravings. Werthmann, Jansen, & Roefs (2015) reviewed the wider literature on populations with obesity, restrained eating, and disordered eating, and found that AB for food could be attributable to both food craving but also concern about over-eating, weight and body shape (Field et al., 2016; Neimeijer, Roefs, & de Jong, 2017). Taken together, these previous reviews support the notion that individuals with overweight and obesity experience motivational conflict when in the presence of food cues.

To our knowledge, there have been only two systematic reviews concerning differences in food AB in individuals with overweight/obesity versus individuals with healthy body weight. Hendrikse et al. (2015) included 19 studies which measured food-related AB using response latency-based paradigms (e.g. visual probe task, food-stroop task), direct measurements of eye movements (i.e., eye-tracking) or neuroimaging methods, and compared participants with overweight (BMI = 25.0–29.9 kg/m2) or obesity (BMI ≥ 30 kg/m2) to a healthy weight control group (BMI = 18.5–24.9 kg/m2). They reported that 15 of the 19 included studies found evidence for enhanced food AB in participants with overweight/obesity relative to healthy weight participants. However, many of the included studies employed multiple measures of food AB, and often these were differentially associated with overweight and obesity. For example, one of the 15 “positive” studies (Graham, Hoover, Ceballos, & Komogortsev, 2011) found that the overweight group had an AB in initial attentional orientation towards low-calorie foods (not high-calorie foods) using eye-tracking; however there was no difference between the overweight and healthy weight groups on average gaze duration to food images (i.e., a measure of the maintained attention). In another study, there was weak evidence for increased AB in the group with overweight/obesity, relative to the healthy weight group, in the visual probe task with stimuli presented for 100 ms (interpreted as an index of early attentional processing) (Nijs, Franken, & Muris, 2010); however there were no differences between the groups on any of the other measures of food AB which were taken in this study (eye-tracking to assess gaze direction and duration, visual probe task with 500 ms stimuli presentation, and recordings of ERPs). Thus, by discounting null or more nuanced results, the results of this previous systematic review are likely to be overly simplistic.

In a more recent systematic review (Hagan, Alasmar, Exum, Chinn, & Forbush, 2020), the effects of different attentional bias paradigms were taken into account by conducting separate meta-analyses per task type (dot probe, emotional stroop, eye-tracking, ERPs). Studies using the dot probe task, eye tracking measures and ERPs were also separately aggregated based on the attentional component measured (e.g., stimulus presentations of ≤200 ms and ≥500 ms for the dot probe task) as a means of distinguishing between early and late attentional processing. In contrast to Hendrikse et al. (2015), there was little evidence for weight status differences across the different task types and attentional components with the exception of ERP measures, where there was preliminary evidence for an automatic food-related AB in participants with obesity relative to participants of healthy weights. However, this conclusion was based on qualitative assessment of only two studies (meta-analysis was not conducted due to an insufficient number of ERP studies). A further issue with the Hagan et al. (2020) analysis relates to their conclusion that people with overweight/obesity ‘did not differ’ from individuals with a healthy weight; it is not statistically correct to conclude the absence of an effect using null hypothesis testing alone (Lakens, McLatchie, Isager, Scheel, & Dienes, 2020).

In summary, previous systematic reviews/meta-analyses have yielded inconsistent findings on the nature of the relationship between food-related AB and obesity. There are also methodological issues with these previous analyses which have hampered understanding and interpretation of the existing evidence base. Furthermore, to our knowledge, there has been little systematic investigation and synthesis of the associations between food-related AB and indices of appetitive motivational state, such as hunger, food cravings, and ad libitum food intake, which are key components of existing theoretical accounts of AB.

There are many definitions of hunger in the literature, however in appetite research it is commonly operationalised as a “conscious sensation reflecting a mental urge to eat. Can be traced to changes in physical sensations in parts of the body – stomach, limbs or head. In its strong form may include feelings of light headedness, weakness or emptiness in stomach” (Blundell et al., 2010) (p.252). In line with this definition, self-report scales (e.g., visual analogue scales) are widely accepted as a standard, sensitive, reliable and valid methodology to quantify current hunger state (Blundell et al., 2010).

Craving for a substance can be defined as “a subjectively experienced motivational state that fluctuates over time” (Field et al., 2009, p. 594). In relation to food, cravings are commonly defined as an intense desire which is directed towards a particular food, drink or taste (Hill, 2007). It is the intensity and specificity that distinguishes food cravings from feelings of hunger, and cravings frequently occur when hunger is low (e.g. craving something sweet after a filling savoury meal) (Hill, 2007). In addition, highly-craved foods such as chocolate are often associated with ambivalence due to a conflict between the pleasure of consuming the food and the guilt associated with over-consumption (Rogers & Smit, 2000). Food cravings are not synonymous with increased food intake, and restriction of a particular food is typically associated with increased craving for that food (Hill, 2007). The subjective experience of food craving is typically measured using single-item visual analogue scales or multi-item craving questionnaires (Cepeda-Benito, Gleaves, Williams, & Erath, 2000).

According to incentive sensitization theory (Robinson & Berridge, 1993, 2008), and associated theoretical accounts applied to food (Appelhans et al., 2016; Nijs & Franken, 2012), food AB is indicative of underlying appetitive motivational processes. Eating is more rewarding when one is hungry (i.e. with hunger being indicated by gastro-intestinal and post-absorptive signals, as well as the time elapsed since the previous meal) (Rogers & Hardman, 2015), therefore it follows that food AB and strength of hunger should be positively correlated. A meta-analysis of AB for positive emotional stimuli versus neutral stimuli included 28 studies with food stimuli (Pool, Brosch, Delplanque, & Sander, 2016). Results revealed a relatively small albeit statistically significant attentional bias for food as compared with neutral stimuli. Importantly, this bias increased when food stimuli were more relevant to the participants' current motivational state (i.e. when they were hungry) relative to when food stimuli were less relevant. This finding supports the idea that food stimuli attract visual attention more than neutral (non-food) stimuli in general, and that current motivational state (hunger) amplifies this relation. However, the Pool et al. meta-analysis did not include information on food intake, weight status of participants or problematic eating behaviour traits and thus cannot inform on the associations between food AB, obesity and consumption. As stated previously, food cravings are intense desires directed towards specific foods, and exposure to palatable food cues can elicit craving and desire towards the cued food, in the absence of energy depletion (Cornell, Rodin, & Weingarten, 1989; Fedoroff, Polivy, & Herman, 1997; Nederkoorn, Smulders, & Jansen, 2000). A meta-analysis from the addiction literature found a small but robust association between drug-related AB and subjective craving (Field et al., 2009), however to the authors’ knowledge, no previous meta-analyses have examined the strength of the AB-craving association in the context of food.

To date, existing reviews on the association between food AB and obesity have provided mixed conclusions. In order to reconcile disparate findings, an alternative theoretical account has been proposed whereby AB is the expression of the momentary motivational evaluation of substance-related stimuli (Field et al., 2016). Specifically, AB for food- and drug-related stimuli arises from momentary changes in evaluations of these stimuli that can be either positive (when the incentive value of the food or drug is high), negative (when individuals have a goal to change their behaviour, and those stimuli are perceived as aversive), or both (when individuals experience motivational conflict, or ambivalence). Importantly, these evaluations of substance-related stimuli and AB are likely to fluctuate substantially within-individuals, and this differs from previous conceptualisations of food AB as a relatively stable trait-like index of preoccupation with food (Appelhans et al., 2016; Berridge, 2009; Nijs & Franken, 2012). The notion that food AB fluctuates within individuals is consistent with novel conceptualisations of more general AB as a “dynamic process in time” (Amir, Zvielli, & Bernstein, 2016, p. 979); specifically, Zvielli, Bernstein, and Koster (2015) and Amir et al. (2016) provide evidence that AB to threat- and substance-related cues is expressed in fluctuating, phasic bursts towards and/or away from the relevant stimuli from moment-to-moment in time.

If momentary evaluations of food-related cues are key determinants of AB, we would expect subjective motivational states such as hunger and craving to be closely associated with food-related AB. Furthermore, in Field et al.’s (2016) model, both AB and consumption behaviour are outputs of the motivational value of food at that moment in time. Therefore, when AB is measured immediately before food intake and in the same context, there should be a close association between the two (though it is important to note that food intake is not simply a proxy for the incentive value of food and is influenced by an array of other factors including food availability, dietary restraint and social influences (Rogers & Hardman, 2015)). Field et al.’s (2016) model further predicts that AB for food cues should only be weakly related to individual differences in body weight and BMI (i.e., consistent with the findings of the Hagan et al. (2020) meta-analysis). This is because within-subject fluctuations in motivational state are postulated to be more influential determinants of AB than more stable between-subject differences. In support of this idea, a recent study found that higher state chocolate craving was associated with more positive implicit evaluation of chocolate (assessed using an implicit association task) when current hunger was also high. However trait chocolate craving was only indirectly associated with implicit evaluation via its association with state craving (Richard, Meule, & Blechert, 2018). Taken together, it would appear that implicit food evaluations are more complex than previously assumed and this may explain why simple between-group comparisons (e.g., individuals with obesity vs. individuals with healthy body weight) do not reveal consistent findings.

Furthermore, as highlighted earlier, individuals with obesity may be particularly likely to experience motivational conflict between the desire to eat palatable foods and the desire to lose weight. Food cues might therefore provoke concerns about eating, and these individuals may attempt to override their food-related AB in order to regulate their emotional and behavioural responses (Field et al., 2016). This motivational conflict may further explain the inconsistent pattern of findings in between-subjects designs when participants with overweight/obesity are compared to participants of healthy body weight.

In order to test these novel theoretical predictions and resolve equivocal findings from previous reviews, we conducted a systematic review and meta-analysis in order to quantify the relationships between food-related AB and: (1.) BMI, (2.) hunger, (3.) subjective craving, and (4.) food intake. A key objective was to provide an inclusive, well-powered overview of the strength of the associations between food-related AB, body weight and appetitive motivation across a range of empirical studies which concurrently measured these variables. This is important as previous systematic reviews/meta-analyses (Hagan et al., 2020; Hendrikse et al., 2015) have been limited to studies which compared a group of participants with overweight/obesity to a healthy weight control group. This has resulted in smaller numbers of studies being included, most notably in sub-group analyses (e.g., meta-analysis was not conducted on the ERP studies in Hagan et al. due to insufficient sample size).

In line with the novel model of AB proposed by Field et al. (2016), we hypothesized that AB for food cues would be more strongly related to the momentary motivational value of food than to individual differences in body weight. On this basis, we predicted that food AB would be closely associated with hunger, subjective craving and food intake, but only weakly associated with BMI.

In contrast to previous reviews, we conducted formal sub-group analyses to examine the impact of the following moderators on the associations between food AB and the main variables of interest:

Direct versus indirect measures. AB can be assessed indirectly based on response latencies during experimental tasks or directly by assessing eye movements or ERPs. We hypothesized that the associations between food AB and our variables of interest (BMI, hunger, craving, food intake) would be larger for direct versus indirect measures of attentional bias, as direct measures arguably provide more reliable and valid indices of selective attention (Christiansen et al., 2015; Field et al., 2009).

Early versus later attentional processes. There is an important distinction between the initial orienting of selective attention and the maintenance/disengagement of attention (Corbetta & Shulman, 2002; Treue, 2003; Weierich, Treat, & Hollingworth, 2008). This is particularly relevant in the current context as there is some evidence that individuals with overweight/obesity show enhanced initial attention to food stimuli followed by reduced maintenance of attention to these stimuli (i.e., approach-avoid attentional response) (Nijs & Franken, 2012; Werthmann et al., 2011; Werthmann, Jansen, & Roefs, 2015). On this basis, we tentatively predicted that the association between food AB and BMI would be larger for measures of early attentional processing relative to measures of late attentional processing. We had no a priori hypothesis that the magnitude of the associations between food AB and appetitive motivation (hunger, craving and food intake) would be larger for any particular attentional subcomponent (i.e., early vs. late).

Type of food-related cue presented (high-calorie vs. low-calorie). Several studies have separately assessed AB towards high-calorie food cues (e.g., chocolate, cake, fried foods) and low-calorie food cues (e.g., vegetables, fruit). High-calorie foods, which are typically high in fat and/or sugar, are highly rewarding (Rogers & Brunstrom, 2016) and thus would be expected to capture attention to a greater extent than low-calorie foods. In addition, high-calorie foods such as chocolate, cakes, biscuits, and various salty and savoury snack foods appear high on lists of craved foods (Hetherington & MacDiarmid, 1993; Hill, Weaver, & Blundell, 1991; Rogers & Smit, 2000; Ruddock, Dickson, Field, & Hardman, 2015). On this basis, we tentatively predicted that the associations between food AB and our variables of interest (particularly BMI and craving) would be larger for high-calorie food cues relative to low-calorie food cues.

Sample characteristics of the different studies may also influence results, in particular the weight status of the participants and number of individuals with overweight/obesity in the sample. For example, in studies where there are few individuals with higher BMI there may be insufficient variability in body weight in order to capture weight-related differences in AB to food. In view of this, we separately examined studies which had directly compared a group of participants with overweight/obesity to a healthy weight control group (in line with the previous systematic reviews/meta-analyses by Hagan et al., 2020 and Hendrikse et al., 2015). We tentatively predicted that any differences in food-AB would be most apparent when comparing these more polarized groups of participants.

In summary, our aim was to conduct a systematic review and meta-analysis to determine the strength of the associations between food AB and BMI, hunger, subjective craving and food intake. We further examined whether these associations would be moderated by (1.) the type of assessment method for AB (direct vs. indirect), (2.) the subcomponent of AB (early vs. late), and (3.) the type of food stimuli used in the attention task (high-calorie food stimuli vs. low-calorie food stimuli).

Section snippets

Literature search

Literature searches were guided by Preferred Reporting Items for Systematic Review (PRISMA). The searches were performed during the month of April 2015 and were updated on 26 April 2018 and 11 August 2019 using the databases Pubmed, PsycInfo, Web of Knowledge and Scopus. The following search terms were used: (Attention* bias OR visual probe OR dot probe OR visual search OR Stroop OR eye movements OR event-related potential OR electroencephalic) AND (Food OR eating behav* OR eating OR hunger OR

BMI analysis

The included studies and associated r values for the association between AB and BMI in the main analysis are shown in Supplementary Table 1 (number of effect sizes (k) in main analysis = 110 drawn from 90 articles). The data pertaining to all subgroup analyses are provided in separate tabs in Supplementary Table 1.

There was no significant overall relationship between BMI and AB, (r = 0.008, 95% CI -0.020, 0.035); Z = 0.549, p = .583, I2 = 11.98%). Attentional bias predicted <1% variance in BMI

Discussion

According to many theoretical accounts, food AB should be most pronounced in people who have obesity relative to individuals of healthy body weight. It should also be (causally) associated with craving and consummatory behaviour. However, empirical evidence for the precise role of AB in obesity and appetitive motivation has remained equivocal. The current study conducted a systematic review and a series of meta-analyses to determine if there is a relationship between food-related AB and: (1.)

Ethical statement

Authorship of the paper: All authors made a significant contribution to the conception, design, execution, and/or interpretation of the reported study.

Originality and plagiarism: The article represents entirely original work. The work of others has been appropriately cited or quoted.

Data access and retention: Raw data are provided with the paper for editorial review, and this has been prepared to provide public access to the data.

Multiple, redundant or concurrent publication: This manuscript is

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. CAH has received research funding from the American Beverage Association and speaker fees from the International Sweeteners Association for work outside of the research reported in this manuscript.

Data availability

The corresponding data files for the analyses can be found on Open Science Framework, https://osf.io/ks7x5/

Acknowledgements

We thank all authors who provided data in a format that enabled their inclusion in the meta-analyses we report.

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