Abstract
Purpose of Review
Automatic approach–avoidance tendencies drive excessive intake of drugs and unhealthy food. Dual-process models of behaviour propose that strong approach biases predict excessive intake when reflective processes are weak. Consistent with theory, early findings indicated that approach biases predicted excessive use of drugs, including alcohol and tobacco. Given that reviews on approach bias for appetitive substances are lacking, the current review aimed to synthesise the recent findings on automatic approach biases across three of the most commonly assessed substances: alcohol, food and tobacco.
Recent Findings
The findings suggest that approach biases exist for a range of substances, are mostly stronger in clinical samples than healthy controls and predict consumption behaviour, albeit under certain conditions.
Summary
Approach biases for appetitive substances are related to excessive consumption in line with theoretical premises. Further longitudinal research is needed, particularly in the domains of tobacco and food, to determine the prediction of consumption of these substances over time. Nevertheless, the findings highlight a continued need for approach bias modification techniques aimed at changing this underlying mechanism.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Excessive consumption behaviours in the domains of smoking and drinking as well as overeating are thought to be influenced by automatically activated approach biases for appetitive substances, such as tobacco, alcohol and unhealthy foods [1, 2]. Approach bias refers to the automatically activated action tendency to reach out toward appetitive cues [3]. Indeed, early research demonstrated the existence of approach bias for substance-specific cues in obesity [4] as well as in heavy drinkers [5], and smokers [6]. These theoretical premises and empirical findings have resulted in the use of approach bias modification techniques, which are aimed at modifying automatic approach biases in order to influence the related consumption behaviour. A key outcome in approach bias modification studies is a change in the underlying mechanism, namely, approach bias. In this review, we aimed to synthesise the recent research on approach biases for a range of appetitive substances.
The Theoretical Role of Approach Bias
Neurocognitive theories such as dual-process models have been prominent in explaining the role of automatically activated cognitive processes, including approach bias, in maladaptive consumption behaviours [2]. For example, the key theoretical premise of reflective-impulsive models is that our behaviour is regulated by two separate, but interactive information processing systems [7]. The impulsive (or automatic) system is fast and regulates behaviour based on previously formed associations (e.g. approach appetitive, high-calorie foods). In contrast, the slower reflective system relies on explicit knowledge (e.g. the health consequences of food). Automatic approach tendencies arise when an appetitive stimulus activates the impulsive system, which is not able to be overridden by cognitive control processes due to a weakened reflective system. Dual-process models have prompted the idea of targeting automatic processes to change problematic consumption behaviour [3].
Although dual-process models provide a theoretical framework for this review, it is important to note that these models have been subject to recent criticism and are currently being revised in the area of addiction [8]. Dual-process models have been criticised because of neural implausibility and theoretical problems [9, 10]. Rather than two separate systems, the revised perspective emphasises iterative processing, in which early information processing becomes biased by conditioned reward stimuli [11], or addiction-related cues, which increases the chance that the corresponding behavioural option is pursued [2, 12, 13]. Important in both original dual-process models and in recent reformulations is the concept of motivation [13, 14]. Specifically, automatically activated tendencies in health behaviours are only moderated when there is sufficient ability (i.e. executive control), and motivation to control unhealthy behavioural tendencies, which suggests that there are three interactive processes at play [15].
Approach Bias Assessment Paradigms
Approach biases can be assessed using computerised tasks that measure reaction times (RTs) to a visual stimulus. The three main paradigms that have been used to measure such biases are the Approach–Avoidance Task (AAT), the Stimulus-Response Compatibility (SRC) task and the approach–avoidance variant of the Implicit Association Test (IAT). One important difference between these three commonly used paradigms is whether the task measures symbolic approach–avoidance responses (as with the SRC task), actual approach–avoidance motor movements (as with the AAT) or approach–avoidance associations (as with the IAT; [16]).
In the AAT, participants respond to images that appear on the centre of a screen by moving a joystick toward or away from themselves using actual motor movements [17]. Pulling the joystick toward themselves (closer to their body) simulates approaching the image, whilst pushing the joystick away from themselves (further from their body) simulates avoiding the image. The AAT has a perceptual zooming feature such that pushing the joystick (avoidance) increases the image size, whereas pulling the joystick (approach) decreases it. In the SRC task, participants respond to presented images by pressing keys on a keyboard to perform symbolic approach–avoidance movements [18]. Images are also presented in the centre of a screen with a manikin figure positioned above or below. Participants press the up or down keys to move the manikin toward (approach) or away from (avoid) the image. For both the AAT and SRC task, relative RTs on approach versus avoidance trials indicate the strength of automatically activated action tendencies. That is, faster RTs for approaching versus avoiding indicate an approach bias, whilst the opposite indicates an avoidance bias.
Furthermore, for both tasks, relevant feature and irrelevant feature versions have been created. In a relevant feature version, participants respond to the image based on the content (e.g. ‘avoid’ alcohol images and ‘approach’ soft drinks images). In an irrelevant feature version, participants respond to a feature unrelated to the contents of the picture, such as format (e.g. approach portrait; avoid landscape) or orientation (e.g. pull when picture has a slight left tilt and push when it has a slight right tilt). The irrelevant feature version of the AAT has been widely used for intervention studies as the assessment task can easily be modified to a modification task by changing the task contingencies without changing the instructions. Specifically, in the assessment phase, images are presented in push format on 50% of trials and in pull format on the other 50% of trials. In the training phase, the target images (e.g. alcohol) can be presented in push format on 90% of trials (or 100% or 80%) and in pull format on 10% of trials (or 0% or 20%), and vice versa for control images (e.g. soft drinks).
The approach–avoidance variant of the IAT measures the strength of association between concepts [19,20,21]. The task is to categorise stimuli presented consecutively on a screen according to a target (e.g. soft drinks vs. alcohol) or an attribute dimension (e.g. approach vs. avoid). In the first block, one target and one attribute dimension (e.g. alcohol and approach stimuli) are paired together on one side of the screen (with soft drinks and avoid on the other side), such that they share a response key (e.g. left). In the second block, the pairing is reversed for the target dimension (e.g. alcohol and avoid stimuli vs. soft drinks and approach stimuli). The difference in mean RTs is calculated between these two combined blocks. Participants are expected to be faster to respond when two associated target/attribute dimensions share a response key (e.g. alcohol and approach stimuli) than when two non-associated target/attribute dimensions share a response key (e.g. alcohol and avoid).
Scope of the Review
In the last decade, a vast body of literature has emerged on approach bias for appetitive substances. Most reviews have focused on intervention studies using approach bias modification (e.g. [22,23,24]), rather than assessment of such biases. Although a recent review evaluated the assessment of approach biases only for drug-related cues [25•], it remains unclear as to how such biases function across appetitive substances. Thus, in the current review, our goal is to provide an integrated summary of recent research on approach biases for several substances, namely, alcohol, cigarettes and food. The aim is threefold: (1) to compare the strength of approach bias for substances across groups with differing levels of consumption and in clinical groups versus healthy controls, (2) to determine whether the strength of the approach bias predicts overt or self-reported consumption behaviour and (3) to characterise experimentally manipulated moderators of approach bias for substances and its relationship with consumption. Finally, we provide recommendations for future research on approach bias assessment and discuss theoretical and practical implications for intervention studies. We have focused on research published in the last 5 years to ensure timeliness.
Review of Recent Findings
The included articles were organised into three categories: (1) studies comparing groups with differing levels of consumption, (2) predictive validity studies and (3) experimental validity studies, based on previous reviews on implicit cognitive processing (e.g. [26]). The first category includes studies that used a quasi-experimental design to compare groups with differing levels of substance-related issues (e.g. heavy vs. social drinkers) or to compare a group with a clinical disorder (e.g. binge eating disorder) with a healthy control group. The general hypothesis is that the groups with a higher level of substance use or a disorder will show a stronger approach bias for disorder-relevant stimuli (e.g. alcohol or high-calorie food cues). The second category of studies examined whether approach bias predicts consumption, including both self-reported and overt behaviour. The third category of studies manipulated an aspect of the task or the participants and examined the effect on approach bias or its relationship with behaviour.
Studies Comparing Groups with Differing Levels of Substance-Related Issues
Alcohol
In the last 5 years, only a few studies have examined approach bias for alcohol cues in groups with differing levels of alcohol use. For example, among undergraduate students, there is emerging evidence of heightened approach bias toward alcohol cues (e.g. scenes of drinking alcohol with friends) in heavy social drinkers compared with light social drinkers using a novel virtual reality paradigm [27], and in individuals with low, but not high sensitivity to the acute effects of alcohol (AAT; [28]). In contrast, evidence from research on clinical populations found that approach biases for alcohol cues were neither observed in current or abstinent problematic drinkers nor in light drinkers in individuals with an IQ ranging from 50 to 85 [29, 30]. The authors concluded that approach–avoidance paradigms may lack sensitivity in certain populations, but their conclusions were drawn based upon assessment with the AAT. It remains to be determined whether other measures (e.g. the IAT or the SRC task) are suitable for assessing automatically activated approach–avoidance tendencies in such populations [31].
Tobacco
Several recent studies have emerged on approach bias for tobacco-related cues. Specifically, approach bias for cigarettes using the AAT did not differ between smokers and non-smokers among adolescents [32, 33•] or adults [34]. Similarly, in smokers, cravers, ex-smokers and non-smokers, there was no difference in approach bias on the AAT or the SRC, but on the IAT, cravers showed a stronger approach-association and non-smokers showed a stronger avoidance-association, both of which were not observed in the smokers or ex-smokers [16]. Interestingly, these findings contrast with earlier research showing that heavy smokers had a stronger approach bias for smoking cues than non-smokers and ex-smokers [6]. Recent studies have also examined approach biases for other appetitive cues in smokers. Among risky drinkers, smokers showed a stronger alcohol-approach association than non-smokers (IAT; [35]), whilst smokers, unlike non-smokers, did not show an approach bias for food (AAT; [34]). Thus, smoking may increase the risk of engaging in other addictive behaviours [36], but reduce sensitivity to natural rewards such as food [37].
Food
Research has also compared approach bias for food in groups with differing levels of eating-related issues. Several of these studies found that individuals with overweight or obesity, but not healthy-weight controls, showed an automatic approach bias toward food cues using the approach–avoidance version of the IAT [38] and the AAT [39, 40]. In contrast, a recent study found that children with overweight or obesity did not show a stronger approach bias for unhealthy food cues than healthy-weight children [41]. However, the children with excess weight may have received reminders to avoid unhealthy foods as they were mostly recruited from weight loss facilities (for an experimental demonstration of such a context effect in adults cf. [42]). Similarly, the only study to examine approach bias in binge eating disorder (BED) and obesity found that both obese individuals with BED and healthy-weight controls displayed an avoidance bias for low-calorie food cues, whilst those with obesity alone displayed an approach bias for such cues [43]. However, it may be that affect influences approach bias for food in BED as binge episodes often occur during negative mood states.
In non-clinical samples, research using the AAT has also shown that approach bias for high-calorie food (relative to non-food cues) was stronger in individuals with higher food craving [44], and reward sensitivity [45]. Similarly, individuals bothered by their snack eating habit were slower to avoid both high-calorie and low-calorie food than healthy controls, but there was no group difference in approach responses toward such cues [46]. Thus, evidence points toward faster approach responses for high-calorie versus low-calorie or non-food cues, in line with earlier research showing that highly rewarding cues elicit stronger approach tendencies [47]. The mostly consistent findings in the food domain are noteworthy given the use of different assessment tasks and samples.
Predictive Validity Studies
Alcohol
Early research demonstrated a positive relationship between approach bias for alcohol and alcohol use (e.g. [4]), supporting the idea that approach bias predicts consumption. However, these promising findings can be contrasted with an emerging body of research revealing largely mixed findings. For example, some studies found that in current drinkers, a stronger approach bias (on the AAT) was positively associated with hazardous [48] and future drinking [49••], whilst other studies found no relationship between approach bias and current or future drinking [50••, 51, 52].
The mixed findings may largely be explained by differences in sample characteristics (e.g. abstinent vs. currently drinking, treatment seeking vs. non-treatment seeking, age and gender), the assessment task and how approach bias was calculated and/or analysed (for more detail, see [53]). Indeed, in some studies, an approach bias toward alcohol cues was not observed at all (e.g. [50••]), and in other studies, an avoidance bias was observed instead (particularly in abstinent patients who had undergone detoxification treatment; SRC task, [54]; IAT, [55•]). One reason for these inconsistent findings is that the majority of approach bias assessment tasks use an index of approach that is relative to avoidance tendencies. However, approach and avoidance tendencies likely reflect two independent processes and, hence, a score that combines the two may fail to reveal the actual contribution of each tendency [53].
Aiming to address this limitation of the standard SRC task, a few recent studies have used a modified SRC task, which allows for the assessment of approach and avoidance tendencies separately [53]. In a sample of current heavy drinkers, Baker et al. [53] did not find an approach bias for alcohol cues compared to control (neutral) cues. Likewise, in a sample of abstinent outpatients who had undergone detoxification treatment, Field et al. [50••] did not find evidence of an approach bias for alcohol use at baseline (following detoxification) using the modified SRC task, nor did they find that it predicted current dependence or consumption, or relapse at any future time point (in contrast to stronger avoidance, which was found to predict relapse at follow-up and supports the findings of earlier research, e.g. [56]).
The approach–avoidance variant of the IAT has also produced mixed results, with some studies finding a stronger approach bias on this task was positively associated with alcohol use [55•], and other studies finding no such relationship [52], or even the opposite relationship, namely, prediction of less drinking in the future, albeit only in adolescents with low positive alcohol expectancies [57]. Interestingly, in adolescents, greater approach bias on the SRC task has been shown to predict current drinking ([58], but see [51]) and future drinking (but, only in those with low negative alcohol expectancies [57]).
Tobacco
In contrast to alcohol, there has been a paucity of recent research on the relationship between approach bias for tobacco and smoking behaviour. One study observed that approach bias for cigarette-related cues did not predict self-reported nicotine dependence nor daily cigarette use [32]. However, these findings are yet to be confirmed in samples of heavy adolescent smokers or adult smokers.
Food
There are relatively fewer studies examining the link between approach bias for food and consumption, but the findings have been more consistent. Specifically, approach bias for unhealthy food predicted uncontrolled eating in adolescents with higher impulsivity [59] and increased food consumption in adults with poor inhibitory control [60], as well as overweight external or emotional eaters [39]. The finding that cognitive control moderates the effect of approach bias on consumption is in line with dual-process models. Recent studies examining the relationship between approach bias and a proxy of adiposity, namely, body mass index (BMI, kg/m2), found that increased BMI was related towards reduced avoidance of sweet food (on the AAT, but not the IAT; [61•]), and faster approach of food using a virtual reality paradigm [62]. However, it should be noted that BMI may not be an indicator of excessive eating given that it is related to many different factors (e.g. exercise). Given that most of these studies used healthy convenience samples, the nature of the relationship between approach bias for food and eating behaviour remains unclear in clinical samples. Nevertheless, these findings indicate the potential relevance of approach–avoidance tendencies for food in eating behaviour.
Experimental Validity Studies
Experimental studies can provide valuable insights into potential moderators in line with dual-process models, which predict that the relationship between automatic and reflective processes is influenced by a number of variables, including temporal dynamics [12] and control resources (e.g. affect, cognitive load, hunger; [63]) or substance use [15].
Alcohol
Only one recent study has examined the potential role of temporal dynamics in approach bias. Specifically, Gladwin et al. [63] found that in a sample of social drinkers, approach bias for alcohol cues variably decreased as a function of manipulated delay between stimulus presentation and response, albeit for both the alcohol and soft drink images. Nevertheless, their finding that such biases are time-dependent processes that decay over longer delays has clinical implications. Specifically, training participants to delay their responses to substance-related cues may assist in reducing approach tendencies, which could contribute to improving the efficacy of approach bias modification interventions [20].
Another variable that has been shown to influence the relationship between approach bias and alcohol use is whether the assessment task instructions are explicitly related to alcohol (relevant feature versions), or whether approach–avoidance responses are elicited implicitly (irrelevant feature versions). Kersbergen et al. [48] compared the ability of four measures of approach bias to predict alcohol use in a sample of college students (who drank at least once a month), and found that the two relevant feature versions of the SRT and AAT predicted hazardous drinking, whilst the two irrelevant versions of the same tasks did not. Thus, the relevant versions may be more reliable and valid and, hence, better suited to the assessment of approach bias [48]. Nevertheless, the irrelevant feature versions, in particular of the AAT, can be more easily adapted for use as an approach bias modification paradigm [2].
Alcohol research has also examined whether affective stimuli influence approach bias and consumption. Specifically, video clips depicting positive or negative aspects of drinking behaviour successfully increased approach and avoidance of alcohol cues, respectively [64], further supporting the idea that approach and avoidance biases are independent processes [53]. Similarly, using a sample of occasional and heavy drinkers, Cousijn et al. [65] paired alcohol images with positive, negative or appetitive (non-alcoholic) images on the AAT. They found that a negative context increased approach bias for alcohol relative to the appetitive context, an effect driven by slower avoidance rather than faster approach. Alcohol-promoting advertisements were also shown to increase approach bias relative to non-alcohol advertisements, but there was no effect on consumption [66••]. In contrast, priming of sexual identity ambiguity through a writing exercise was associated with a discernible increase in alcohol-approach bias and consumption in a laboratory taste test [67].
Cognitive load has also been experimentally manipulated in alcohol research with mixed findings. Specifically, some research has found a relationship between alcohol-approach bias and consumption under low, but not high cognitive load among social drinkers (AAT; [68]) whilst others found no such moderating effect in heavy drinkers (IAT; [52]). A potential reason for the discrepant findings is that these studies differed in the assessment tasks used (AAT vs. IAT). Specifically, the AAT may provide a more accurate assessment of approach bias given that it can be used to directly assess approach–avoidance motor movements rather than symbolic movements (SRC) or associations (IAT). Another reason may be the type and timing of the cognitive load manipulation. Specifically, the interference suppression manipulation was embedded within the AAT task [68], whilst the ego depletion paradigm (‘cross out e’ task) was administered prior to the AAT assessment [52]. However, both studies used a laboratory taste test paradigm to measure alcohol consumption, which has been subject to long-standing concerns regarding ecological validity ([69], but see [70]).
Research has also examined the moderating role of substance use in the relationship between approach bias and consumption behaviour. Cohn et al. [71] found that approach bias toward alcohol, but not cigarette cues, predicted proportion of heavy drinking days during nicotine deprivation. Their findings suggest that cigarette withdrawal may increase motivation to drink, but not smoke. It remains to be seen whether these findings can be applied to the consumption of other substances, such as food, given recent evidence that nicotine withdrawal increases food consumption, which was not explained by metabolic factors [72]. More recently, Jünger et al. (2017) [73] demonstrated that alcohol intoxication did not affect approach bias for alcohol (on the AAT) in healthy young males, however, it is important to note that approach bias was low at baseline, which was possibly due to the nature of the sample.
Food
The idea that affective stimuli modulates approach bias has also been applied to eating research. Recent evidence showed that health beliefs could be induced by labelling images of identical beverages as ‘high-calorie’ or ‘low-calorie’, such that participants showed a stronger approach bias for the preferred beverage (the one labelled as low-calorie), as indicated by a subsequent choice task [74]. Exposure to socially framed norms (‘approval of fruit consumption’ and ‘disapproval of candy consumption’ messages) heightened approach bias for healthy relative to unhealthy food [75], whilst attentional priming increased approach bias for unhealthy food [76]. However, it is unknown whether health beliefs, social norms or attentional priming influence the relationship between approach bias and food consumption.
Another variable predicted to reduce cognitive control resources is hunger. Research has been mixed, with one study showing no difference in approach bias for appealing foods between hungry and non-hungry participants [77], and another finding that hungry participants had a stronger approach bias than sated participants [78]. Approach bias also moderated the link between hunger and sweets consumption, such that the relationship was weaker when approach bias for healthy food was higher [79]. Thus, homeostatic state (i.e. hunger vs. satiety) may influence approach bias for food-related cues. Furthermore, approach bias for unhealthy food cues was observed with the irrelevant feature version of the AAT, but not the irrelevant version [80••], which mirrors findings from the alcohol domain [48].
Summary
We reviewed recent empirical evidence for approach bias across a range of substances, including alcohol, cigarettes and food. There is considerable evidence from the reviewed studies that approach biases for disorder-specific stimuli are heightened in heavy drinking [27, 28] and obesity [38,39,40], although some studies found no such evidence [29, 34, 41, 58] or even observed an avoidance bias [54]. Overall, the reviewed studies in the eating domain support the general hypothesis that groups with a disorder will show a stronger approach bias for disorder-relevant cues than healthy controls, whilst the evidence was somewhat mixed for alcohol. Whilst our review of the literature illustrated the presence of approach biases for all three appetitive substances, it should be noted that the evidence was inconclusive in the smoking domain. Nevertheless, identifying the existence of approach biases in individuals with obesity and social or heavy drinkers supports the continued need for interventions, such as approach bias modification, to modify these automatic processes.
Automatic approach biases were also shown to play an important role in the consumption of alcohol [48, 49••] and unhealthy food, particularly in individuals with poor inhibitory control or high impulsivity [59, 60]. However, other studies found no relationship between approach bias and behaviour (e.g. [32, 54, 55•]), but importantly these were the studies that did not observe an approach bias for the disorder-related substance. Finally, the experimental studies highlighted a number of moderators that strengthened approach bias and its relationship with consumption behaviour, such as relevant task instructions [48, 80••], negative or positive affect [64, 65, 66••], low cognitive load [68], hunger [78] and withdrawal from other substances [71]. Together, these findings support the main premises of dual-process models of behaviour, namely, that automatically activated tendencies predict behaviour when the reflective system is relatively weak (e.g. due to poor inhibitory control or high trait impulsivity), or when control resources are depleted (e.g. due to hunger, cognitive load or negative affect; [6]).
Implications for Future Research
The discrepant findings can be attributed to a number of differences across studies, such as the type of assessment task. Despite differences in operationalisation, approach–avoidance biases were still observed in studies using each of the tasks. Furthermore, both relevant and irrelevant feature versions of the tasks detected approach biases, although only two studies to date have compared different tasks (SRC and AAT) and versions of these tasks within the same sample [48, 81]. Whilst concerns have been raised regarding the reliability of irrelevant feature versions, it is important to consider that in such tasks (typically, the AAT), participants respond to a feature of the stimulus that is not related to the content that the task aims to assess, which has the advantage of masking the research question and reducing the use of response strategies [17]. Nevertheless, concerns regarding the reliability of approach–avoidance assessment tasks have led to the development of the Visual Approach/Avoidance by the Self Task (VAAST), which simulates the visual effects of whole-body rather than arm movements [82]. Future studies should aim to establish the validity of the VAAST in assessing approach–avoidance biases for appetitive substances. In addition, technological advances have begun to shift how approach bias assessment tasks are delivered. Of note, two of the reviewed studies used virtual reality paradigms [27, 62]. Using virtual environments to measure approach bias may more accurately capture automatically activated approach–avoidance tendencies given that participants have the opportunity to engage with realistic stimuli related to both the substance (e.g. alcohol) and the environment (e.g. drinking situation). Future studies should consider using more ecologically valid and engaging paradigms not only for assessing, but also for modifying approach biases.
Another important difference is the type of sample used. Most reviewed studies in the eating domain examined approach bias for food and consumption in healthy, unselected samples. Looking forward, research should aim to establish whether the substance-specific approach bias predicts consumption behaviour in clinically relevant samples, particularly in the eating domain. Furthermore, future research should examine the role of automatic approach biases for alcohol and tobacco cues across the spectrum of mild to severe substance use disorder given evidence that the relationship between automatic processing of substance-related stimuli and use is not linear [83]. It is also important to consider that most studies examining the relationship between approach bias and behaviour have relied on laboratory taste tests or self-report, both of which have their limitations, such as reliance on retrospective memory and lack of ecological validity [69, 70]. Future studies should examine the behavioural outcomes of approach action tendencies through the inclusion of more naturalistic consumption measures ‘in vivo’, such as ecological momentary assessment techniques. Finally, given the cross-sectional nature of most of the reviewed studies, more longitudinal research is needed to uncover the long-term effects of approach biases for appetitive cues on consumption.
Another challenge for the understanding of automatically activated approach–avoidance tendencies is co-morbidity. Indeed, research examined in this review highlighted that tobacco and alcohol use were interrelated [35, 71]. Thus, another direction for future research is the concurrent assessment of approach–avoidance biases and related behaviours for multiple substances. Such research will further our understanding of the shared mechanisms underlying these disorders in line with a transdiagnostic approach, which advocates for the assessment of interdependent processes and behaviours [84]. Indeed, transdiagnostic research on approach bias will have implications for modification paradigms aiming to target multiple excessive consumption behaviours. We expect that future reviews will also include studies on approach–avoidance biases for appetitive cues in behavioural addictions, such as gambling disorder [85], or so-called addiction to social media sites [86] given the emerging research in this field.
Finally, despite the wealth of studies on the assessment of approach bias for appetitive substances, the underlying mechanisms remain unclear. Recent iterations of dual-process models posit that automatically activated action tendencies are learned by early information processing that becomes biased through associative conditioning of rewarding stimuli [11]. Indeed, recent research using eye tracking to assess value-modulated attentional capture has demonstrated that the influence of reward on attentional capture by non-drug (i.e. monetary) reward was positively related to illicit substance use among individuals with lower levels of cognitive control [87]. Theoretically, these latter findings suggest that attentional capture by reward-related stimuli may underpin automatically activated biases and excessive consumption behaviour, although the causal direction is yet to be established [11]. Future research should examine whether attentional capture by reward-related cues is associated with other automatically activated processes (e.g. approach bias) and other types of excessive consumption behaviour (e.g. unhealthy food intake).
Conclusions
Automatic approach–avoidance action tendencies for food, alcohol and cigarettes were observed, although a range of assessment paradigms were used to measure such biases. The existence of approach biases and their relationship with problematic consumption behaviours, including unhealthy eating, heavy drinking and smoking highlights the importance of approach bias modification interventions. Future research should prioritise longitudinal designs to determine whether approach bias predicts the development of problematic behaviours. There is also a need for more research using novel assessment paradigms and ecologically valid measures of consumption behaviour. Finally, future reviews on approach biases should aim to synthesise the emerging research in the field of behavioural addictions, including gambling disorder. Given that a change in the underlying mechanism is a key outcome in intervention studies, advances in approach bias assessment will also help to improve the efficacy of approach bias modification.
References
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
Marteau TM, Hollands GJ, Fletcher PC. Changing human behavior to prevent disease: the importance of targeting automatic processes. Science. 2012;337(6101):1492–5 http://www.ncbi.nlm.nih.gov/pubmed/22997327.
Wiers RW, Gladwin TE, Hofmann W, Salemink E, Ridderinkhof KR. Cognitive bias modification and cognitive control training in addiction and related psychopathology. Clin Psychol Sci. 2013;1(2):192–212. https://doi.org/10.1177/2167702612466547.
Wiers RW, Rinck M, Kordts R, Houben K, Strack F. Retraining automatic action-tendencies to approach alcohol in hazardous drinkers. Addiction. 2010;105(2):279–87. https://doi.org/10.1111/j.1360-0443.2009.02775.x.
Havermans RC, Giesen JCAH, Houben K, Jansen A. Weight, gender, and snack appeal. Eat Behav. 2011;12(2):126–30.
Field M, Kiernan A, Eastwood B, Child R. Rapid approach responses to alcohol cues in heavy drinkers. J Behav Ther Exp Psychiatry. 2008;39(3):209–18 http://www.ncbi.nlm.nih.gov/pubmed/17640615.
Wiers CE, Kühn S, Javadi AH, Korucuoglu O, Wiers RW, Walter H, et al. Automatic approach bias towards smoking cues is present in smokers but not in ex-smokers. Psychopharmacology. 2013;229(1):187–97. https://doi.org/10.1007/s00213-013-3098-5.
Strack F, Deutsch R. Reflective and impulsive determinants of social behavior. Personal Soc Psychol Rev. 2004;8(3):220–47. https://doi.org/10.1207/s15327957pspr0803_1.
Ramirez JJ, Hendershot CS, Rangel-Gomez M, Lindgren KP, Peterson KP, Bernat E, et al. A dual process perspective on advances in cognitive science and alcohol use disorder. Clin Psychol Rev. 2018;69:83–96. https://doi.org/10.1016/j.cpr.2018.04.002.
Hommel B, Wiers RW. Towards a unitary approach to human action control. Trends Cogn Sci. 2017;21(12):940–9.
Keren G, Schul Y. Two is not always better than one: a critical evaluation of two-system theories. Perspect Psychol Sci. 2009;4(6):533–50.
Watson P, Pearson D, Wiers RW, Le Pelley M. Prioritising pleasure and pain: attentional capture by reward- and punishment-related stimuli. Curr Opin Behav Sci. 2019;26:107–13. https://doi.org/10.1016/j.cobeha.2018.12.002.
Gladwin TE, Figner B, Crone EA, Wiers RW. Addiction, adolescence, and the integration of control and motivation. Dev Cogn Neurosci. 2011;1(4):364–76.
Wiers RW, Gladwin TE. Reflective and impulsive processes in addiction and the role of motivation. In: Deutsch R, Gawronski B, Hofmann W, editors. Reflective and impulsive determinants of human behavior. Abingdon: Routledge; 2017. p. 173–88.
Gladwin TE, Figner B. Hot cognition and dual systems: introduction, criticisms, and ways forward. In: Wilhelms E, Reyna VF, editors. Frontiers of cognitive psychology series: neuroeconomics, judgment and decision making. New York: Psycholoy Press; 2014. p. 157–80.
Wiers RW, Anderson KG, Van Bockstaele B, Salemink E, Hommel BE. Affect, dual-processing, developmental psychopathology, and health behaviors. In: Affective determinants of health behavior, vol. 16. Oxford: Oxford University Press; 2018. p. 158.
Woud ML, Maas J, Wiers RW, Becker ES, Rinck M. Assessment of tobacco-related approach and attentional biases in smokers, cravers, ex-smokers, and non-smokers. Front Psychol. 2016;7:172. https://doi.org/10.3389/fpsyg.2016.00172/abstract.
Rinck M, Becker ES. Approach and avoidance in fear of spiders. J Behav Ther Exp Psychiatry. 2007;38(2):105–20 https://linkinghub.elsevier.com/retrieve/pii/S0005791606000620.
De Houwer J, Crombez G, Baeyens F, Hermans D. On the generality of the affective Simon effect. Cogn Emot. 2001;15(2):189–206. https://doi.org/10.1080/02699930125883.
Roefs A, Huijding J, Smulders FTY, MacLeod CM, de Jong PJ, Wiers RW, et al. Implicit measures of association in psychopathology research. Psychol Bull. 2011;137(1):149–93. https://doi.org/10.1037/a0021729.
Wiers RW, Eberl C, Rinck M, Becker ES, Lindenmeyer J. Retraining automatic action tendencies changes alcoholic patients’ approach bias for alcohol and improves treatment putcome. Psychol Sci. 2011;22(4):490–7. https://doi.org/10.1177/0956797611400615.
Ostafin BD, Palfai TP. Compelled to consume: the implicit association test and automatic alcohol motivation. Psych Addict Behav. 2006;20(3):322–7.
Cristea IA, Kok RN, Cuijpers P. The effectiveness of cognitive bias modification interventions for substance addictions: a meta-analysis. PLoS One. 2016;11(9):e0162226. https://doi.org/10.1371/journal.pone.0162226.
Kakoschke N, Kemps E, Tiggemann M. Approach bias modification training and consumption: a review of the literature. Addict Behav. 2017;64:21–8.
Wiers RW, Boffo M, Field M. What’s in a trial? On the importance of distinguishing between experimental lab studies and randomized controlled trials: the case of cognitive bias modification and alcohol use disorders. J Stud Alcohol Drugs. 2018;79(3):333–43 http://www.ncbi.nlm.nih.gov/pubmed/29885138.
• Zhang MWB, Ying J, Wing T, Song G, Fung DSS, Smith HE. Cognitive biases in cannabis, opioid, and stimulant disorders: a systematic review. Front Psychiatry. 2018;9:376. https://doi.org/10.3389/fpsyt.2018.00376/full In this systematic review, the authors documented the existence of substance-specific attentional and approach biases across opioid, cannabis and stimulant use disorders.
Roefs A, Huijding J, Smulders FTY, Macleod CM, De Jong PJ, Wiers RW, et al. Implicit measures of association in psychopathology research. Psych Bull. 2011;137(1):149. http://eetonderzoek.nl/wp-content/uploads/2015/03/IJA29.pdf–93.
Kim D-Y, Lee J-H. Development of a virtual approach–avoidance task to assess alcohol cravings. Cyberpsychol Behav Soc Netw. 2015;18(12):763–6. https://doi.org/10.1089/cyber.2014.0490.
Fleming KA, Bartholow BD. Alcohol cues, approach bias, and inhibitory control: applying a dual process model of addiction to alcohol sensitivity. Psychol Addict Behav. 2014;28(1):85–96 http://www.ncbi.nlm.nih.gov/pubmed/23438245.
van Duijvenbode N, Didden R, Korzilius HPLM, Engels RCME. The addicted brain: cognitive biases in problematic drinkers with mild to borderline intellectual disability. J Intellect Disabil Res. 2015;60(3):242–53.
van Duijvenbode N, Didden R, Korzilius HPLM, Engels RCME. The usefulness of implicit measures for the screening, assessment and treatment of problematic alcohol use in individuals with mild to borderline intellectual disability. Adv Neurodev Disord. 2017;1(1):42–51. https://doi.org/10.1007/s41252-016-0005-1.
Wiers RW. The potential usefulness of cognitive bias modification (CBM) in the treatment of problematic alcohol use in individuals with mild to borderline intellectual disability. Adv Neurodev Disord. 2017;1(2):105–6. https://doi.org/10.1007/s41252-017-0009-5.
Larsen H, Kong G, Becker D, Cousijn J, Boendermaker W, Cavallo D, et al. Implicit motivational processes underlying smoking in American and Dutch adolescents. Front Psychiatry. 2014;5:51. https://doi.org/10.3389/fpsyt.2014.00051/abstract.
• Weckler H, Kong G, Larsen H, Cousijn J, Wiers RW, Krishnan-Sarin S. Impulsivity and approach tendencies towards cigarette stimuli: implications for cigarette smoking and cessation behaviors among youth. Exp Clin Psychopharmacol. 2017;25(5):363–72 This study shows that adolescent smokers with a stronger approach bias and higher levels of impulsivity increased the odds of being a smoker.
Machulska A, Zlomuzica A, Adolph D, Rinck M, Margraf J. “A cigarette a day keeps the goodies away”: smokers show automatic approach tendencies for smoking—but not for food-related stimuli. PLoS One. 2015;10(2):e0116464. https://doi.org/10.1371/journal.pone.0116464.
Cohn AM, Cobb C, Hagman BT, Cameron A, Ehlke S, Mitchell JN. Implicit alcohol cognitions in risky drinking nicotine users with and without co-morbid major depressive disorder. Addict Behav. 2014;39(4):797–802 http://www.ncbi.nlm.nih.gov/pubmed/24531633.
Creemers HE, Korhonen T, Kaprio J, Vollebergh WAM, Ormel J, Verhulst FC, et al. The role of temperament in the relationship between early onset of tobacco and cannabis use: the TRAILS study. Drug Alcohol Depend. 2009;104(1–2):113–8.
Rubinstein ML, Luks TL, Dryden WY, Rait MA, Simpson GV. Adolescent smokers show decreased brain responses to pleasurable food images compared with nonsmokers. Nicotine Tob Res. 2011;13(8):751–5. https://doi.org/10.1093/ntr/ntr046.
Kemps E, Tiggemann M. Approach bias for food cues in obese individuals. Psychol Health. 2015;30(3):370–80. https://doi.org/10.1080/08870446.2014.974605.
Kakoschke N, Kemps E, Tiggemann M. Differential effects of approach bias and eating style on unhealthy food consumption in overweight and normal weight women. Psychol Health. 2017;32(11):1371–85.
Mehl N, Mueller-Wieland L, Mathar D, Horstmann A. Retraining automatic action tendencies in obesity. Physiol Behav. 2018;192:50–8.
Schmidt R, Sebert C, Kösling C, Grunwald M, Hilbert A, Hübner C, et al. Neuropsychological and neurophysiological indicators of general and food-specific impulsivity in children with overweight and obesity: a pilot study. Nutrients. 2018;10(12):1983 Available from: https://www.mdpi.com/2072-6643/10/12/1983.
Roefs A, Quaedackers L, Werrij MQ, Wolters G, Havermans R, Nederkoorn C, et al. The environment influences whether high-fat foods are associated with palatable or with unhealthy. Behav Res Ther. 2006;44:715–36.
Paslakis G, Kühn S, Grunert S, Erim Y. Explicit and implicit approach vs. avoidance tendencies towards high vs. low calorie food cues in patients with obesity and active binge eating disorder. Nutrients. 2017;9(10):1068.
Brockmeyer T, Hahn C, Reetz C, Schmidt U, Friederich H-C. Approach bias and cue reactivity towards food in people with high versus low levels of food craving. Appetite. 2015;95:197–202.
May CN, Juergensen J, Demaree HA. Yum, cake!: how reward sensitivity relates to automatic approach motivation for dessert food images. Pers Individ Dif. 2016;90:265–8.
Maas J, Keijsers GPJ, Rinck M, Sharbanee JM, Vroling MS, Becker ES. Implicit action tendencies and evaluations in unwanted snacking behavior. Int J Cog Ther. 2017;10(1):79–91.
Brignell C, Griffiths T, Bradley BP, Mogg K. Attentional and approach biases for pictorial food cues. Influence of external eating. Appetite. 2009;52(2):299–306 http://www.ncbi.nlm.nih.gov/pubmed/19027808.
Kersbergen I, Woud ML, Field M. The validity of different measures of automatic alcohol action tendencies. Psychol Addict Behav. 2015;29(1):225–30. https://doi.org/10.1037/adb0000009.
•• Martin Braunstein L, Kuerbis A, Ochsner K, Morgenstern J. Implicit alcohol approach and avoidance tendencies predict future drinking in problem drinkers. Alcohol Clin Exp Res. 2016;40(9):1945–52 http://www.ncbi.nlm.nih.gov/pubmed/27421061. This is an important longitudinal study showing that a stronger approach bias for alcohol cues predicts future drinking in problem drinkers with alcohol-use disorder.
•• Field M, Di Lemma L, Christiansen P, Dickson J. Automatic avoidance tendencies for alcohol cues predict drinking after detoxification treatment in alcohol dependence. Psychol Addict Behav. 2017;31(2):171–9 An important longitudinal study showing that approach–avoidance biases for alcohol cues predict treatment responses among individuals with alcohol use disorder.
Janssen T, Larsen H, Vollebergh WAM, Wiers RW. Longitudinal relations between cognitive bias and adolescent alcohol use. Addict Behav. 2015;44:51–7. https://doi.org/10.1016/j.addbeh.2014.11.018.
Lindgren KP, Baldwin SA, Ramirez JJ, Olin CC, Peterson KP, Wiers RW, et al. Self-control, implicit alcohol associations, and the (lack of) prediction of consumption in an alcohol taste test with college student heavy episodic drinkers. PLoS One. 2019;14(1):e0209940. Available from. https://doi.org/10.1371/journal.pone.0209940.
Baker S, Dickson JM, Field M. Implicit priming of conflicting motivational orientations in heavy drinkers. BMC Psychol. 2014;2(1):28. https://doi.org/10.1186/s40359-014-0028-1.
Snelleman M, Schoenmakers TM, van de Mheen D. Attentional bias and approach/avoidance tendencies do not predict relapse or time to relapse in alcohol dependency. Alcohol Clin Exp Res. 2015;39(9):1734–9 http://www.ncbi.nlm.nih.gov/pubmed/26247388.
• Wiers CE, Gladwin TE, Ludwig VU, Gröpper S, Stuke H, Gawron CK, et al. Comparing three cognitive biases for alcohol cues in alcohol dependence. Alcohol Alcohol. 2016;52(2):242–8 This paper was the first to investigate three conceptually distinct automatically activated cognitive biases for alcohol cues: attentional bias, approach–avoidance bias, and implicit alcohol-approach associations among patients with alcohol use disorder.
Spruyt A, De Houwer J, Tibboel H, Verschuere B, Crombez G, Verbanck P, et al. On the predictive validity of automatically activated approach/avoidance tendencies in abstaining alcohol-dependent patients. Drug Alcohol Depend. 2013;127(1–3):81–6.
Pieters S, Burk WJ, Van der Vorst H, Engels RC, Wiers RW. Impulsive and reflective processes related to alcohol use in young adolescents. Front Psychiatry. 2014;5:56. https://doi.org/10.3389/fpsyt.2014.00056/abstract.
Janssen T, Wood MD, Larsen H, Peeters M, Vollebergh WAM, Wiers RW. Investigating the joint development of approach bias and adolescent alcohol use. Alcohol Clin Exp Res. 2015;39(12):2447–54.
Booth C, Spronk D, Grol M, Fox E. Uncontrolled eating in adolescents: the role of impulsivity and automatic approach bias for food. Appetite. 2018;120:636–43.
Kakoschke N, Kemps E, Tiggemann M. Combined effects of cognitive bias for food cues and poor inhibitory control on unhealthy food intake. Appetite. 2015;87:358–64.
• Maas J, Woud ML, Keijsers GPJ, Rinck M, Becker ES, Wiers RW. The attraction of sugar: an association between body mass index and impaired avoidance of sweet snacks. J Exp Psychopathol. 2017;8(1):40–54. https://doi.org/10.5127/jep.052415 This human laboratory study showed that higher body mass index was related to impaired avoidance, but not increased approach, of sweet snacks on the AAT.
Schroeder PA, Lohmann J, Butz MV, Plewnia C. Behavioral bias for food reflected in hand movements: a preliminary study with healthy subjects. Cyberpsychology, Behav Soc Netw. 2016;19(2):120–6. https://doi.org/10.1089/cyber.2015.0311.
Gladwin TE, Mohr SE, Wiers RW. The potential role of temporal dynamics in approach biases: delay-dependence of a general approach bias in an alcohol approach–avoidance task. Front Psychol. 2014;5:1398. https://doi.org/10.3389/fpsyg.2014.01398/abstract.
Di Lemma LCG, Dickson JM, Jedras P, Roefs A, Field M. Priming of conflicting motivational orientations in heavy drinkers: robust effects on self-report but not implicit measures. Front Psychol. 2015;6:1465. https://doi.org/10.3389/fpsyg.2015.01465/abstract.
Cousijn J, Luijten M, Wiers RW. Mechanisms underlying alcohol-approach action tendencies: the role of emotional primes and drinking motives. Front Psychiatry. 2014;5:44. https://doi.org/10.3389/fpsyt.2014.00044/abstract.
. Stautz K, Frings D, Albery IP, Moss AC, Marteau TM. Impact of alcohol-promoting and alcohol-warning advertisements on alcohol consumption, affect, and implicit cognition in heavy-drinking young adults: a laboratory-based randomized controlled trial. Br J Health Psychol. 2017;22(1):128–50. https://doi.org/10.1111/bjhp.12221 This is an important experimental laboratory study examining the effects of alcohol advertisements on approach bias and alcohol use in young adults. The results show that exposure to alcohol-promoting advertisements increased approach and reduced avoidance of alcohol on the AAT relative to exposure to non-alcohol advertisements.
Talley AE, Fleming K, Hancock DW, Sher KJ. The impact of sexual self-concept ambiguity on alcohol approach bias and consumption. Addict Behav. 2019;92:155–60.
Sharbanee JM, Stritzke WG, Jamalludin ME, Wiers RW. Approach-alcohol action tendencies can be inhibited by cognitive load. Psychopharmacology. 2014;231(5):967–75.
Sayette MA, Creswell KG, Dimoff JD, Fairbairn CE, Cohn JF, Heckman BW, et al. Alcohol and group formation: a multimodal investigation of the effects of alcohol on emotion and social bonding. Psychol Sci. 2012;23(8):869–78.
Jones A, Button E, Rose AK, Robinson E, Christiansen P, Di Lemma L, et al. The ad-libitum alcohol ‘taste test’: secondary analyses of potential confounds and construct validity. Psychopharmacology. 2016;233(5):917–24. Available from. https://doi.org/10.1007/s00213-015-4171-z.
Cohn A, Ehlke S, Cobb CO. Relationship of nicotine deprivation and indices of alcohol use behavior to implicit alcohol and cigarette approach cognitions in smokers. Addict Behav. 2017;67:58–65.
Yannakoulia M, Anastasiou CA, Zachari K, Sidiropoulou M, Katsaounou P, Tenta R. Acute effect of smoking and smoking abstinence on energy intake and appetite-related hormones blood concentrations. Physiol Behav. 2018;184:78–82.
Jünger E, Javadi AH, Wiers CE, Sommer C, Garbusow M, Bernhardt N, et al. Acute alcohol effects on explicit and implicit motivation to drink alcohol in socially drinking adolescents. J Psychopharmacol. 2017;31(7):893–905.
Wegman J, van Loon I, Smeets PAM, Cools R, Aarts E. Top-down expectation effects of food labels on motivation. Neuroimage. 2018;173:13–24.
Mollen S, Holland RW, Ruiter RAC, Rimal RN, Kok G. When the frame fits the social picture: the effects of framed social norm messages on healthy and unhealthy food consumption. Communic Res. 2016;1:33.
Kotynski AE, Demaree HA. A study named desire: local focus increases approach motivation for desserts. Motiv Emot. 2017;41(4):455–64. https://doi.org/10.1007/s11031-017-9622-x.
Piqueras-Fiszman B, Kraus AA, Spence C. “Yummy” versus “yucky”! Explicit and implicit approach–avoidance motivations towards appealing and disgusting foods. Appetite. 2014;78:193–202.
Kraus AA, Piqueras-Fiszman B. Sandwich or sweets? An assessment of two novel implicit association tasks to capture dynamic motivational tendencies and stable evaluations towards foods. Food Qual Prefer. 2016;49:11–9.
Cheval B, Audrin C, Sarrazin P, Pelletier L. When hunger does (or doesn’t) increase unhealthy and healthy food consumption through food wanting: the distinctive role of impulsive approach tendencies toward healthy food. Appetite. 2017;116:99–107.
•• Lender A, Meule A, Rinck M, Brockmeyer T, Blechert J. Measurement of food-related approach–avoidance biases: larger biases when food stimuli are task relevant. Appetite. 2018;125:42–7 This study is the first to show that approach bias for food-related cues is stronger when participants are instructed to respond to the content of the pictures rather than to a task irrelevant feature (e.g. the outline of the picture) measured using the AAT.
Field M, Caren R, Fernie G, De Houwer J. Alcohol approach tendencies in heavy drinkers: comparison of effects in a relevant stimulus-response compatibility task and an approach/avoidance Simon task. Psychol Addict Behav. 2011;25(4):697–701. https://doi.org/10.1037/a0023285.
Rougier M, Muller D, Ric F, Alexopoulos T, Batailler C, Smeding A, et al. A new look at sensorimotor aspects in approach/avoidance tendencies: the role of visual whole-body movement information. J Exp Social Psych. 2018;76:42–53.
Tibboel H, De Houwer J, Spruyt A, Brevers D, Roy E, Noël X. Heavy social drinkers score higher on implicit wanting and liking for alcohol than alcohol-dependent patients and light social drinkers. J Behav Ther Exp Psych. 2015;48:185–91.
Keck ME, Kappelmann N, Kopf-Beck J. Translational research as prerequisite for personalized psychiatry. Eur Arch Psychiatry Clin Neurosci. 2018;268(3):215–7. https://doi.org/10.1007/s00406-018-0885-3.
Boffo M, Smits R, Salmon JP, Cowie ME, de Jong DTHA, Salemink E, et al. Luck, come here! Automatic approach tendencies toward gambling cues in moderate- to high-risk gamblers. Addiction. 2018;113(2):289–98. https://doi.org/10.1111/add.14071.
Juergensen J, Leckfor C. Stop pushing me away: relative level of Facebook addiction is associated with implicit approach motivation for Facebook stimuli. Psychol Rep 2018;003329411879862. https://doi.org/10.1177/0033294118798624
Albertella L, Copeland J, Pearson D, Watson P, Wiers RW, Le Pelley ME. Selective attention moderates the relationship between attentional capture by signals of nondrug reward and illicit drug use. Drug Alcohol Depend. 2017;175:99–105.
Acknowledgements
The authors would like to thank Antonio Verdejo-Garcia for his helpful feedback on an earlier version of this paper.
Funding
RSCL was supported by funding from the National Health and Medical Research Council (No. 1162031).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
Dr. Albertella has nothing to disclose.
Dr. Wiers has nothing to disclose.
Dr. Kakoschke has nothing to disclose.
Dr. Lee reports funding from the National Health & Medical Research Council (No. 1162031) during the conduct of the study. The funders had no input to the study design, data collection, or interpretation, writing of the report, or submission for publication.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the Topical Collection on Food Addiction
Rights and permissions
About this article
Cite this article
Kakoschke, N., Albertella, L., Lee, R.S.C. et al. Assessment of Automatically Activated Approach–Avoidance Biases Across Appetitive Substances. Curr Addict Rep 6, 200–209 (2019). https://doi.org/10.1007/s40429-019-00254-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40429-019-00254-2