Applications of the dot probe task in attentional bias research in eating disorders: A review.
@@Recent years have seen an increasing interest in the cognitive approach to eating disorders, which postulates that patients selectively attend to information associated with eating, body shape, and body weight. The unreliability of self-report measures in eating disorders due to strong denial of illness gave rise to experimental studies inspired by research into anxiety disorders involving attentional bias, with the prevalent method being a modified color-naming Stroop task. Unfortunately, that tool was shown to exhibit many limitations, especially in terms of attentional bias measurement. Thus, researchers started to seek alternative methods of evaluating attention in persons with eating disorders. Along with the Stroop test and the Posner paradigm, one of the most frequently used methods is the dot probe task. This paper presents the dot probe protocol as well as the rationale underpinning its use, including its advantages and drawbacks. Furthermore, a modification of the task is proposed to enable the assessment of all components of attentional bias in patients with eating disorders. The paper also discusses practical implications of the modification for the treatment of these patients. For several years now there has been an increasingly widespread use of so-called attentional training employing, amongst others, the dot probe task, which may be modified for the purpose of reducing or eliminating of attentional biases in patients with eating disorders. Unfortunately, due to the absence of studies providing a reliable account of all types of attentional bias in eating disorders, this field of research lags considerably behind anxiety research and does not enable therapeutic applications.Introduction
The issue of treatment of eating disorders, such as anorexia nervosa and bulimia nervosa, continues to be very contentious. Recent years have seen a surge of interest in the cognitive approach, in which patients evaluate themselves almost exclusively in terms of their body shape, weight, eating habits, and their ability to control them. Jones, Leung, and Harris (2007) argue that these are "essentially cognitive disorders in which the main cognitive disturbance is manifested in a characteristic set of attitudes and values concerning body weight and shape" (Jones et al., 2007, p. 157). One of the cognitive processes that may perpetuate these disorders is selective attention. According to cognitive theories of eating disorders, patients selectively attend to material related to eating and body appearance (Vitousek & Hollon, 1990; see also Johansson, 2006; Dobson & Dozois, 2004). In this field, the most promising avenues are experimental studies into selective attention, including attentional bias, as in the case of patients with eating disorders self-report instruments are unreliable due to the fact that such patients tend to deliberately distort their responses (Faunce, 2002). Therefore, studies in this field are increasingly often conducted using information processing paradigms incorporating stimuli related to food, body shape, and weight (Ainsworth, Waller, & Kennedy, 2002).
Attentional bias or attentional biases?
According to Quimet, Gawronski, and Dozois (2009), many studies have shown that attention cannot be treated as a uniform, one-dimensional construct (see also Posner, 1980), while Koster and colleagues (2006) have observed that the exact nature of attentional biases remains elusive (see also Fox et al., 2001). In particular, the specific components of attentional bias associated with threat-related stimuli are still the subject of ongoing debate (Bannerman, Milders, & Sahraie, 2010). It should be emphasized that while attentional bias linked to positive stimuli is noteworthy, it is negative stimuli that are particularly "attention grabbing" (Fox, Russo, & Dutton, 2002, p. 376; see also Pratto & John, 1991, p. 380; Morgan, Rees, & Curran, 2008, p. 1331). According to Cisler and Koster (2010), the components of attentional bias refer to its measurable characteristics (what attentional bias "looks like"). In the case of threat-related attentional bias, these authors distinguish several components, which may also be termed "phases" (Ouimet et al., 2009, p. 461) or "mental operations" (Koster et al., 2006, p. 636). The first one is facilitated attention to threat, which is the relative facility or speed with which attention is drawn toward a threat. This represents attentional orienting toward threat, and so threatening stimuli are detected faster than non-threatening ones. The second component of attentional bias is difficulty in disengaging attention away from threat, which is the degree to which a threat-related stimulus captures, attention, preventing the reorientation of attention towards a different location. Consequently, it is more difficult to disengage attention from a threatening stimulus than from a neutral one. The third component is attentional avoidance of threat, which causes attention to be preferentially allocated towards locations other than the location of a threatening stimulus (Cisler & Koster, 2010). As can be seen, researching attentional bias is non-trivial as a distinction must be made between the three components. Thus, studies of persons with eating disorders should make it possible to reveal the nature of the attentional bias of those patients upon presentation of relevant (i.e., eating-related) stimuli.
Is the Stroop test the right method of measuring attentional bias in eating disordered patients?
The paradigm most frequently used in attentional bias research involving eating disordered patients is the Stroop task (1935). The original version of the task consisted of presenting neutral words in different color inks written on large cards, often with several words per card (Faunce, 2002). Participants were first requested to name the color of the words and then read the words irrespective of their color. This task was later revised to include color words (e.g., red and green) written in either the corresponding color, e.g., the word "red" printed in red (congruent trials) or other competing colors, e.g., the word "green" printed in blue (incongruent trials) (Wells & Matthews, 1994; Dobson & Dozois, 2004; Johansson, 2006; BarHaim et al., 2007). In this version, the participants are required to ignore the meaning of the words and to name the color of each stimulus as quickly as possible (Ainsworth et al., 2002; Cisler, Bacon, & Williams, 2009; Faunce, 2002; Johansson, 2006; Cisler & Koster, 2010). Many studies have found that subjects take significantly longer to color-name the words presented in incongruent trials than in congruent ones (Wells & Matthews, 1994; Johansson, 2006). This difference is attributed to the interference effect, which is calculated as the total time taken to name the words written in incongruent colors divided by the total time for the words written in congruent ones (Dobson & Dozois, 2004).
The discovery that performance on the classical Stroop task depends on the meaning of stimuli enabled researchers to modify that task for testing information processing in emotional disorders (Ainsworth et al., 2002; Johansson, 2006; Cisler et al., 2009; see also Faunce, 2002). Modifications of the original Stroop test were developed in the 1980s and are popularly known as "emotional Stroop tasks" (Wells & Matthews, 1994; Krejtz & Scdek, 2001; Lee & Shafran, 2004; Bar-Haim et al., 2007; Asanowicz & Wolski, 2007). The new versions were meant to evaluate attentional bias in emotional disorders, such as anxiety and depression (Lee & Shafran, 2004), as well as in other psychiatric conditions, e.g., eating disorders. (Dobson & Dozois, 2004). Two new types of trials were proposed: emotional and control ones (Johansson, 2006; see also Cisler et al., 2009; Cisler & Koster, 2010; Szymura, 2007). In emotional trials, researchers manipulated the content (Dobson & Dozois, 2004) and the valence of stimuli employed (Bar-Haim et al., 2007), making the meaning of the presented words relevant to the specific concerns of the patients arising from their emotional disorder. Similarly to the classical Stroop task, in modified clinical versions of the test the subjects were asked to name the color of words linked to their psychopathology; for instance a person with arachnophobia was asked to name the color of the word "cobweb." Also in the 1980s, special versions of the Stroop test were developed to test patients with eating disorders; these are known as the "Food Stroop" (Ben-Tovim et al., 1989) and the "Body Stroop" (Channon, Hemsley, & deSilva, 1988) and include words related to food and body shape. In control trials the meaning of the presented words was neutral with respect to valence (see also Dobson & Dozois, 2004).
Although tests employing the Stroop task have shown that individuals with eating disorders tend to name the colors of words related to food and body more slowly (e.g. Faunce, 2002; Dobson & Dozois, 2004; Johansson, 2006), some researchers (e.g. Lee & Shafran, 2004) have also emphasized that this task is poorly suited for measuring attentional bias as such. First of all, it is unclear what mechanisms are responsible for the results obtained in the Stroop test. Despite the very high number of tests using the classic Stroop task, it has not been conclusively determined whether the effect is caused by differences in the relative speed of processing color vs. language (Klein, 1964), the automaticity of language use (e.g., Logan, 1980), the perceptual encoding of appropriate stimulus attributes (Dyer, 1973), or the interference related to the unequal strengths of competing processing pathways (Cohen, Dunbar, & McClelland, 1990; see also Dobson & Dozois, 2004). It is not even certain whether Williams, Mathews, and MacLeod (1996) were right in claiming that Stroop interference is indicative of attention being automatically directed toward a given type of information, e.g. related to eating, which makes the test inadequate for studying attentional bias.
Due to the numerous concerns as to the legitimacy of the Stroop task for the measurement of attentional bias, researchers started to seek other methods of exploring the relationship between attention and psychopathology (Lee & Shafran, 2004), especially in terms of assessing attentional bias (Lee & Shafran, 2008). One of the predominant objectives was to design tests that would provide more consistent results concerning threat-related attentional biases. Thus, new information processing paradigms (Shafran et al., 2007) were developed largely in response to the methodological shortcomings of the Stroop task (Shafran et al., 2007; Lee & Shafran, 2008). The most prominent of them are the Posner paradigm (Cisler et al., 2009) and the dot probe task (Ainsworth et al., 2002; Cisler et al., 2009; Cisler & Koster, 2010), which represent significant advances in the approach to attentional bias research. These modifications show an interesting evolution of methods in the quest for a tool that would be best suited for evaluating attentional bias.
While a detailed description of the Posner paradigm is beyond the scope of this paper, it should be noted that in its most widely used variant, employed by Fox and colleagues (2001), a single word (threatening or neutral) is presented on the right or left side of the screen. Immediately following the word, a target is displayed. In validly cued, or test, trials (Hoppitt & Mackintosh, 2009; Fox et al., 2002) the cue and target appear at the same location, while in invalidly cued, or control, trials (Hoppitt and Mackintosh, 2009; Fox et al., 2002) they appear in different locations (Fox et al., 2001; see also Jaskowski, 2009). This paradigm enables evaluation of attentional engagement as measured by the subjects' responses (expressed as reaction times, RTs) to valid targets and attentional disengagement as measured by their responses to invalid targets (Derakshan, Eysenck, & Myers, 2007). It is believed that faster RTs to targets on valid trials reflect attentional capture by the cue, whereas slower RTs on invalid trials indicate difficulty in disengaging attention from the cue (Bannerman et al., 2010). If the subjects respond faster only on valid trials, following negative cues as compared to valid trials containing neutral stimuli, it may be assumed that bias is linked to attention being attracted and captured by negative information. In turn, if the subjects' responses are slower in invalid trials following negative words as compared to responses on invalid trials following neutral words, this suggests that they have a problem with disengaging attention from a negative stimulus (Amir et al., 2003; Derakshan et al., 2007; Hoppitt & Mackintosh, 2009; Cisler et al., 2009; Klumpp & Amir, 2009, Bannerman et al., 2010; see also Fox, 2002; Cisler & Koster, 2010). Attentional disengagement from threat is manifested by the additional time the subject takes to respond to invalid threatening trials as compared to invalid neutral trials (Klumpp & Amir, 2009). A major criticism of the Posner trial has been voiced by Klumpp and Amir (2009), who argue that this task essentially does not measure the degree to which attention is attracted by a given stimulus, which means that it is not sensitive to vigilance effects, because trials display only one stimulus so that there is no competition for attentional resources (see also Fox et al., 2002). This implies that the Posner test can be used only for invalid trials, where the target appears in the location previously not occupied by the stimulus (Fox et al., 2002). Therefore, it is perhaps fortunate that this test has not been used for patients with eating disorders, as the results would be just as non-informative as in the case of the Stroop task.
The dot probe task
As mentioned above, researchers of attentional bias (e.g., Cisler et al., 2009; Cisler & Koster, 2010) distinguish three components of attention: vigilance/facilitated attention towards relevant cues, cue avoidance, and difficulty in disengagement from cues. In this context, the dot probe task has been shown to measure more components of attentional bias than the Stroop test, which at best records vigilance to relevant cues. This task is a modification of the Posner paradigm (1980) proposed by MacLeod, Mathews, and Tata (1986), and is particularly well-suited for testing anxiety. Differences between the dot probe and the Posner tasks include the fact that the former simultaneously presents two stimuli, which are personally relevant or threatening, while in the Posner task it is not always the case (see also Faunce, 2002; Lee & Shafran, 2004; Mogg & Bradley, 2005; Bar-Haim et al., 2007, Asanowicz & Wolski, 2007; Shafran et al., 2007; Lee & Shafran, 2008; Cisler & Koster, 2010). The dot probe task is considered by many researchers a methodologically stronger test of attentional bias than the Stroop color naming task (Placanica, Faunce, & Job, 2002; see also Lee & Shafran, 2004; Mogg & Bradley, 1998), and it is also deemed to be a superior and more direct test of allocation of attention and attentional bias (Rieger et al., 1998; Mogg & Bradley, 1998; see also Faunce, 2002).
A study of patients with anxiety disorders (MacLeod et al., 1986) showed that this procedure is sensitive to attentional biases (Rieger et al., 1998) and can determine whether the subject's attention is directed towards or away from a given class of stimulus words. This is achieved by forcing the subjects to simultaneously process two distinct stimuli rather than different attributes of one stimulus, as in the Stroop task. Due to the fact that in this task a pair of stimuli, such as words, are presented separately, reaction times to the probes that replace them (one at a time) show whether the subject's attention is preferentially directed towards or away from the stimuli.
The dot probe task protocol and rationale
In the dot probe task, subjects are seated in front of a computer screen with their chins securely positioned on a chin rest. They are asked to stare at a fixation cross at the center of the screen (the cross was absent in the original version of the task), and then a pair of stimuli (words, facial expressions, or pictures) are displayed simultaneously near the fixation cross, approx. 5 cm apart (one above and the other below it, or one to the left and the other to the right) for a certain amount of time, which is usually 500 ms (Cisler et al., 2009). Subsequently, a neutral item known as a probe (e.g., a dot, asterisk, or letter) is presented at the location of one of the stimuli. Participants are requested to indicate the location of the probe (i.e., signal whether the probe has replaced the top, bottom, left, or right stimulus) using either a keyboard (pressing two differently colored buttons) or a special response box with two buttons, also differently colored. Latency is measured automatically by the computer (MacLeod et al., 1986; Rieger et al., 1998; Aisnworth et al., 2002; Fox et al., 2002; Asanowicz & Wolski, 2007; Frewen et al., 2008; Cisler et al., 2009; Klumpp & Amir, 2009; Ouimet et al., 2009; Bannerman et al., 2010; Cisler & Koster, 2010).
The methodological rationale underpinning this measure of attention allocation (Fox et al., 2002) is that that the subjects' reaction time will vary between trials partly as a function of the stimulus on which they first focus their attention (Frewen et al., 2008). It is expected that the subjects will detect more quickly those probes that are displayed in the same spatial locations as the stimuli they focused on as opposed to locations they did not focus on (Posner, Snyder, & Davidson, 1980; Navon & Margalit, 1983; Faunce, 2002; Lee & Shafran, 2004; Mogg & Bradley, 2005; Frewen et al., 2008; Shafran et al., 2007; Lee & Shafran, 2008) because of the additional time it takes for the subject to shift his or her attention toward the location of the probe in the latter case (Frewen et al., 2008). In other words, response latency to the probe will be reduced when it appears in an attended rather than unattended region of the computer screen (Mogg & Bradley, 2005). It is thought that reaction times to the probe are faster if at the time of probe display the subject's attention is already allocated to the location of the probe (Koster et al., 2004). For instance, if the subject's attention was initially oriented towards a negative word (i.e., the subject exhibits a negative attentional bias), the reaction time should be shorter if the probe appears in the region where that word was displayed. On the other hand, if the subject's attention was oriented away from the negative word, then the reaction time should be shorter if the probe is displayed in the location previously occupied by a positive word (Bar-Haim et al., 2006; Bar-Haim et al., 2007; Hoppit & Mackintosh, 2009).
In the "emotional" variant of the task, subjects are presented with a pair of words that differ in their emotional valence (threatening vs. neutral, or positive vs. negative), usually for 100-1250 ms (MacLeod et al., 1986; Mogg & Bradley, 2005; see also Asanowicz & Wolski, 2007; Frewen et al., 2008, Ouimet et al., 2009). Later versions of the dot probe task included masked exposure conditions with word pairs presented for 14 ms. It should be noted that over time the range of stimuli was expanded to include schematic facial expressions (e.g., Bradley et al., 1999; see also Mogg & Bradley, 2005) and pictures (Asanowicz & Wolski, 2007; Frewen et al., 2008; see also Shafran et al., 2008). Researchers typically use two types of stimuli - neutral and threat-related (Fox et al., 2001; Bar-Haim et al., 2007, Derakshan et al., 2007, Klumpp & Amir, 2009; see also Koster et al., 2004; Lee & Shafran, 2004; Szymura, 2007).
Attentional biases are diagnosed based on differences in reaction times towards probes replacing threatening and neutral stimuli in congruent and incongruent trials, respectively (Cisler et al., 2009; Cisler & Koster, 2010). Shorter RTs in congruent trials show that the individual had attended to the location where the probe was displayed (suggesting vigilance to threat), whereas longer RTs in incongruent trials indicate that he or she detected the probe by shifting attention to a previously unattended location (suggesting difficulty with attentional disengagement from threat). In turn, shorter RTs in incongruent trials than in congruent ones indicate that the individual avoids threatening stimuli. This interpretation was corroborated by reports that probe detection latencies are correlated with eye shifts between the two regions of the computer screen (Bradley, Mogg, & Millar, 2000; see also Ouimet et al., 2009) (Figure 1).
Advantages of the dot probe task
Thanks to the fact that participants performing the dot probe task respond to a neutral probe, latency is not affected by any emotional reaction to that probe or some general arousal. An additional advantage of this paradigm is the ability to manipulate the time interval between the presentation of the stimulus pair and the probe (i.e., stimulus onset asynchrony, SOA, also known as the Inter-Stimulus Interval, ISI), which allows for exploring the time course of attentional allocation (Bar-Haim et al., 2007). When exposure time is very short, then attentional orientation towards or away from a threat is probably unconscious, while in the case of longer exposure times this orientation may be linked to conscious processing. The dot probe task is also advantageous in that it enables measurement of whether attention is preferentially directed toward certain stimuli or perhaps the stimuli are avoided (Placanica et al., 2002).
Reservations as to what kind of attentional bias(es) the dot probe task actually measures
Despite the fact that some studies using the Stroop test on non-clinical and clinical groups, and especially on anxious individuals (e.g. Bradley et al., 1998; see also MacLeod et al., 1986), showed the procedure to be sensitive to attentional biases (MacLeod et al., 1986; Rieger et al., 1998), other studies have called this into question (Koster et al., 2004). The original interpretations of the patterns obtained with the dot probe task have been challenged (Fox et al., 2002; see also Klumpp & Amir, 2009). These concerns have not been allayed even by the study of Bradley and colleagues (2000), who reported that subjects exhibiting eye movements towards threatening faces also responded faster to probes that replaced those faces as compared to non-threatening faces. According to Bradley et al. (2000), these results seem to confirm that anxious individuals (in contrast to nonanxious ones) are characterized by attentional vigilance to threat faces (see also Klumpp & Amir, 2009).
However, their argument has been criticized on three main grounds.
First criticism
First, it has been observed that in dot-probe trials where the probe is displayed at the location of a threat-related stimulus, short reaction times (positive attentional bias manifested as the difference in RTs between congruent and incongruent trials, e.g. Bar-Haim et al., 2007) may be linked to either vigilance to threat or difficulty in disengaging attention from it (Bar-Haim et al., 2007; see also Koster et al., 2004). The Posner paradigm appears to be better suited for investigation of the disengagement mechanism: since a threat-related or neutral stimulus is displayed briefly in one of two possible locations followed by a probe in one location, slower responses on invalidly cued trials are interpreted as difficulty in disengagement from threat (Fox et al., 2002; Koster et al. 2004; Bar-Haim et al., 2007). Even though according to Bar-Haim et al. (2007) the task was intended to determine the relative proportion of two components of attention, namely engagement and disengagement, it actually only identifies disengagement and is not sensitive to vigilance effects, because only one stimulus precedes the probe on each trial and there is no competition for attentional resources (Fox et al., 2002; Bar-Haim et al., 2007; Klumpp & Amir, 2009). Thus, it may be concluded that while the dot probe task does detect the presence of attentional biases, it cannot determine the specific type of bias observed (Cisler et al., 2009), and in particular it cannot distinguish between vigilance and difficulty in disengagement from threat (Koster et al., 2004).
In response to the limitations of the dot probe task and the Posner paradigm, Koster et al. (2004) proposed a modification of the dot probe task observing that the previous versions of the dot probe task lacked a neutral baseline (neutral-neutral stimuli) with which to compare the reaction times of congruent and incongruent trials. In the original version of the test, RTs were compared only between the latter two types of trials. However, if a baseline were used, RTs on congruent and incongruent trials could be compared with it to check for vigilance to threat and difficulty in attentional disengagement from threat, respectively (Cisler et al., 2009; Cisler & Koster, 2010; see also Shane & Peterson, 2007; Cisler et al., 2009; Klumpp & Amir, 2009). Koster et al. (2004) proposed two methods of analyzing data obtained in tests using the modified dot probe task: a comparison of RTs on congruent and incongruent trials and a comparison of RTs between trials displaying two neutral stimuli and those displaying one neutral and one threatening stimulus. The first analysis determines whether subjects engage with the threatening stimulus (subtraction of RTs on congruent trials from those on incongruent trials gives a positive bias score) or avoid that stimulus (subtraction of RTs on congruent trials from those on incongruent trials gives a negative bias score). The other analysis shows whether a positive attentional bias score reflects vigilance to threat or difficulty in disengagement from threat. Vigilance should yield shorter RTs on congruent trials than on neutral-neutral baseline trials (the difference obtained by subtracting RTs on baseline trials from those on congruent trials should be negative), while difficulty in disengagement from threat should result in longer reaction times on incongruent trials than on baseline trials (the difference obtained by subtracting reaction times on baseline trials from reaction times on incongruent trials should be positive). In the latter case, the longer reaction times on incongruent trials result from the additional time needed to shift attention from a threatening location to a neutral one (Koster et al., 2004; see also Koster et al., 2006) (Figure 2).
Results from studies employing the modified dot probe task have shown that RTs on trials containing a pair of neutral stimuli do not differ from RTs on congruent trials, which suggests that threat-related stimuli do not lead to attentional facilitation. In turn, RTs on neutral-neutral trials are shorter than on incongruent trials, which corroborates difficulty in attentional disengagement from threat (Koster et al., 2004; Koster et al., 2006).
Second criticism
Secondly, it seems that response latencies in the dot probe task provide only a snapshot of the distribution of subjects' attention, with faster responses to probes displayed in the attended location relative to the unattended location (Cooper & Langton, 2006, p. 1322; see also Koster et al., 2004; Mogg & Bradley, 2005; Bar-Haim et al., 2006, 2007; Posner et al., 1980; Navon & Margalit, 1983).
The problem may be elucidated by studying the components of attentional bias using variable stimulus presentation durations (different time intervals between the presentation of a pair of stimuli and a probe, also known as stimulus onset asynchrony). Ouimet et al. (2009; see also Fox et al., 2001) discuss the temporal characteristics of attentional components; according to them attentional orienting occurs at <30 ms, engagement at 30-500 ms, disengagement at 500-1000 ms, and avoidance at >1000 ms. Koster et al. (2004) note that attentional orientation can be studied at <200 ms (e.g., in the dot probe task pictures should not be displayed for more than 200 ms). Calvo and Avero (2005) emphasize that the nature of attentional bias changes over time in the case of processing of emotional pictures. Indeed, research has revealed that a specific component of the observed attentional bias may be the function of exposure time, or stimulus onset asynchrony (this parameter makes it possible to elucidate the time course of attentional allocation, Bar Haim et al., 2007).
Third criticism
Third, Klumpp and Amir (2009) suggest that if a pair of stimuli is displayed for a longer period of time, more than one shift of attention between them may occur. In their objections to the adopted dot probe methodology, Asanowicz and Wolski (2007) argue that at a SOA of 500 ms subjects' reaction times do not reflect the initial orienting toward affective stimulation, because the processes of attentional disengagement, shift, and engagement with a new object proceed much faster, and their direction and strength may change within that time (see also Posner, 1994). Therefore, it seems that at a SOA of 500 ms the dot probe task does not record processes linked to attentional orientation; instead emotional information is processed more thoroughly, due to which disengagement from the stimulus and response to the probe require more time. According to Asanowicz and Wolski (2007), 500 ms is long enough to reorient attention twice; following initial engagement with the emotional stimulus, attention may be shifted to a neutral stimulus. Consequently, while a very short display of affective stimuli triggers an automatic orientation reaction leading to attentional engagement, a longer exposure (500 ms) to affective stimuli does not reflect the processes of attentional orientation, but rather the costs of emotional information processing by the attentional control mechanism. Thus, only very short SOAs afford an insight in the process of initial attentional shifts, which proceed faster than the control processes.
Holas and Brzezicka (2009) argue that anxiety affects preattentive processing and causes heightened detection of threat-related stimuli. In anxious individuals, attention is initially "stuck" to the stimulus (attentional component associated with engagement), which may result in difficulties in disengagement, most often observed at SOAs of 150 to 600 ms. Anxious individuals often avoid looking at threatening stimuli at longer exposure times. This effect has been found in studies tracking eye movements at SOAs of 1500 to 3000 ms. Also Cooper and Langton (2006) report that 500 ms is sufficient time to enable more than one shift in covert attention (i.e., attending to a stimulus without shifting the subject's gaze towards it). Similarly, according to Holas and Brzezicka (2009), 500 ms of stimulus display is sufficient for several attentional shifts between different locations. This is corroborated by Wells and Matthews (1994), who conclude that a SOA of 500 ms or more provides enough time for a strategic shift of attention. Cooper and Langton (2006) note that following 100 ms of face stimulus presentation, the subject's attention is preferentially directed towards the location of threatening faces, while at 500 ms that effect is absent. Furthermore, Fox et al. (2001) and Fox et al. (2002) observe that since in the probe-detection task stimuli are presented in the central visual field, both locations on the screen (top/bottom or right/left) are task relevant, and display time is relatively long (500 ms), attention may be directed to different locations and the subjects may have a tendency to dwell on threat-related stimuli once they have been detected. If this is indeed the case, the traditional probe detection task (with a presentation time of 500 ms) cannot be used to determine whether the threatening stimulus draws attention or rather holds attention once detected (Fox et al., 2001, p. 682). In other words, it is impossible to differentiate between initial vigilance to threat and difficulty in disengagement from it (see Koster et al., 2004; Bar-Haim et al., 2007; Klumpp & Amir, 2009).
In their recent study, Derakshan et al. (2007) investigated the time course of processing emotional information in repressors using the dot probe task and the Posner paradigm. According to these authors, manipulation of stimulus exposure duration enables investigation of the time course of attentional bias, including automatic vigilance to threat upon short exposure times and conscious vigilance towards threat and/or avoidance of threat when exposure times are long. Davidson (1998) described such research efforts as affective chronometry, or "the temporal dynamics of affective responding" (p. 310). It should be noted that while measuring changes in attentional bias over time requires variation in stimulus exposure times, in practice it is difficult to use more than two or three exposure durations (e.g., 100 and 500 ms), without making the task excessively long and fatiguing for participants. Alternatively, one can assess the direction and latency of eye movements in response to emotional stimuli (Mogg & Bradley, 2005). It should be noted here that many researchers have used the eye-tracking paradigm in patients with eating disorders and analogue conditions, obtaining some conflicting results.
As far as body-related stimuli are concerned, while certain patients (especially those with low BMI) exhibited greater attentional engagement with body shape than healthy controls (Blechert et al., 2009; Pinhas et al., 2014), but other experiments failed to produce evidence for such attentional bias (Horndasch et al., 2012, a study of children). Similarly inconclusive results have been obtained for subclinical groups. In this case, inconsistency may be attributed to differences between study groups. Some of the studies in question reported that patients directed increased attention towards their own unattractive body parts (in eating symptomatic participants) (Jansen, Nederkoorn, & Mulkens, 2005; see also Roefs et al., 2008), whereas others indicated avoidance of those stimuli (participants high in body dissatisfaction) (Janelle et al., 2009). Yet another study of overweight subjects (Warschburger et al., 2015) gave the opposite result reporting greater attentional engagement with attractive vs. unattractive regions of one's body. In turn, experiments with stimuli such as pictures of specific body parts (hips and upper legs) and fat body words have consistently revealed vigilance towards those stimuli in subclinical samples in participants with high scores on the Drive-for-Thinness subscale of the Eating Disorder Inventory (EDI) (Garner, 1991) and in weight-dissatisfied individuals (Hewig et al., 2008; Gao et al., 2011, respectively). Furthermore, Cho, Kwak, and Lee (2013) found that healthy participants with high levels of avoidance coping exhibited greater attention towards slim body shapes than subjects with low levels of avoidance coping, especially after exposure to oversized body pictures.
Regarding food stimuli, researchers using the eye-tracking paradigm obtained mixed results, similarly to the case of body stimuli. Giel and coworkers (2011a) failed to produce evidence for greater attentional engagement with food cues in eating-disordered patients. In subclinical groups, the findings have not been conclusive either, as some researchers reported vigilance towards low calorie foods and decreased vigilance towards high calorie foods in overweight participants (Graham et al., 2011), while others found fixation on both high-calorie and low-calorie food items in individuals with nonclinical BED (Popien et al., 2015). According to Werthmann and colleagues (2013a), who studied high and low chocolate cravers, the former group was characterized by a longer initial gaze on chocolate and reduced total dwell time for chocolate stimuli than high cravers. Furthermore, the same research team (2013b) found that normalweight high-restrained and low-restrained eaters showed attentional biases for food stimuli in comparison to control stimuli, regardless of restraint status. Finally, Folkvord and colleagues (2015) reported that children with a higher gaze duration and faster latency of initial fixation for food cues ate more of the advertised snacks. Undoubtedly, research using the eye-tracking paradigm should be continued because the obtained results, albeit mixed, are very interesting and may provide valuable insights, especially if applied in conjunction with the dot probe task. So far, several studies of this type have been conducted (i.e., Castellanos et al., 2009; Nijs et al., 2010; Werthmann et al., 2011, 2014; Doolan et al.; 2014) (see Table 2). They are discussed at length in the following section on dot probe task research.
Results of dot probe studies with a focus on eating disordered individuals in the context of attentional bias research
The dot probe task was initially used to study populations with anxiety disorders, including Generalized Anxiety Disorder (GAD) (MacLeod et al., 1986) with the findings consistently indicating preferential processing of information linked to potentially threatening stimuli (MacLeod et al., 1986; see also Koster et al., 2004; Asanowicz & Wolski, 2007; Ouimet et al., 2009). It has been found that individuals with anxiety disorders, including GAD, respond faster to probes replacing threat-related stimuli than to those replacing neutral stimuli, even upon subliminal stimulus display. Their reaction times are also shorter than those of individuals in the control group, which seems to prove that persons with anxiety disorders exhibit attentional vigilance for threat (Macleod et al., 1986; Lee & Shafran, 2004; see also Fox et al., 2001; Koster et al., 2004; Szymura, 2007). This is true both for words (e.g., MacLeod et al., 1986; Mogg & Bradley, 2005; Fox et al. 2002) and faces (Bradley et al., 1999; see also Cisler et al., 2009; Cisler & Koster, 2010). It appears that individuals with stronger anxiety tend to select a subsequent source of data taking into consideration the previous threatening stimulus to make sure it does not recur in their selected attentional "window" (Szymura, 2007). The dot probe task has also been used for investigating attentional bias in other anxiety disorders, such as: panic disorder, arachnophobia, and blood-injury phobia (e.g., Wenzel & Holt, 1999; see also Ainsworth et al., 2002) and have usually reported attentional bias toward threat. Moreover, this paradigm has been used in patients with depression (e.g., Platt, Murphy, & Lau, 2015), alcohol dependence (e.g., Sinclair et al., 2016), complicated grief (e.g., Bullock & Bonanno, 2013), psychopathy (e.g., Edalati, Walsh, & Kosson, 2016), insomnia (Spiegelhalder et al., 2010), chronic pain (e.g., Asmundson, Wright, & Hadjistavropoulos, 2005), and asthma (e.g., Fritzsche et al., 2010), as well as in repressors (e.g., Derakshan et al., 2007). Recent years have seen numerous studies using the dot probe task in patients with eating disorders.
This paper reviews the literature concerning attentional bias in eating disorders and analogue conditions, with a particular focus on tests using the dot probe paradigm. Even though this work sets out to characterize a part of the dot probe paradigm rather than offer a systematic overview, relevant studies where sought based on PRISMA recommendations (Moher et al., 2009). Key search terms ("eating disorders", "anorexia nervosa", "bulimia nervosa", "eating", "body shape", "weight", "attention bias", "attentional bias", "information processing", "dot probe task") were used to query the online databases EBSCOhost, Google, PubMed, and ScienceDirect. No limits were set on publication date. In terms of the participants, the inclusion criterion was a diagnosis of clinical or subclinical eating disorder or analogue condition (e.g., obesity, restrained eating); however to obtain a fuller understanding of attentional bias with respect to the health-illness continuum, this review also covered studies involving healthy individuals exposed to experimental manipulation (e.g. fasted vs. fed exposure to eating-, body shape-, and weight-related cues). There was no limit on the age or sex of participants. Initially, 186 papers (168 original articles and 18 reviews) were identified based on the above criteria. Subsequently, the eligibility of studies was evaluated in a two-step process. First of all, taking into consideration the multitude of diverse research results, original studies which did not employ the dot probe task (n=116) were excluded for the sake of clarity of meta-analysis. Ultimately, the original studies included in qualitative synthesis were: 27 articles describing applications of the dot probe task in eating disorders and analogue conditions, 16 works using combined measures in eating disorders and analogue conditions, and 11 papers on applications of attentional bias modification training (ABMT) based on the dot probe task in obese and healthy samples (ABMT was not applied in eating disorders in any of the studies). Second, the present meta-analysis included all of the identified 18 review articles, even if they did not discuss the one-dot probe paradigm, as their rejection would have implied discarding information on some of the most critical developments in attentional bias research in eating disorders and analogue conditions.
Thus to the best of the present author's knowledge, 27 original studies employing the dot probe task on patients with eating disorders (11) and analogue conditions (16) have been published to date (Table 1, appendices).
A brief presentation of the results is given below, complete with a critical discussion pointing to similarities and differences between them.
Several studies on patients with eating disorders have consistently reported attentional bias towards words reflecting a large physique (Rieger et al., 1998), negative eating stimuli (i.e., images depicting high-calorie foods) (Shafran et al., 2007, studies 1 and 2) and negative body shape stimuli (i.e., images reflecting thin models) (Shafran et al., 2007, study 2).
However, those results were not reproduced when the standard interstimulus interval (ISI) of 500 ms was changed to 2,000 ms (Lee & Shafran, 2008). Other findings included attentional avoidance of words reflecting a thin physique (which constituted positive body shape stimuli from the perspective of eating disordered individuals) (Rieger et al., 1998) and positive eating stimuli (i.e., images reflecting low-calorie foods) (Shafran et al., 2007). In contrast to the above, in the study of Blechert, Ansorge, and Tuschen-Caffier (2010) anorexic patients exhibited attentional bias towards self-photos (i.e., thin body cues).
In terms of stimuli unrelated to eating disorders, Cardi and colleagues (2013) found that patients with those disorders exhibited attentional bias towards rejecting faces, difficulty disengaging attention from them, and attentional avoidance of accepting faces. In another study, Cardi and colleagues (2014) identified vigilance towards dominant and submissive faces both in eating disordered patients and in individuals recovered from an eating disorder. Hughes-Scalise and Connell (2014) reported attentional bias towards angry faces in eating disordered teens; importantly, in participants with high attentional bias towards angry faces maladaptive parental response to sadness predicted seriousness of the eating disorder. Finally, the study by Schober and colleagues (2014) showed no evidence for a differential attentional bias towards threatening words in patients with anorexia nervosa as compared to healthy controls.
A considerable body of research using the dot probe task has also focused on the outcomes of eating disorder treatments. One study (Shafran et al., 2008, study 2) indicated a decrease in attentional biases for positive and negative eating stimuli following treatment (20 weeks of Cognitive Behavioral Therapy, CBT). Furthermore, Kim et al. (2014a) reported reduction in vigilance towards eating-related stimuli and negative shape stimuli in anorexic patients treated with oxytocin. Moreover, Kim and colleagues (2014b) found that patients exhibited changed attentional bias for angry faces following oxytocin administration (avoidance of angry faces was replaced with vigilance towards them).
Of particular importance are studies of overweight and obese samples because, although they may not be assigned to any particular group of eating-disordered patients (except for binge eating disorder, BED), they are very close to them in terms of body dissatisfaction, increased anxiety, and excessive attempts at weight control (e.g. Day, Ternouth, & Collier, 2009). Three studies on overweight and/or obese patients were found to use the dot probe task. Loeber et al. (2012) reported that food stimuli did not modulate attention allocation either in obese or in healthy participants at a very early stage of information processing. Very different results were obtained in the other two studies. First, the group of obese participants examined by Kemps, Tiggemann, and Hollitt (2014, study 1) revealed faster RTs to high calorie food than animal words. Second, Oh and Taylor (2013) showed that attentional bias to chocolate images could trigger uncontrolled consumption both in overweight/obese individuals and normal chocolate eaters.
Research conducted on a variety of subclinical and healthy samples provides a very interesting, albeit sometimes contradictory, body of evidence, with discrepancies being probably attributable to differences between the subclinical groups. Some studies have confirmed that problematic food-related beliefs and behaviors are associated with attentional biases towards food cues. For instance, Placanica et al. (2002) reported that high EDI-2 scorers exhibit greater attentional bias towards low-calorie food as compared to low EDI-2 scorers, and greater attentional bias towards low-calorie food when nonfasted as compared to fasted. Brignell and colleagues (2009) found that high-external eating was associated with greater attentional bias for food cues. In another study (Hou et al., 2011), attentional bias towards food pictures was positively correlated with external eating. In subjects with high food neophobia Maratos and Staples (2015) identified greater vigilance towards unfamiliar fruit and vegetable stimuli than in those with low food neophobia. Furthermore, in participants with high hunger Mogg and colleagues (1998) detected attentional bias for food-related stimuli, but only if those stimuli were presented for a longer time (500 ms). Other noteworthy results come from two studies reporting attentional bias for food cues. First, Papies, Stroebe, and Aarts (2008) found that in restrained eaters pre-exposure to food cues elicited an attentional bias towards palatable food words. Second, Shank and colleagues (2015) identified a positive correlation between attentional bias towards highly palatable food and BMI in children with loss of control eating.
Importantly, another group of studies did not reveal differences between subclinical individuals and healthy participants in terms of attentional biases. Glauert et al. (2010) reported that healthy participants exhibited vigilance towards thin bodies for an ISI of both 500 ms and 150 ms and irrespective of whether stimuli (images of thin and fat female bodies) were less or more extreme. In turn, Wilson and Wallis (2013, study 3) did not identify any effect of restrained eating and/or mood on attention processing. Very surprisingly, healthy participants examined by Freijy, Mullan, and Sharpe (2014) revealed attentional bias towards high-calorie pictures, away from high-calorie words, towards low-calorie words, and away from low-calorie pictures.
Finally, one should take note of experimental manipulations in healthy samples. Smith and Rieger (2010) did not detect increased attention towards negative shape/weight words in participants in the body dissatisfaction condition versus negative mood and neutral conditions, but participants in the negative mood condition exhibited increased attention towards negative shape/weight words relative to the body dissatisfaction condition. Similarly, in a study by Hepworth and colleagues (2010) negative mood increased attentional bias for food pictures in healthy participants. In turn, di Pellegrino, Magarelli, and Mengarelli (2011) reported that attentional bias for food eaten decreased from pre-to post-satiety, along with the subjective pleasantness of that food.
In summary, despite certain inconsistency in the reported results, most studies of patients with eating disorders indicated vigilance towards stimuli related to those disorders (e.g., Shafran et al., 2007). Investigations using stimuli unrelated to the disorders (faces) were less consistent: some authors reported vigilance to those stimuli (e.g., Cardi et al., 2013), while others did not find differences between eating disordered patients and healthy controls (e.g., Schober et al., 2014). Nevertheless, it is noteworthy that attentional bias patterns changed after treatment (CBT or oxytocin), resulting in reduced vigilance towards eating-related stimuli (e.g., Shafran et al., 2008, study 2; Kim et al., 2014a).
Studies using the dot probe task in subclinical and healthy samples revealed considerable discrepancies in attentional biases for cues related to eating disorders. While some authors reported vigilance to these stimuli (e.g., Brignell et al., 2009), others did not identify any attentional biases in this respect (Loeber et al., 2012) or did not detect differences between subclinical and healthy participants (e.g., Werthmann et al., 2013b).
Finally, only three studies (i.e. Mogg et al., 1998; Lee & Shafran, 2008; Glauert et al., 2010), explored the relationship between ISI and attentional bias, so further research in this area is needed.
Of note are also studies employing the dot probe task and other paradigms. To the best of the present author's knowledge, sixteen papers on the subject have been published to date, including one on eating disordered patients and fifteen on subclinical and healthy samples (Table 2, appendices).
The only such study involving patients with eating disorders was conducted by Chamberlain and co-authors (2012), who examined subjects with BED before and after treatment. Although they proved that GSK1521498 (a selective mu-opioid receptor antagonist) at 5 mg/day significantly reduced attentional bias for food pictures (dot probe task) versus placebo at longer stimulus duration (2000 ms), the treatment had no effect on the Stroop task.
Similarly as in the case of studies using exclusively the dot probe paradigm, one should separately analyze reports on overweight and obese samples. To the best of the present author's knowledge, to date 7 studies of this type have been carried out. A study using the eye-tracking paradigm (Castellanos et al., 2009) reported increased gaze duration for food compared to non-food images in the fasted condition both in obese and healthy participants, but no attentional bias was found for cues related to an eating disorder on the dot probe task. Nijs and co-authors (2010), who investigated both obese/overweight and healthy participants, identified enhanced automatic orientation towards food cues in hungry versus satiated participants, and in overweight/obese versus normal-weight individuals, but only on the dot probe task. Furthermore, in the same study Nijs and coauthors (2010) observed increased intentional allocation of attention to food pictures in hunger versus satiety, but only using event-related potentials (ERP). At the same time, they did not find differences between obese and healthy participants in eye-tracking data. Werthmann et al. (2011) obtained statistically significant results only for experiments involving the eye-tracking paradigm, but not the dot probe task. In overweight (versus healthy) participants they detected more frequent initial gazing towards food pictures, accompanied by subsequent reduced maintenance of attention on those pictures. Furthermore, according to Werthmann and colleagues (2011) craving was related to initial orientation towards food. Doolan et al. (2014), who employed both the dot probe task and the eye-tracking paradigm, obtained statistically significant results only for the latter: both overweight/obese participants and healthy participants showed greater attentional bias towards high-energy-density (vs. low-energy-density) food images regardless of hunger condition. A study of both obese and healthy participants by Garcia-Garcia et al. (2013) reported higher reaction times both for food and rewarding non-food stimuli (dot probe task) and decreased activation of the bilateral occipital lobe, lateral prefrontal cortex, medial prefrontal cortex, precentral gyrus, paracingulate gyrus, anterior cingulate gyrus, precuneous/posterior cingulate cortex, and lateral occipital cortex (functional magnetic resonance imaging MRI). One should also mention works which did not identify any statistically significant effects. In a study of obese and overweight patients, low- and high-restrained eaters and healthy participants, Ahern and colleagues (2010) did not find any differences in attentional bias for food-related images on the dot probe task or in approach tendencies elicited by food images on the stimulus-response compatibility task. Nathan et al. (2012), who examined overweight/obese participants using the Stroop task and the dot probe task, did not report any effects of the D3 receptor antagonist GSK598809 on attentional bias.
Interestingly, many studies involving subclinical and healthy groups, combining the dot probe task with other paradigms identified attentional bias for stimuli related to an eating disorder using one of the measures, but not the other. For instance, Boon, Vogelzang, and Jansen (2000) did not find any attentional bias for food and weight/shape stimuli on the dot probe task, but identified such bias towards food stimuli on the word recognition task. In turn, in a dot probe task study carried out by Johansson, Ghaderi, and Andersson (2004) individuals high in responsiveness to external food cues exhibited avoidance of food words, while those low in responsiveness revealed vigilance towards food words; at the same time no attentional bias was detected for food or body words on the Stroop task. Calitri and colleagues (2010) did not find any effects of cognitive bias (as measured by the dot probe task) on weight change over a one-year period, but reported that cognitive bias (as measured by the Stroop task) towards unhealthy foods predicted an increase in BMI whereas cognitive bias towards healthy foods was associated with a BMI decrease.
Only one study reported statistically significant results for all (in this case--two) paradigms used. In the study employing the dot probe task and the eye-tracking paradigm in healthy participants, Werthmann and coworkers (2014) found that self-reported emotional eating did not account for changes in attention allocation for food or food intake and that in participants in neutral condition attention, maintenance on food cues was significantly related to increased intake in contrast to the sad mood condition.
Some other studies used the dot probe task in conjunction with paradigms designed to examine inhibitory control. Loeber and colleagues (2013), who used the dot probe task and a go/no-go task, identified an influence of self-reported hunger on behavioral response inhibition. Interesting results were reported by Kakoschke, Kemps, and Tiggemann (2015), who applied three tools: the dot probe task, an approach-avoidance task, and a food-specific go/no-go task. They found that in healthy participants neither attentional nor approach bias alone made a significant contribution to food intake (dot probe task, approach-avoidance task). On the other hand, they detected a significant effect of the interaction between approach bias (approach-avoidance task) and inhibitory control (foodspecific go/no-go task) on unhealthy snack food intake. Participants who showed a strong approach bias combined with low inhibitory control consumed the most snack food. While Lattimore and Mead (2015) obtained interesting results with the dot probe task, such as slower disengagement from pictorial food stimuli (for 2000 ms duration) in high-impulsive participants and faster detection of pictorial food cues (for 500 ms duration) in low-impulsive participants, differences on the go/no-go task were not statistically significant. Pothos et al. (2009), who used the Stroop task, the dot probe task, a recognition task, and the extrinsic affective Simon task, found that correlations between the various cognitive measures were weak and evident only in certain subsets of the population sample, as defined by gender and emotional-, restrained- and external-eating characteristics of healthy participants.
In summary, the use of the dot probe task in conjunction with other paradigms has led to mixed results. Some studies employing that research pattern (e.g., Castellanos, 2009) reported statistically significant results for one of the tests only, while others (e.g., Nathan et al., 2012) did not obtain any significant findings whatsoever. Only a few studies (i.e., Garcia-Garcia et al., 2013; Werthmann et al., 2014) yielded statistically significant results on all the measures applied. Studies focused on inhibitory control were also inconclusive (e.g., Lattimore & Mead, 2015). Hence, the question arises as to what is actually measured by those tools, especially in light of the seminal work by Pothos and co-authors (2009), which revealed poor correlations between the various tests (Stroop task, dot probe task, recognition task, extrinsic affective Simon task). Perhaps the best solution would be to use only one methodologically sound measure of attentional bias?
Despite the above-mentioned difficulties, one should carefully consider systematic reviews and meta-analyses focused on the results of attentional bias tests in eating disorders and analogue conditions. To the best knowledge of the present author, eighteen papers of this kind have been published to date, thirteen of which included results from patients with eating disorders (Table 3, appendices).
Most reviews of works involving patients with eating disorders (beside other groups) were devoted to analysis of results produced by different paradigms. Exceptions were reported in five papers. Aspen, Darcy, and Lock (2013) and Renwick, Campbell, and Schmidt (2013a) analyzed four and twelve dot probe task studies, respectively. The conclusions were that patients with eating disorders exhibited attentional bias towards negative eating disorder-related stimuli (greater for negative eating-related than shape-related stimuli) and away from positive eating disorder-related stimuli (Aspen et al., 2013; Renwick et al., 2013a). Second, these patients were characterized by attentional bias towards rejecting faces and disengagement from accepting faces (Renwick et al., 2013 a). Two papers were devoted to studies using the Stroop task only (Dobson & Dozois, 2004; Johansson, Ghaderi, & Andersson, 2005). According to Dobson and Dozois (2004), who reviewed 26 papers, patients with anorexia nervosa are characterized by attentional bias for body/weight stimuli, while patients with bulimia nervosa additionally exhibit attentional biases for food and neutral stimuli. In another work analyzing 27 studies, Johansson and colleagues (2005) came to the conclusion that patients with anorexia nervosa exhibit greater Stroop interference for food than for body words while bulimic patients are characterized by moderate Stroop interference for body and food words. Finally, Zhu et al. (2012) offered a meta-analysis of 17 studies using fMRI in patients with anorexia nervosa and found that negative emotional arousal was related to cognitive processing bias of food and body stimuli.
Additionally, in their review covering 31 studies employing the dot probe task and the Stroop task Lee and Shafran (2004) suggested that eating disordered patients exhibit greater Stroop interference for food and shape words than healthy controls. Moreover patients with anorexia nervosa reveal Stroop interference for food, body, and size words and vigilance towards positive emotional words. On the other hand, patients with bulimia nervosa are characterized by Stroop interference for food, shape, weight, body and ego threat words and avoidance of positive emotional words. Generally, findings for anorexia nervosa seem to be more consistent than for bulimia nervosa.
The other twelve publications analyzed studies using multiple paradigms (see Table 3, appendices), leading to the following conclusions. First, patients with anorexia nervosa and bulimia nervosa exhibit attentional bias for disorder-relevant (food, body shape, and body weight) words, but results across various studies are inconsistent (Duchesne et al., 2004, meta-analysis of 19 papers). Second, those patients reveal hypervigilance towards high calorie food pictures and avoidance of low-calorie food images; in addition anorexic patients are characterized by greater Stroop interference than bulimic patients (Brooks et al., 2011, meta-analysis of 43 studies). In their review of 15 works, Giel and colleagues (2011b) argued that they consistently prove attentional bias for food pictures in patients with eating disorders. In turn, Oldershaw and co-authors (2011), who reviewed 13 studies, concluded that attentional bias towards food, shape, and weight stimuli extends to emotional stimuli and patients with eating disorders exhibit greater attentional bias towards social threat words than healthy controls. Attentional bias towards threat appears to be most specific to anorexia nervosa, while threat avoidance is linked to bulimia nervosa. Having analyzed 66 studies, Lydecker (2013) proposed that patients with eating disorders are susceptible to an interference effect of eating-disorder relevant words and identified certain associations between attention and core eating disorder symptomatology. Werthmann, Jansen, and Roefs (2015), who reviewed 30 reports, observed inconsistent results for eating-disordered patients in comparison to non-clinical groups and no significant differences in attentional bias for food cues between restrained eaters and unrestrained ones. In one of the latest meta-analyses, involving 21 studies, Wolz and colleagues (2015) found a consistent attentional bias towards food pictures vs. neutral pictures for patients and control groups, while group comparisons between individuals with abnormal eating and healthy eating participants were inconsistent.
To the best of the present author's knowledge, five meta-analyses did not involve patients with eating disorders. Nijs and Franken (2012), who reviewed seven papers, identified a specific pattern of attention to food stimuli in the group of overweight/obese participants. Doolan and colleagues (2015), who analyzed eight papers, claimed that in obese participants there is a positive correlation between reaction time bias scores and food craving scores and in overweight/obese participants there is increased gaze direction bias to food images as compared with healthy controls. In turn, Hendrikse et al. (2015) reported that only four out of nineteen studies supported the notion of enhanced reactivity to food stimuli overweight/obese individuals. Asmaro and Liotti (2014), who reviewed 33 papers devoted to fMRI and event-related potentials (ERP), reported that stimuli related to high-calorie food activated brain areas involved in reward processing, which were similar to those activated in substance users viewing drug-related stimuli. Finally, of special note is the review by Pool and co-authors (2016), which incorporated data from 243 reports, of which 58 employed the dot probe task. While no studies involving participants with eating disorders or analogue conditions were analyzed, some investigations did use food-related stimuli. The review suggested the occurrence of attentional biases towards positive stimuli as opposed to neutral ones.
Summing up the review results, it should be stressed that their conclusions are quite divergent. These discrepancies may be partially attributed to differences in the number of studies analyzed (from 4 to 66, except for Pool et al., 2016). Meta-analyses involving patients with eating disorders consistently showed the presence of attentional bias towards negative eating disorder-related stimuli (i.e., Duchesne et al., 2004; Lee & Shafran, 2004; Giel et al., 2011b; Aspen et al., 2013; Lydecker, 2013; Renwick et al., 2013a; see also Brooks et al., 2011) and away from positive eating disorder-related stimuli (i.e., Aspen et al., 2013; Lydecker, 2013; Renwick et al., 2013a; see also Brooks et al., 2011). However, there are considerable discrepancies between the results of meta-analyses in terms of the type of stimuli. While according to Dobson and Dozois (2004) patients with anorexia nervosa exhibit an attentional bias for body/weight stimuli and those with bulimia nervosa additionally reveal attentional biases for food cues, Johansson and colleagues (2005) concluded that patients with anorexia nervosa are characterized by greater attentional bias (Stroop interference) for food than for body words. Moreover, Oldershaw and colleagues (2011) suggested that attentional bias towards food, shape, and weight stimuli extends to emotional stimuli and patients with eating disorders exhibit greater attentional bias towards social threat words than healthy controls. Importantly, Lydecker (2013) observed associations between attention and core eating disorder symptomatology. Unfortunately, Werthmann and colleagues (2015) reported inconsistent results for eating-disorder patients in comparison to non-clinical groups. Also some other researchers (i.e., Wolz et al, 2015) found that attentional biases towards food pictures occur both in patients with eating disorders and in healthy controls; moreover, group comparisons between individuals with abnormal eating and healthy eating participants were inconsistent. Meta-analyses which did not involve patients with eating disorders suggested a specific pattern of attention to food stimuli in the group of overweight/obese participants (Nijs & Franken, 2012), which is vigilance to food images (linked to food craving scores) (Doolan et al., 2015), while Hendrikse and colleagues (2015) reported that some studies confirmed attentional bias to these stimuli in overweight/obese individuals. Very promising are metaanalyses of studies using psychophysiol ogical measures (Zhu et al., 2012; Asmaro and Liotti, 2014), which corroborate reports from meta-analyses on experimental tasks evaluating attentional bias.
Attentional Bias Modification Treatment as an interesting treatment option for patients with eating disorders
As the efficacy of existing treatments for patients with eating disorders, including CBT, has been found unsatisfactory (e.g., Zipfel et al. 2014), efforts have been undertaken to develop novel, more effective therapy options. Several interesting methods have been designed on the basis of the cognitive approach to eating disorders.
For several years now, intensive studies have been under way on neurocognitive deficits in the executive function involving a set of neuropsychological processes primarily centered in prefrontal regions and governing higher-level, goal-directed behavior. The existing body of research suggests that impairments in this function, particularly in the domains of set-shifting (e.g., Tchanturia et al., 2011), central coherence (e.g., Lopez et al. 2009), inhibitory control (e.g., Wu et al., 2013), and working memory (e.g., Svaldi, Brand, & Tuschen-Caffier, 2010) are related to the development and maintenance of disordered eating behavior. These observations formed the basis for developing neurocognitive training known as cognitive remediation therapy (CRT) (e.g., Tchanturia, 2014) consisting of cognitive flexibility training, inhibitory control training, and working memory training. CRT shows initial promise for improving executive function, though more research is needed to establish whether these programs result in symptom reduction or greater responsiveness to conventional behavioral treatment (Juarascio et al., 2015).
Another interesting treatment option for patients with eating disorders is Attentional Bias Modification Treatment (ABMT). Although Bar-Haim (2010), who initiated work on the program, used the initialism ABM (attentional bias modification), this work has adopted ABMT for the sake of clarity, following the practice of Renwick et al. (2013a) and Renwick, Campbell, and Schmidt (2013b). ABMT is a novel method of treating anxiety disorders that arose from contemporary cognitive theories of anxiety and from experimental studies concerning threat-related attentional biases in anxiety disorders (Bar-Haim, 2010). In the ABMT protocol using the dot probe task, the location of the target probes is manipulated to increase the proportion of trials with targets appearing at the location of the intended training bias. For example, in attentional training aimed to induce attentional bias away from threat and towards neutral stimuli, targets would appear more frequently at the location of the neutral stimulus rather than threat. Researchers assume that a bias away from threat is gradually induced with the systematic repetition of trials, typically hundreds of times (Bar-Haim, 2010; see Figure 3).
Indeed, ABMT is a promising method for the treatment of anxiety disorders. Many researchers have shown that it leads to considerable reduction in anxiety symptoms, remission, and continued beneficial treatment effects for at least four months following the treatment course (Mathews & MacLeod, 2002; see also MacLeod et al., 2002; Shafran et al., 2008; Amir et al., 2009; Schmidt et al., 2009; Hoppitt & Mackintosh, 2009, see also Lopes, Viacava, & Bizarro, 2015).
Since anxiety is considered to play a major role in the development and maintenance of eating disorders, ABMT should be widely used in patients suffering from such disorders (Renwick et al., 2013a, b). Until several years ago, when very few papers on attentional bias in eating disorders were available, it may be said to have prevented the application of the dot probe task in the treatment of eating disorders. However, recent years have seen a proliferation of studies using the dot probe task in individuals with eating disorders and analogue conditions (Tables: 1, 2, see also table 3). To the best of the present author's knowledge, a total of ten reports have been published on ABMT effectiveness. Only two of them involved patients with clinical eating disorders (anorexia nervosa and binge eating disorder--Cardi et al., 2015 and Boutelle et al., 2016, respectively), while the others were conducted on individuals with subclinical eating conditions and healthy subjects (Table 4, appendices).
Although the last group of studies should be regarded as preliminary, they indicate that such interventions are effective: attentional bias for food increased in the "attend" group and decreased in the "avoid" group (Boutelle et al., 2014, 2016; Kakoschke, Kemps, & Tiggemann, 2014; Kemps, Tiggemann, & Hollitt, 2014 study 2; Kemps et al., 2014; Kemps, Tiggemann, & Elford, 2015; Kemps, Tiggemann, & Hollitt, 2016). Moreover, participants who are trained to attend to negative shape/weight-related stimuli are more likely to develop body dissatisfaction and more prone to dietary restraint when exposed to a body image challenge as compared with participants trained to attend to positive stimuli (Smith & Rieger, 2006, 2009). Additionally, Cardi and co-authors (2015) found that in patients with AN this type of training led to a moderate increase in attention to positive faces, accompanied by both lower levels of anxiety and higher self-compassion in response to a judgmental video clip.
Admittedly, it is far from obvious whether ABMT results could be generalized to real-life contexts, but this question may be addressed using an approach similar to CBT and CRT, in which the abilities acquired by the patient are practiced in real life as behavioral experiments (Tchanturia & Hambrook, 2010). In the case of ABMT such experiments would provide an opportunity to implicitly retrain early attention orientating which happens outside conscious control. However, it should be remembered that this route of translating newly acquired skills into everyday situations may be more difficult than in the case of the other two treatment methods, which rely on the individual's ability to utilize effortful attention control strategies. Nevertheless, behavioral experiments involving ABMT can be conducted, and are indeed necessary.
Designing attentional training for eating disordered patients requires thorough investigation of the time course of attentional allocation in this group. For this purpose, it is necessary to develop a novel modification of the dot probe task to distinguish between all three components of attention. Results obtained with such a tool should enable a comprehensive description of the temporal aspects of attention in patients with eating disorders and development of an appropriate training protocol with a view to alleviating their conditions. It remains to be established whether ABMT can be used as a stand-alone treatment or whether it will be more effective when used as adjunct to more traditional psychological interventions.
Summary
For many years now researchers have endeavored to determine the etiology of eating disorders and discover how they are maintained. Recently, an increasing focus has been directed to the cognitive approach, including selective attention. There is robust evidence that eating disordered individuals display an attentional bias for eating disorder-salient stimuli. Scholars have at their disposal a wide array of measures which have been successfully deployed in studies on fear. The attentional bias test most frequently used on patients with eating disorders is the Stroop test, which is unfortunately fraught with some shortcomings and it is debatable whether it actually measures attentional bias or distraction. One of the most promising measures of attentional bias, which is increasingly often used in eating disorder research, is the dot probe task. Again, the popularly used version of this tool has a number of shortcomings; for instance, the obtained results do not permit a distinction between the various components of attention (i.e., vigilance to relevant stimuli vs. difficulty disengaging attention from those stimuli). The modification introduced by Koster and colleagues (2004) is particularly promising as it solves the problem by means of baseline trials (with both stimuli being neutral). On the one hand, meta-analyses concerning the dot probe task in patients with eating disorders and analogue conditions indicate that this task needs to be methodologically perfected (it sometimes leads to inconclusive results). On the other hand, the rapidly increasing numbers of studies in this area make it possible to attain a more comprehensive view of attentional bias in this group of patients. In recent years, it has been suggested that the dot probe task could be used as a tool for training attentional bias in eating disordered patients (e.g., Renwick 2013a,b). Some researchers (e.g., Kemps et al., 2016) have already successfully undertaken such efforts (albeit only on subclinical groups). Indeed, this type of attention training has been found to result in very good outcomes. It should be emphasized that the use of such an intervention in eating disorders, whether as a stand-alone treatment or as an adjunct to the standard therapy, offers promising prospects to the patients as long as the training is designed to include all components of attentional bias.
(Manuscript received: 29 May 2015; accepted: 1 February 2017)
APPENDICES
Table 1. Attention bias in eating disorders and analogue
conditions--dot probe task studies
Reference Subjects * N Stimuli
Studies in clinical samples
Rieger et al. ED: Stimulus words
(1998) -AN 16 reflecting a
-BN 17 thin, a large
RES 32 physique and
positively or
negatively
valenced
emotion
words
Shafran et al. ED: Food, shape pictures
(2007) -AN 3 (positive,
Study 1 -BN 6 neutral and
-EDNOS 14 negative), weight
Anxious 19 pictures (all
HC: neutral) and
-Low 31 control pictures
-Moderate 21 (positive,
-High shape 23 neutral, negative)
concerns
Shafran et al. ED: Food, shape pictures
(2007) -AN 5 (positive, neutral
Study 2 -BN 27 and negative),
-EDNOS (BED) 6 weight pictures
-EDNOS (other) 44 (all neutral)
HC 44 and control
pictures
(positive,
neutral, negative)
Lee and ED: Food, shape pictures
Shafran -AN 3 (positive,
(2008) -BN 6 neutral and
-EDNOS 14 negative),
Anxious 19 weight pictures
HC: (all neutral)
-Low 31 and control
-Moderate 21 pictures
-High shape 23 (positive,
concerns neutral, negative)
Shafran ED: Food, shape pictures
et al. -AN 5 (positive, neutral
(2008) -BN 27 and negative),
Study 1 -EDNOS (BED) 6 weight pictures
-EDNOS (other) 44 (all neutral)
HC 44 and control
pictures
(positive,
neutral, negative)
Shafran ED (before and Food, shape pictures
et al. after 20 (positive, neutral
(2008) weeks of CBT): and negative),
Study 2 -BN 13 weight pictures
-EDNOS (BED) 6 (all neutral)
-EDNOS (other) 12 and control
ED-Waiting List 24 pictures
Controls (positive,
neutral, negative)
Blechert, ED: Self and other
Ansorge, and -AN 19 body photos
Tuschen- -BN 18
Caffier HC 21
(2010)
Cardi et al. ED: Faces expressing
(2013) -AN 29 rejection and
-BN 17 acceptance,
ANRec 13 neutral faces
BNRec 9
HC 50
Cardi et al. ED 46 Grey-scale pictures
(2014) EDRec 22 of neutral faces
HC 50 and social rank
stimuli, i.e.
dominant and
submissive faces
of different
people (males
and females)
Hughes- ED <18 25 Emotional faces:
Scalise, ChP <18 25 happy, sad and
and Connell angry
(2014)
Kim et al. AN (the oxytocin 31 Food, weight,
(2014a) condition and the and shape images
placebo condition)
HC (the oxytocin 33
condition and the
placebo condition)
Kim et al. AN (the oxytocin 31 Social faces
(2014b) condition and the representing
placebo condition) anger, disgust,
HC (the oxytocin 33 and happiness
condition and the
placebo condition)
Schober AN: Threatening words
et al. -AN-R 20 (denoting negative
(2014) -AN-BP 17 emotional states,
-EDNOS-AN 12 physical illness
HC 44 or death,
catastrophe
/trauma
/victimization)
and control
words
Studies in overweight and obese samples
Loeber et al. OB: Food words and
(2012) -Females 13 control words
-Males 7
HW:
-Females 13
-Males 7
Oh and OW/OB regular Chocolate and
Taylor chocolate eaters: control images
(2013) -after 24 h 21
abstinence
-after [greater 17
than or equal
to]1 week 20
abstinence
during Lent
Normal
chocolate eaters
Kemps, OB 58 Food (high and low
Tiggeman, HW 58 calorie) words
and and neutral,
Hollitt control (animal)
(2014) words
Study 1
Studies in subclinical and healthy samples
Mogg et al. HP (low and high Food-related words
(1998) hunger): and control,
-Females 16 transport-related
-Males 16 words
Placanica, HP (fasted and High-calorie food,
Faunce, and nonfasted): low-calorie
Soames Job -Low EDI-2 19 food, negative
(2002) scorers shape/weight,
-High EDI-2 19 positive shape
scorers /weight and
control,
household-related
and transport-
related words
Papies, HP (RES and uRES): Food words,
Stroebe, -Females 79 palatable food
and -Males 25 words and control
Aarts words
(2008)
Study 1
Papies, HP (RES and uRES): Food words,
Stroebe, -Females 98 palatable food
and -Males 40 words and control
Aarts words
(2008)
Study 2
Brignell HP: Food and control
et al. -Females 44 stimuli pictures
(2009) -Males 11
-Low external 24
eaters
-High external 19
eaters
Glauert HP 50 Images of thin and
et al. fat female
(2010) bodies (extreme
Study 1 stimuli)
Glauert HP 50 Images of thin and
et al. fat female
(2010) bodies (extreme
Study 2 stimuli)
Glauert HP 50 Images of thin and
et al. fat female
(2010) bodies (less
Study 3 extreme stimuli)
Hepworth et HP: Food and control
al. (2010) -The negative 40 pictures
mood condition
-The neutral 40
condition
Smith and HP (the body 54 Negative shape/
Rieger dissatisfaction weight words,
(2010) condition, the control words
negative mood
condition, and
the neutral
condition)
di HP (before 26 Food and control
Pellegrino, satiation and pictures
Magarelli, after satiation)
and
Mengarelli
(2011)
Hou et al. HP (with high Food and control
(2011) or low stimuli pictures
external
eating scores):
-Females 29
-Males 13
Wilson and HP: Food and control
Wallis -LRES 31 pictures
(2013) -HRES 29
Study 1
Wilson and HP: Food and control
Wallis -LRES 31 pictures (based
(2013) -HRES 29 on ratings)
Study 2
Wilson and HP: Food and control
Wallis -Negative mood 38 pictures (based
(2013) -Neutral mood 39 on additional
Study 3 ratings)
Freijy, HP 99 Word and pictorial
Mullan, food (high-
and Sharpe calorie and low-
(2014) calorie) stimuli
Maratos and HP with food Photographs of
Staples neophobia, familiar and
(2015) <18: unfamiliar
-Females 35 fruits
-Males 35 and vegetables
Shank et al. HP <18: Pictures of
(2015) -With loss of 47 high palatable
control eating foods, low
-Without loss of 29 palatable
control eating foods and
neutral,
household
objects
Reference Main findings
Studies in clinical samples
Rieger et al. ED:
(1998) -Trend attentional bias towards words reflecting
a large physique,
-Attentional bias away from words reflecting
a thin physique.
AN:
-Trend attentional bias towards words
reflecting a large physique.
BN:
-Trend attentional bias away from words
reflecting a thin physique.
RES:
-No attentional bias.
Shafran et al. ED:
(2007) -Attentional bias towards negative eating
Study 1 stimuli and neutral weight stimuli,
-Avoidance of positive eating stimuli,
-No attentional bias for shape stimuli and
neutral eating stimuli,
-Greater attentional bias for positive
eating stimuli than in all comparison groups,
-Greater attentional bias for negative eating
stimuli than in anxious, moderate and low
shape concern controls,
-No attention bias for positive, negative
and neutral shape stimuli across groups,
-Greater attention bias for neutral weight
stimuli than in all comparison groups.
Shafran et al. ED:
(2007) -Attentional bias towards negative eating
Study 2 stimuli, negative and neutral shape stimuli
and neutral weight stimuli,
-Attentional bias away from positive eating
stimuli,
-Greater attentional bias for positive and
negative eating stimuli than for HC,
-Greater attentional bias for negative shape
stimuli than for HC,
-Greater attentional bias for weight stimuli
than for HC.
ED and HC:
-No differences in attentional bias for
positive or neutral shape stimuli.
Lee and ED:
Shafran -Attentional bias towards negative eating
(2008) stimuli, negative and neutral shape stimuli
and neutral weight stimuli when Inter-
Stimulus Interval (ISI)=500 ms,
-Attentional bias away from positive eating
stimuli when ISI=500 ms,
-Attentional bias towards neutral weight
stimuli when ISI=2,000 ms.
Shafran ED:
et al. -Attentional bias towards negative eating
(2008) stimuli, negative and neutral shape stimuli
Study 1 and neutral weight stimuli,
-Attentional bias away from positive eating
stimuli,
-Greater attention bias for positive and
negative eating stimuli than for HC,
-Greater attention bias for negative shape
stimuli than for HC,
-Greater attention bias for weight stimuli
than for HC.
ED and HC:
-No differences in attentional bias for
positive or neutral shape stimuli.
Shafran ED:
et al. -Decrease in attentional biases for positive
(2008) and negative eating stimuli following
Study 2 treatment,
-Decrease in attentional bias for weight
stimuli following treatment.
Blechert, AN:
Ansorge, and -Attentional bias towards self-photo.
Tuschen- BN:
Caffier -Nonsignificant attentional bias towards
(2010) other photos.
HC:
-No attentional bias.
Cardi et al. ED:
(2013) -Attentional bias towards rejecting faces,
-Difficulty disengaging attention from
rejecting faces,
-Sustained attentional avoidance of
accepting faces,
-No significant differences between AN and BN.
ANRec and BNRec:
-Similar pattern to ED: no significant
differences between ANRec and BNRec.
HC:
-Attentional bias towards accepting faces,
-Difficulty disengaging attention from
accepting faces,
-Sustained attentional avoidance of
rejecting faces.
ED and HC:
-Attentional bias towards rejection was
correlated with adverse childhood
experiences.
Cardi et al. ED and EDRec:
(2014) -Vigilance towards dominant and submissive faces.
HC:
-Attentional disengagement from dominant and
submissive faces,
-Vigilance towards neutral faces.
Hughes- ED:
Scalise, -Attentional bias towards angry faces
and Connell moderated the relationship between parental
(2014) response to sadness and teen ED status:
for teens with high attentional bias towards
angry faces, maladaptive parental response
to sadness predicted seriousness of ED
status versus chronic pain status.
Kim et al. AN:
(2014a) -Reductions in vigilance towards eating'-
related stimuli and towards negative shape
stimuli under the influence of oxytocin.
HC:
-No changes.
Kim et al. AN:
(2014b) -Avoidance of angry faces under the
placebo condition,
-Changed attentional bias for angry faces
after administration of oxytocin (avoidance
of angry faces before the administration
of oxytocin and vigilance towards angry
faces after the administration of oxytocin).
AN and HC:
-Attentional bias towards disgust faces
under the placebo condition,
-Reductions in attentional bias towards
disgust faces under the oxytocin condition,
-No attentional bias for happy/smiling
faces under either the placebo or oxytocin
conditions.
HC:
-Vigilance towards angry faces under the
placebo condition,
-Reduced vigilance towards angry faces
after administration of oxytocin.
Schober AN:
et al. -No evidence for a differential attentional
(2014) bias for threatening words as compared
to HC.
Studies in overweight and obese samples
Loeber et al. Food stimuli did not modulate attention
(2012) allocation in a very early stage of
information processing.
Oh and -Attentional bias to chocolate images can
Taylor trigger uncontrolled consumption,
(2013) - Lower vigilance towards chocolate images
after exercise than after the rest,
-Similar effect for normal chocolate
eaters and OW/OB,
-Similar effects after restraint for eating
chocolate for 1day and 1week.
Kemps, OB:
Tiggeman, -Faster RT for high calorie food words
and than for animal words.
Hollitt HW:
(2014) -No attentional bias.
Study 1
Studies in subclinical and healthy samples
Mogg et al. HP (low hunger):
(1998) -No attentional bias for brief (14 ms) and
longer (500 ms) duration food'-related stimuli.
HP (high hunger):
-Attentional bias for food'-related stimuli
when presented for longer duration only
(500 ms).
Placanica, High and low EDI'-2 scorers:
Faunce, and -No vigilance towards negative and positive
Soames Job shape/weight stimuli.
(2002) Low EDI'-2 scorers:
-Greater attentional bias towards high'-calorie
foods when fasted compared with nonfasted.
High EDI'-2 scorers:
-Greater attentional bias towards low'-calorie
food compared with low EDI'-2 scorers,
-Greater attentional bias towards low'-calorie
food when nonfasted compared with fasted.
Papies, RES and uRES:
Stroebe, -No selective attention for control words.
and RES:
Aarts -The pre'-exposure to food cues elicited
(2008) an attentional bias for palatable food,
Study 1 -Higher hedonic ratings of palatable food
were associated with increased selective
attention for these food items.
uRES
-No shifts in selective attention.
Papies, The results of Study 1 were replicated.
Stroebe, Additional results
and RES:
Aarts -Attentional bias for palatable food did
(2008) not emerge when they were exposed to
Study 2 subliminally presented diet words after
the pre'-exposure to food cues.
Brignell High'-external eating was associated with
et al. a greater attentional bias for food cues, as
(2009) well as with a bias to evaluate them more
positively.
Glauert -Vigilance towards thin bodies when ISI=500 ms,
et al. -This attentional bias existed regardless
(2010) of how dissatisfied women were with their
Study 1 bodies.
Glauert -Vigilance towards thin bodies when ISI=150 ms,
et al. -This attentional bias existed regardless
(2010) of how dissatisfied women were with their
Study 2 bodies.
Glauert -Vigilance towards thin bodies when ISI=150 ms,
et al. - Attentional bias was significantly negatively
(2010) correlated with both body dissatisfaction
Study 3 and BMI,
-The significant correlation between
attentional bias and body dissatisfaction was
eliminated when BMI was controlled,
-The significant correlation between BMI and
attentional bias was eliminated when
body dissatisfaction was controlled.
Hepworth et -Negative mood increased both attentional
al. (2010) bias for food pictures and subjective
appetite,
-Attentional bias and subjective appetite
were positively inter'-correlated,
-Attentional bias was associated with
external and restrained eating.
Smith and HP in the body dissatisfaction condition:
Rieger -No increase in attention towards negative
(2010) shape/weight words compared with the
negative mood and neutral conditions.
HP in the negative mood condition:
-Increase in attention towards negative
shape/weight words relative to the body
dissatisfaction condition.
di -Attentional bias for food eaten decreased
Pellegrino, from pre'-to post'-satiety, along with the
Magarelli, subjective pleasantness for that food,
and -Subjective pleasantness and attentional
Mengarelli bias for the food not eaten did not show
(2011) any such decrease.
Hou et al. -Attentional bias towards food cues
(2011) correlated positively with external eating,
-Attentional bias for food cues was
positively related to trait impulsivity,
-Attentional bias for food cues remained
related to attention impulsivity after
controlling for external eating.
Wilson and -No evidence of attentional orientation
Wallis or disengagement,
(2013) -Slight attentional avoidance of
Study 1 food'-related pictures.
Wilson and No evidence of attentional bias.
Wallis
(2013)
Study 2
Wilson and -No evidence of attentional bias,
Wallis -No effect of restrained eating and/or
(2013) mood on attention processing.
Study 3
Freijy, -No main effects for stimuli type (pictures
Mullan, vs words) or calorific value (high vs low),
and Sharpe -Attentional bias towards high'-calorie
(2014) pictures,
-Attentional bias away from high'-calorie words,
-Attentional bias towards low'-calorie words,
-Attentional bias away from low'-calorie
pictures.
Maratos and HP with food neophobia:
Staples -Vigilance towards unfamiliar fruit and
(2015) vegetable stimuli,
-Willingness to try the food stimuli was
inversely correlated with vigilance towards
unfamiliar fruits/vegetables.
HP with high food neophobia:
-Greater vigilance towards unfamiliar fruit
and vegetable stimuli than for HP with
low food neophobia.
Shank et al. HP with and without loss of control eating:
(2015) -No differences in sustained attentional
bias and this component of attentional bias
was unrelated to body weight.
HP with loss of control eating:
-Attentional bias towards high palatable
foods was positively associated with BMI.
* Studies mostly involved females; hence, the studies where males
were included besides females are marked accordingly: Females,
Males.
* Abbreviations: ED, diagnosis of an eating disorder; AN, diagnosis
of anorexia nervosa; BN, diagnosis of bulimia nervosa; RES,
restrained eaters; uRES, unrestrained eaters; EDNOS, diagnosis of
eating disorder not otherwise specified; HC, healthy controls; BED,
binge eating disorder; CBT, cognitive behavioral therapy; ANRec,
recovered from anorexia nervosa; BNRec, recovered from bulimia
nervosa; EDRec, recovered from an eating disorder; <18, under 18
years of age, ChP, diagnosis of chronic pain, AN-R, anorexia
nervosa, restricting subtype; AN-BP Anorexia nervosa binge eating/
purging type; EDNOS-AN, diagnosis of eating disorder not otherwise
specified, anorexia nervosa type; OB, obese participants; HW,
healthy weight participants; OW, overweight participants; HP,
healthy participants; EDI-2, Eating Disorder Inventory-2; HRES,
high-restrained eaters; LRES, low-restrained eaters.
Table 2. Attention bias in eating disorders and analogue
conditions--results of studies that used combined paradigms (the
dot probe task and other paradigms)
Reference Subjects * N
Studies in clinical samples
Chamberlain, BED before and after
at al. (2012) therapy (with a mu
opioid receptor
antagonist GSK1521498
-2 or 5 mg per day) or
placebo condition.
-Female 35
-Male 28
Studies in overweight and obese samples
Castellanos et OB (fasted and fed 18
al. (2009) conditions)
HW (fasted and fed 18
conditions)
Ahern et al. OW, OB, HW, LRES, 63
(2010) HRES
Nijs et al. OW/OB (hungry and 26
(2010) satiated)
HW (hungry and 40
satiated)
Werthmann et OW/OB 22
al. (2011) HW 29
Nathan et al. OW/OB (LRES and 26
(2012) HRES) (before and
after administration
of D3 receptor
antagonist GSK598809
175 mg/day or
placebo)
Garcia-Garcia OW/OB 15
et. al. HW 19
(2013)
Doolan et al. OW/OB (fasted and
(2014) nonfasted):
-Females 12
-Males 14
HW (fasted and fed
conditions):
-Females 12
-Males 14
Studies in subclinical and healthy samples
Boon, HP:
Vogelzang, -RES 29
and Jansen -uRES 30
(2000)
Johansson, HP (individuals high 43
Ghaderi, and and low in
Andersson responsiveness to
(2004) external food cues)
Pothos et al. HP 25
(2009)
Calitri et al. HP 102
(2010)
Loeber et al. HP 48
(2013)
Werthmann et HP 85
al. (2014) (the sad mood
condition,
the neutral
condition)
Kakoschke, HP 146
Kemps, and
Tiggemann
(2015)
Lattimore and HP (high-impulsive 50
Mead (2015) and low-impulsive
individuals
Reference Measure of attention Stimuli
Studies in clinical samples
Chamberlain, Stroop task, dot -Palatable, non-
at al. (2012) probe task palatable and
neutral, control
words (Stroop
task),
-Food and non-
food words (dot
probe task).
Studies in overweight and obese samples
Castellanos et Dot probe task, eye- Food and non-
al. (2009) tracking paradigm food images
Ahern et al. Dot probe task, Food and control
(2010) stimulus-response pictures
compatibility task
Nijs et al. Dot probe task, eye- Food and control
(2010) tracking paradigm, pictures
P300 event-related
potentials (ERP)
Werthmann et Dot probe task, eye- Palatable high-fat
al. (2011) tracking paradigm food, musical
instruments,
office and traffic
pictures
Nathan et al. Stroop task, dot Food images and
(2012) probe task control images
Garcia-Garcia Dot probe task, High and low
et. al. functional Magnetic calorie food
(2013) Resonance Imaging pictures, control
and rewarding
non-food pictures
Doolan et al. Dot probe task, eye- High-energy-density
(2014) tracking paradigm food images,
low-energy-density
food images
Studies in subclinical and healthy samples
Boon, Dot probe task, word Food-, weight/
Vogelzang, recognition task shape-related and
and Jansen control: home-
(2000) related and office-
related words
Johansson, Stroop task, dot Food-, body-weight-,
Ghaderi, and probe task and shape-related
Andersson words, control
(2004) words
Pothos et al. Stroop task, dot probe Healthy foods,
(2009) task, a recognition unhealthy foods,
task, extrinsic and control
affective Simon task (office) words
Calitri et al. Stroop task, dot probe Healthy and
(2010) task unhealthy food
words, control,
office words
Loeber et al. Dot probe task, go/no- Food and control
(2013) go task words (Go/No-
Go Task), food
and control
pictures (Dot
Probe Task)
Werthmann et Dot probe task, eye- Palatable high-fat
al. (2014) tracking paradigm food, musical
instruments,
office and traffic
pictures
Kakoschke, Dot probe task, Food and animal
Kemps, and approach-avoidance pictures
Tiggemann task; food-specific
(2015) go/no-go task
Lattimore and Dot probe task, stop- Food and control
Mead (2015) signal task stimuli
Reference Main findings
Studies in clinical samples
Chamberlain, BED:
at al. (2012) -GSK1521498--5 mg/day significantly reduced
attentional bias for food pictures versus
placebo (dot probe task),
-The effect on attentional bias was limited
to the longer stimulus duration condition
(2000 ms),
-No effects of treatment on Stroop task.
Studies in overweight and obese samples
Castellanos et OB and HW:
al. (2009) -Increased gaze duration for food compared
to non-food images in the fasted condition
(eye-tracking paradigm).
OB:
-Increased attention to food images in the
fed condition,
-Preferential orienting towards food images
at the onset of each image (eye-tracking
paradigm).
HW:
-Similar gaze duration for food and non-food
images in the fed condition (eye-tracking
paradigm).
Ahern et al. LRES and HRES:
(2010) -No differences in attentional bias for food-
related images (dot probe task),
-No differences in approach tendencies
elicited by food images (stimulus-response
compatibility task).
Nijs et al. OW/OB and HW:
(2010) -No differences between groups or conditions
in the eye-tracking data (eye-tracking
paradigm),
-No differences between groups or conditions
in maintained attention (dot probe task),
-Enhanced automatic orientation towards food
cues in hungry versus satiated, and in
overweight/obese versus normal-weight
individuals (dot probe task).
HW:
-The intentional allocation of attention to
food pictures was enhanced in hunger
versus satiety (P300 ERP).
Werthmann et OW:
al. (2011) -An approach-avoidance pattern of attention
towards high-fat food (eye-tracking
paradigm),
-Initial gaze towards food pictures more
often than in HW, but subsequently reduced
maintenance of attention on these pictures
(eye-tracking paradigm),
-Craving was related to initial orientation
towards food (eye-tracking paradigm).
Nathan et al. OW/OB:
(2012) -No effect of the D3 receptor antagonist
GSK598809 on attentional bias (Stroop task,
dot probe task).
LRES:
-Significant attentional bias towards food
cues in both tasks under placebo, and this
was attenuated by GSK598809.
HRES:
-No attentional bias to food cues following
either placebo or GSK598809.
Garcia-Garcia OB and HW:
et. al. (2013) -Both higher reaction times for food and
rewarding non-food stimuli (dot probe task).
OB:
-Decreased activation in bilateral
activation of occipital lobe, lateral
prefrontal cortex, medial prefrontal cortex,
precentral gyrus, paracingulate gyrus and
anterior cingulate gyrus, precuneous,
posterior cingulate cortex and lateral
occipital cortex (fMRI).
Doolan et al. OW/OB and HC:
(2014) -Greater attentional bias towards high-
energy-density food images compared to
low-energy-density food images regardless
of hunger condition (eye-tracking paradigm).
OW/OB--males:
-Greater maintained attention towards high-
energy-density food images when compared
with HC (eye-tracking paradigm).
Studies in subclinical and healthy samples
Boon, HP:
Vogelzang, -No attentional bias for food and weight
and Jansen /shape stimuli (dot probetask),
(2000) -Attentional bias towards food stimuli (word
recognition task).
Johansson, HP:
Ghaderi, and Individuals high in responsiveness to
Andersson external food cues:
(2004) -Avoidance of food words (dot probe task).
Individuals low in responsiveness to external
food cues:
-Vigilance towards food words (dot probe task).
Individuals high and low in responsiveness
to external food cues:
-No significant differences in attentional
bias for body words on the dot probe task
or for food or body words on the Stroop task.
Pothos et al. The relation between the cognitive measures
(2009) was weak and evident only in certain subsets
of the population sample, as defined by
gender and emotional-, restrained- and
external-eating characteristics of HP.
Calitri et al. -No effects of cognitive bias (measured by
(2010) dot probe task) onweight change over a
1-year period,
-Cognitive bias (measured by Stroop task) to
unhealthy foods predicted an increase in
BMI whereas cognitive bias to healthy foods
was associated with a decrease in BMI,
-Cognitive biases appear to predict behavior
change.
Loeber et al. HP:
(2013) -The influence of self-reported hunger on
behavioral response inhibition (go/no-go
task),
-The blood glucose level was associated with
an attentional bias towards food-associated
cues (dot probe task).
Werthmann et HP:
al. (2014) -Self-reported emotional eating did not account
for changes in attention allocation for food
or food intake.
HP in the neutral condition:
-Attention maintenance on food cues was
significantly related to increased intake
in contrast to the sad mood condition.
Kakoschke, HP:
Kemps, and -Neither attentional nor approach bias alone
Tiggemann made a significant contribution to food
(2015) intake (dot probe task, approach-avoidance
task),
-A significant effect of interaction between
approach bias (approach-avoidance task) and
inhibitory control (food-specific
go/no-go task) on unhealthy snack food intake,
-Participants who showed a strong approach
bias combined with low inhibitory control
consumed the most snack food.
Lattimore and High-impulsive participants:
Mead (2015) -Slowed disengagement (longer RTs for 2000
ms duration) of pictorial food stimuli
compared to low-impulsive participants (dot
probe task).
Low-impulsive participants:
-Speeded detection of pictorial food cues
(for 500 ms duration) compared to high-
impulsive participants (dot probe task).
* Studies mostly involved females; hence, the studies where males
were included besides females are marked accordingly: Females, Males.
* Abbreviations: BED, binge eating disorder; GSK1521498, a selective
mu-opioid receptor antagonist; OB, obese participants; HW, healthy
weight participants; OW, overweight participants; LRES, low-
restrained eaters; HRES, high-restrained eaters; HP, healthy
participants; RES, restrained eaters; uRES; unrestrained eaters.
Table 3. Attentional bias in eating disorders and analogue
conditions--systematic reviews and meta-analyses
Study No. of
studies Study groups N
Dobson and 26 AN 211
Dozois (2004) BN 509
ANRec 23
BNRec 11
LDFT 22
HDFT 37
Dieters 100
RES 64
uRES 61
TMJ 45
HC 461
HP<18 120
Duchesne et al. 19 AN 210
(2004) BN 399
EDNOS 10
OB 51
NC 622
Lee and Shafran 31 ED 20
(2004) AN 306
BN 525
ANRec 23
BNRec 11
LDFT 37
HDFT 29
Dieters 24
RES 65
uRES 76
obRES 45
NED 19
HC 873
Johansson, 27 ED (separated into 759
Ghaderi, and AN and BN)
Andersson (2005) NED 244
(see also HC 589
Johansson, 2006,
the same
metaanalysis).
Brooks et al. 43 ED 262
(2011) AN 355
BN 253
ANRec 23
BNRec 11
RES 437
uRES 607
HC 1076
Giel et al. 15 ED 272
(2011b) AN 127
BN 99
EDNOS 7
ANRec 9
RES 11
Anxiety Disorders 38
HC 480
Oldershaw et al. 13 ED 83
(2011) (Construct AN 339
1: BN 132
social-affective ANRec 35
values Depression and/or 21
and responses) anxiety disorder
Obsessive- 16
compulsive
disorder
HC 464
Nijs and Franken 7 OB 72
(2012) OW/OB 63
Long-term 15
successful WLM
NW 167
Zhu et al., 17 AN 248
(2012) HC 241
Aspen, Darcy, 4 ED 117
and Lock HC 226
(2013)
Lydecker 66 ED 236
(2013) AN 482
BN 844
EDNOS 30
OB 72
ANRec 58
BNRec 11
Symptomatic 12
dieters
Healthy dieters 83
RES 104
obRES 45
Fasting 58
Nonfasting 59
Weight dissatisfied 20
Weight satisfied 20
LDFT 37
HDFT 29
Psychiatric patients 19
Depressed 12
Anxiety disorder 19
TMJ 45
HC 1630
HP<18 120
Renwick, 12 AN 129
Campbell, and BN 131
Schmidt (2013a) EDNOS 146
ANRec 13
BNRec 9
High EDI-2 19
Low EDI-2 19
RES 29
uRES 30
Anxious 38
With low shape 62
concerns
With moderate 42
shape concerns
With high shape 46
concerns
HC 267
Asmaro and 33 RES 45
Liotti (2014) uRES 49
Emotional eaters 10
Non-emotional 11
eaters
Chocolate cravers 22
Non-cravers 20
HP 484
HP<18 190
Doolan et al. 8 OB 72
(2015) OW/OB 148
WLM 15
HC with high BMI 15
HC with low BMI 21
NW 146
Hendrikse 19 OB 301
et al. OW 3
(2015) OW/OB 41
HW + OW + OB 102
WLM 15
UW/HW 21
HW 368
Werthmann, 30 AN 71
Jansen, BN 55
and Roefs EDNOS 64
(2015) OB 18
OW/OB 242
OW/OB BED 27
OW/OB <18 29
RES 263
uRES 288
RES-AN-like 88
patients
Anxious 19
With low shape 31
concerns
With moderate 21
shape concerns
With high shape 23
concerns
HC 217
HW 403
HP (students) 460
Wolz et al. 21 AN 48
(2015) BN 22
BED 22
OB 102
OW/OB 26
Chocolate cravers 14
Non-cravers 12
Successful dieters 18
Non dieting 24
subjects
Low external eaters 24
High external eaters 25
Low emotional 20
eating style
High emotional 25
eating style
RES 39
uRES 41
UW 16
HC 97
HP 73
HP<18 64
NW 177
Pool et 243 HP 9120
al. (2016)
Study Measure of attention* Stimuli
Dobson and Stroop task (n=26) Body/weight,
Dozois (2004) food words,
neutral words
Duchesne et al. Encoding test (n=1), Eating, weight,
(2004) free recall test body shape
(n=1), dot probe and body size
task (n=1), Stroop words, control
task (n=16), words
cued recall
test (n=1),
vocabulary
(WAIS-R) (n=1),
word completion
test (n=1)
Lee and Shafran Stroop task (n=27), Eating, food,
(2004) dot probe task body shape/
(n=4) weight,
positive and
negative
emotional,
social threat
and control
words, body
shape images
Johansson, Stroop task (n=27) Body, food and
Ghaderi, and control words
Andersson (2005)
(see also
Johansson, 2006,
the same
metaanalysis).
Brooks et al. Stroop task (n=27), Food stimuli
(2011) dot probe task and control
(n=3), distracter stimuli-images
task (n=2), and words
memory task (n=5),
verbalising task
(n=2), cue
reactivity (n=3),
perception
estimation (n=1)
Giel et al. Functional Magnetic Food, shape,
(2011b) Resonance Imaging face, emotional
(FMRI) (n = 3), and neutral
psychophysiological images
measures (e.g.
electroencephalography,
EEG and
electromyography,
EMG ) (n=4),
behavioural measures
(e.g. dot-probe task)
(n = 8)
Oldershaw et al. Dot probe task (n = 1), Shape, weight,
(2011) (Construct Stroop task (n=2), food,
1: conditional emotional,
social-affective associative task social threat,
values (n = 2), recall appetitive and
and responses) task (n = 3), control words,
startle reflex auditory
(n=1), visual search emotional
task (n=2), anagram specific to
solving (n = 1), fast AN and neutral
response decision task stimuli, food,
(n=1) emotional and
facial images
Nijs and Franken Dot probe task (n=4), High and low
(2012) Stroop task (n=2), calorie foods
eye-tracking pictures, high-
paradigm (n=4), fat food
event-related pictures, high
potentials (n=2) calorie sweet
foods pictures
and high calorie
savoury foods
pictures, non-
food pictures,
high and low
foods words,
non-food words
Zhu et al., Functional Magnetic Food, body,
(2012) Resonance Imagining emotional and
(fMRI) (n=17) neutral stimuli
(oral and
visual)
Aspen, Darcy, Dot probe task (n=4) Words related to
and Lock thin and
(2013) large physique,
positively and
negatively
valenced emotion
words, pictures
related to
eating, body
shape, and body
weight
Lydecker Stroop task (n=49), Eating, food,
(2013) dot probe task body shape and
(n=9), eye-tracking weight words,
paradigm (n=8) forbidden and
unforbidden
foods words,
positive body
words, self-
directed ego
threat words,
threatening
words
(soociotropy
threat words,
autonomy threat
words,
discomfort
anxiety threat
words, ego-
others threat
words, ego-self
threat words),
emotional words,
body shape
(figures) and
neutral images,
self-photo,
other-photo,
thin figure,
normal figure
and fat figure
images,
endomorph,
ectomorph,
mesomorph
figures, images
of attractive
men and women,
higher and
lower BMI images
Renwick, Dot probe task (n=12) Positive,
Campbell, and negative,
Schmidt (2013a) neutral
eating shape
and weight
images, high-
calorie food,
low-calorie
food, negative
and positive
shape/weight
words, self and
other body
images,
rejecting,
accepting and
neutral facial
images, negative
emotional words
(social threat,
physical
illness, death
and catastrophe
/trauma/
victimisation),
control images
(animals,
transport words,
home-related
and office-
related words)
Asmaro and Electroencephalography High-caloric
Liotti (2014) (EEG)/Event-related food and
potentials (ERP) chocolate
(n=10), functional cues (images,
Magnetic Resonance words, odors)
Imaging (fMRI)
(n=23)
Doolan et al. Stroop task (n=2), eye- Food and non-food
(2015) tracking paradigm images, high
(n=4), dot probe and low energy
task (n=5) dense food
words, control
words, pictures
related to high
calorie sweet
foods, high
calorie savoury
foods, and low
calorie foods
Hendrikse Dot probe task (7), High calorie and
et al. Stroop task (n=3), low calorie
(2015) presentation of food food words and
pair pictures--passive pictures, high
(n=1), randomised- calorie sweet
blocked passive and savoury
picture presentation foods pictures,
/viewing (n=6), one- appetizing and
back visual non-appetizing
recognition task food pictures,
(n=1), food attention neutral non-
network test (n=1) food pictures,
scenery, car,
geometric
shapes,
objects, office
items,
pictures,
rewarding
pictures,
"pleasant"
positive
valenced
pictures,
neutral
(utensil) items
pictures, animal
words, negative
emotion words,
neutral "glass
of water"
pictures,
neutral words
Werthmann, Dot probe task (n=16), High calorie and
Jansen, Stroop task (n=4), low calorie
and Roefs free viewing task food cues
(2015) (n=4), visual search (words and
task (n=4), pictures),
clarification task healthy and
(n=1), spatial cueing unhealthy food
task (n=3), eye- words and
tracking paradigm pictures, high
(n=1), event-related calorie savoury
potentials (ERP) food pictures,
(n=1), flanker task high calorie
(n=2), rapid serial sweet food
visual presentation pictures,
task (n=1), anti- palatable
saccade task (n=1) high calorie
food pictures
and words, bland
low calorie food
pictures,
pictures
connected to
food with high
added fat, food
with high added
sugar, food
with low natural
sugar, food
with low natural
fat, "positive
eating",
"negative
eating",
"neutral eating"
pictures, high-
fat food
pictures, low-
fat food
pictures, weight
/shape words,
appetising food,
non-appetising
food pictures,
high-fat cake
pictures,
chocolate
and non-
chocolate
pictures,
non-food cues,
e.g. shoes
(words and
pictures)
Wolz et al. Event-related High-and low-
(2015) potentials (ERP) calorie food
(n=21), Stroop task pictures,
(n=2), dot probe emotional and
task (n=1), eye- neutral
tracking paradigm pictures, food
(n=1). go/no-go and non-food
paradigm (n=1), images and
oddball paradigm words, images of
(n=4) chocolate, bland
and uncooked
foods, chairs,
images of
landscapes and
faces
Pool et Dot probe task (n=58), Positively
al. (2016) free viewing task valenced
(n=24), rapid visual stimuli:
serial presentation Baby/child;
task (n=24), spatial erotic/
cuing task (n=24), attractive;
Stroop task (n=35), food; general
visual search task mixed; money;
(n=51), other self-relevant;
adaptations of these smiling face and
tasks (n=27) neutral stimuli
(illustrations,
photos, words)
Study Main findings
Dobson and BN:
Dozois (2004) -Attentional biases for body/weight,
food, and neutral stimuli.
AN:
-Attentional bias for body/weight stimuli.
Dieters and RES:
-No attentional bias.
Duchesne et al. AN, BN:
(2004) -Attentional bias for disorder-relevant
words, but results across
various studies are inconsistent.
OB under eating restriction:
-Attentional bias for eating and body
size words.
BN:
-Decrease in the attentional bias for
eating, weight and body-shape words
after treatment.
EDNOS:
-Attentional bias for weight and body
shape words.
HC with restrictive attitudes:
-Attentional bias for eating words.
HC with high eating restriction:
-Attentional bias for eating, weight
and body-shape words.
Lee and Shafran ED:
(2004) -Greater Stroop interference for food
and shape words than in the HC,
-Avoidance of positive words.
AN:
-Stroop interference for food, body
and size words and vigilance
towards positive words.
BN:
-Stroop interference for food, shape,
weight, body and ego threat words
and avoidance of positive words,
-Discrepancies between different studies,
-Findings for AN are more consistent
than for BN.
Johansson, AN:
Ghaderi, and -Greater Stroop interference for food
Andersson (2005) than for body words.
(see also BN:
Johansson, 2006, -Moderate Stroop interference for body
the same and food words.
metaanalysis). ED, NED and HC:
-Significant differences between ED
and NED/HC in response latency.
NED and HC:
-No differences between NED and HC in
response latency.
Brooks et al. ED:
(2011) -Hypervigilance towards high calorie
food pictures,
-Avoidance of low-calorie food images,
-High calorie food words distract
attention.
AN:
-Greater Stroop interference than in
the BN.
RES:
-No attentional bias for food stimuli.
Giel et al. ED:
(2011b) -Sensory disengagement and higher
emotional involvement (fMRI),
-Experience food as less pleasurable
(self-reported data and facial EMG),
-Attentional bias for food pictures
(dot probe task).
Oldershaw et al. -Attentional bias towards food, shape
(2011) (Construct and weight stimuli extends to
1: emotional stimuli,
social-affective -Attentional bias towards threat
values appears most specific to AN,
and responses) -Threat avoidance is more strongly
associated with BN than with AN,
-Greater attentional bias towards
social threat words for ED than for HC.
Nijs and Franken OW/OB:
(2012) -Specific (different from NW) pattern
of attention to food stimuli,
-After an enhanced initial automatic
orientation of attention to
high-calorie stimuli, tendency to
strategic attentional disengagement
from these stimuli.
Zhu et al., AN:
(2012) Although no robust brain activation has
been found in response to emotional
stimuli, emotion-related neural
networks are involved in the processing
of food and body stimuli. Negative
emotional arousal is related to
cognitive processing bias of food
and body stimuli.
Aspen, Darcy, ED:
and Lock -Attentional bias towards negative
(2013) stimuli (greater bias for negative
eating-related stimuli than for
negative shape-related stimuli) and
away from positive stimuli (greater
bias for positive eating-related
stimuli than for positive shape-related
stimuli).
Lydecker ED:
(2013) -Susceptibility to an interference
effect of eating-disorder relevant
words,
-Initial, automatic attentional bias
for eating disorder-relevant stimuli,
-Associations between attention and core
eating disorder symptomatology.
Renwick, ED:
Campbell, and -Attentional bias towards negative
Schmidt (2013a) eating, neutral weight, negative
and neutral shape stimuli,
-Attentional bias away from positive
eating stimuli; greater bias for
these stimuli than in HC,
-Attentional bias towards rejecting
faces and disengagement from
accepting faces,
-Trend attentional bias towards
positive emotion stimuli (AN),
-No difference in attentional bias
to negative emotional words
compared with HC,
-Trend attentional bias away from
positive emotion stimuli
(BN).
HC:
-In participants with high hunger
attentional bias towards food
stimuli when presented for longer
duration only,
-Greater attentional bias to high-
calorie foods when fasted
compared with nonfasted and
greater attentional bias to low-
calorie food when nonfasted
compared with fasted,
-Greater attentional bias to low-
calorie food in participants with
high EDI-2 scores compared with
participants with low EDI-2
scores.
Asmaro and Stimuli related to high-calorie
Liotti (2014) food activate brain areas involved
in reward processing, similar to
those activated when substance
users view drug stimuli.
Doolan et al. OB:
(2015) -Positive correlation between
reaction time bias scores and food
craving scores.
OW/OB:
-Increased gaze direction bias to
food images as compared with the HC,
-Positive correlation between BMI
and reaction times to food
images high in fat and/or sugar.
OB and HC:
-Increased gaze direction and
duration to food images for all
participants when hungry,
maintained in OB females when fed,
-Increased gaze duration and
direction to high dense food
images compared with low dense
food images.
Weight loss maintainers:
-Slower reaction times to high
energy dense words than in HC
or OB participants.
HC:
-Faster visual probe task reaction
times in 500 ms trials to food
images as compared with the OW/OB,
-Increased direction bias to high
dense sweet food images in HC
with low BMI as compared with HC
with high BMI.
Hendrikse -Only four studies support the
et al. notion of enhanced reactivity to
(2015) food stimuli in OW and OB,
-This support was observed
primarily (3 from 4 studies) in
studies that employed
psychophysiological techniques
(i.e. eye-tracking paradigm,
functional Magnetic Resonance
Imagining).
Werthmann, -Conflicting evidence for an
Jansen, increased attention bias for high
and Roefs calorie food in OW and OB in
(2015) comparison with HW,
-Inconsistent results for eating-
disorder patients in comparison
to non-clinical groups,
-No differences in an attention
bias for food cues between RES
and uRES,
-Food-related attentional biases
in HW and RES.
Wolz et al. -Consistent attentional bias
(2015) towards food pictures compared to
neutral pictures for patient and
control groups,
-Group comparisons between
individuals with abnormal eating
and healthy eating participants
were more inconsistent.
OB:
-Early attention engagement to
food is followed by relative
disengagement,
-Loss of control eating, as well
as external and emotional eating,
are associated with a sustained
maintenance of attention towards
high-caloric food.
Pool et -Attentional biases towards
al. (2016) positive stimuli when compared
with neutral stimuli,
-This effect is larger during
early than later stages of
attentional processing, this means
that emotional stimuli are processed
rapidly and independently of
voluntary processes,
-This effect is significantly
larger for positive stimuli that
are relevant to the current
concerns of the observer.
* For the sake of clarity, full names of the measures of attention
were included. The gender of participants was not specified,
similarly as in other overviews of meta-analyses (e.g., Renwick,
Campbell, & Schmidt, 2013a), since more than 90% of the studies
involved females.
* Abbreviations: AN, diagnosis of anorexia nervosa; BN, diagnosis
of bulimia nervosa; ANRec, recovered from anorexia nervosa; BNRec,
recovered from bulimia nervosa; LDFT, Low scores on the Drive-for-
Thinness subscale of the EDI; HDFT, High scores on the Drive-for-
Thinness subscale of the EDI; RES, restrained eaters; uRES;
unrestrained eaters; TMJ, temporomandibular joint disorders; HC,
healthy controls; HP, healthy participans; <18, under 18 years of
age; EDNOS, diagnosis of eating disorder not otherwise specified,
OB, obese participants; NC, normal controls; ED, diagnosis of an
eating disorder; obRES, obese restrained eaters; NED, non-eating-
disordered nevertheless over-concerned with eating and body
weight; OW, overweight participants; WLM, Weight Loss
Maintainters; NW, normal weight participants; BMI, Body Mass
Index; EDI-2, Eating Disorder Inventory-2; HW, healthy weight
participants; BED, binge eating disorder; UW, underweight
participants.
Table 4. Attentional bias in eating disorders and analogue
conditions: Results of attentional bias modification studies
(only those based on the dot probe task) involving clinical,
subclinical, and healthy samples
Reference Subjects* N Stimuli
Studies in clinical samples
Cardi et al. AN 28 Positive, negative and
(2015) neutral faces
Boutelle et BED (OW and OB) 9 Food words and neutral
al. (2016) words
Studies in overweight and obese samples
Boutelle et OW/OB <18 24 Food words and non-food
al. (2014) (females- words
44.8%,
males 55.2%)
(the "avoid
food" group
and control
group)
Kemps, OB (the "attend" 96 Food pictures and
Tiggeman, group, neutral, control
and the "avoid" pictures
Hollitt group) before
(2014) and after the
Study 2 induction of
an attentional
bias
towards or
away from
food words)
Kemps, OW and OB 104 Food pictures
Tiggeman,
and
Hollitt
(2016)
Studies in healthy samples
Smith and HP (before and 70 Negative shape/weight-
Rieger after the related words,
(2006) induction of negatively valenced
an attentional emotion words,
bias toward neutral, control
shape/weight- words
related
information)
Smith and HP (before and 98 Negative shape/weight
Rieger after the words, positive
(2009) induction of shape/weight words,
an attentional negative (high
bias toward calorie) food words,
shape/weight- positive (low calorie)
related food words or neutral,
information) control words
Kakoschke, HP (the "attend 146 Healthy food and
Kemps, and healthy unhealthy food pictures
Tiggeman food" group
(2014) and the
"avoid healthy
food" group)
Kemps et HP 110 Chocolate and non-
al. (2014) (the "attend chocolate pictures
Study 1 chocolate"
group and the
"avoid
chocolate"
group)
Kemps et al. HP 88 Chocolate and non-
(2014) (the "attend chocolate pictures
Study 2 chocolate"
group and
the "avoid
chocolate"
group)
Kemps, HP (the "attend 149 Chocolate and non-
Tiggeman, chocolate" chocolate pictures
and group and the
Elford "avoid
(2015) chocolate"
group)
Reference Main findings
Studies in clinical samples
Cardi et al. At baseline patients displayed an attention bias
(2015) towards negative faces. At the end of
intervention there was a medium sized increase
in attention to positive faces There were also
lower levels of anxiety and higher levels of
self-compassion in response to a judgemental video
clip.
Boutelle et -Beneficial changes in attentional bias,
al. (2016) -Decrease in weight, eating disorder symptoms,
binge eating, loss of control and responsivity to
food in the environment,
-The majority of these effects were sustained
at 3-month follow-up.
Studies in overweight and obese samples
Boutelle et The "avoid group":
al. (2014) -Beneficial outcome of the training as compared
to the control group for attentional bias,
-Decreased number of calories consumed.
Control group:
-Attentional bias for food,
-Upward food intake trend.
Kemps, OB:
Tiggeman, Attentional bias for food increased in the
and "attend" group, and decreased in the
Hollitt "avoid" group.
(2014)
Study 2
Kemps, OW and OB:
Tiggeman, -Attentional bias for food increased in the
and "attend" group and decreased in the "avoid"
Hollitt group. These retraining effects were
(2016) maintained at 24 h and one-week follow-up,
and extended to new food pictures,
-Participants in the "avoid" group also
produced relatively fewer food words on the
word stem task than those in the "attend" group.
Studies in healthy samples
Smith and HP:
Rieger Participants who are trained to attend to negative
(2006) shape/weight-related stimuli will be more
vulnerable to the development of body
dissatisfaction when exposed to a body image
challenge compared with participants who are
trained to attend to either neutral stimuli or
negative emotion stimuli.
Smith and HP:
Rieger Participants who are trained to attend to
(2009) negative shape/weight-related stimuli will be more
vulnerable to the development of body
dissatisfaction and will be more prone to dietary
restraint when exposed to a body image challenge
compared with participants who are trained
to attend to positive shape/weight-related
stimuli.
Kakoschke, HP:
Kemps, and Participants trained to attend to healthy food
Tiggeman cues demonstrated an increased attentional bias
(2014) for such cues and ate relatively more of the
healthy than unhealthy snacks compared to the
"attend unhealthy food" group.
Kemps et HP:
al. (2014) -Attentional bias for chocolate cues increased
Study 1 in the "attend chocolate" group, and decreased
in the "avoid chocolate" group,
-Participants in the "avoid chocolate" group
ate significantly less of the chocolate muffin
than those in the "attend chocolate" group, by
contrast, blueberry muffin consumption did not
differ between the two training conditions,
-Attentional retraining also affected chocolate
craving.
Kemps et al. HP:
(2014) -Training effects from the first experiment
Study 2 generalized to novel, previously unseen
chocolate pictures,
-Participants in the "avoid chocolate" group
ate significantly less of the chocolate muffin
than those in the "attend chocolate" group,
by contrast, blueberry muffin consumption did
not differ between the two training conditions,
-Additionally, the "attend chocolate" group
reported stronger chocolate cravings following
training, whereas the "avoid chocolate" group
reported less intense cravings.
Kemps, HP:
Tiggeman, -Attentional bias for chocolate cues increased
and in the "attend chocolate" group, and decreased
Elford in the "avoid chocolate" group after training,
(2015) -Participants in the "avoid chocolate" group
also ate disproportionately less of a
chocolate food product than participants
in the "attend chocolate" group,
-The observed re-training effects were
maintained 24 h later and also one week later.
* Studies mostly involved females; hence, the studies where males
were included besides females are marked accordingly: Females,
Males.
* Abbreviations: AN, anorexia nervosa; BED, binge eating disorder;
OW, overweight participants; OB, obese participants; HP, healthy
participants.
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Malgorzata Starzomska *
Institute of Psychology, Faculty of Christian Philosophy Cardinal Stefan Wyszynski University, Warsaw, Poland
* Acknowledgments: This research project was supported by a grant for maintaining research potential awarded by the Cardinal Stefan Wyszynski University in Warsaw, No. PBF-31/16. Corresponding author: Malgorzata Starzomska. Institute of Psychology, Faculty of Christian Philosophy. Cardinal Stefan Wyszynski University. Woycickiego 1/3, building No. 14, 01-938 Warsaw, Poland. Telephone: +48 (504217443). E-mail: [email protected]
Caption: Figure 3. Schematic of attentional bias modification study evaluating treatment efficacy and stress vulnerability (source: Bar-Haim, 2010, p. 861, with the publisher's permission).
Figure 1. Three components of attentional bias and their testing with the dot probe task (based on Frewen et al., 2008, with the publisher's permission). It is impossible to distinguish between vigilance for threat (initial orienting) and disengagement from threat (attentional dwell time) Condition A (Threat Facilitation) Subject attending threat stimulus, probe appears in place of threat, RT should be fast. Condition B (Threat Disengagement) Subject attending threat stimulus, probe appears in place of neutral stimulus, RT should be slower, because of added time required to shift attention. Condition C (Threat Avoidance) Subject attending neutral stimulus, probe appears In place of neutral stimulus, RT should be fast. Figure 2. Modification of the dot probe task. Condition A (Threat Facilitation) Subject attending threat stimulus, probe appears in place of threat, RT should be faster than RT for baseline trials. Condition B (Threat Disengagement) Subject attending threat stimulus, probe appears in place of neutral stimulus, RT should be slower than RT for baseline trials, because of added time required to shift attention. Condition C (Threat Avoidance) Subject attending neutral stimulus, probe appears in place of neutral stimulus, RT should be fast.
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| Author: | Starzomska, Malgorzata |
|---|---|
| Publication: | Psicologica |
| Date: | Jul 1, 2017 |
| Words: | 23982 |
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