A new approach to facilitating attentional disengagement from food cues in unsuccessful dieters: The bouncing image training task

A systematic review of the effectiveness of attentional bias modification to support weight management in individuals who are overweight or obese

Attentional bias modification (ABM) is a novel cognitive-based treatment that may help individuals reduce overeating by decreasing food processing bias and food cravings. However, most empirical studies have evaluated the short-term effects of ABM in populations that have body mass indexes that are within healthy ranges; therefore, the extent to which this training method can influence eating behavior in individuals who are overweight or obese remains unclear. This systematic review explored the efficacy of ABM as a tool to support weight management for individuals who are overweight or obese. Ten studies met the inclusion criteria for the review. The findings reveal that although training is effective at manipulating attentional bias to food cues, there is limited evidence that training significantly impacted appetite and eating behaviors or led to maintained weight loss.

Beyond the modified dot-probe task: A meta-analysis of the efficacy of alternate attention bias modification tasks across domains

Attention biases towards disease-relevant cues have been implicated in numerous disorders and health conditions, such as anxiety, cancer, drug-use disorders, and chronic pain. Attention bias modification (ABM) has shown that changing attention biases can change related emotional processes. ABM most commonly uses a modified dot-probe task, which has received increasing criticism regarding its reliability and inconsistent findings. The purpose of the present review was thus to systematically review and meta-analyse alternative tasks used in ABM research. We sought to examine whether alternative tasks significantly changed attention biases and emotional outcomes, and critically examined whether relevant sample, task and intervention characteristics moderated each of these effect sizes. Seventy-four (completer n = 15,294) study level comparisons were included in the meta-analysis. Overall, alternative ABM designs had a medium effect on changing biases (g = 0.488), and a small, but significant effect on improving clinical outcomes (g = 0.117). We found this effect to be significantly larger for studies which successfully changed biases compared to those that did not. Across all tasks, it appeared that targeting engagement biases results in the largest change to attention biases. Importantly, we found tasks incorporating gaze-contingency - encouraging engagement with non-biased stimuli - show the most promise for improving emotional outcomes.

The independent roles of attentional engagement with, and disengagement from, negative information in intrusive re-experiencing of negative events

BACKGROUND AND OBJECTIVES

Intrusive re-experiencing of negative events represents a key vulnerability factor for Post-Traumatic Stress Disorder. Thus, delineating attentional mechanisms that might contribute to key facets of intrusive re-experiencing would be highly advantageous. The present study sought to evaluate the novel hypothesis that biased attentional engagement and disengagement differentially contribute to two central facets of intrusive re-experiencing, frequency and controllability, respectively.

METHODS

One hundred undergraduates were exposed to an acutely negative event, followed by completion of an attentional task that permitted the discrete assessment of biased attentional engagement with, and biased attentional disengagement from, negative vs. non-negative information. Intrusions concerning this negative event were assessed daily, for the subsequent seven days, by means of an electronic diary.

RESULTS

Results revealed that enhanced attentional engagement with negative vs. non-negative information predicted unique variance in intrusion frequency, whereas impaired attentional disengagement from negative vs. non-negative information predicted unique variance in intrusion controllability. These intrusion-linked patterns of attentional selectivity were evident at shorter (500 ms) stimulus exposure durations only, and not at longer (1000 ms) stimulus exposure durations.

LIMITATIONS

The current study did not assess attentional selectivity prior to the negative event. Additionally, a time-based approach, rather than an event-based approach, was employed in the assessment of intrusions.

CONCLUSIONS

The current findings suggest that engagement bias and disengagement bias may underpin differing facets of intrusive re-experiencing, raising the possibility that therapeutically targeting each type of attentional bias may attenuate a distinctive aspect of intrusive re-experiencing.

Application of Unsupervised Transfer Technique Based on Deep Learning Model in Physical Training

The research purpose is to study the standardization and scientizing of physical training actions. Stacking denoising auto encoder (SDAE), a BiLSTM deep network model (SDAL-DNM) (a kind of training action model), and an unsupervised transfer model are used to deeply study the action problem of physical training. Initially, the physical training action discrimination model adopted here is a combination of stacked noise reduction self-encoder and bidirectional depth network model. Then, this model can collect data for five actions in physical training and further analyze the importance of action standardization for physical training. Afterward, the SDAL-DNM implemented here fully integrates the advantages of SDAE and BiLSTM. Finally, the unsupervised transfer model adopted here is based on SDAL-DNM deep learning (DL). The movement data of the physical training crowd are collected, and then the unsupervised transfer model is trained. According to the movement characteristics of physical training, the data difference between trainers is calculated so that the actions of each trainer can be continuously adapted according to the model, and finally, the benefits of effectively distinguishing the training actions can be achieved. The research shows that before and after unsupervised learning, the average decline of the model used is 1.69%, while the average decline of extreme learning machine (ELM) is 5.5%. The conclusion is that the unsupervised transfer model can improve the discrimination accuracy of physical training actions and provide theoretical support to effectively correct mistakes in physical training actions.

Enhanced capacity to switch but not to maintain: The basis of attentional bias to high calorie foods in restrained eaters

It has been argued that high restrained eaters (i.e., people who fluctuate between restrictive food intake and overeating) are characterised by a heightened attentional bias to high calorie foods. However, the validity of this hypothesis has not yet been convincingly established. The current study sought to empirically evaluate this hypothesis using two directional measures of attentional bias: the well-established dot probe bias assessment task and the more novel Chase the Food bias assessment task. The latter attentional assessment approach has the capacity to differentiate between attentional switching and attentional maintenance within a complex and dynamic food environment. Participants (61 high restrained eaters and 38 low restrained eaters) completed the dot probe task and the Chase the Food task. Findings obtained on the dot probe task did not reveal a group difference in terms of biased attentional responding towards high calorie vs. low calorie food. Conversely, the two groups were found to differ on one of the measures obtained on the Chase the Food task. Specifically, high restrained eaters, as compared to low restrained eaters, demonstrated speeded attentional switching to high calorie foods, rather than a greater ability to maintain attention on high calorie foods when required to do so. These novel findings imply that high restrained eaters are potentially characterised by facilitated attentional switching towards high calorie foods. Implications are discussed including the possibility of targeting biased attentional switching using training variants of the Chase the Food task in interventions designed to reduce maladaptive eating behaviours.

The evidential value of research on cognitive training to change food-related biases and unhealthy eating behavior: A systematic review and p-curve analysis

Cognitive bias modification (CBM), which retrains implicit biases towards unhealthy foods, has been proposed as a promising adjunct to improve the efficacy of weight loss interventions. We conducted a systematic review of research on three CBM approaches (i.e., cue-specific inhibitory control, approach bias modification, and attentional bias modification) for reducing unhealthy eating biases and behavior. We performed a p-curve analysis to determine the evidential value of this research; this method is optimally suited to clarify whether published results reflect true effects or false positives due to publication and reporting biases. When considering all CBM approaches, our results suggested that the findings of CBM trials targeting unhealthy eating are unlikely to be false positives. However, only research on attentional bias modification reached acceptable levels of power. These results suggest that CBM interventions may be an effective strategy to enhance the efficacy of weight loss interventions. However, there is room for improvement in the methodological standards of this area of research, especially increasing the statistical power can help to fully clarify the clinical potential of CBM, and determine the role of potential moderators.

Effectiveness of attentional bias modification training as add-on to regular treatment in alcohol and cannabis use disorder: A multicenter randomized control trial

Background

Attentional bias for substance-relevant cues has been found to contribute to the persistence of addiction. Attentional bias modification (ABM) interventions might, therefore, increase positive treatment outcome and reduce relapse rates. The current study investigated the effectiveness of a newly developed home-delivered, multi-session, internet-based ABM intervention, the Bouncing Image Training Task (BITT), as an add-on to treatment as usual (TAU).

Methods

Participants (N = 169), diagnosed with alcohol or cannabis use disorder, were randomly assigned to one of two conditions: the experimental ABM group (50%; TAU+ABM); or the control group (50%; split in two subgroups the TAU+placebo group and TAU-only group, 25% each). Participants completed baseline, post-test, and 6 and 12 months follow-up measures of substance use and craving allowing to assess long-term treatment success and relapse rates. In addition, attentional bias (both engagement and disengagement), as well as secondary physical and psychological complaints (depression, anxiety, and stress) were assessed.

Results

No significant differences were found between conditions with regard to substance use, craving, relapse rates, attentional bias, or physical and psychological complaints.

Conclusions

The findings may reflect unsuccessful modification of attentional bias, the BITT not targeting the relevant process (engagement vs. disengagement bias), or may relate to the diverse treatment goals of the current sample (i.e., moderation or abstinence). The current findings provide no support for the efficacy of this ABM approach as an add-on to TAU in alcohol or cannabis use disorder. Future studies need to delineate the role of engagement and disengagement bias in the persistence of addiction, and the role of treatment goal in the effectiveness of ABM interventions.

Attentional Bias in Alcohol and Cannabis Use Disorder Outpatients as Indexed by an Odd-One-Out Visual Search Task: Evidence for Speeded Detection of Substance Cues but Not for Heightened Distraction

Current cognitive models of addiction imply that speeded detection and increased distraction from substance cues might both independently contribute to the persistence of addictive behavior. Speeded detection might lower the threshold for experiencing craving, whereas increased distraction might further increase the probability of entering a bias-craving-bias cycle, thereby lowering the threshold for repeated substance use. This study was designed to examine whether indeed both attentional processes are involved in substance use disorders. Both attentional processes were indexed by an Odd-One-Out visual search task in individuals diagnosed with alcohol use disorder (AUD; n = 63) and cannabis use disorder (CUD; n = 28). To test whether the detection and/or the distraction component are characteristic for AUD and CUD, their indices were compared with matched individuals without these diagnoses (respectively, n = 63 and n = 28). Individuals with CUD showed speeded detection of cannabis cues; the difference in detection between AUD and the comparison group remained inconclusive. Neither the AUD nor the CUD group showed more distraction than the comparison groups. The sample size of the CUD group was relatively small. In addition, participants made relatively many errors in the attentional bias (AB) task, which might have lowered its sensitivity to detect ABs. The current study provided no support for the proposed role of increased distraction in CUD and AUD. The findings did, however, provide support for the view that speeded detection might be involved in CUD. Although a similar trend was evident for AUD, the evidence was weak and remained therefore inconclusive.

A Promising Candidate to Reliably Index Attentional Bias Toward Alcohol Cues-An Adapted Odd-One-Out Visual Search Task

Attentional bias (AB) has been suggested to contribute to the persistence of substance use behavior. However, the empirical evidence for its proposed role in addiction is inconsistent. This might be due to the inability of commonly used measures to differentiate between attentional engagement and attentional disengagement. Attesting to the importance of differentiating between both components of AB, a recent study using the odd-one-out task (OOOT) showed that substance use was differentially related to engagement and disengagement bias. However, the AB measures derived from the OOOT showed insufficient reliability to be used as a solid measure of individual differences. Therefore, the current study aimed to improve the reliability of the AB measures derived from the OOOT by using more distinct contrast stimuli, adding practice trials, increasing the number of trials, and by having participants perform the task in an alcohol-relevant context. We contrasted the original OOOT with the adapted OOOT (i.e., OOOT-adapt) and assessed AB in low- and high-drinking individuals. Participants were 245 undergraduate students who typically tend to drink either low or high amounts of alcohol. In one condition, AB was measured with the original OOOT in a typical laboratory context, whereas in the other condition, AB was measured with the OOOT-adapt in a bar (i.e., alcohol-relevant) context. The OOOT-adapt showed superior internal consistency, especially for the high-drinking group. Further, specifically the OOOT-adapt differentiated between low- and high-drinking participants showing that high drinkers engaged faster with alcohol cues than did low drinkers. Thus, the OOOT-adapt was found to be a promising candidate to reliably index AB in the context of alcohol use. The OOOT-adapt further showed superior criterion validity as it could differentiate between low- and high-drinking individuals, thereby adding to the evidence that AB might be involved in substance use behavior.

Influence of hunger on attentional engagement with and disengagement from pictorial food cues in women with a healthy weight

Because of inconsistencies in the field of attentional bias to food cues in eating behavior, this study aimed to re-examine the assumption that hungry healthy weight individuals have an attentional bias to food cues, but satiated healthy weight individuals do not. Since attentional engagement and attentional disengagement have been proposed to play a distinct role in behavior, we used a performance measure that is specifically designed to differentiate between these two attentional processes. Participants were healthy weight women who normally eat breakfast. In the satiated condition (n = 54), participants were instructed to have breakfast just before coming to the lab. In the fasted condition (n = 50), participants fasted on average 14 h before coming into the lab. Satiated women showed no stronger attentional engagement or attentional disengagement bias to food cues than to neutral cues. Fasted women did show stronger attentional engagement to food cues than to neutral cues that were shown briefly (100 ms). They showed no bias in attentional engagement to food cues that were shown longer (500 ms) or in attentional disengagement from food cues. These findings are in line with the assumption that healthy weight individuals show an attentional bias to food cues when food stimuli are motivationally salient. Furthermore, the findings point to the importance of differentiating between attentional engagement and attentional disengagement.