Go/no-go for food: Attention bias and intention to eat unhealthy food

The effectiveness of Go/No-Go and Stop-Signal training in reducing food consumption and choice: A systematic review and meta-analysis

The Go/No-Go and Stop-Signal tasks have been used to reduce excess food intake via repeated pairing of food cues with response inhibition. A meta analysis of 32 studies was conducted to determine whether, and under which conditions, the Go/No-Go and Stop-Signal training tasks are effective in reducing food consumption or choice. Moderators included task parameters (e.g., number of sessions, stop signal), sample differences (e.g., age, weight), and the measure of food consumption or choice. Overall, there was a small effect for Go/No-Go and Stop-Signal training in reducing food consumption or choice, g = -0.21, CI95 = [-0.31, -0.11], p < .001, with this holding individually only for a single session of the Go/No-Go Task, g = -0.31, CI95 = [-0.45, -0.18], p < .001. Comprehensive investigation of the impact of varying moderators indicated that the effect for Go/No-Go training was robust. Nevertheless, there was significant variation in the specific parameters of the task. Overall, the present meta-analysis extends previous findings by providing comprehensive evidence that the Go/No-Go Task is effective in reducing food consumption and choice, as well as providing optimal parameter recommendations for the task.

Food go/no-go training alters neural circuits for food evaluation for appetite reduction

Food go/no-go training has been traditionally categorized as a type of inhibitory training that decreases the desire for high-calorie food consumption. This training requires participants to either respond or withhold their responses to presented items with go cues or food items with no-go cues, respectively. Recent findings have suggested that this training may devalue food items associated with no-go cues instead of facilitating inhibitory control, leading to reduced food consumption. We thus hypothesized that food go/no-go training would alter the brain response to food items with no-go cues in food evaluation regions. To examine this hypothesis, we conducted a repeated measures functional magnetic resonance imaging using food images in healthy participants, who underwent 3 weeks of food go/no-go training (n = 26) using high- and low-calorie food items paired with no-go cues (no-go food) and go cues (go food), respectively, and control training (n = 24). The food go/no-go training reduced the ratings for the desire to eat no-go foods and increased the ratings for go foods. The reduction in no-go food rating was positively associated with a decrease in daily snack intake. The neural responses in the food evaluation regions increased for go foods. Moreover, the functional connectivity of those regions was altered. The food go/no-go training did not decrease impulsivity traits or increase restrained eating, which are associated with inhibitory control. Overall, food go/no-go training influenced the brain regions associated with food evaluation, thus devaluating no-go foods and reducing the daily snack intake. Accordingly, food go/no-go training could promote healthier food choices.

Dynamic attentional bias for pictorial and textual food cues in the visual search paradigm

Previous studies have found that individuals have an attentional bias for food cues, which may be related to the energy level or the type of stimulus (e.g., pictorial or textual food cues) of the food cues. However, the available evidence is inconsistent, and there is no consensus about how the type of stimulus and food energy modulate food-related attentional bias. Searching for food is one of the most important daily behaviors. In this study, a modified visual search paradigm was used to explore the attentional bias for food cues, and eye movements were recorded. Food cues consisted of both food words and food pictures with different energy levels (i.e., high- and low-calorie foods). The results showed that there was an attentional avoidance in the early stage but a later-stage attentional approach for all food cues in the pictorial condition. This was especially true for high-calorie food pictures. Participants showed a later-stage conflicting attentional bias for foods with different energy levels in the textual condition. They showed an attentional approach to high-calorie food words but an attentional avoidance of low-calorie food words. These data show that food-related attentional bias varied along with different time courses, which was also modulated by the type of stimulus and food energy. These findings regarding dynamic attentional bias could be explained using the Goal Conflict Model of eating behavior.

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.

Measuring Food-Related Attentional Bias

Objective: Food-related attentional bias has been defined as the tendency to give preferential attention to food-related stimuli. Attentional bias is of interest as studies have found that increased attentional bias is associated with obesity; others, however, have not. A possible reason for mixed results may be that there is no agreed upon measure of attentional bias: studies differ in both measurement and scoring of attentional bias. Additionally, little is known about the stability of attentional bias over time. The present study aims to compare attentional bias measures generated from commonly used attentional bias tasks and scoring protocols, and to test re-test reliability.

Methods: As part of a larger study, 69 participants (67% female) completed two food-related visual probe tasks at baseline: lexical (words as stimuli), and pictorial (pictures as stimuli). Reaction time bias scores (attentional bias scores) for each task were calculated in three different ways: by subtracting the reaction times for the trials where probes replaced (1) neutral stimuli from the trials where the probes replaced all food stimuli, (2) neutral stimuli from the trials where probes replaced high caloric food stimuli, and (3) neutral stimuli from low caloric food stimuli. This resulted in three separate attentional bias scores for each task. These reaction time results were then correlated. The pictorial visual probe task was administered a second time 14-days later to assess test-retest reliability.

Results: Regardless of the scoring use, lexical attentional bias scores were minimal, suggesting minimal attentional bias. Pictorial task attentional bias scores were larger, suggesting greater attentional bias. The correlation between the various scores was relatively small (r = 0.13–0.20). Similarly, test-retest reliability for the pictorial task was poor regardless of how the test was scored (r = 0.20–0.41).

Conclusion: These results suggest that at least some of the variation in findings across attentional bias studies could be due to differences in the way that attentional bias is measured. Future research may benefit from either combining eye-tracking measurements in addition to reaction times.