ArticlePloS one2021
Attentional bias modification for chocolate: Sham-n training as a new control group.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although attentional bias modification has been shown effective in several appetitive domains, results have been mixed. A major contributor seems to be the choice of control condition. The aim of the present study was to compare attentional bias modification for chocolate against a new control condition, sham-n (neutral or no-contingency) training. Using a modified dot probe protocol, participants (N = 192; 17-30 years) were randomly trained to attend to chocolate pictures, avoid chocolate pictures, or received sham-n training. In the attend and avoid conditions, stimulus pairs consisted of one chocolate and one non-chocolate picture, and probes replaced most often (90/10) chocolate or non-chocolate pictures, respectively. In the sham-n training condition, stimulus pairs consisted of two chocolate or two non-chocolate pictures, and probes replaced pictures within pairs with equal frequency (50/50). Attentional bias for chocolate increased following attend training, decreased following avoidance training, and did not change following sham-n training. The findings clearly demonstrate that both attend and avoidance training alter (in opposite direction) attentional bias for chocolate, whereas sham-n training is inert. This makes sham-n training particularly promising for use in clinical samples who tend to show strong initial biases.
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