Abstract

Background: Theoretical models of addiction predict that an attentional bias toward substance-related cues plays a role in development and maintenance of addictive behaviors, although empirical data testing these predictions are somewhat equivocal. This may in part be a consequence of substantial variability in methods used to operationalize attentional bias. Our aim was to examine the variability in key design and analysis decisions of the addiction Stroop.

Method: Using a pre-registered design, we identified 95 studies utilizing an addiction Stroop (46 alcohol, 25 smoking, 24 drug-related). We extracted key information about the design of the Stroop tasks, including; administration (paper-and-pencil vs. computerized), response (key-press vs. voice), design (block vs. mixed). For analysis decisions we extracted information on upper- and lower-bound reaction time cutoffs, removal of data based on standard error cutoffs, removal of participants based on overall performance, type of outcome used, and removal of errors.

Results: Based on variability from previous research there are at least 1,451,520 different possible designs of the computerized Alcohol Stroop, 77,760 designs of the computerized Smoking Stroop and 112,640 for the Drug Stroop. Many key design decisions were unreported. Similarly, variability in analyses decisions would allow for 9,000 different methods for analyzing the Alcohol Stroop, 5,376 for the Smoking Stroop and 768 for the Drug Stroop. P-curves suggest data provided evidential value and exploratory meta-regressions suggest that the addiction Stroop effect was not associated with design and analysis decisions.

Conclusions: The addiction Stroop effect is seemingly robust, however the adoption of consistent reporting guidelines is necessary to aid reliability and reproducibility.

摘要

背景：成瘾的理论模型预测，对物质相关线索的注意力偏向在成瘾行为的发展和维持中起作用，尽管测试这些预测的经验数据有些模棱两可。这可能部分是由于用于操作注意偏差的方法存在很大差异的结果。我们的目标是检查成瘾 Stroop 的关键设计和分析决策的可变性。

Method: Using a pre-registered design, we identified 95 studies utilizing an addiction Stroop (46 alcohol, 25 smoking, 24 drug-related). We extracted key information about the design of the Stroop tasks, including; administration (paper-and-pencil vs. computerized), response (key-press vs. voice), design (block vs. mixed). For analysis decisions we extracted information on upper- and lower-bound reaction time cutoffs, removal of data based on standard error cutoffs, removal of participants based on overall performance, type of outcome used, and removal of errors.

结果：基于先前研究的可变性，至少有 1,451,520 种不同的计算机酒精 Stroop 设计、77,760 种计算机化 Smoking Stroop 设计和 112,640 种药物 Stroop 设计。许多关键的设计决策没有报告。同样，分析决策的可变性将允许使用 9,000 种不同的方法来分析酒精 Stroop，5,376 种用于 Smoking Stroop，768 种用于分析药物 Stroop。P 曲线表明数据提供了证据价值，探索性元回归表明成瘾 Stroop 效应与设计和分析决策无关。

结论：成瘾 Stroop 效应似乎很强大，但必须采用一致的报告指南，以提高可靠性和可重复性。