Onie Sandersan, Notebaert Lies, Clarke Patrick, Most Steven B
School of Psychology, UNSW Sydney, Sydney, NSW, Australia.
School of Psychological Science, University of Western Australia, Perth, WA, Australia.
Front Psychol. 2019 Jan 21;9:2782. doi: 10.3389/fpsyg.2018.02782. eCollection 2018.
Attention bias modification (ABM), in which participants are trained to direct attention away from negative information, has been shown to reduce anxiety. However, such findings have been inconsistent. Changes in attentional bias are often absent, suggesting need for further investigation of the underlying mechanisms of ABM, as well as better statistical methods to analyze ABM data in order to reduce inferential error. In this study, we (a) compared inhibition control training to standard ABM training conditions, and (b) demonstrated the benefits of using simple Bayesian analyses to analyze ABM data. We recruited 116 participants and assessed their attentional bias prior to and after training, which involved practice avoiding negative stimuli, attending to negative stimuli, or avoiding a non-emotional, exogenous attentional cue (inhibitory control training). Our results suggested no impact of any of the training conditions on attentional bias. We further demonstrate Bayesian analyses may help control for both Type I and Type II error relative to a frequentist approach.
注意力偏差修正（ABM），即训练参与者将注意力从负面信息上转移开，已被证明可以减轻焦虑。然而，这些研究结果并不一致。注意力偏差的变化往往并不明显，这表明需要进一步研究ABM的潜在机制，以及更好的统计方法来分析ABM数据，以减少推理误差。在本研究中，我们（a）将抑制控制训练与标准ABM训练条件进行了比较，（b）展示了使用简单贝叶斯分析来分析ABM数据的好处。我们招募了116名参与者，并在训练前后评估了他们的注意力偏差，训练内容包括练习避免负面刺激、关注负面刺激或避免非情绪化的外部注意力线索（抑制控制训练）。我们的结果表明，任何一种训练条件对注意力偏差均无影响。我们进一步证明，相对于频率论方法，贝叶斯分析可能有助于控制I型和II型错误。
