Nishiguchi Yuki, Sakamoto Jiro, Kunisato Yoshihiko, Takano Keisuke
Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan.
Faculty of Human Sciences, Sophia University, Tokyo, Japan.
Front Psychol. 2019 Nov 7;10:2447. doi: 10.3389/fpsyg.2019.02447. eCollection 2019.
In recent years, several attentional bias modification (ABM) studies have been conducted. Previous studies have suggested that explicit instruction (i.e., informing participants of the contingency of stimuli) enhances the effect of ABM. However, the specific working mechanism has not been identified. This is partly because reaction time (RT) data are typically reduced to an attention bias score, which is a mere difference of RT between experimental and control conditions. This data reduction causes a loss of information, as RT reflects various cognitive processes at play while making a response or decision. To overcome this issue, the present study applied linear ballistic accumulator (LBA) modeling to the outcomes (RT measures) of explicitly guided (compared to standard) ABM. This computational modeling approach allowed us to dissociate RTs into distinct components that can be relevant for attentional bias, such as efficiency of information processing or prior knowledge of the task; this provides an understanding of the mechanism of action underlying explicitly guided ABM. The analyzed data were RT-observed in the dot-probe task, which was administered before and after 3-days of ABM training. Our main focus was on the changes in LBA components that would be induced by the training. Additionally, we analyzed in-session performances over the 3 days of training. The LBA analysis revealed a significant reduction in processing efficiency (i.e., drift rate) in the congruent condition, where the target probe is presented in the same location as a negative stimulus. This explains the reduction in the overall attentional bias score, suggesting that explicit ABM suppresses processing of negative stimuli. Moreover, the results suggest that explicitly guided ABM may influence prior knowledge of the target location in the training task and make participants prepared to respond to the task. These findings highlight the usefulness of LBA-based analysis to explore the underlying cognitive mechanisms in ABM, and indeed our analyses revealed the differences between the explicit and the standard ABM that could not be identified by traditional RT analysis or attentional bias scores.
近年来，已经开展了多项注意力偏差修正（ABM）研究。先前的研究表明，明确的指导（即告知参与者刺激的关联性）会增强ABM的效果。然而，具体的作用机制尚未明确。部分原因在于，反应时间（RT）数据通常被简化为一个注意力偏差分数，该分数仅仅是实验条件和对照条件下RT的差值。这种数据简化导致了信息的丢失，因为RT反映了做出反应或决策时起作用的各种认知过程。为了克服这个问题，本研究将线性弹道累加器（LBA）模型应用于明确指导（与标准指导相比）的ABM的结果（RT测量值）。这种计算建模方法使我们能够将RT分解为与注意力偏差相关的不同成分，例如信息处理效率或任务的先验知识；这有助于理解明确指导的ABM背后的作用机制。分析的数据是在点探测任务中观察到的RT，该任务在ABM训练的3天前后进行。我们主要关注训练所引发的LBA成分的变化。此外，我们还分析了训练3天期间的 session 内表现。LBA分析显示，在一致条件下（即目标探测刺激出现在与负面刺激相同的位置），处理效率（即漂移率）显著降低。这解释了整体注意力偏差分数的降低，表明明确的ABM抑制了对负面刺激的处理。此外，结果表明明确指导的ABM可能会影响训练任务中目标位置的先验知识，并使参与者为应对任务做好准备。这些发现凸显了基于LBA的分析在探索ABM潜在认知机制方面的有用性，实际上我们的分析揭示了明确ABM和标准ABM之间的差异，而这些差异是传统RT分析或注意力偏差分数无法识别的。
