Number_task

Below are all the raw data of the perceptual number-matching task results. For testing the self-prioritization effect in the perceptual tasks, we analyzed each task separately. Correct responses shorter than 200ms were excluded from the analyses, eliminating less than 1% of the trials overall. For the reaction times (RTs), we used a one-way ANOVA for each of the tasks, testing within-subject differences for shape/number. This was first performed for all conditions together, and then separately for matched pairs. For testing the interaction between MA and self-prioritization in each perceptual task, we used Pearson correlation, creating a single measure of self-prioritization. To this end (following Sui et al., 2013), we first created a single measure named “behavioral response efficiency,” which is the Self-minus-Stranger difference in RT/ Accuracy (note that the stronger the self-prioritization, the higher the behavioral response efficiency). We than created a single measure created only from RT difference between self-stranger, since previous findings yeilded a reduced self-prioritization effect only in RT (David et al., 2022; Sui et al., 2016). To answer the fifth research question, we first conducted a Pearson correlation test between MA (math anxiety) and performance in the two self-prioritization tasks. Examining the relationship between behavioral response efficiency and MA yielded no significant correlations for the perceptual shape-matching task, nor for the number-matching task ([r = -0.12, n = 127, p = 0.19] , [r = 0.05, n = 126, p = 0.54] , respectively). Moreover, no significant correlations between MA and the single RT measure were found, not for the shape-matching task [r = 0.09, n = 127, p = 0.92] nor the number-matching task [r = -0.02, n = 126, p = 0.80]. In sum, we did not find any significant correlations between both the behavioral tasks and MA.

以下为知觉数字匹配任务结果的全部原始数据。为检验知觉任务中的自我优先效应（self-prioritization effect），我们对每项任务分别开展分析。分析过程中剔除了时长低于200ms的正确试次，整体剔除占比不足1%。针对反应时（reaction time, RT），我们针对每项任务采用单因素方差分析（one-way ANOVA），检验形状/数字维度下的被试内差异；该分析首先针对所有实验条件整体进行，随后针对匹配试次对单独展开。为检验每项知觉任务中数学焦虑（math anxiety, MA）与自我优先效应的交互作用，我们采用皮尔逊相关分析（Pearson correlation），并构建了单一的自我优先效应量化指标。为此（参照Sui等人2013年的研究），我们首先构建了名为“行为反应效率（behavioral response efficiency）”的单一量化指标，其计算方式为自我条件与陌生人条件下的反应时/准确率差值（需注意：自我优先效应越强，行为反应效率越高）。随后我们又构建了仅基于自我-陌生人条件下反应时差值的单一量化指标，因既往研究仅在反应时维度发现了减弱的自我优先效应（David等人，2022；Sui等人，2016）。为解答第五个研究问题，我们首先针对数学焦虑与两项自我优先效应任务的作业表现开展了皮尔逊相关分析。对行为反应效率与数学焦虑的相关性分析结果显示，知觉形状匹配任务与数字匹配任务均未出现显著相关（分别为[r=-0.12, n=127, p=0.19]、[r=0.05, n=126, p=0.54]）。此外，数学焦虑与单一反应时量化指标之间也未发现显著相关：形状匹配任务[r=0.09, n=127, p=0.92]与数字匹配任务[r=-0.02, n=126, p=0.80]均无显著关联。综上，本研究未发现两项行为任务与数学焦虑之间存在任何显著相关。