Data analysis
REGRESSION
To benchmark the BGM and Silence groups, we used Mann-Whitney U tests (for ordinal variables such as BGM Listening Frequency and Extraversion) and independent sample T-tests (for numerical variables such as English ability and working memory capacity) to compare individual factors. This benchmarking can verify the BGM and Silence groups were comparable in individual factors.To answer RQ1, differences on various measures between the conditions with and without BGM, we utilized independent sample T-tests or Mann-Whitney U tests to compare reading task performance, eye movement measures (numerical variables) and metacognition (ordinal variables) between the BGM and Silence groups. Benjamini-Hochberg procedureTo answer RQ2, relationships between learners’ individual factors and various measures, multiple linear regression models were fit to predict reading task performance, metacognitive ratings, and eye movement measures (as dependent variables) using individual factors including English ability, working memory capacity, training on music, BGM listening frequency, and extraversion (as independent variables) in each background audio condition (with vs. without BGM). Simultaneous F tests with a significant level of 0.05 were used to correct multiple comparisonsAll statistical analyses were carried out using JASP (jasp-stats.org). Both classical and Bayesian statistical analyses were adopted. In Bayes Factor analyses, parameters were configured using their default prior options in JASP: for t-tests, scaled information prior option with scale r = 0.707 was utilized given that a medium effect size was expectedAudio recordings of the interviews were transcribed and coded adopting thematic content analysis