Data for: Inhibitory control training reveals a common neurofunctional basis for generic executive functions and language switching in bilinguals
Description
Sixty-six Chinese-English bilinguals were randomly assigned to the training group, the active control group, or the passive control group. The training group engaged in an eight-day training programme with a modified version of the Simon task (Liu et al., 2015); the active control group engaged in an eight-day training programme with a color judgment task that does not tap onto inhibitory control; and the control group did not receive any training between the pre- and post-training testing sessions. To quantify transfer effects from IC training to language control, all three groups performed a language-switching task before and after the training while hemodynamic fluctuations were monitored using functional magnetic resonance imaging (fMRI; Kang et al., 2017; Kang et al., 2018). Behavioral data: Trials with a reaction time below 200 ms or above 1500 ms were excluded from the analysis as outliers. In addition, we excluded reaction times more than 2.5 standard deviations below or above each participant’s mean value. Statistical analyses were then performed on the remaining correct trials. The uploaded data presented each participant' s reaction time and accuracy in each group. fMRI data: Image preprocessing and whole-brain analyses were performed using SPM8 (Wellcome Department of Cognitive Neurology, London, UK) implemented in MATLAB. The first four scans were discarded to allow magnetization to reach the equilibrium state for each participant. For the rest of the images, slice-timing correction was used to reduce differences in acquisition time between slices. The images were realigned to the first scan to correct for head motion. The realignment parameters were examined to check head motion. The criterion for inclusion was that a participant did not show absolute motion greater than 3 mm. Seven participants were excluded due to large head motions. The remaining images were then normalized to the T1 template provided by SPM8 to minimize cerebral differences between participants and re-sampled with 3×3×3 mm voxels. The images were smoothed with a cubic Gaussian filter (6-mm full width at half-maximum). At the individual level, a General Linear Model was used to estimate the contrast of interest for each participant. Statistical analyses were performed by modeling different conditions on a voxel-by-voxel basis. The data were globally scaled and high-pass-filtered at 128 s. The contrast of interest at the individual level was the difference between switch trials and non-switch trials in the pre-test session and post-test session. The uploaded data are the contrast maps at the individual level.