Neuroimaging data analyses
REGRESSION
Pre-processed fMRI data were analyzed using the general linear model implemented in Statistical Parametric Mapping (SPM) software (SPM12, Wellcome Department of Cognitive Neurology, Institute of Neurology, London, UK). The first-level model included three regressors modeling cue screens pooled by condition (one regressor each for ‘Describe Yourself’, ‘Help Somebody’ and ‘Spread Information’ trials), three regressors modeling the entire period during which the article was on the screen (read and share screens) pooled by condition and one regressor for rating screens. In addition, we included a regressor of no-interest pooling trials with missing ratings and nine nuisance motion regressors. A high-pass filter was applied to remove low-frequency noise (128 s).We computed first-level contrasts for all pair-wise condition comparisons for trial periods during which articles were visible on screen. For each ROI and each participant, we then averaged the resulting voxel-wise parameter estimates for all voxels within a given ROI (yielding one number per person, per ROI) using functions provided by Numpy (Version 1.20.3, For exploratory multilevel modeling of indirect effects, we estimated an additional first-level model including separate regressors per trial to extract brain activity in response to each article (i.e. a beta-series model producing one value per article, per participant). Each regressor included the entire period within the trial during which the article was visible on screen. We then conducted exploratory multilevel regression analysis using indirect.mlm in R (