Computational protocol: Neural Representations of Belief Concepts: A Representational Similarity Approach to Social Semantics

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Protocol publication

[…] Functional data were preprocessed using AFNI software (). Slices in each volume were corrected for acquisition timing using Fourier interpolation (3dTshift). Each volume was then spatially aligned to the fourth volume of the first scan (3dVolReg). In each run, a Fourier high-pass temporal filter (0.008 Hz) was applied to remove low-frequency trends (3dDetrend), and image intensities were normalized. The data were spatially smoothed with a 4-mm full-width, half-maximum Gaussian kernel for the experimental runs, and a 6-mm kernel for the localizer runs. Two types of general linear models were fit to the experimental data: one that modeled activation individually for each of the 20 social groups, and one that modeled each of the 4 quadrants of the 2D belief space (Liberal Spiritualists, Liberal Materialists, Conservative Spiritualists, and Conservative Materialists). For the theory of mind localizer, predictors were created for the false photograph and false belief conditions, spanning both story and question presentation periods.In all linear models, regressors were created by convolving their time-courses in the experiment with a gamma-modeled hemodynamic response. These convolved time-courses were used as predictors in a least-squares regression over the signal time-course in each voxel. The models also included regressors for motion, based on realignment parameter estimates in each of 4 directions and 2 rotations; as well as predictors for low-frequency linear trends across runs. The regression procedure produced a statistical map for each condition, representing a beta weight and t-statistic for each voxel, indicating the partial correlation between the signal in that voxel over the course of the experiment and the occurrence of that condition. The beta values are commonly interpreted as percent blood oxygen level-dependent (BOLD) signal change relative to the baseline condition, which here were the null trials. [...] Anatomical data were processed using the Freesurfer software function recon-all (), which skull-stripped the volumes and used intensity gradients to segregate white and gray matter and generate inflated cortical surface maps for each individual. Inter-individual alignment was performed over the surfaces as follows: first, functional maps were aligned to each individual's native-space anatomical volume; the inflated surface based on this volume were then registered with other participants’ surfaces using the AFNI function MapIcosohedron, and the alignment parameters from the volume to the resampled surface were used to align the functional data. These procedures were implemented using the Surfing Toolbox (available at and described in more detail by . […]

Pipeline specifications

Software tools AFNI, Surfing
Application Functional magnetic resonance imaging
Organisms Homo sapiens