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Chunk #34 — EXPERIMENTAL PROCEDURES — Image Processing

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States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning.
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Image processing and statistical analyses were performed using SPM5 (available at http://www.fil.ion.ucl.ac.uk/spm). All volumes from all sessions were corrected for differences in slice acquisition, realigned to the first volume, spatially normalized to a standard echo planar imaging template included in the SPM software package (Friston et al., 1995) using fourth-degree B-spline interpolation, and finally smoothed with an isotropic 8 mm FWHM Gaussian filter to account for anatomical differences between subjects and to allow for valid statistical inference at the group level. Images contaminated by movement artifacts were identified using a velocity cutoff of 0.2 mm/TR. Furthermore, unphysiological global signal changes were identified using a cutoff for the global image mean of ± 2.5 SD above or below the session-specific mean. Nuisance regressors were created for these scans (with a single 1 for the questionable scan and 0’s elsewhere) to be included as covariates of no interest in the first level design matrices.