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The ability to recognise that another's belief is false is a hallmark of our capacity to understand others' mental states. It has been suggested that the computational and neural mechanisms that underpin learning about others' mental states may be similar to those that underpin first-person Reinforcement Learning (RL). In RL, unexpected decision-making outcomes constitute prediction errors (PE), which are coded for by neurons in the Anterior Cingulate Cortex (ACC). Does the ACC signal the PEs (false beliefs) of others about the outcomes of their decisions? We scanned subjects using fMRI while they monitored a third-person's decisions and similar responses made by a computer. The outcomes of the trials were manipulated, such that the actual outcome was unexpectedly different from the predicted outcome on 1/3 of trials. We examined activity time-locked to privileged information which indicated the actual outcomes only to subjects. Activity in the gyral ACC was found when the outcomes of the third-person's decisions were unexpectedly positive. Activity in the sulcal ACC was found when the third-person's or computer's outcomes were unexpectedly positive. We suggest that a property of the ACC is that it codes PEs, with a portion of the gyral ACC specialised for processing the PEs of others.
Pubmed ID: 22982355
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Software library of image analysis and statistical tools for fMRI, MRI and DTI brain imaging data. Include registration, atlases, diffusion MRI tools for parameter reconstruction and probabilistic taractography, and viewer. Several brain atlases, integrated into FSLView and Featquery, allow viewing of structural and cytoarchitectonic standard space labels and probability maps for cortical and subcortical structures and white matter tracts. Includes Harvard-Oxford cortical and subcortical structural atlases, Julich histological atlas, JHU DTI-based white-matter atlases, Oxford thalamic connectivity atlas, Talairach atlas, MNI structural atlas, and Cerebellum atlas.
View all literature mentionsSoftware package for analysis of brain imaging data sequences. Sequences can be a series of images from different cohorts, or time-series from same subject. Current release is designed for analysis of fMRI, PET, SPECT, EEG and MEG.
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