Understanding the actions and intentions of other humans around us is crucial for everyday social interactions. How the brain associates visual and motor represen-tations of our own actions and those of others is poorly understood, however. The first part of this Thesis is methodological and describes the implementation of surface-based information mapping using multi-voxel pattern analysis (MVPA) of functional magnetic resonance imaging (£MRI) data. Based on experimental data, I argue that surface based information mapping improves sensitivity and selectivity compared to traditional volume-based methods. The second part is experimental and applies the methods developed in the first part to identify and characterize areas that show action-specific representations across the visual and motor domains. In other words, where in the brain is performing an action represented similarly as observing someone else performing the same action? Or are there areas with spatially overlapping but different neural populations for observing and executing actions? Unlike typical £MRI analyses, MVPA can distinguish between these possibilities while information mapping al-lows for a data-driven approach that does not require specifying brain regions of interest a priori. In five experimental studies object presence, action goal, effectors, viewpoint, and imagery of observed and executed actions were systematically manipulated. The analyses suggest that two areas-anterior parietal and occipitotemporal cortex-are engaged in visuo-motor representations involving the observation of actions performed by ourselves or others, and that one area-ventral premotor cortex-is only engaged in representing our own actions. These results provide t he first evidence of action-specific visuo-motor representations using MVPA. I conclude t hat visuo-motor representations of actions can be understood through a general associative mechanism that transcends unimodal visual or motor repre-sentations, and that the application of MVPA and information mapping forms a promising approach to characterize such representations in more detail.