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Human face perception is modulated by both emotional valence and social relevance, but their interaction has rarely been examined. Event-related brain potentials (ERP) to happy, neutral, and angry facial expressions with different degrees of social relevance were recorded. To implement a social anticipation task, relevance was manipulated by presenting faces of two specific actors as future interaction partners (socially relevant), whereas two other face actors remained non-relevant. In a further control task all stimuli were presented without specific relevance instructions (passive viewing). Face stimuli of four actors (2 women, from the KDEF) were randomly presented for 1s to 26 participants (16 female). Results showed an augmented N170, early posterior negativity (EPN), and late positive potential (LPP) for emotional in contrast to neutral facial expressions. Of particular interest, face processing varied as a function of experimental tasks. Whereas task effects were observed for P1 and EPN regardless of instructed relevance, LPP amplitudes were modulated by emotional facial expression and relevance manipulation. The LPP was specifically enhanced for happy facial expressions of the anticipated future interaction partners. This underscores that social relevance can impact face processing already at an early stage of visual processing. These findings are discussed within the framework of motivated attention and face processing theories.

Algorithms for 3-D segmentation and reconstruction of anatomical surfaces from magnetic resonance imaging (MRI) data are presented. The 3-D extension of the Marr-Hildreth operator is described, and it is shown that its zero crossings are related to anatomical surfaces. For an improved surface definition, morphological filters-dilation and erosion-are applied. From these contours, 3-D reconstructions of skin, bone, brain, and the ventricular system can be generated. Results obtained with different segmentation parameters and surface rendering methods are presented. The fidelity of the generated images comes close to anatomical reality. It is noted that both the convolution and the morphological filtering are computationally expensive, and thus take a long time on a general-purpose computer. Another problem is assigning labels to the constituents of the head; in the current implementation, this is done interactively.