Subjective experience suggests that negatively arousing memories are harder to control than neutral ones. Here, we investigate this issue in an item-cued directed forgetting experiment. Electroencephalogram event-related potentials (ERPs) were recorded as participants viewed un-arousing neutral and highly arousing negative photographs, each followed by a cue to remember or forget it. Directed forgetting, that is reduced recognition of 'to-be-forgotten' items, occurred for neutral but not negative pictures. ERPs revealed three underlying effects: first, during picture viewing a late parietal positive potential (LPP) was more pronounced for negative than for neutral pictures. Second, 'remember' cues were associated with larger LPPs than 'forget' cues. Third, an enhanced frontal positivity appeared for 'forget' cues. This frontal positivity was generated in right dorso-lateral prefrontal regions following neutral pictures and in medial frontal cortex following negative pictures. LPP magnitude when viewing negative pictures was correlated with reduced directed forgetting, whereas both the enhanced frontal positivity for forget cues and the larger parietal positivity for remember cues predicted more directed forgetting. This study indicates that both processes of selective rehearsal (parietal positivities) and frontally controlled inhibition contribute to successful directed forgetting. However, due to their deeper incidental processing, highly arousing negative pictures are exempt from directed forgetting.

Low resolution electromagnetic tomography (LORETA) is a well-known method for the solution of the l2-based minimization problem for EEG/MEG source reconstruction. LORETA with a volume-based source space is widely used and much effort has been invested in the theory and the application of the method in an experimental context. However, it is especially interesting to use anatomical prior knowledge and constrain the LORETA's solution to the cortical surface. This strongly reduces the number of unknowns in the inverse approach. Unlike the Laplace operator in the volume case with a rectangular and regular grid, the mesh is triangulated and highly irregular in the surface case. Thus, it is not trivial to choose or construct a Laplace operator (termed Laplace-Beltrami operator when applied to surfaces) that has the desired properties and takes into account the geometry of the mesh. In this paper, the basic methodology behind cortical LORETA is discussed and the method is applied for source reconstruction of simulated data using different Laplace-Beltrami operators in the smoothing term. The results achieved with the different operators are compared with respect to their accuracy using various measures. Conclusions about the choice of an appropriate operator are deduced from the results.

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