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Dear list, I would be grateful for some advice on the following: I am conducting an evoked response DCM analysis on grand-average ERPs from a visual object processing study on young and old adults. I have some idea from previous studies of the network involved in this task from previous studies, and this is largely confirmed in an MSP source analysis on this data. My hypotheses relate specifically to the values of delay and connection strength parameters across groups. However I would like to use model selection to find the best model for the data before doing statistics on parameter values. This would be both to find the best connection topology and the optimal subset of nodes (via their connections). So Question 1: is this an advisable / recommended analysis strategy, given the above? If roughly 'yes': My experiment has a 2x2x2 factorial design (living vs. nonliving objects; basic vs. domain level naming; young vs old adults). The first two factors are within-subject, the third one is between subjects. These result in 8 ERPs in the grand average. So Question 2: When using BMS to compare alternative models and/or families, should I also specify the full set of contrasts in the between trial effects? Or does my specification to use all 8 ERPs in the .mat with the (1,2,3,4,5,6,7,8) vector already set up the cells of the factorial design? Or is this factor information not necessary for the model evidence? Thanks, John -- Mr. John Griffiths, MSc PhD Candidate Centre for Speech, Language, and the Brain**** Department of Experimental Psychology University of Cambridge, UK