786 As the ﬁrst step, reported foci were interpreted as cen- ters for 3D Gaussian probability distributions that capture the spatial uncertainty associated with each focus. This uncertainty is mostly a function of between-template (attributable to different normalization strategies and tem- plates across laboratories) and between-subject (due to small sample sizes) variance. In fact, the between-template and between-subject variability are acknowledged based on empirical estimates, the latter being additionally gauged by individual sample size (Eickhoff et al. 2009). In a second step, the probabilities of all activation foci in a certain experiment were combined for each voxel, yielding a modeled activation (MA) map (Turkeltaub et al. 2011). Voxel-wise ALE scores resulted from the union across these MA maps and quantiﬁed the convergence across experiments at each particular location in the brain. The third and last step distinguished between random and ‘true’ convergence by comparing the ensuing ALE scores against an empirical distribution reﬂecting a random spatial association between the experiments’ MA maps (Eickhoff et al. 2012). The within-experiment dis- tribution of foci, however, was regarded to be ﬁxed (Eickhoff et al. 2009). Thus, a random-effects inference was invoked, focusing on the above-chance convergence across different experiments (Eickhoff et al. 2009; Caspers et al. 2010; Kurth et al. 2010). The resulting ALE scores were tested against the earlier calculated ‘true’ ALE scores and cut off at a cluster-level-corrected threshold of p \ 0.05. For cluster-level correction, the statistical image of uncorrected voxel-wise p values was ﬁrst cut off by the cluster-forming threshold. Then, the size of the supra- threshold clusters was compared against a distribution of cluster sizes derived from simulating 1,000 datasets with the same properties (number of foci, uncertainty, etc.) as the original experiments but random location of foci. The p value associated with each cluster was then given by the chance of observing a cluster of the given size in any particular simulation. Additional conjunction and difference analyses were conducted to explore how different meta-analyses relate to each other. Conjunction-analyses testing for convergence between different meta-analyses employed inference by the minimum statistic, i.e., computing the intersection of the thresholded Z-maps (Caspers et al. 2010). That is, any voxel determined to be signiﬁcant by the conjunction analysis constitutes a location in the brain which survived inference corrected for multiple comparisons in each of the individual meta-analyses. Difference analyses calculated the difference between corresponding voxels’ ALE scores for two sets of experiments. Then, the experiments con- tributing to either analysis were pooled and randomly divided into two analogous sets of experiments (Eickhoff et al. 2011). Voxel-wise ALE scores for these two sets 123 Brain Struct Funct (2012) 217:783–796 were calculated and subtracted from each other. Repeating this process 10,000 times yielded a distribution of recorded differences in ALE scores between two sets of experiments. The ‘true’ difference in ALE scores was then tested against these differences obtained under the distribution yielding voxel-wise p values for the difference. These resulting non-parametric p values were thresholded at p \ 0.001. Unfortunately, a statistical method to correct for multiple comparisons when assessing the differences between ALE maps has not yet been established. It should be mentioned, however, that the randomization procedure employed to compute the contrast between ALE-analyses is in itself highly conservative as it estimates the proba- bility for a true difference between the two datasets. Functional characterization The converging activation patterns of tasks requiring moral cognition, theory of mind, or empathy were ﬁrst determined by ALE meta-analysis. The conjunction across all three individual ALE analyses then yielded a computationally derived seed region for functional characterisation through quantitative correspondence with cognitive and experi- mental descriptions of the BrainMap taxonomy. In fact, BrainMap metadata describe the category of mental pro- cesses isolated by the statistical contrast of each experiment stored in the database (http://www.brainmap.org; Laird et al. 2009a). More speciﬁcally, behavioral domains (BD) include the main categories cognition, action, perception, emotion, interoception, as well as their subcategories. The respective paradigm classes (PC) categorize the speciﬁc task employed (a complete list of BDs and PCs can be found at http://www.brainmap.org/scribe/). We analyzed the behavioral domain and paradigm class metadata of Brain- Map experiments associated with seed voxels to determine the frequency of domain ‘hits’ relative to its likelihood across the entire database. Using a binomial test, Bonfer- roni-corrected for multiple comparisons, the functional roles of the convergent network as a whole were identiﬁed by determining those BDs and PCs that showed a signiﬁcant over-representation in experiments activating within the seed regions, relative to the entire BrainMap database (Laird et al. 2009b; Eickhoff et al. 2011). Results Individual meta-analyses of moral cognition, theory of mind, and empathy All areas resulting from the ALE meta-analyses were anatomically labeled by reference to probabilistic cytoar- chitectonic maps of the human brain using the SPM