Computational methods emerged as novel tools in the quest for our human origins (Piszczek et al., 2024). Fusing archaic genomes, human brain gene expression data, and fMRI allowed to extrapolate multi-genic selection during the 60 Mya of Anatomically Modern Humans (AMH) ancestral evolution (Kaczanowska et al., 2022). This approach reconstructed traces of adaptive selection across cognitive traits, offering a comprehensive perspective on evolutionary pressures shaping diverse cognitive functions. Here, we build on this workflow to specifically target and expand on the evolution of socio-affective networks in the human lineage by expanding the fMRI task sets with synthetic databases. With this, we aim to decode the intricate web of genetic evolution and brain network changes that govern complex behaviors and traits from affective processing to social interaction and communication. Specifically, our analysis focuses on genes associated with these socio-affective functions, distinguishing between those that have undergone adaptive evolution (high dN/dS ratios) and those that have been under fixed selection pressure (low dN/dS ratios). This approach identified specific genetic sets that may have driven the development of socio-affective competencies, which are at the core of human interaction and communication. The data allow a deeper understanding of the genetic foundations underlying human socio-affective networks, that have shaped these critical aspects of human behavior. Our research showcases computational neuroarchaeology as a bridge between detailed molecular/cellular insights and broader systems-level understanding. With this, we grasp inaccessible aspects of human cognitive evolution and open new avenues for interdisciplinary collaboration among evolutionary geneticists, neuroscientists, and archaeologists.