Very recently, studies have begun to uncover the role of these two vACC regions in social decision-making through the use of computational modelling during social interactions (Diaconescu et al., 2017; Lockwood et al., 2016; Will et al., 2017; Wittmann et al., 2016). Whilst several models of decision-making exist, one particularly influential model comes from reinforcement/associative learning theory (RLT). At a basic level, RLT proposes that two key signals drive learning, a prediction error that measures the discrepancy between predicted and actual outcomes and an expected value signal that is updated by the prediction error (Rescorla and Wagner, 1972; Sutton and Barto, 1998). These models have been shown to be consistent with the firing rates of dopaminergic neurons in the midbrain (Schultz, 2013; Schultz et al., 1997) and several fMRI studies have shown prediction error and expected value signals that covary with responses in cortical and subcortical brain areas (see Chase et al., (2015) for a meta-analysis). RLT models have been used to study social cognition (Ruff & Fehr, 2014), often during so called strategic games like an iterated prisoners dilemma
