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The moral foundations theory supports that people, across cultures, tend toconsider a small number of dimensions when classifying issues on a moral basis.The data also show that the statistics of weights attributed to each moraldimension is related to self-declared political affiliation, which in turn hasbeen connected to cognitive learning styles by recent literature inneuroscience and psychology. Inspired by these data, we propose a simplestatistical mechanics model with interacting neural networks classifyingvectors and learning from members of their social neighborhood about theiraverage opinion on a large set of issues. The purpose of learning is to reducedissension among agents even when disagreeing. We consider a family of learningalgorithms parametrized by \delta, that represents the importance given tocorroborating (same sign) opinions. We define an order parameter thatquantifies the diversity of opinions in a group with homogeneous learningstyle. Using Monte Carlo simulations and a mean field approximation we find therelation between the order parameter and the learning parameter \delta at atemperature we associate with the importance of social influence in a givengroup. In concordance with data, groups that rely more strongly oncorroborating evidence sustains less opinion diversity. We discuss predictionsof the model and propose possible experimental tests.

Moral foundations in an interacting neural networks society