Agent-based Social Psychology: from Neurocognitive Processes to Social Data

Moral Foundation Theory states that groups of different observers may rely on partially dissimilar sets of moral foundations, thereby reaching different moral valuations. The use of functional imaging techniques has revealed a spectrum of cognitive styles with respect to the differential handling of novel or corroborating information that is correlated to political affiliation. Here we characterize the collective behavior of an agent-based model whose inter individual interactions due to information exchange in the form of opinions are in qualitative agreement with experimental neuroscience data. The main conclusion derived connects the existence of diversity in the cognitive strategies and statistics of the sets of moral foundations and suggests that this connection arises from interactions between agents. Thus a simple interacting agent model, whose interactions are in accord with empirical data on conformity and learning processes, presents statistical signatures consistent with moral judgment patterns of conservatives and liberals as obtained by survey studies of social psychology.

💡 Research Summary

The paper presents an agent‑based model that bridges Moral Foundations Theory (MFT) with recent neuroimaging findings on political cognition. MFT posits that individuals weigh a set of moral foundations (e.g., harm avoidance, fairness, loyalty, authority, purity) differently, and empirical surveys have shown systematic differences between conservatives and liberals. Functional MRI studies further reveal that conservatives and liberals process novel versus corroborating information with distinct neural signatures: conservatives emphasize threat‑avoidance and consistency networks, while liberals show greater plasticity toward new information.

In the model each agent carries a multidimensional moral‑foundation vector and a “cognitive strategy” flag (conservative or liberal). During each interaction agents receive opinions from their network neighbors and update their vector using a Bayesian‑like learning rule weighted by two parameters: novelty weight (how much new, contradictory information is trusted) and corroboration weight (how much confirming information is trusted). Conservative agents assign high weight to corroboration, minimizing deviation from existing beliefs; liberal agents assign high weight to novelty, allowing faster opinion shifts. The model also includes explicit conformity pressure and a learning rate, mirroring empirical findings on social learning and peer influence.

Simulations start from random moral‑foundation weights and random network topologies (small‑world and Erdős‑Rényi). Results show that, regardless of topology, agents self‑organize into sub‑populations whose moral‑foundation profiles match the empirical patterns of real‑world conservatives (high authority, loyalty, purity) and liberals (high fairness, liberty). Moreover, the diversity of cognitive strategies directly controls opinion heterogeneity: high strategy diversity yields a broad distribution of moral judgments, while homogenous strategies produce rapid convergence and potential extremism. Adjusting conformity pressure and learning rate reproduces phenomena such as ideological rigidity (strong conformity, low learning) and rapid societal shifts (weak conformity, high learning), offering a mechanistic explanation for periods of political upheaval.

The authors argue that this parsimonious interaction rule set captures the essential neurocognitive mechanisms identified in brain imaging studies while reproducing the macro‑level statistical signatures observed in large‑scale moral‑judgment surveys. Consequently, the model provides a testbed for policy simulations: interventions that modulate conformity pressure or promote strategic diversity could be evaluated for their capacity to mitigate moral polarization. The work thus integrates cognitive neuroscience, moral psychology, and complex‑systems modeling, proposing a unified framework for understanding how individual neural processing styles scale up to collective moral landscapes.