Have you ever noticed yourself distancing yourself away from a person who has widely different opinions from you, or get tired of explaining yourself for your opinion? What about finding someone with whom you get along and who shares similar opinions to you? While catching up with friends or scrolling through comments on a social media post, it is easy to notice how clusters of opinions are formed and how individuals create a social group for themselves based on shared beliefs. But how does our brain perceive this polarization? Norberto Grzywacz, in his study Comparison of Distance and Reinforcement-Learning Rules in Social-Influence Models, studied how decision-making and social influence could follow the same learning rules that direct artificial intelligence. In past research, models of social influence automatically assume that individuals are swayed by those who already think like them, which is where the ‘distance-based’ rule, where influence takes precedence as opinions diverge. However, in Grzywacz’s research, we think about whether our brains use reinforcement learning, which is the same process used in AI systems. This allows people to learn which values or opinions ‘ pay off’ socially and show that behaviors that are rewarded (like by acceptance or appraisal) are then strengthened. Grzywacz’s simulation showed that both systems create polarization, but reinforcement learning produces realistic group patterns, like groups aligning or dividing suddenly due to unforeseeable shifts.
This idea is closely connected to a 2020 study by Levorsen et al., Testing the Reinforcement Learning Hypothesis of Social Influence. By using fMRIs and computational modeling, researchers were able to assess if social conformity, the tendency to match others’ opinions, operates on the same neural mechanism as reinforcement learning. Participants received both social feedback, on how their opinions compared to others, and reward feedback, monetary gains or losses. According to the study, the brain didn’t treat these two forms of feedback the same way, showing that while reinforcement depends on reward-processing regions of the brain, like the striatum, social influence, on the other hand, engaged additional areas related to social reasoning and self-evaluation. These findings show that Grzywacz’s reinforcement-learning model captures many aspects of social polarization, but real-world influence could be even more complex. Our brains don't just chase rewards, but respond to social norms or identity. This idea confirms that polarization does not only occur because opinions are ‘rewarded’ but because it defines who a person is within a group. Both Grzywacz and Levorsen’s findings show how individuals learn from one another. Rules like reinforcement learning can help to explain why opinions spread and cluster, but the emotional and social context of the opinions themselves makes the process human. Understanding both sides, the algorithmic and the social, can help our machines, like AI, learn values using reinforcement like systems, that pick up our biases and polarization patterns. Designing systems that can distinguish between social learning and reward learning can help prevent the divisive act of polarization.
Works Cited:
Grzywacz, Norberto M. Comparison of Distance and Reinforcement-Learning Rules in Social-Influence Models, Neurocomputing, Volume 649, 2025. https://doi.org/10.1016/j.ne ucom.2025.130870
Levorsen, Marie et al. “Testing the reinforcement learning hypothesis of social conformity.” Human brain mapping vol. 42,5 (2021): 1328-1342. doi:10.1002/hbm.25296
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