van Baar, J. M. | Search bioRxiv

bioRxiv2020

Intolerance to uncertainty modulates neural synchrony between political partisans

Political partisans see the world through an ideologically biased lens. What drives political polarization? It has been posited that polarization arises because holding extreme political views satisfies a need for certain and stable beliefs about the world. We examined the relationship between uncertainty tolerance and political polarization using brain-to-brain synchrony analysis, which measured committed liberals and conservatives subjective interpretation of a continuous political narrative. Participants (N=44) watched a political debate while undergoing fMRI. Shared ideology between participants increased neural synchrony in many brain areas including key regions of the valuation and theory-of-mind networks (e.g. temporoparietal junction). The degree of neural synchrony was modulated by uncertainty aversion: Uncertainty-intolerant individuals experienced greater brain-to-brain synchrony with politically like-minded peers and lower synchrony with political opponents. This effect was observed for liberals and conservatives alike. Moreover, increasing neural synchrony between committed partisans predicted subsequent polarized attitude formation about the debate after the scanning session. These results suggest that uncertainty attitudes gate the shared neural processing of political narratives, thereby fueling polarized attitude formation about hot-button issues.

neuroscience↗

bioRxiv2020

Latent motives guide structure learning during adaptive social choice

Predicting the behavior of others is an essential part of human cognition that enables strategic social behavior (e.g., cooperation), and is impaired in multiple clinical populations. Despite its ubiquity, social prediction poses a generalization problem that remains poorly understood: We can neither assume that others will simply repeat their past behavior in new settings, nor that their future actions are entirely unrelated to the past. Here we demonstrate that humans solve this challenge using a structure learning mechanism that uncovers other people’s latent, unobservable motives, such as greed and risk aversion. In three studies, participants were tasked with predicting the decisions of another player in multiple unique economic games such as the Prisoner’s Dilemma. Participants achieved accurate social prediction by learning the hidden motivational structure underlying the player’s actions to cooperate or defect (e.g., that greed led to defecting in some cases but cooperation in others). This motive-based abstraction enabled participants to attend to information diagnostic of the player’s next move and disregard irrelevant contextual cues. Moreover, participants who successfully learned another’s motives were more strategic in a subsequent competitive interaction with that player, reflecting that accurate social structure learning can lead to more optimal social behaviors. These findings demonstrate that advantageous social behavior hinges on parsimonious and generalizable mental models that leverage others’ latent intentions.Significance statement A hallmark of human cognition is being able to predict the behavior of others. How do we achieve social prediction given that we routinely encounter others in a dizzying array of social situations? We find people achieve accurate social prediction by inferring another’s hidden motives—motives that do not necessarily have a one-to-one correspondence with observable behaviors. Participants were able to infer another’s motives using a structure learning mechanism that enabled generalization. Individuals used what they learned about others in one setting to predict their actions in an entirely new setting. This cognitive process can explain a wealth of social behaviors, ranging from strategic economic decisions to stereotyping and racial bias.Competing Interest StatementThe authors have declared no competing interest.View Full Text

animal behavior and cognition↗