Tweeting under uncertainty: The relationship between uncertain language and negative emotions in the wild.

Despite decades of research characterizing the relationship between uncertainty and emotion, little is known about how these constructs interact in the wild. Using naturalistic, large-scale language produced on Twitter, we ask whether increases in environmental uncertainty and associated aversive emotional reactions can be captured by the millions of digital traces of people sharing their thoughts online. Analyzing more than 20 million tweets from more than 7.5 million unique users, we find that uncertainty expressions peak when environmental uncertainty is high. This effect, however, is modulated by the type of trigger that increases uncertainty. Pandemics (COVID-19 in 2020) and national U.S. elections (2021) exhibit an increase in uncertainty language and negative sentiment in the real world, illustrating the well-documented relationship between uncertainty and aversive emotional reactions acting in lockstep. In contrast, when uncertain events involve a moral violation (i.e., the 2021 U.S. Capitol attack), specific negative emotions (i.e., anger, fear, and moral outrage) sharply increase, while uncertainty language abruptly decreases. This reveals that in the real world, uncertainty and emotion have a more complex relationship than originally assumed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Shared neural representations and temporal segmentation of political content predict ideological similarity.

Despite receiving the same sensory input, opposing partisans often interpret political content in disparate ways. Jointly analyzing controlled and naturalistic functional magnetic resonance imaging data, we uncover the neurobiological mechanisms explaining how these divergent political viewpoints arise. Individuals who share an ideology have more similar neural representations of political words, experience greater neural synchrony during naturalistic political content, and temporally segment real-world information into the same meaningful units. In the striatum and amygdala, increasing intersubject similarity in neural representations of political concepts during a word reading task predicts enhanced synchronization of blood oxygen level-dependent time courses when viewing real-time, inflammatory political videos, revealing that polarization can arise from differences in the brain's affective valuations of political concepts. Together, this research shows that political ideology is shaped by semantic representations of political concepts processed in an environment free of any polarizing agenda and that these representations bias how real-world political information is construed into a polarized perspective.

Uncertainty aversion predicts the neural expansion of semantic representations.

Correctly identifying the meaning of a stimulus requires activating the appropriate semantic representation among many alternatives. One way to reduce this uncertainty is to differentiate semantic representations from each other, thereby expanding the semantic space. Here, in four experiments, we test this semantic-expansion hypothesis, finding that uncertainty-averse individuals exhibit increasingly differentiated and separated semantic representations. This effect is mirrored at the neural level, where uncertainty aversion predicts greater distances between activity patterns in the left inferior frontal gyrus when reading words, and enhanced sensitivity to the semantic ambiguity of these words in the ventromedial prefrontal cortex. Two direct tests of the behavioural consequences of semantic expansion further reveal that uncertainty-averse individuals exhibit reduced semantic interference and poorer generalization. Together, these findings show that the internal structure of our semantic representations acts as an organizing principle to make the world more identifiable.