@article{HoeySchroeder, author = {Hoey, Jesse and Schr{\"o}der, Tobias}, title = {Disruption of Social Orders in Societal Transitions as Affective Control of Uncertainty}, series = {American behavioral scientist}, volume = {67}, journal = {American behavioral scientist}, number = {2}, publisher = {Sage Publ.}, address = {Thousand Oaks}, issn = {1552-3381}, doi = {10.1177/00027642211066055}, pages = {311 -- 331}, abstract = {Bayesian affect control theory is a model of affect-driven social interaction underconditions of uncertainty. In this paper, we investigate how the operationalization of uncertainty in the model can be related to the disruption of social orders—societal pressures to adapt to ongoing environmental and technological change. First, we study the theoretical tradeoffs between three kinds of uncertainty as groups navigate external problems: validity (the predictability of the environment, including of other agents), coherence (the predictability of interpersonal affective dynamics), and dependence (the predictability of affective meanings). Second, we discuss how these uncertainty tradeoffs are related to contemporary political conflict and polarization in the context of societal transitions. To illustrate the potential of our model to analyze the socio-emotional consequences of uncertainty, we present a simulation of diverging individual affective meanings of occupational identities under uncertainty in a climate change mitigation scenario based on events in Germany. Finally, we sketch a possible research agenda to substantiate the novel, but yet mostly conjectural, ideas put forward in this paper.}, language = {en} } @article{CombsDamettoBlaisonetal., author = {Combs, Aidan and Dametto, Diego and Blaison, Christophe and Leung, Renee and Malhotra, Aarti and Schr{\"o}der, Tobias and Hoey, Jesse and Smith-Lovin, Lynn}, title = {Affective connotations according to LLMs}, series = {Cognition and Emotion}, journal = {Cognition and Emotion}, publisher = {Routledge, Taylor \& Francis}, address = {London}, issn = {0269-9931}, doi = {10.1080/02699931.2025.2568551}, pages = {17}, abstract = {The affective connotations of words are central to meaning and important predictors of many social processes. As such, understanding the degree to which commercially-available generative language models (LLMs) replicate human judgements of affective connotations may help better understand human-model interactions. LLMs may also serve as useful tools for researchers seeking affective meaning estimates. We test the ability of three LLMs - GPT-4o, Mistral Large, and Llama 3.1 - to estimate human affective connotation ratings of words representing social identities, behaviours, modifiers, and settings in three language cultures: English (US), French (France), and German (Germany). We find that LLM ratings of terms correlate strongly with human ratings. However, their ratings tend to be overly extreme and patterns of correlations between meaning dimensions only loosely approximate those of human ratings. Consistent with previous findings of English-language and American biases in LLMs, we find that LLMs tend to perform better on English terms, though this pattern varies somewhat by meaning dimension and the type of term in question. We explore how LLMs might contribute to scholarship on affective connotations - by acting as tools for measurement - and how scholarship on affective connotations might contribute to generative language models - by guiding exploration of model biases.}, subject = {Affektivit{\"a}t}, language = {en} }