Perils and opportunities in using large language models in psychological research.

The emergence of large language models (LLMs) has sparked considerable interest in their potential application in psychological research, mainly as a model of the human psyche or as a general text-analysis tool. However, the trend of using LLMs without sufficient attention to their limitations and risks, which we rhetorically refer to as "GPTology", can be detrimental given the easy access to models such as ChatGPT. Beyond existing general guidelines, we investigate the current limitations, ethical implications, and potential of LLMs specifically for psychological research, and show their concrete impact in various empirical studies. Our results highlight the importance of recognizing global psychological diversity, cautioning against treating LLMs (especially in zero-shot settings) as universal solutions for text analysis, and developing transparent, open methods to address LLMs' opaque nature for reliable, reproducible, and robust inference from AI-generated data. Acknowledging LLMs' utility for task automation, such as text annotation, or to expand our understanding of human psychology, we argue for diversifying human samples and expanding psychology's methodological toolbox to promote an inclusive, generalizable science, countering homogenization, and over-reliance on LLMs.

The (moral) language of hate.

Humans use language toward hateful ends, inciting violence and genocide, intimidating and denigrating others based on their identity. Despite efforts to better address the language of hate in the public sphere, the psychological processes involved in hateful language remain unclear. In this work, we hypothesize that morality and hate are concomitant in language. In a series of studies, we find evidence in support of this hypothesis using language from a diverse array of contexts, including the use of hateful language in propaganda to inspire genocide (Study 1), hateful slurs as they occur in large text corpora across a multitude of languages (Study 2), and hate speech on social-media platforms (Study 3). In post hoc analyses focusing on particular moral concerns, we found that the type of moral content invoked through hate speech varied by context, with Purity language prominent in hateful propaganda and online hate speech and Loyalty language invoked in hateful slurs across languages. Our findings provide a new psychological lens for understanding hateful language and points to further research into the intersection of morality and hate, with practical implications for mitigating hateful rhetoric online.

Moral values predict county-level COVID-19 vaccination rates in the United States.

Despite the widespread availability of COVID-19 vaccines, the United States has a depressed rate of vaccination relative to similar countries. Understanding the psychology of vaccine refusal, particularly the possible sources of variation in vaccine resistance across U.S. subpopulations, can aid in designing effective intervention strategies to increase vaccination across different regions. Here, we demonstrate that county-level moral values (i.e., Care, Fairness, Loyalty, Authority, and Purity) are associated with COVID-19 vaccination rates across 3,106 counties in the contiguous United States. Specifically, in line with our hypothesis, we find that fewer people are vaccinated in counties whose residents prioritize moral concerns about bodily and spiritual purity. Further, we find that stronger endorsements of concerns about Fairness and Loyalty to the group predict higher vaccination rates. These associations are robust after adjusting for structural barriers to vaccination, the demographic makeup of the counties, and their residents' political voting behavior. Our findings have implications for health communication, intervention strategies based on targeted messaging, and our fundamental understanding of the moral psychology of vaccination hesitancy and behavior. (PsycInfo Database Record (c) 2022 APA, all rights reserved).