The future of large language models in social science research: Reply to Berger (2024) and Carrillo et al. (2024).

In their commentaries, Berger (2024) and Carrillo et al. (2024) raise several thoughtful questions regarding machine-assisted hypothesis generation in the social sciences. We discuss their ideas and build upon them.

Machine-assisted social psychology hypothesis generation.

Social psychology research projects begin with generating a testable idea that relies heavily on a researcher's ability to assimilate, recall, and accurately process available research findings. However, an exponential increase in new research findings is making the task of synthesizing ideas across the multitude of topics challenging, which could result in important overlooked research connections. In this research, we leverage the fact that social psychology research is based on verbal models and employ large natural language models to generate hypotheses that can aid social psychology researchers in developing new research hypotheses.

The moral foundations of cryptocurrency: evidence from Twitter and survey research.

Despite its relatively brief history, cryptocurrency has already had a profound impact on the economy, with some predicting that it will eventually replace traditional fiat currencies. Historically, it had dark associations with illegal activities in the early days, although perceptions and associations likely have, in recent years, changed for the better. Thus, understanding how people perceive the morality of cryptocurrency currently forms the motivation of the current research.

Neural mechanisms of credit card spending.

Credit cards have often been blamed for consumer overspending and for the growth in household debt. Indeed, laboratory studies of purchase behavior have shown that credit cards can facilitate spending in ways that are difficult to justify on purely financial grounds. However, the psychological mechanisms behind this spending facilitation effect remain conjectural.