Papers for

psychology research teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Interacting with historical ai changes perceptions of moral decline

Time Machine Experiments: Using Historically-Bounded AI for Inquiry into the Human Mind

Abstract: Can interacting with someone from 1930, with no knowledge of what happened after, influence a person's perception of the past? People reason about the present against a picture of the past without observing it. The past is reconstructed from memory and testimony, but this reconstruction has been filtered through everything that happened since. Historically-bounded large language models (LLMs) make that past available for interaction. As a proof-of-concept for the impact of interacting with historical minds, we ran a preregistered randomized experiment ($N=240$), where participants interacted with an LLM trained on pre-1930 text. The interaction reduced the illusion of moral decline, the tendency to view the past as more moral than the present, compared to the contemporary-model control. This Time Machine Experiment paradigm informs new forms of interactive experiments, where temporal knowledge boundaries become experimental variables, and expands the realm of science fiction science, which turns thought experiments into actual experiments.

Mon 14 SeptHuman-Computer InteractionComputers and Society

The gist

People often think the past was more moral than today, partly because they imagine history filtered through their current views. The authors tested whether talking with an AI that only knows information up to 1930 changes this perception. They found that interacting with this historically limited AI reduced the feeling that morality has declined over time. This experiment shows how AI models bounded by time can help explore how people think about history.

Open → 2609.15468v1