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

2026-09-14

AI Summary

研究使用1930年前文本训练的AI模型与人互动，以减少人们认为过去比现在更道德的错觉，探索历史视角对现代认知的影响。

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.

Problem

• Historically-bounded AI
• Human Mind
• Moral Decline

Innovation

• Historically-bounded LLMs
• Time Machine Experiment
• moral decline illusion