Tabula rasa agents display emergent in-group behavior

• 成果类型: Article
• 署名作者: Koster, Raphael; Duenez-Guzman, Edgar A.; Cunningham, William A.; Leibo, Joel Z.
• 署名单位: Alphabet Inc.; Google Incorporated; DeepMind; University of Toronto; Vector Institute for Artificial Intelligence; University of Toronto
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-12897
• DOI: 10.1073/pnas.2319947121
• 发表日期: 2025-06-24
• 关键词: group bias; social categorization; self-categorization; mere exposure; intergroup; IDENTITY; RACE

摘要

Theories on group-bias often posit an internal preparedness to bias one's cognition to favor the in-group (often envisioned as a product of evolution). In contrast, other theories suggest that group-biases can emerge from nonspecialized cognitive processes. These perspectives have historically been difficult to disambiguate given that observed behavior can often be attributed to innate processes, even when groups are experimentally assigned. Here, we use modern techniques from the field of AI that allow us to ask what group biases can be expected from a learning agent that is a pure blank slate without any intrinsic social biases, and whose lifetime of experiences can be tightly controlled. This is possible because deep reinforcement-learning agents learn to convert raw sensory input (i.e. pixels) to reward-driven action, a unique feature among cognitive models. We find that blank slate agents do develop group biases based on arbitrary group differences (i.e. color). We show that the bias develops as a result of familiarity of experience and depends on the visual patterns becoming associated with reward through interaction. The bias artificial agents display is not a static reflection of the bias in their stream of experiences. In this minimal environment, the bias can be overcome given enough positive experiences, although unlearning the bias takes longer than acquiring it. Further, we show how this style of tabula rasa group behavior model can be used to test fine-grained predictions of psychological theories.