芝诺的恶魔与基于Transformer的人类行为计算模型的领域普适性否定
Zenons Demon and the Denial of Domain-Generality for Transformer-Based Computational Models of Human Behavior
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中文总结 AI 辅助
该研究针对基于Transformer的人类行为模型的领域普适性主张提出质疑，提出领域普适性论题，通过示例证明这类模型不符合结构标准，将单一任务参数变化与认知多样性混淆，否定其作为通用认知理论发展起点的地位。
中文摘要 AI 辅助
基于Transformer的人类行为模型（例如Binz等人2025年提出的Centaur模型）声称自己是人类行为的领域普适计算模型。这一领域普适性主张基于其被认为具备在大量认知和感知领域中预测与模拟人类行为的能力。此外，有观点认为这种性能水平使这些模型朝着通用、统一的认知理论（Newell，1990）发展。我们对这一描述提出质疑。我们提出领域普适性论题：一个计算模型是领域普适的，当且仅当它在结构不同的任务集合上表现良好。尽管基于Transformer的人类行为模型实现了令人印象深刻的统计广度，但我们通过示例证明，它们不符合这一结构标准，将单一任务的参数变化与真正的认知多样性混为一谈。我们构建了一个论点，否定基于Transformer的人类行为模型的领域普适性，从而否定其作为通往通用、统一认知理论之路起点的所谓地位。
英文摘要
Transformer-based models of human behavior (e.g., the Centaur model by Binz, et al., 2025) posit to be domain general computational models of human behavior. The claim of domain-generality is by virtue of the supposed capability to predict and simulate human behavior across a vast range of cognitive and perceptual domains. Further, it is argued that this degree of performance places such models on a path toward general, unified theories of cognition (Newell, 1990). We contest this characterization. We propose the Domain-Generality Thesis: A computational model is domain-general if and only if it performs well across a structurally distinct set of tasks. While transformer-based models of human behavior achieve impressive statistical breadth, we demonstrate that, by example, they fail this structural criterion, conflating parametric variations of a single task with genuine cognitive diversity. We construct an argument that denies the domain-generality of transformer-based models of human behavior and thus denies the purported status as a start on the path towards general, unified theories of cognition.
