基础模型在认知科学中的应用
On the use of foundation models in cognitive science
- Georgia Institute of Technology（佐治亚理工学院）
- University of California, San Diego（加利福尼亚大学圣迭戈分校）
- Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对将基础模型作为认知模型的挑战，提出四阶段推理框架，明确连接假设作用，强调行为对齐需结合理论承诺等才具科学意义。
AI中文摘要：
近年来大量研究评估了基础模型（Foundation Models, FMs）的认知与发展对齐性，这些研究涵盖了它们在多个认知领域与成人表现的对应关系，以及模型训练是否追踪儿童认知发展的问题。然而，将FMs作为候选认知模型存在重大方法论和概念挑战，核心问题是：在何种条件下，行为对齐可证明将FMs视为认知解释模型是合理的？本文提出了一个四阶段推理框架，用于评估FMs作为认知与发展模型：将人类实验任务调整为模型兼容格式、指定将模型输出映射到人类测量指标的连接假设、评估行为对应关系，以及在候选模型或操作间进行比较。我们阐明了连接假设在模型输出与人类行为测量间的映射作用，识别了限制对齐主张的挑战，并提出了理论驱动与比较评估的原则。我们强调，仅行为拟合是不够的，只有当对齐嵌入明确的理论承诺、理论诊断任务及跨候选模型的系统对比评估时，才具有科学意义。
英文摘要：
A host of recent studies have evaluated the cognitive and developmental alignment of Foundation Models (FMs). These investigations include evaluations of their correspondence to adult performance across a range of cognitive domains, as well as whether aspects of model training track children's cognitive development. However, using FMs as candidate cognitive models poses significant methodological and conceptual challenges. A key question underlies this effort: under what conditions does behavioral alignment justify treating FMs as explanatory models of cognition? In this paper, we articulate a four-stage inferential framework for evaluating FMs as cognitive and developmental models: adapting human experimental tasks to model-compatible formats, specifying linking hypotheses that map model outputs to human measures, evaluating behavioral correspondence, and comparing across candidate models or manipulations. We clarify the role of linking hypotheses in mapping model outputs to human behavioral measures, identify challenges that constrain alignment claims, and propose principles for theory-driven and comparative evaluation. Throughout, we argue that behavioral fit alone is insufficient. Alignment becomes scientifically meaningful only when embedded within explicit theoretical commitments, theory-diagnostic tasks, and systematic contrastive evaluation across candidate models.
