Deviating from conventional perspectives that frame artificial intelligence (AI) systems solely as logic emulators, we propose a novel program of heuristic reasoning. We distinguish between the 'instrumental' use of heuristics to match resources with objectives, and 'mimetic absorption,' whereby heuristics manifest randomly and universally. Through a series of innovative experiments, including variations of the classic Linda problem and a novel application of the Beauty Contest game, we uncover trade-offs between maximizing accuracy and reducing effort that shape the conditions under which AIs transition between exhaustive logical processing and the use of cognitive shortcuts (heuristics). We provide evidence that AIs manifest an adaptive balancing of precision and efficiency, consistent with principles of resource-rational human cognition as explicated in classical theories of bounded rationality and dual-process theory. Our findings reveal a nuanced picture of AI cognition, where trade-offs between resources and objectives lead to the emulation of biological systems, especially human cognition, despite AIs being designed without a sense of self and lacking introspective capabilities.

翻译：不同于将人工智能（AI）系统单纯视为逻辑模拟器的传统观点，我们提出了一项关于启发式推理的新研究框架。我们区分了启发式的"工具性"使用（即通过匹配资源与目标）与"模仿性吸收"（即启发式以随机且普遍的方式显现）。通过一系列创新实验，包括经典琳达问题的变体以及对"选美比赛"博弈的新应用，我们揭示了在最大化准确率与降低努力程度之间的权衡关系，这些权衡塑造了AI在穷举逻辑处理与使用认知捷径（启发式）之间转换的条件。我们提供证据表明，AI展现出对精确性与效率的适应性平衡，这与资源理性人类认知的原则一致，正如有限理性经典理论和双过程理论所阐释的那样。我们的研究结果揭示了AI认知的微妙图景：尽管AI设计时缺乏自我意识且不具备内省能力，但资源与目标之间的权衡仍导致其对生物系统（尤其是人类认知）的模仿。