In 1950, Alan Turing proposed replacing the question "Can machines think?" with a behavioral test: if a machine's outputs are indistinguishable from those of a thinking being, the question of whether it truly thinks can be set aside. This paper argues that Turing's move was not only a pragmatic simplification but also an epistemological commitment, a decision about what kind of evidence counts as relevant to intelligence attribution, and that this commitment has quietly constrained AI research for seven decades. We trace how Turing's behavioral epistemology became embedded in the field's evaluative infrastructure, rendering unaskable a class of questions about process, mechanism, and internal organization that cognitive psychology, neuroscience, and related disciplines learned to ask. We draw a structural parallel to the behaviorist-to-cognitivist transition in psychology: just as psychology's commitment to studying only observable behavior prevented it from asking productive questions about internal mental processes until that commitment was abandoned, AI's commitment to behavioral evaluation prevents it from distinguishing between systems that achieve identical outputs through fundamentally different computational processes, a distinction on which intelligence attribution depends. We argue that the field requires an epistemological transition comparable to the cognitive revolution: not an abandonment of behavioral evidence, but a recognition that behavioral evidence alone is insufficient for the construct claims the field wishes to make. We articulate what a post-behaviorist epistemology for AI would involve and identify the specific questions it would make askable that the field currently has no way to ask.

翻译：1950年，艾伦·图灵提出用行为测试取代“机器能思考吗？”这一问题：如果机器的输出与有思维能力的个体无法区分，那么它是否真正思考的问题可以暂且搁置。本文认为，图灵的这一转变不仅是实用主义的简化，更是一种认识论承诺——一种关于何种证据与智能归因相关的决策，且这一承诺在七十年间悄然制约了人工智能研究。我们追溯了图灵的行为认识论如何嵌入该领域的评估基础设施，使得认知心理学、神经科学及相关学科早已学会提出的有关过程、机制与内部组织的一类问题无从追问。我们将其与心理学中行为主义向认知主义的转变进行结构类比：正如心理学仅研究可观察行为的承诺，在放弃之前阻止了其提出关于内部心理过程的富有成效的问题，人工智能对行为评估的承诺亦阻碍了区分通过根本不同计算过程实现相同输出的系统，而这种区分正是智能归因的关键所在。我们主张，该领域需要一场堪比认知革命的认识论转型：并非抛弃行为证据，而是认识到仅凭行为证据不足以支撑该领域希望提出的构念主张。我们阐述了后行为主义认识论对人工智能意味着什么，并明确了当前该领域无法追问、但这一认识论将使其成为可能的具体问题。