arXiv:2507.22893cs.HCcs.AI2025-07被引 6

把AI看作无形认知基础设施,揭示其如何悄然改变人类思考方式。

Toward a New Science of AI as Cognitive Infrastructure

  • 将AI视为潜移默化的认知基础设施,重塑人类认知前提
  • 提出通过断开预处理来暴露认知依赖的实验方法
  • 适合关注人机认知关系与社会智能的跨学科研究者

当代人机交互研究忽视了AI系统在潜意识层面根本性重塑人类认知这一关键盲点,阻碍了对分布式认知的理解。本文提出“认知基础设施研究”(CIS)作为新交叉领域,将AI重新定义为‘认知基础设施’:基础性、常被忽视的系统,决定数字社会中哪些知识可及、哪些行动可行。这些语义基础设施通过预测性个性化传递意义,具备自适应隐形特征,使其影响难以察觉。关键在于,它们自动化了‘相关性判断’,将‘认知主体性’转移至非人类系统。通过个体(认知依赖)、集体(民主讨论)、社会(治理)三个层面的叙事场景,描述认知基础设施如何重塑人类认知、公共推理与社会知识体系。CIS旨在解决AI预处理在个体、集体与文化层面重构分布式认知的问题,需前所未有地整合多学科方法。该框架弥补了各学科短板:认知科学缺乏大规模预处理分析能力,数字社会学无法触及个体认知机制,计算方法忽略文化传播动态。为此,CIS提出创新方法:‘基础设施崩溃方法’——通过系统性撤除长期习惯后的AI预处理,揭示认知依赖。

原文摘要 · Abstract (English)

Contemporary human-AI interaction research overlooks how AI systems fundamentally reshape human cognition pre-consciously, a critical blind spot for understanding distributed cognition. This paper introduces "Cognitive Infrastructure Studies" (CIS) as a new interdisciplinary domain to reconceptualize AI as "cognitive infrastructures": foundational, often invisible systems conditioning what is knowable and actionable in digital societies. These semantic infrastructures transport meaning, operate through anticipatory personalization, and exhibit adaptive invisibility, making their influence difficult to detect. Critically, they automate "relevance judgment," shifting the "locus of epistemic agency" to non-human systems. Through narrative scenarios spanning individual (cognitive dependency), collective (democratic deliberation), and societal (governance) scales, we describe how cognitive infrastructures reshape human cognition, public reasoning, and social epistemologies. CIS aims to address how AI preprocessing reshapes distributed cognition across individual, collective, and cultural scales, requiring unprecedented integration of diverse disciplinary methods. The framework also addresses critical gaps across disciplines: cognitive science lacks population-scale preprocessing analysis capabilities, digital sociology cannot access individual cognitive mechanisms, and computational approaches miss cultural transmission dynamics. To achieve this goal CIS also provides methodological innovations for studying invisible algorithmic influence: "infrastructure breakdown methodologies", experimental approaches that reveal cognitive dependencies by systematically withdrawing AI preprocessing after periods of habituation.

认知基础设施人机交互分布式认知社会智能

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