认知暗物质：测量AI所缺失的东西
Cognitive Dark Matter: Measuring What AI Misses
AI总结：
本文提出认知暗物质概念，旨在通过测量AI缺失的认知功能提升模型的通用智能，同时促进对人类智能的理解。
摘要：
我们提出现代人工智能系统崎岖的智能景观源于一种缺失的训练信号，我们称之为“认知暗物质”（CDM）：那些有意义地塑造行为但难以仅从行为本身推断出的脑功能。我们识别出关键的CDM领域——元认知、认知灵活性、情景记忆、终身学习、归纳推理、社会和常识推理以及情绪智能，并提出当前AI基准和大规模神经科学数据集都严重偏向于已经掌握的能力，CDM负载的功能大多未被测量。然后我们概述了一个以三种互补的数据类型为中心的研究计划，旨在为模型训练揭示CDM：（i）来自大规模认知模型的潜在变量，（ii）过程追踪数据如眼动追踪和think-aloud协议，（iii）配对的神经行为数据。这些数据将使AI能够在认知过程而非仅行为结果上进行训练，产生更具一般性和更少崎岖的智能模型。作为双重利益，相同的数据将促进我们对人类智能本身的理解。
英文摘要：
We propose that the jagged intelligence landscape of modern AI systems arises from a missing training signal that we call ``cognitive dark matter'' (CDM): brain functions that meaningfully shape behavior yet are hard to infer from behavior alone. We identify key CDM domains---metacognition, cognitive flexibility, lifelong learning, abductive reasoning, social and common-sense reasoning, and emotional intelligence---and present evidence CDM-loaded functions are largely unmeasured in current AI benchmarks, and that large-scale neuroscience training datasets which could be used to instill these capabilities do not yet exist. We then outline a research program centered on three complementary data types designed to surface CDM for model training: (i) latent variables from large-scale cognitive models, (ii) process-tracing data such as eye-tracking and think-aloud protocols, and (iii) paired neural--behavioral data. These data will enable AI training on cognitive process rather than behavioral outcome alone, producing models with more general, less jagged intelligence. As a dual benefit, the same data will advance our understanding of human intelligence itself.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
