AI能预测并辅助人类记忆搜索，表现优于真人。
AI Models Can Predict and Collaboratively Modulate Human Memory Search
- 用语义流畅性任务测试AI追踪人类思维轨迹的能力。
- AI预测人类记忆搜索路径的准确率高于人类自身。
- 适合研究人机协作认知、创意生成的学者参考。
大型语言模型（LLMs）展现出前所未有的自然语言生成能力和多种文本问题求解能力。在许多基于语言的任务中，例如常规编程，这些人工智能模型已减少甚至消除了对人类输入的需求。然而，与其取代人类的认知努力，这些模型可能作为认知工具来拓展人类能力，特别是在需要开放性概念探索和创造性构思的任务中。但目前我们尚不清楚这些模型如何在人机交互中增强人类的生成性认知能力。本研究探讨并评估了LLM在语义记忆搜索过程中跟踪和增强人类心理轨迹的能力。为此，我们采用语义流畅性任务（SFT），这是一种经典认知范式，要求生成性语义记忆检索，长期用于刻画人类的聚合性与发散性思维。研究结果表明，LLM在此任务中跟踪和预测人类记忆轨迹的能力超越其他人类。
原文摘要 · Abstract (English)
Large language models (LLMs) exhibit unprecedented natural language generation and many text-based problem-solving capabilities. Indeed, in many language-based tasks, for example routine coding, these artificial intelligence models have reduced, or even eliminated, the need for human input. But rather than replacing human cognitive effort, LLMs may instead serve as cognitive tools to extend human abilities, particularly when they are engaged in a task requiring open-ended conceptual exploration and creative ideation. However, we are yet to understand how these models may enhance such generative human cognitive abilities in human--AI interactions. In this study, we explore and evaluate the ability of LLMs to follow and enhance human mental trajectories during semantic memory search. To test this, we use the semantic fluency task (SFT), a classic cognitive paradigm requiring generative semantic memory retrieval that has long served to characterize convergent and divergent thinking in humans. We demonstrate that an LLM's abilities to track and predict human memory trajectories in this task exceed those of other humans.
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