计算机科学
任务(项目管理)
概率逻辑
人机交互
人工智能
认知
自然(考古学)
合理规划模型
计算模型
工作(物理)
理性
认知模型
博弈论
计算智能
认知科学
通用人工智能
环境智能
认知系统
管理科学
任务分析
心理学
理性代理人
人类智力
游戏
作者
Katherine M. Collins,Cedegao E. Zhang,Lionel Wong,Mauricio Barba da Costa,Graham Todd,Adrian Weller,Samuel J. Cheyette,Thomas L. Griffiths,Joshua B. Tenenbaum
出处
期刊:Nature
[Nature Portfolio]
日期:2026-07-15
卷期号:655 (8123): 598-607
标识
DOI:10.1038/s41586-026-10722-1
摘要
. But real life also pushes human intelligence along a different frontier, requiring people to flexibly navigate decision-making problems that they have never thought about before. Here we use novice gameplay to study how people reason about new problem settings. Through a series of large-scale behavioural studies with over 1,000 participants and 121 two-player strategic board games (almost all novel to our participants), we show that people are systematic and adaptively rational in how they play a game for the first time or evaluate a game (for example, how fair or how fun it is likely to be) before they have played it even once. We explain these capacities via a computational cognitive model that we call the 'Intuitive Gamer': a model based on mechanisms of fast and flat (depth-limited) goal-directed probabilistic simulation. Our work offers insights into how people rapidly evaluate, act and make suggestions when encountering novel problems, and could inform the design of more flexible and human-like artificial intelligence systems that can determine not just how to solve new tasks but also whether a task is worth thinking about at all.
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