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深度强化学习的进步使我们能够更好地预测时间连续任务中的人类行为

Sabine Haberland¹, Hannes Ruge¹, Holger Frimmel¹

¹Institut of General Psychology, TUD Dresden University of Technology, Dresden, Germany.

|December 4, 2025

概括

先进的深度强化学习 (RL) 模型,如SEED,在复杂的,时间连续的任务中显著改善了对人类运动反应的预测. 这提高了我们利用人工智能模拟人类行为的能力.

科学领域:

认知科学认知科学
人工智能的人工智能
神经科学是一个神经科学.

背景情况:

在复杂的,时间连续的环境中对人类行为进行建模是具有挑战性的.
深度Q网络 (DQN) 提供了一种将高维刺激与运动反应联系起来的方法.
最近的DQN进步对人类行为建模的影响仍然不清楚.

研究的目的:

调查先进的深度强化学习 (RL) 模型是否可以改善人类运动反应的预测.
将不同DQN架构 (基线,Ape-X,SEED) 的预测精度与人类行为数据进行比较.
评估时间分辨率对时间连续任务中的预测准确性的影响.

主要方法:

记录了23名人类参与者在玩三个街机游戏时的运动反应.
利用基线DQN,Ape-X和SEED模型中的刺激特征作为预测因素.
通过使用线性模型和分析时间光滑效应,将DQN响应概率与人类运动响应相匹配.

主要成果:

这三个DQN模型都预测了人类的行为明显高于机会水平.
结合决斗,双重Q学习和LSTM的SEED模型在所有游戏中都显示出卓越的预测准确性.
与基线和Ape-X.相比,SEED在细粒度的时间尺度上对人类行为建模的增强能力得到了证明.

结论:

深度RL的进步,特别是像SEED这样的复杂模型,增强了在动态环境中对人类行为的建模.
这种方法为传统的实验方法提供了有价值的补充,使得在更精细的时间分辨率下进行分析.
未来的研究可以利用复杂的RL代理来进一步了解人类的决策和运动控制.

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