Article

Advances in deep reinforcement learning enable better predictions of human behavior in time-continuous tasks

2025-05-26

Abstract excerpt

Humans have to respond to everyday tasks with goal-directed actions in complex and time-continuous environments. However, modeling human behavior in such environments has been challenging. Deep Q-networks (DQNs), an application of deep learning used in reinforcement learning (RL), enable the investigation of how humans transform high-dimensional, time-continuous visual stimuli into appropriate motor responses. Whi...

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Literature Corpus work: 064bca03-9d4a-5dfb-81fa-3a46147e036e
DOI: 10.1101/2025.05.20.655119

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