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

ABSTRACT: 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. While recent advances in DQNs have led to significant performance improvements, it has remained unclear whether these advancements translate into improved modeling of human behavior. Here, we recorded motor responses in human participants (N = 23) while playing three distinct arcade games. We used stimulus features generated by a DQN as predictors for human data by fitting the DQN's

SUBMITTER: Haberland S 

PROVIDER: S-EPMC12677501 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature