Results of the one-sample t-test.

<div><p>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)...

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Bibliographic Details
Main Author:	Sabine Haberland (22783613) (author)
Other Authors:	Hannes Ruge (5815964) (author), Holger Frimmel (22783616) (author)
Published:	2025
Subjects:	Sociology Science Policy Mental Health Biological Sciences not elsewhere classified xlink "> humans significant performance improvements remained unclear whether recorded motor responses humans transform high appropriate motor responses conventional experimental approach human motor responses playing arcade games grained temporal scale human behavior across dqn &# 8217 continuous visual stimuli rl ), enable compare prediction accuracy modeling human behavior third baseline dqn model human behavior continuous experimental tasks deep learning used human behavior experimental tasks prediction accuracy deep rl baseline dqn human participants human data used features three games temporal resolution improved modeling everyday tasks deep q better prediction varying degrees trial structure thereby opening term memory results suggest response probabilities reinforcement learning long short linear model interesting avenue future research dqns ), double q directed actions considerably improved chance level
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Results of the one-sample t-test.

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