To achieve successful human-robot interaction, we have to study human decision making rules. This work investigates human learning rules in games with the presence of intelligent decision makers. Particularly, we analyze human behavior in a congestion game where a player distributes vehicles across two roads. This game is repeated for several rounds, so players can formulate a strategy. After each round, the cost of the roads is presented to the human player. The goal of all players is to minimize the total congestion experienced by the vehicles they control. To demonstrate our results, we first simulated a human player using the Fictitious Play and Regret Matching algorithms. Then, we demonstrated the passivity property of these algorithms after adjusting the passivity condition to suit discrete time formulation. Next, we conducted the experiment online where 83 participants played a total of 147 games. A similar analysis was performed on the collected data, to study the passivity of the human decision making rule. We observe different performances with the four types of different virtual players. However, in all cases, the human decision making rule conforms to the passivity condition. This result implies that humans can be modeled as passive decision makers, and systems can be designed to exploit this result, to obtain desirable outcomes.