Argumentation theory examines how conclusions are derived or refuted through logical reasoning, playing a crucial role in human interaction and decision-making. In artificial intelligence, computational argumentation leverages formal models to aid in decision-making processes. This paper investigates the influence of argument content (specifically the naturalness bias) and graphical representation on participants' adherence to the simple principles of reinstatement and void precedence principles. We conducted experiments testing three hypotheses related to participants' rationality in evaluating arguments with and without graphical aids and bias-provoking content. Contrary to our expectations, neither the presence of graphical representations nor the type of content significantly impacted participants' compliance with the reinstatement principle. Additionally, the graph did not enhance understanding, suggesting the need for instructional aids. Our findings challenge previous studies and highlight the complexity of factors influencing argument evaluation.