Generalized Additive Models (GAMs) offer a balance between performance and interpretability in machine learning. The interpretability aspect of GAMs is expressed through shape plots, representing the model's decision-making process. However, the visual properties of these plots, e.g. number of kinks (number of local maxima and minima), can impact their complexity and the cognitive load imposed on the viewer, compromising interpretability. Our study, including 57 participants, investigates the relationship between the visual properties of GAM shape plots and cognitive load they induce. We quantify various visual properties of shape plots and evaluate their alignment with participants' perceived cognitive load, based on 144 plots. Our results indicate that the number of kinks metric is the most effective, explaining 86.4% of the variance in users' ratings. We develop a simple model based on number of kinks that provides a practical tool for predicting cognitive load, enabling the assessment of one aspect of GAM interpretability without direct user involvement.

翻译：广义可加模型（GAMs）在机器学习中实现了性能与可解释性之间的平衡。GAMs的可解释性通过形状图得以体现，这些图形展示了模型的决策过程。然而，这些图形的视觉特性（例如转折点数量，即局部极大值与极小值的数量）会影响其复杂程度，并增加观察者的认知负荷，从而损害可解释性。本研究包含57名参与者，探究了GAM形状图的视觉特性与其引发的认知负荷之间的关系。我们量化了144张形状图的多项视觉特性，并评估了这些特性与参与者感知认知负荷的一致性。结果表明，转折点数量指标最为有效，能够解释用户评分中86.4%的方差。我们基于转折点数量开发了一个简易模型，为预测认知负荷提供了实用工具，使得无需直接用户参与即可评估GAM可解释性的一个方面。