Quantifying Visual Properties of GAM Shape Plots: Impact on Perceived Cognitive Load and Interpretability

TL;DR

This study examines how visual features of GAM shape plots, especially the number of kinks, influence perceived cognitive load, and introduces a model to predict interpretability based on these properties.

Contribution

The paper quantifies visual properties of GAM shape plots and develops a model predicting cognitive load from the number of kinks, enhancing interpretability assessment tools.

Findings

Number of kinks explains 86.4% of variance in cognitive load.

A simple model based on kinks predicts interpretability effectively.

Shape plot complexity impacts viewer's cognitive load.

Abstract

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.