COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations
Abstract
Interpretable machine learning models trade -off accuracy for simplicity to make explanations more readable and easier to comprehend. Drawing from cognitive psychology theories in graph comprehension, we formalize readability as visual cognitive chunks to measure and moderate the cognitive load in explanation visualizations. We present Cognitive-GAM (COGAM) to generate explanations with desired cognitive load and accuracy by combining the expressive nonlinear generalized additive models (GAM) with simpler sparse linear models. We calibrated visual cognitive chunks with reading time in a user study, characterized the trade-off between cognitive load and accuracy for four datasets in simulation studies, and evaluated COGAM against baselines with users. We found that COGAM can decrease cognitive load without decreasing accuracy and/or increase accuracy without increasing cognitive load. Our framework and empirical measurement instruments for cognitive load will enable more rigorous assessment of the human interpretability of explainable AI.
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From Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust (2023) · cites this paper as [1]
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How Well do Feature Visualizations Support Causal Understanding of CNN Activations?Preprint
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IF-City: Intelligible Fair City Planning to Measure, Explain and Mitigate Inequality
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“Recently, there has been some limited recognition that consideration of CLT could improve explainability and interpretability [17–20].”
BackgroundIncremental XAI: Memorable Understanding of AI with Incremental Explanations
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The Who in Explainable AI: How AI Background Shapes Perceptions of AI ExplanationsPreprint
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Symbolic Regression Enhanced Decision Trees for Classification Tasks
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Helping Users Tackle Algorithmic Threats on Social Media: A Multimedia Research Agenda
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Self-citationGenerating multi-level explanations for process outcome predictions
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Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning
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Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments
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DistilSR: A Distilled Version of Gene Expression Programming Symbolic Regression
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TimeREISE: Time Series Randomized Evolving Input Sample Explanation
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XAI in Automated Fact-Checking? The Benefits Are Modest and There's No One-Explanation-Fits-All
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FXAM: A Unified and Fast Interpretable Model for Predictive Analytics
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Modeling Cognitive Load in Mobile Human Computer Interaction Using Eye Tracking Metrics
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- Authors
- Abdul, A., von der Weth, C., Kankanhalli, M., & Lim, B. Y.
- Affiliations
- National University of Singapore (SG)
- National University of Singapore, Singapore, Singapore
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-14
- OpenAlex
- W3028689275
- Cited by
- 96
- References
- 74
- Citations by year
- 2026: 12 · 2025: 14 · 2024: 19 · 2023: 17 · 2022: 14 · 2021: 18 · 2020: 2
- FWCI
- 4.87
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- National University of Singapore (submittedVersion)
- Funders
- National Research Foundation Prime Minister's Office Singapore
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- cognitive load, explainable AI, model explanation, generalized additive models, readability, graph interpretation, explanation visualization, sparse linear models, human interpretability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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