# Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study

arXiv: https://arxiv.org/abs/2304.08861 · HTML: https://mlsanity.com/paper/2304.08861/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 85 references that are in the map.
- First posted on arXiv 18 Apr 2023.

## Builds on (most cited first, 17 of 17)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems](https://mlsanity.com/paper/2001.08298/) (2020)
- [Human-Centered Explainable AI (XAI): From Algorithms to User Experiences](https://mlsanity.com/paper/2110.10790/) (2021)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [Counterfactual Explanations Can Be Manipulated](https://mlsanity.com/paper/2106.02666/) (2021)
- ["How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations](https://mlsanity.com/paper/1911.06473/) (2019)
- [Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making](https://mlsanity.com/paper/1902.02960/) (2019)
- [Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support](https://mlsanity.com/paper/2302.12389/) (2023)
- [A Human-Grounded Evaluation of SHAP for Alert Processing](https://mlsanity.com/paper/1907.03324/) (2019)
- [Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability](https://mlsanity.com/paper/2206.10610/) (2022)
- [On Cognitive Preferences and the Plausibility of Rule-based Models](https://mlsanity.com/paper/1803.01316/) (2018)
- [Roadmap of Designing Cognitive Metrics for Explainable Artificial Intelligence (XAI)](https://mlsanity.com/paper/2108.01737/) (2021)
- [Measure Utility, Gain Trust: Practical Advice for XAI Researcher](https://mlsanity.com/paper/2009.12924/) (2020)

## Built on by (most cited first, 15 of 15)

- [Explain Any Concept: Segment Anything Meets Concept-Based Explanation](https://mlsanity.com/paper/2305.10289/) (2023)
- [MemeIntel: Explainable Detection of Propagandistic and Hateful Memes](https://mlsanity.com/paper/2502.16612/) (2025)
- [Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration](https://mlsanity.com/paper/2409.12809/) (2024)
- [Navigating Explanatory Multiverse Through Counterfactual Path Geometry](https://mlsanity.com/paper/2306.02786/) (2023)
- [Human Delegation Behavior in Human-AI Collaboration: The Effect of Contextual Information](https://mlsanity.com/paper/2401.04729/) (2024)
- [User Decision Guidance with Selective Explanation Presentation from Explainable-AI](https://mlsanity.com/paper/2402.18016/) (2024)
- [Transferring Domain Knowledge with (X)AI-Based Learning Systems](https://mlsanity.com/paper/2406.01329/) (2024)
- [The Contribution of XAI for the Safe Development and Certification of AI: An Expert-Based Analysis](https://mlsanity.com/paper/2408.02379/) (2024)
- [The Effect of Explainable AI-based Decision Support on Human Task Performance: A Meta-Analysis](https://mlsanity.com/paper/2504.13858/) (2025)
- [Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisions](https://mlsanity.com/paper/2505.01192/) (2025)
- [A Pattern Language for Resilient Visual Agents](https://mlsanity.com/paper/2604.28001/) (2026)
- [Dynamic Explanation Emphasis in Human-XAI Interaction with Communication Robot](https://mlsanity.com/paper/2403.14550/) (2024)
- [Improving the Explain-Any-Concept by Introducing Nonlinearity to the Trainable Surrogate Model](https://mlsanity.com/paper/2405.11837/) (2024)
- [Should XAI Nudge Human Decisions with Explanation Biasing?](https://mlsanity.com/paper/2406.07323/) (2024)
- [An Empirical Examination of the Evaluative AI Framework](https://mlsanity.com/paper/2411.08583/) (2024)

## Abstract (arXiv)

While the emerging research field of explainable artificial intelligence (XAI) claims to address the lack of explainability in high-performance machine learning models, in practice, XAI targets developers rather than actual end-users. Unsurprisingly, end-users are often unwilling to use XAI-based decision support systems. Similarly, there is limited interdisciplinary research on end-users' behavior during XAI explanations usage, rendering it unknown how explanations may impact cognitive load and further affect end-user performance. Therefore, we conducted an empirical study with 271 prospective physicians, measuring their cognitive load, task performance, and task time for distinct implementation-independent XAI explanation types using a COVID-19 use case. We found that these explanation types strongly influence end-users' cognitive load, task performance, and task time. Further, we contextualized a mental efficiency metric, ranking local XAI explanation types best, to provide recommendations for future applications and implications for sociotechnical XAI research.