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A Cognitive Load Theory (CLT) Analysis of Machine Learning Explainability, Transparency, Interpretability, and Shared Interpretability

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/sce.21097openalex-snapshot
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inferredcites →https://doi.org/10.1002/widm.1312openalex-snapshot
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inferredcites →https://doi.org/10.1006/ijhc.1981.0309openalex-snapshot
The black box inside the glass box: presenting computing concepts to novices
inferredcites →https://doi.org/10.1007/978-1-4419-8126-4_11openalex-snapshot
The Redundancy Effect
inferredcites →https://doi.org/10.1007/978-1-4419-8126-4_5openalex-snapshot
Intrinsic and Extraneous Cognitive Load
inferredcites →https://doi.org/10.1007/s00422-010-0364-zopenalex-snapshot
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inferredcites →https://doi.org/10.1007/s00426-022-01697-5openalex-snapshot
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inferredcites →https://doi.org/10.1007/s10462-022-10275-5openalex-snapshot
Recent advances in decision trees: an updated survey
inferredcites →https://doi.org/10.1007/s11747-019-00710-5openalex-snapshot
Explainable AI: from black box to glass box
inferredcites →https://doi.org/10.1016/0959-4752(94)90003-5openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.artint.2021.103458openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.chb.2007.02.016openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.chb.2022.107574openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.cognition.2018.06.011openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.conb.2022.102668openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.ijhcs.2020.102551openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.jmp.2020.102447openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.jpsychores.2009.09.004openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.learninstruc.2006.02.007openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.neubiorev.2018.03.001openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.neunet.2021.09.011openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0007-8506(07)63249-1openalex-snapshot
Design for Assembly and Disassembly
inferredcites →https://doi.org/10.1017/9781316494233openalex-snapshot
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inferredcites →https://doi.org/10.1023/b:truc.0000021806.17516.d0openalex-snapshot
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inferredcites →https://doi.org/10.1037//0278-7393.16.4.569openalex-snapshot
Phonological ambiguity and lexical ambiguity: Effects on visual and auditory word recognition.
inferredcites →https://doi.org/10.1037/0033-2909.129.3.339openalex-snapshot
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inferredcites →https://doi.org/10.1037/xlm0000391openalex-snapshot
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inferredcites →https://doi.org/10.1038/538020aopenalex-snapshot
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inferredcites →https://doi.org/10.1080/02699052.2023.2165158openalex-snapshot
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inferredcites →https://doi.org/10.1080/10410236.2014.974131openalex-snapshot
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inferredcites →https://doi.org/10.1093/biosci/biab133openalex-snapshot
Writing Accessible Theory in Ecology and Evolution: Insights from Cognitive Load Theory
inferredcites →https://doi.org/10.1093/oxfordhb/9780199279739.001.0001openalex-snapshot
The Oxford Handbook of Causation
inferredcites →https://doi.org/10.1108/01409171311306427openalex-snapshot
Getting real about ICT
inferredcites →https://doi.org/10.1109/access.2018.2870052openalex-snapshot
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
inferredcites →https://doi.org/10.1109/cig.2018.8490433openalex-snapshot
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inferredcites →https://doi.org/10.1109/ijcnn.2019.8852110openalex-snapshot
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inferredcites →https://doi.org/10.1109/tvcg.2020.3030459openalex-snapshot
Scalability of Network Visualisation from a Cognitive Load Perspective
inferredcites →https://doi.org/10.1111/bjep.12612openalex-snapshot
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inferredcites →https://doi.org/10.1111/j.1467-9272.2006.00527.xopenalex-snapshot
The Cognitive Load of Geographic Information
inferredcites →https://doi.org/10.1111/j.1540-5915.1997.tb01333.xopenalex-snapshot
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inferredcites →https://doi.org/10.1111/j.1747-9991.2011.00436.xopenalex-snapshot
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inferredcites →https://doi.org/10.1145/3313831.3376615openalex-snapshot
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inferredcites →https://doi.org/10.1145/3411764.3445296openalex-snapshot
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inferredcites →https://doi.org/10.1177/0956797610385950openalex-snapshot
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inferredcites →https://doi.org/10.1200/jco.2016.68.4316openalex-snapshot
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inferredcites →https://doi.org/10.1207/s15326985ep3801_1openalex-snapshot
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inferredcites →https://doi.org/10.3389/fnbot.2018.00083openalex-snapshot
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inferredcites →https://doi.org/10.4324/9781315044712-4openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/10447318.2026.2623213openalex-snapshot
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inferred← citeshttps://doi.org/10.1108/apjml-01-2026-0052openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/iccr67387.2025.11292208openalex-snapshot
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inferred← citeshttps://doi.org/10.1145/3749012.3749070openalex-snapshot
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inferred← citeshttps://doi.org/10.12973/ejmse.6.1.1openalex-snapshot
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inferred← citeshttps://doi.org/10.3103/s1068798x25703307openalex-snapshot
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