Dear HPM-TG members,
During our recent TG business meeting, people discussed the need for learning materials related to HPM. Where can we obtain such knowledge? How do professors usually teach students? What will be the relationship between HPM and machine learning?
Personally, I think HPM can play an important role in the development of intelligent automation and testing human-automation collaboration. While artificial intelligence methods using artificial neural network have gained significant attention in recent years, such AI models are usually difficult to explain. In contrast, HPM models typically use symbolic methods that human researchers can explain. Perhaps combining symbolic models with connectionism models can provide a solution to explainable AI.
Regarding learning resources, my students usually learn theories and statistics from courses, learn task analysis methods from projects, and study cognitive architecture, e.g., ACT-R from its online tutorials and published papers. And there are many online courses and tutorials for machine learning.
Below I listed a table of how I usually put different types of models into categories. Do you think any methods will become more popular? Do you know any good educational materials? What do you think are the future needs in this field?
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Category
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Sub-category
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Verbal descriptive
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(not computational)
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Classical statistics
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Descriptive statistics
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Inference statistics
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Signal detection theory
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Regression
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Advanced statistics /
machine learning
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Machine learning
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Stochastic process
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Bayesian models
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Artificial neural network
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Mathematical
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Empirical and physical
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Control theory models
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Queueing network
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Predetermined predictive systems
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Perceptual and cognitive elements
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Motion elements
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Simulation
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Cognitive architectures
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Digital human models
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Work flow / layout
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Best regards,
Shi
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Shi Cao (pronounced like SHER TSAO), PhD, PEng
Associate Professor
Department of Systems Design Engineering
University of Waterloo
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