Summary
This chapter discusses two applications of the theory of fuzzy sets in investigating and evaluating human learning abilities and cognitive processes. They are an integral part of the Interactivist-Expectative Theory on Agency and Learning (IETAL) and its multiagent expansion known as Multi-Agent Systems Interactive Virtual Environments (MASIVE).
In the first application presented, a fuzzy set is defined to ease and automate the process of detection of negative variation in filtered brain waves during the Dynamic Cognitive Negative Variation (CNV) experiment. The automatic detection of brain waveforms that are contingent of negative variation is a crucial part of the experiment that measures individual human learning parameters. By eliminating the direct influence of the human expert, a level of objectivity is being maintained over the duration of the whole experiment. The decision process is significantly shorter, which contributes to more accurate measuring, as is the case in numerous experiments involving human subjects and learning.
In the second application, fuzzy sets serve as tools in the process of grading, which is a highly cognitive, but ill-defined problem. The fuzzy evaluation framework that is given is very general, and straightforwardly applicable in any evaluation process when the evaluator is expected to quantize one or several aspects of a given artifact.
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Trajkovski, G. (2004). Fuzzy Sets in Investigation of Human Cognition Processes. In: Abraham, A., Jain, L., van der Zwaag, B.J. (eds) Innovations in Intelligent Systems. Studies in Fuzziness and Soft Computing, vol 140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39615-4_15
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DOI: https://doi.org/10.1007/978-3-540-39615-4_15
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