On the Morphology of Uncertainty in Human Perception and Cognition

Human cognitive and perception processes have a great tolerance for imprecision or uncertainty. For this reason, the notions of perception and cognition have great importance in solving many decision making problems in engineering, medicine, science, and social science as there are innumerable uncertainties in real-world phenomena. These uncertainties can be broadly classified as either type one uncertainty arising from the random behavior of physical processes or type two uncertainty arising from human perception and cognition processes. Statistical theory can be used to model the former, but lacks the sophistication to process the latter. The theory of fuzzy logic has proven to be very effective in processing type two uncertainty. New computing methods based on fuzzy logic can lead to greater adaptability, tractability, robustness, a lower cost solution, and better rapport with reality in the development of intelligent systems. Fuzzy logic is needed to properly pose and answer queries about quantitatively defining imprecise linguistic terms like middle class, poor, low inflation, medium inflation, and high inflation. Imprecise terms like these in natural languages should be considered

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“…However, they can be classified under two broad categories: type one uncertainty and type two uncertainty [8][9][10].…”

Section: Uncertainty and Imprecision In Perception And Cognitionmentioning

confidence: 99%

“…These advances were mostly inspired by certain biological aspects of the human brain. One of the intriguing aspects of human perception and cognition is its tolerance for imprecision and uncertainty [1][2][3][4][5][6][7][8][9][10], which characterize most real-world phenomena.…”

Section: Introductionmentioning

confidence: 99%

Important New Terms and Classifications in Uncertainty and Fuzzy Logic

Gupta

Solo

2015

Fifty Years of Fuzzy Logic and Its Applications

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“…Certainty and precision are much too often used as an absolute standard in reasoning and decision making. Fuzzy logic [1][2][3][4][5][6] is based on the notion of relative graded membership and can deal with information arising from computational perception and cognition that is uncertain, imprecise, vague, partially true, or without sharp boundaries. Dr. Zadeh published his first famous paper [1] on fuzzy sets in 1965.…”

Section: Introductionmentioning

confidence: 99%

Warren, McCain, and Obama Needed Fuzzy Sets at Presidential Forum

Solo

2012

Advances in Fuzzy Systems

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During a presidential forum in the 2008 US presidential campaign, the moderator, Pastor Rick Warren, wanted Senator John McCain and then-Senator Barack Obama to definerichwith a specific number. Warren wanted to know at what specific income level a person goes from being not rich to rich. The problem with this question is that there is no specific income at which a person makes the leap from being not rich to being rich. This is becauserichis a fuzzy set, not a crisp set, with different incomes having different degrees of membership in therichfuzzy set. Fuzzy logic is needed to properly ask and answer Warren's question about quantitatively definingrich. An imprecise natural language word likerichshould be considered to havequalitative definitions, crisp quantitative definitions,andfuzzy quantitative definitions.

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Curriculum for a New Four-Year Bachelor's Degree in Intelligent Systems Engineering

Solo

2023

2023 Congress in Computer Science, Computer Engineering, &Amp;amp; Applied Computing (CSCE)

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On the Morphology of Uncertainty in Human Perception and Cognition

Cited by 8 publications

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Important New Terms and Classifications in Uncertainty and Fuzzy Logic

Important New Terms and Classifications in Uncertainty and Fuzzy Logic

Warren, McCain, and Obama Needed Fuzzy Sets at Presidential Forum

Curriculum for a New Four-Year Bachelor's Degree in Intelligent Systems Engineering

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