Mamdani fuzzy rule-based models for psychological research
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Mamdani fuzzy rule‑based models for psychological research Deepak Chandra Pandey1 · Govind Singh Kushwaha2 · Sanjay Kumar1 Received: 14 October 2019 / Accepted: 8 April 2020 © Springer Nature Switzerland AG 2020
Abstract The biasness of the participants in psychological research cannot be ignored during answering various psychological questioners or inventory. Hence, the prediction of psychological parameters can be deemed an ambiguous endeavor and fuzzy modeling provides a mean to account for this ambiguity and uncertainty. In the present study, two fuzzy rulebased models that use single input and generate single output are developed to convert the raw scores of neuroticism and extraversion to standard scores. Maudsley personality inventory (MPI) and Sinha’s comprehensive anxiety test (SCAT) were used to collect raw data of neuroticism, extraversion and anxiety from participants. Using the standard scores for neuroticism and extroversion, third fuzzy rule-based model is also developed to predict the anxiety level of the participants. Each model is a collection of fuzzy rules that express the relationship of each input to the output. The performance of all developed models is tested by estimating mean absolute percentage error (MAPE) and paired two-tailed t test. Keywords Fuzzy model · Extraversion · Neuroticism · Anxiety · Uncertainty
1 Introduction Human behavior depends on number of psychological parameters, and extraversion, neuroticism, anxiety are few of them. Extraversion represents tendency to be sociable, assertive, active and directive. Neuroticism represents a tendency to exhibit poor emotional adjustment and experiences negative effect such as fear, anxiety, and impulsivity [12, 20]. Studies show that extraversion is associated with happiness, whereas neuroticism is associated with unhappiness [4, 22]. Many studies have established an association between anxiety and neuroticism [2]. Anxiety represents a ‘state of arousal’ caused by threat to wellbeing [28]. It means a condition of tension, uneasiness, threat and readiness which involves an entire organism to act and respond. ‘Threat’ means anticipation of pain, danger or serious interference with goal seeking activities.
Simulation of human behavior as an interdisciplinary research field has attracted the keen interest of mathematician and psychologist. In recent years, it has been extensively studied and applied in psychological research [21]. Zadeh [33] gave the notion of fuzzy set to handle the uncertainty which is caused by imprecise information and vague data. The interest of psychologist in fuzzy logic has visibly been growing since mid-1980s [1, 11, 25, 26]. Psychology is not only a field in which profound applications of fuzzy logic is anticipated, but is also very important for the development of fuzzy set theory itself [34]. Fuzzy logic allows researchers to handle the imprecision and vague inherence of input data in depth and develop more reliable model for computing input–output relations [17]. Many researchers [6, 7, 13, 16, 18, 19, 29,
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