A Review on Role of Fuzzy Logic in Psychology

Srivastava, Shilpa; Pant, Millie

doi:10.1007/978-981-10-0451-3_70

Cited by 9 publications

(3 citation statements)

References 73 publications

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“…The performance comparison between the prediction models was conducted using MSE, RMSE, MAE, and MAPE, where the results showed that Mamdani model performance was slightly better than the ANN model. This may be due to the ability of FL to deal with uncertainties and ambiguity by imbibing human perception through linguistic variables, which is very suitable for psychological concepts in particular (Singh Kushwaha & Kumar, 2009; Srivastava, Pant, & Agarwal, 2016). However, ANFIS outperformed both models.…”

Section: Discussionmentioning

confidence: 99%

Forgiveness and trust dynamics on social networks

2018

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Social media users can easily be offended or hurt on those platforms, which leads to discomfort and health issues such as stress and anxiety. Forgiveness plays an important role to maintain healthy online relationships, which is the central constituent of social dynamics, from cooperation to social cohesion. While most prior studies have focused on analyzing forgiveness factors in offline settings using statistical methods, this study offers a new perspective using a two-staged approach whereby a research model was tested using structural equation modeling (SEM), and then the results were used as inputs for artificial neural network (ANN) and fuzzy logic (FL) models. An agent-based simulation was then performed to shed light on a possible use of the implemented models. Combining ANN and FL provided more accurate prediction results. In addition, simulation experiments call attention to the potential benefits of forgiveness in maintaining connectedness in a social network. The main purpose of this investigation was to evaluate the applicability of soft computing techniques on forgiveness prediction. Instead of relying on data mining techniques, we looked into questions that can improve our understanding of how society works in a digital age. In addition, this study provides an interesting example of a different and insightful way of doing computational social science that is useful to both researchers and practitioners.

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Section: Discussionmentioning

confidence: 99%

Forgiveness and trust dynamics on social networks

2018

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“…Nickel has a wide variety of biological [1][2][3] and chemical applications, such as Ni alloys for coins, magnetism, and organocatalysis. 4,5 Ni compounds with oxidation states 0 and +2 are the most common and most extensively studied.…”

Section: Introductionmentioning

confidence: 99%

Ni(i)–TPA stabilization by hydrogen bond formation on the second coordination sphere: a DFT characterization

et al. 2022

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“…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 [6,7,13,16,18,19,29,30] proposed integration of fuzzy logic in psychological research for more logical outcomes. Recently Devi [5] and Laifa [15] integrated fuzzy set theory with neural network to develop intelligent models for psychological research.…”

Section: Introductionmentioning

confidence: 99%

Mamdani fuzzy rule-based models for psychological research

2020

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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.

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A Review on Role of Fuzzy Logic in Psychology

Cited by 9 publications

References 73 publications

Forgiveness and trust dynamics on social networks

Forgiveness and trust dynamics on social networks

Ni(i)–TPA stabilization by hydrogen bond formation on the second coordination sphere: a DFT characterization

Mamdani fuzzy rule-based models for psychological research

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