Skip to main content
SpringerLink
Log in

Fuzzy Set Theory and Psychology

  • Chapter
  • First Online:
An Introduction to Artificial Psychology

  • 262 Accesses

Abstract

Fuzzy set theory and its role in psychology are introduced in this chapter. Fuzzy models accept that the human perception of the world is not black and white but includes a degree of grayness (e.g., in diagnosis where the presence or absence symptoms may or may not lead to a diagnosis of a particular illness and in the use of language where, for example, a person saying “I am famous” is only their personal subjective opinion). These models attempt to account for this uncertainty in perception when model building by using a fuzzy layer based upon expert perception expressed in language and quantifying these opinions using probabilities in a fuzzy perceptual map. Properties of the set of perceptions are presented and simple mathematical distributions used to illustrate the membership of these fuzzy sets of perceptions are defined and illustrated, such as cardinality, support, core, height, normalization, and crossover points. Finally, a worked example with code using procedures in R is given, looking at the relationship between depression and multiple sclerosis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Arfi, B. (2010). Linguistic fuzzy logic methods in social sciences (Vol. 253). Springer.

    Google Scholar 

  • Ashish, K., Dasari, A., Chattopadhyay, S., & Hui, N. B. (2018). Genetic-neuro-fuzzy system for grading depression. Applied Computing and Informatics, 14(1), 98–105.

    Article  Google Scholar 

  • Avci, E. (2008). Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system. Applied Soft Computing, 8(1), 225–231.

    Article  Google Scholar 

  • Avci, E., & Akpolat, Z. H. (2006). Speech recognition using a wavelet packet adaptive network based fuzzy inference system. Expert Systems with Applications, 31(3), 495–503.

    Article  Google Scholar 

  • Avci, E., Turkoglu, I., & Poyraz, M. (2005, June). Intelligent target recognition based on wavelet adaptive network based fuzzy inference system. In Iberian conference on pattern recognition and image analysis (pp. 594–603). Springer.

    Google Scholar 

  • Avci, E., Hanbay, D., & Varol, A. (2007). An expert discrete wavelet adaptive network based fuzzy inference system for digital modulation recognition. Expert Systems with Applications, 33(3), 582–589.

    Article  Google Scholar 

  • Baig, F., Khan, M. S., Noor, Y., Imran, M., & Baig, F. (2011). Design model of fuzzy logic medical diagnosis control system. International Journal on Computer Science and Engineering, 3(5), 2093–2108.

    Google Scholar 

  • Blake, C. (1998). UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html

  • Blazer, D. G. (1982). Social support and mortality in an elderly community population. American Journal of Epidemiology, 115(5), 684–694.

    Article  PubMed  Google Scholar 

  • Bodenhofer, U., De Baets, B., & Fodor, J. (2007). A compendium of fuzzy weak orders: Representations and constructions. Fuzzy Sets and Systems, 158(8), 811–829.

    Article  Google Scholar 

  • Cao, S. G., Rees, N. W., & Feng, G. (2001). Mamdani-type fuzzy controllers are universal fuzzy controllers. Fuzzy Sets and Systems, 123(3), 359–367.

    Article  Google Scholar 

  • Chen, Y. J., Wu, C. H., Chen, Y. M., Li, H. Y., & Chen, H. K. (2017). Enhancement of fraud detection for narratives in annual reports. International Journal of Accounting Information Systems, 26, 32–45.

    Article  Google Scholar 

  • Chopra, S., Dhiman, G., Sharma, A., Shabaz, M., Shukla, P., & Arora, M. (2021). Taxonomy of adaptive neuro-fuzzy inference system in modern engineering sciences. Computational Intelligence and Neuroscience, 2021.

    Google Scholar 

  • Cumming, G. S. (2011). Spatial resilience in social-ecological systems. Springer Science & Business Media.

    Book  Google Scholar 

  • Cvetković, J., Ivanović Kovačevic, S., Cvetkovic, M., & Cvetkovic, S. (2020). Evaluation of the role of stress in patients with breast cancer and depression by paykel’s life event and adaptive neuro-fuzzy approach. Brain and Behavior, 10(4), e01570.

    Article  PubMed  PubMed Central  Google Scholar 

  • Du, H., & Zhang, N. (2008). Application of evolving Takagi–Sugeno fuzzy model to nonlinear system identification. Applied Soft Computing, 8(1), 676–686.

    Article  Google Scholar 

  • Dubois, D., & Prade, H. (1996). What are fuzzy rules and how to use them. Fuzzy Sets and Systems, 84(2), 169–185.

    Article  Google Scholar 

  • Dubois, D., & Prade, H. (2005). Fuzzy elements in a fuzzy set. International fuzzy systems association world congress, Jul 2005. pp. 55–60. ffhal-03367408f.

    Google Scholar 

  • Ekong, V. E., Ekong, U. O., Uwadiae, E. E., Abasiubong, F., & Onibere, E. A. (2013). A fuzzy inference system for predicting depression risk levels. African Journal of Mathematics and Computer Science Research, 6(10), 197–204.

    Google Scholar 

  • Erin, B., & Abiyev, R. H. (2019, January). Diagnosis of common diseases using Type-2 fuzzy system. In Proceedings of the 3rd international conference on machine learning and soft computing (pp. 239–243).

    Chapter  Google Scholar 

  • Fang, X., Van Kleef, G. A., & Sauter, D. A. (2018a). Person perception from changing emotional expressions: Primacy, recency, or averaging effect? Cognition and Emotion, 32(8), 1597–1610.

    Article  PubMed  Google Scholar 

  • Fang, X., Sauter, D. A., & Van Kleef, G. A. (2018b). Seeing mixed emotions: The specificity of emotion perception from static and dynamic facial expressions across cultures. Journal of Cross-Cultural Psychology, 49(1), 130–148.

    Article  PubMed  Google Scholar 

  • Farahani, H., LeighPiotr, L., & Piotr, O. (2018). Fuzzy matrix model as a new method to find optimal diagnostic accuracy points of psychological tests. European Mathematical Psychology Group.

    Google Scholar 

  • Farahani, F. V., Karwowski, W., & Lighthall, N. R. (2019). Application of graph theory for identifying connectivity patterns in human brain networks: A systematic review. Frontiers in Neuroscience, 13, 585.

    Article  PubMed  PubMed Central  Google Scholar 

  • Gandhi, S. P., Heeger, D. J., & Boynton, G. M. (1999). Spatial attention affects brain activity in human primary visual cortex. Proceedings of the National Academy of Sciences, 96(6), 3314–3319.

    Article  Google Scholar 

  • González, A., & Perez, R. (1999). SLAVE: A genetic learning’ system based on an iterative approach. IEEE Transactions on Fuzzy Systems, 7(2), 176–191.

    Article  Google Scholar 

  • González, A., Pérez, R., & Verdegay, J. L. (1994). Learning the structure of a fuzzy rule: A genetic approach. Fuzzy Systems and Artificial Intelligence, 3(1), 57–70.

    Google Scholar 

  • Hájek, P. (2013). Metamathematics of fuzzy logic (Vol. 4). Springer Science & Business Media.

    Google Scholar 

  • Hasan, M. F., & Sobhan, M. A. (2020). Describing fuzzy membership function and detecting the outlier by using five number summary of data. American Journal of Computational Mathematics, 10, 410–424. https://doi.org/10.4236/ajcm.2020.103022

    Article  Google Scholar 

  • Head, M. L., Holman, L., Lanfear, R., Kahn, A. T., & Jennions, M. D. (2015a). v. The extent and consequences of p-hacking in science. PLoS Biology, 13(3), e1002106.

    Article  PubMed  PubMed Central  Google Scholar 

  • Head, M. L., Holman, L., Lanfear, R., Kahn, A. T., & Jennions, M. D. (2015b). The extent and consequences of p-hacking in science. PLoS Biology, 13(3), e1002106.

    Article  PubMed  PubMed Central  Google Scholar 

  • Horowitz, L., & Malle, B. (1993). Fuzzy concepts in psychotherapy research. Psychotherapy Research, 3(2), 131–148.

    Article  Google Scholar 

  • Huette, S., & Spivey, M. (2012). Fuzzy consciousness. In Being in time: Dynamical theories of phenomenal experience (pp. 149–164). John Benjamins Pub. Co.

    Chapter  Google Scholar 

  • Ishibuchi, H., Nakashima, T., & Murata, T. (1999). Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 29(5), 601–618.

    Article  Google Scholar 

  • Ito, M., & Gilbert, C. D. (1999). Attention modulates contextual influences in the primary visual cortex of alert monkeys. Neuron, 22(3), 593–604.

    Article  PubMed  Google Scholar 

  • Izquierdo, S., & Izquierdo, L. R. (2017). Mamdani fuzzy systems for modelling and simulation: A critical assessment. Available at SSRN, 2900827.

    Google Scholar 

  • Jang, J. S. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685.

    Article  Google Scholar 

  • Jiao, L., Pan, Q., Denoeux, T., Liang, Y., & Feng, X. (2015). Belief rule-based classification system: Extension of FRBCS in belief functions framework. Information Sciences, 309, 26–49.

    Article  Google Scholar 

  • Kant, I. (1908). Critique of pure reason. 1781. In Modern classical philosophers (pp. 370–456). Houghton Mifflin.

    Google Scholar 

  • Kello, C. T., & Van Orden, G. C. (2009). Soft-assembly of sensorimotor function. Nonlinear Dynamics, Psychology, and Life Sciences, 13(1), 57.

    PubMed  Google Scholar 

  • Kello, C. T., Anderson, G. G., Holden, J. G., & Van Orden, G. C. (2008). The pervasiveness of 1/f scaling in speech reflects the metastable basis of cognition. Cognitive Science, 32(7), 1217–1231.

    Article  PubMed  Google Scholar 

  • Keltner, D., Sauter, D., Tracy, J., & Cowen, A. (2019). Emotional expression: Advances in basic emotion theory. Journal of Nonverbal Behavior, 43(2), 133–160.

    Article  PubMed  PubMed Central  Google Scholar 

  • Khefacha, I., & Belkacem, L. (2015). Modeling entrepreneurial decision-making process using concepts from fuzzy set theory. Journal of Global Entrepreneurship Research, 5, 1–21.

    Article  Google Scholar 

  • Klawonna, F., & Novák, V. (1996). The relation between inference and interpolation in the framework of fuzzy systems. Fuzzy Sets and Systems, 81(3), 331–354.

    Article  Google Scholar 

  • Klir, G. J., & Yuan, B. (1995a). Fuzzy sets and fuzzy logic: Theory and application. Prentice-Hall.

    Google Scholar 

  • Klir, G., & Yuan, B. (1995b). Fuzzy sets and fuzzy logic (Vol. 4, pp. 1–12). Prentice hall.

    Google Scholar 

  • Lakens, D. (2015). On the challenges of drawing conclusions from p-values just below 0.05. PeerJ, 3, e1142.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lamme, V. A., & Roelfsema, P. R. (2000). The distinct modes of vision offered by feedforward and recurrent processing. Trends in Neurosciences, 23(11), 571–579.

    Article  PubMed  Google Scholar 

  • Larsen, J. T., & McGraw, A. P. (2011). Further evidence for mixed emotions. Journal of Personality and Social Psychology, 100(6), 1095.

    Article  PubMed  Google Scholar 

  • Leggett, D. J. (Ed.). (2013). Computational methods for the determination of formation constants. Springer Science & Business Media.

    Google Scholar 

  • Mamdani, E. H. (1974). Applications of fuzzy algorithms for control of simple dynamic plant. Proceedings of the IEEE, 121, 1585–1588.

    Google Scholar 

  • Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13.

    Article  Google Scholar 

  • Mao, Y., Wong, C. S., Tao, X., & Jiang, C. (2018). The impact of affect on organizational justice perceptions: A test of the affect infusion model. Journal of Management & Organization, 24(6), 893–916.

    Article  Google Scholar 

  • Marsman, M., & Wagenmakers, E. J. (2017). Bayesian benefits with JASP. European Journal of Developmental Psychology, 14(5), 545–555.

    Article  Google Scholar 

  • Martínez-Jiménez, M. A., Ramirez-GarciaLuna, J. L., Kolosovas-Machuca, E. S., Drager, J., & González, F. J. (2018). Development and validation of an algorithm to predict the treatment modality of burn wounds using thermographic scans: Prospective cohort study. PLoS One, 13(11), e0206477.

    Article  PubMed  PubMed Central  Google Scholar 

  • Massaro, D. W. (1989). Testing between the TRACE model and the fuzzy logical model of speech perception. Cognitive Psychology, 21(3), 398–421.

    Article  PubMed  Google Scholar 

  • Massaro, D. W., & Cohen, M. M. (2000). Tests of auditory–visual integration efficiency within the framework of the fuzzy logical model of perception. The Journal of the Acoustical Society of America, 108(2), 784–789.

    Article  PubMed  Google Scholar 

  • Mehran, K. (2008). Takagi-sugeno fuzzy modeling for process control. Industrial Automation, Robotics and Artificial Intelligence (EEE8005), 262, 1–31.

    Google Scholar 

  • Mosoiu, C., Sumedrea, A. G., Burtea, V., & Ifteni, P. (2010, June). Fuzzy system approach to symptoms in schizophrenia. In Proceedings of the 11th WSEAS international conference on neural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on fuzzy systems (pp. 268–272).

    Google Scholar 

  • Motter, B. C. (1993). Focal attention produces spatially selective processing in visual cortical areas V1, V2, and V4 in the presence of competing stimuli. Journal of Neurophysiology, 70(3), 909–919.

    Article  PubMed  Google Scholar 

  • Mushtaq, F., Bland, A. R., & Schaefer, A. (2011). Uncertainty and cognitive control. Frontiers in Psychology, 2, 249.

    Article  PubMed  PubMed Central  Google Scholar 

  • Nguyen, T. H., & Walker, E. (1977). A first course in fuzzy logic. CRC Press.

    Google Scholar 

  • Novák, V. (1994). Fuzzy control from the logical point of view. Fuzzy Sets and Systems, 66(2), 159–173.

    Article  Google Scholar 

  • Oden, G. C., & Massaro, D. W. (1978). Integration of featural information in speech perception. Psychological Review, 85(3), 172.

    Article  PubMed  Google Scholar 

  • Pal, S. K., & Mandal, D. P. (1991). Fuzzy logic and approximate reasoning: An overview. IETE Journal of Research, 37(5-6), 548–560.

    Article  Google Scholar 

  • Perfilieva, I., & Vajgl, M. (2015, June). Autoassociative fuzzy implicative memory on the platform of fuzzy preorder. In 2015 conference of the international fuzzy systems association and the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT-15) (pp. 1598–1603). Atlantis Press.

    Google Scholar 

  • Pourabdollah, A., Mendel, J., & M & John, R.I. (2020). Alpha-cut representation used for defuzzification in rule-based systems. Fuzzy Sets and System, 399(15), 110–132. https://doi.org/10.1016/j.fss.2020.05.008

    Article  Google Scholar 

  • Ragin, C. C. (2000). Fuzzy-set social science. University of Chicago Press.

    Google Scholar 

  • Rayan, F., Nanjayan, S. K., Quah, C., Ramoutar, D., Konan, S., & Haddad, F. S. (2015). Review of evolution of tunnel position in anterior cruciate ligament reconstruction. World Journal of Orthopedics, 6(2), 252.

    Article  PubMed  PubMed Central  Google Scholar 

  • Reinertsen, K. V., Engebraaten, O., Loge, J. H., Cvancarova, M., Naume, B., Wist, E., et al. (2017). Fatigue during and after breast cancer therapy—A prospective study. Journal of Pain and Symptom Management, 53(3), 551–560.

    Article  PubMed  Google Scholar 

  • Riza, L. S., Bergmeir, C., Herrera, F., & Benítez, J. M. (2015). frbs: Fuzzy rule-based systems for classification and regression in R. Journal of Statistical Software, 65, 1–30.

    Article  Google Scholar 

  • Schopenhauer, A., Frauenstädt, J., & Hübscher, A. (1859). Die welt als wille und vorstellung (Vol. 2). Brockhaus.

    Google Scholar 

  • Seising, R. (2008). On the absence of strict boundaries—Vagueness, haziness, and fuzziness in philosophy, science, and medicine. Applied Soft Computing, 8(3), 1232–1242.

    Article  Google Scholar 

  • Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2016). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22, 1359.

    Article  Google Scholar 

  • Smithson, M. (1982). Applications of fuzzy set concepts to behavioral sciences. Mathematical Social Sciences, 2(3), 257–274.

    Article  Google Scholar 

  • Smithson, M., & Oden, G. C. (1999). Fuzzy set theory and applications in psychology. In Practical Applications of fuzzy technologies (pp. 557–585). Kluwer Academic.

    Chapter  Google Scholar 

  • Smithson, M., & Verkuilen, J. (2006). Fuzzy set theory: Applications in the social sciences (Vol. 147).

    Book  Google Scholar 

  • Spence, C., Driver, J., & Driver, J. C. (Eds.). (2004). Crossmodal space and crossmodal attention. Oxford University Press.

    Google Scholar 

  • Spivey, M. J., & Spirn, M. J. (2000). Selective visual attention modulates the direct tilt aftereffect. Perception & Psychophysics, 62(8), 1525–1533.

    Article  Google Scholar 

  • Sporns, O., Tononi, G., & Kötter, R. (2005). The human connectome: A structural description of the human brain. PLoS Computational Biology, 1(4), e42.

    Article  PubMed  PubMed Central  Google Scholar 

  • Stephen, D. G., & Mirman, D. (2010). Interactions dominate the dynamics of visual cognition. Cognition, 115(1), 154–165.

    Article  PubMed  PubMed Central  Google Scholar 

  • Stoklasa, J., Talašová, J., & Holeček, P. (2011). Academic staff performance evaluation–variants of models. Acta Polytechnica Hungarica, 8(3), 91–111.

    Google Scholar 

  • Stoklasa, J., Talášek, T., & Musilová, J. (2014). Fuzzy approach-a new chapter in the methodology of psychology? Human Affairs, 24(2), 189–203.

    Article  Google Scholar 

  • Sugeno, M., & Kang, G. (1988). Structure identification of fuzzy model. Fuzzy Sets and Systems, 28(Oct. 1988), 15–33.

    Article  Google Scholar 

  • Sugeno, M., & Yasukawa, T. (1993). A fuzzy-logic-based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems, 1(1), 7–31.

    Article  Google Scholar 

  • Taylor, S. (2022). The psychology of pandemics. Annual Review of Clinical Psychology, 18, 581–609.

    Article  PubMed  Google Scholar 

  • Terziyska, M., Doukovska, L., & Petrov, M. (2015). Implicit GPC based on semi fuzzy neural network model. In Intelligent Systems’ 2014 (pp. 695–706). Springer.

    Chapter  Google Scholar 

  • Throckmorton, C. S., Mayew, W. J., Venkatachalam, M., & Collins, L. M. (2015). Financial fraud detection using vocal, linguistic and financial cues. Decision Support Systems, 74, 78–87.

    Article  Google Scholar 

  • Torres, A., & Nieto, J. J. (2006). Fuzzy logic in medicine and bioinformatics. Journal of Biomedicine and Biotechnology, 2006.

    Google Scholar 

  • Tsai, C. F., & Chen, M. Y. (2010). Variable selection by association rules for customer churn prediction of multimedia on demand. Expert Systems with Applications, 37(3), 2006–2015.

    Article  Google Scholar 

  • Turkoglu, I., & Avci, E. (2008). Comparison of wavelet-SVM and wavelet-adaptive network based fuzzy inference system for texture classification. Digital Signal Processing, 18(1), 15–24.

    Article  Google Scholar 

  • Van Der Heide, A., Sánchez, D., & Trivino, G. (2011, August). Computational models of affect and fuzzy logic. In Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (pp. 620–627). Atlantis Press.

    Google Scholar 

  • Van Leekwijck, W., & Kerre, E. E. (1999). Defuzzification: Criteria and classification. Fuzzy Sets and Systems, 108(2), 159–178.

    Article  Google Scholar 

  • Wagenmakers, E. J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., Selker, R., Gronau, Q. F., Šmíra, M., Epskamp, S., Matzke, D., Rouder, J. N., & Morey, R. D. (2018). Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications. Psychonomic Bulletin & Review, 25(1), 35–57. https://doi.org/10.3758/s13423-017-1343-3. PMID: 28779455; PMCID: PMC5862936.

    Article  Google Scholar 

  • Wang, L., Dong, J. Y., & Li, S. L. (2015). Fuzzy inference algorithm based on quantitative association rules. Procedia Computer Science, 61, 388–394.

    Article  Google Scholar 

  • Wanga, H., Yanga, B., & Li, W. (2022). Some properties of fuzzy t-norm and vague t-norm. https://doi.org/10.48550/arXiv.2205.09231

  • Wierman, M. J. (2010). An introduction to the mathematics of uncertainty. Creighton University, 149–150.

    Google Scholar 

  • Zeki, S. (2001). Localization and globalization in conscious vision. Annual Review of Neuroscience, 24(1), 57–86.

    Article  PubMed  Google Scholar 

  • Zeki, S. (2003). The disunity of consciousness. Trends in Cognitive Sciences, 7(5), 214–218.

    Article  PubMed  Google Scholar 

  • Zétényi, T. (Ed.). (1988). Fuzzy sets in psychology. Elsevier.

    Google Scholar 

  • Zimmermann, H. J. (2011). Fuzzy set theory—And its applications. Springer Science & Business Media.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tarbiat Modares University, Tehran, Iran

    Hojjatollah Farahani, Parviz Azadfallah, Forough Esrafilian & Sara Saljoughi

  2. University of Kragujevac, Kragujevac, Serbia

    Marija Blagojević

  3. University of Cambridge, Cambridge, UK

    Peter Watson

Authors
  1. Hojjatollah Farahani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marija Blagojević
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Parviz Azadfallah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peter Watson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Forough Esrafilian
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sara Saljoughi
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Farahani, H., Blagojević, M., Azadfallah, P., Watson, P., Esrafilian, F., Saljoughi, S. (2023). Fuzzy Set Theory and Psychology. In: An Introduction to Artificial Psychology. Springer, Cham. https://doi.org/10.1007/978-3-031-31172-7_3

Download citation

Publish with us

Policies and ethics

Search

Navigation