Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-24T01:16:33.296Z Has data issue: false hasContentIssue false
  • English
  • Français

Prediction of Anger Expression of Individuals with Psychiatric Disorders using the Developed Computational Codes based on the Various Soft Computing Algorithms

Published online by Cambridge University Press:  23 December 2019

Maryam Moghadasin
Maryam Moghadasin*
Affiliation:
Kharazmi University (Iran)
*
*Correspondence concerning this article should be addressed to Maryam Moghadasin. Kharazmi University. Faculty of Psychology and Education. 14911–15719 Tehran (Iran). E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Anger is defined as a psychobiological emotional state that consists of feelings varying in intensity from mild irritation or annoyance to intense fury and rage. Dysfunction in anger regulation is marker of most psychiatric disorders. The most important point about anger regulation by the individuals is how to express anger and control it. The purpose of the present study is to predict the anger expression from the anger experience in individuals with psychiatric disorder for assessment of how to express and control the anger. To this end, the number of 3,000 subjects of individuals with clinical disorders had filled in the State-Trait Anger Expression Inventory–II (STAXI–II). After removing the uncertain diagnoses (900 subjects), the number of 2,100 data was considered in the analysis. Then, the computational codes based on three soft computing algorithms, including Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Decision Tree (DT) were developed to predict the scales of anger expression of the individuals with psychiatric disorders. The scales of anger experience were used as input data of the developed computational codes. Comparison between the results obtained from the DT, RBF and ANFIS algorithms show that all the developed soft computing algorithms forecast the anger expression scales with an acceptable accuracy. However, the accuracy of the DT algorithm is better than the other algorithms.

Keywords

anger experienceanger expressionpredictionpsychiatric disorderssoft computing
Type
Research Article
Information
The Spanish Journal of Psychology , Volume 22 , 2019 , E62
Copyright
Copyright © Universidad Complutense de Madrid and Colegio Oficial de Psicólogos de Madrid 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

How to cite this article:

Moghadasin, M. (2019). Prediction of anger expression of individuals with psychiatric disorders using the developed computational codes based on the various soft computing algorithms. The Spanish Journal of Psychology, 22. e62. Doi:10.1017/sjp.2019.59

References

Abraham, A. (2005a). Adaptation of fuzzy inference system using neural learning. In Nedjah, N. & Macedo, M. L. (Eds.), Fuzzy systems engineering (pp. 53–83). Heidelberg, Germany: Springer.CrossRefGoogle Scholar
Abraham, A. (2005b). Artificial neural networks. Handbook of measuring system design. New Jersey; NJ: John Wiley & Sons, Ltd.Google Scholar
Almeida, A., & Azkune, G. (2018). Predicting human behavior with recurrent neural networks. Applied Sciences, 8(2), 305–317. https://doi.org/10.3390/app8020305CrossRefGoogle Scholar
Broomhead, D. S., & Lowe, D. (1988). Radial basis functions, multi–variable functional interpolation and adaptive networks. RSRE-Memorandum No.4148. Royal Signals and Radar Establishment Malvern, London, UK.Google Scholar
Chatterjee, S., & Shi, H. (2010, December). A novel neuro fuzzy approach to human emotion determination. Proceedings of the 2010 International Conference on Digital Image Computing: Techniques and Applications (pp. 282–287). Sydney, Australia.Google Scholar
Clay, D. L., Anderson, W. P., & Dixon, W. A. (1993). Relationship between anger expression and stress in predicting depression. Journal of Counseling & Development, 72(1), 91–94. https://doi.org/10.1002/j.1556-6676.1993.tb02283.xCrossRefGoogle Scholar
Curley, E. E., Tung, E. S., & Keuthen, N. J. (2016). Trait anger, anger expression, and anger control in trichotillomania: Evidence for the emotion regulation model. Journal of Obsessive-Compulsive and Related Disorders, 9, 77–81. https://doi.org/10.1016/j.jocrd.2016.04.005CrossRefGoogle Scholar
Deffenbacher, J. L., Oetting, E. R., Thwaites, G. A., Lynch, R. S., Baker, D. A., Stark, R. S., … Eiswerth-Cox, L. (1996). State–Trait Anger Theory and the utility of the Trait Anger Scale. Journal of Counseling Psychology, 43(2), 131–148. https://doi.org/10.1037/0022-0167.43.2.131CrossRefGoogle Scholar
Devi, S., Kumar, S., & Kushwaha, G. S. (2016, February). An adaptive neuro fuzzy inference system for prediction of anxiety of students. Proceedings of Eighth International Conference on Advanced Computational Intelligence (ICACI) (pp. 7–13). Chiang Mai, Thailand.Google Scholar
Di Nuovo, A. G., Catania, V., Di Nuovo, S., & Buono, S. (2008). Psychology with soft computing: An integrated approach and its applications. Applied Soft Computing, 8(1), 829–837. https://doi.org/10.1016/j.asoc.2007.03.001CrossRefGoogle Scholar
Durham, T. A., Byllesby, B. M., Lv, X., Elhai, J. D., & Wang, L. (2018). Anger as an underlying dimension of posttraumatic stress disorder. Psychiatry Research, 267, 535–540. https://doi.org/10.1016/j.psychres.2018.06.011CrossRefGoogle ScholarPubMed
Eysenck, H. J., & Eysenck, S. B. G. (1975). Manual of the Eysenck Personality Questionnaire (junior and adult). London, UK: Hodder and Stoughton.Google Scholar
Fava, M. (1998). Depression with anger attacks. The Journal of Clinical Psychiatry, 59(Suppl 18), 18–22. https://doi.org/10.1002/(SICI)1520-6394(1998)8:1+%3C59::AID-DA9%3E3.0.CO;2-YGoogle ScholarPubMed
Fava, G. A., Grandi, S., Rafanelli, C., Saviotti, F. M., Ballin, M., & Pesarin, F. (1993). Hostility and irritable mood in panic disorder with agoraphobia. Journal of Affective Disorders, 29(4), 213–217. https://doi.org/10.1016/0165-0327(93)90010CrossRefGoogle ScholarPubMed
Fergus, T. A., & Bardeen, J. R. (2014). Emotion regulation and obsessive–compulsive symptoms: A further examination of associations. Journal of Obsessive-Compulsive and Related Disorders, 3(3), 243–248. https://doi.org/10.1016/j.jocrd.2014.06.001CrossRefGoogle Scholar
Gould, R. A., Ball, S., Kaspi, S. P., Otto, M. W., Pollack, M. H., Shekhar, A., & Fava, M. (1996). Prevalence and correlates of anger attacks: A two site study. Journal of Affective Disorders, 39(1), 31–38. https://doi.org/10.1016/0165-0327(96)00017-1CrossRefGoogle ScholarPubMed
Gross, J. J. (2014). Emotion regulation: Conceptual and empirical foundations In Gross, JJ (Ed.), Handbook of emotion regulation (pp. 3–20). New York, NY: Guilford Press.Google Scholar
Harvey, P. D., Sukhodolsky, D., Parrella, M., White, L., & Davidson, M. (1997). The association between adaptive and cognitive deficits in geriatric chronic schizophrenic patients. Schizophrenia Research, 27(2–3), 211–218. https://doi.org/10.1016/S0920-9964(97)00068-6CrossRefGoogle ScholarPubMed
Hawkins, K. A., & Cougle, J. R. (2013). A test of the unique and interactive roles of anger experience and expression in suicidality: Findings from a population-based study. The Journal of Nervous and Mental Disease, 201(11), 959–963. https://doi.org/10.1097/NMD.0000000000000041CrossRefGoogle ScholarPubMed
Hopfield, J. J. (1988). Artificial neural networks. IEEE Circuits and Devices Magazine, 4(5), 3–10. https://doi.org/10.1109/101.8118CrossRefGoogle Scholar
Ioannou, S. V., Raouzaiou, A. T., Tzouvaras, V. A., Mailis, T. P., Karpouzis, K. C., & Kollias, S. D. (2005). Emotion recognition through facial expression analysis based on a neurofuzzy network. Neural Networks, 18(4), 423–435. https://doi.org/10.1016/j.neunet.2005.03.004CrossRefGoogle ScholarPubMed
Jang, J. S. R. (1991, July). Fuzzy modeling using generalized neural networks and Kalman Filter Algorithm. In Proceedings of 9th National Conference on Artificial Intelligence (AAAI–91), Vol. 4, No. 1 (pp. 762–767). Anaheim, CA.Google Scholar
Jang, J.-S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685. https://doi.org/10.1109/21.256541CrossRefGoogle Scholar
Kalghatgi, M. P., Ramannavar, M., & Sidnal, N. S. (2015). A neural network approach to personality prediction based on the big-five model. International Journal of Innovative Research in Advanced Engineering (IJIRAE), 2(8), 56–63.Google Scholar
Kitayama, S., Park, J., Boylan, J. M., Miyamoto, Y., Levine, C. S., Markus, H. R., … Love, G. D. (2015). Expression of anger and ill health in two cultures: An examination of inflammation and cardiovascular risk. Psychological Science, 26(2), 211–220. https://doi.org/10.1177/0956797614561268CrossRefGoogle ScholarPubMed
Lee, C., Yoo, S. K., Park, Y., Kim, N., Jeong, K., & Lee, B. (2006, January). Using neural network to recognize human emotions from heart rate variability and skin resistance. In IEEE Engineering in Medicine and Biology 27th Annual Conference (pp. 5523–5525). Shanghai, People’s Republic of China.Google Scholar
Lench, H. C. (2004). Anger management: Diagnostic differences and treatment implications. Journal of Social and Clinical Psychology, 23(4), 512–531. https://doi.org/10.1521/jscp.23.4.512.40304CrossRefGoogle Scholar
Levine, D. S. (1989). Neural network principles for theoretical psychology. Behavior Research Methods, Instruments, & Computers, 21(2), 213–224. https://doi.org/10.3758/BF03205585CrossRefGoogle Scholar
Lowe, D. (n.d.). Multi-variable functional interpolation and adaptive networks. Complex Systems, 2, 321–355. https://doi.org/10.4236/jbise.2013.65A003 3Google Scholar
Malkawi, M., & Murad, O. (2013). Artificial neuro fuzzy logic system for detecting human emotions. Human-Centric Computing and Information Sciences, 3(1), 3. https://doi.org/10.1186/2192-1962-3-3CrossRefGoogle Scholar
Martindale, C. (1991). Cognitive psychology: A neural-network approach. London, UK: Thomson Brooks/Cole Publishing Co.Google Scholar
Matson, J. L., Dixon, D. R., & Matson, M. L. (2005). Assessing and treating aggression in children and adolescents with developmental disabilities: A 20 year overview. Educational Psychology, 25(2–3), 151–181. https://doi.org/10.1080/0144341042000301148CrossRefGoogle Scholar
Mauss, I. B., & Butler, E. A. (2010). Cultural context moderates the relationship between emotion control values and cardiovascular challenge versus threat responses. Biological Psychology, 84(3), 521–530. https://doi.org/10.1016/j.biopsycho.2009.09.01CrossRefGoogle ScholarPubMed
Nauck, D., Klawonn, F., & Kruse, R. (1997). Foundations of neuro-fuzzy systems. New York, NY: John Wiley & Sons, Inc.Google Scholar
Nicholson, J., Takahashi, K., & Nakatsu, R. (2000). Emotion recognition in speech using neural networks. Neural Computing & Applications, 9(4), 290–296. https://doi.org/10.1109/ICONIP.1999.845644CrossRefGoogle Scholar
Nicole, S., & Caprara, G. V. (2005). Assessing aggressive behavior in children. European. Journal of Psychological Assessment, 21(4), 255–264. https://doi.org/10.1027/1015-5759.21.4.255CrossRefGoogle Scholar
Norušis, M. J. (2011). IBM SPSS statistics 19 guide to data analysis. Upper Saddle River, NJ: Prentice Hall, Prentice Hall.Google Scholar
Park, J., Kitayama, S., Markus, H. R., Coe, C. L., Miyamoto, Y., Karasawa, M., … Ryff, C. D. (2013). Social status and anger expression: The cultural moderation hypothesis. Emotion, 13(6), 1122–1131. https://doi.org/10.1037/a0034273CrossRefGoogle ScholarPubMed
Potey, M., & Sinha, P. K. (2015). Review and analysis of machine learning and soft computing approaches for user modeling. International Journal of Web & Semantic Technology (IJWesT), 6(1), 39–55. https://doi.org/10.5121/ijwest.2015.6104CrossRefGoogle Scholar
Quinn, C. A., Rollock, D., & Vrana, S. R. (2014). A test of Spielberger’s state-trait theory of anger with adolescents: Five hypotheses. Emotion, 14(1), 74–84. https://doi.org/10.1037/a0034031CrossRefGoogle ScholarPubMed
Rachman, S. (1993). Obsessions, responsibility and guilt. Behaviour Research and Therapy, 31(2), 149–154. https://doi.org/10.1016/0005-7967(93)90066-4CrossRefGoogle ScholarPubMed
Rubenstein, C. S., Altemus, M., Pigott, T. A., Hess, A., & Murphy, D. L. (1995). Symptom overlap between OCD and bulimia nervosa. Journal of Anxiety Disorders, 9(1), 1–9. https://doi.org/10.1016/0887-6185(95)91551-RCrossRefGoogle Scholar
Sese, A., Palmer, A. L., & Montano, J. J. (2004). Psychometric measurement models and artificial neural networks. International Journal of Testing, 4(3), 253–266. https://doi.org/10.1207/s15327574ijt0403_4CrossRefGoogle Scholar
Song, J., Hwang, S., & Jeon, M. (2009). Relationship between anger level and anger-expression mode: Age group comparison. Korean Journal of School Psychology, 6(2), 213–227.CrossRefGoogle Scholar
Spielberger, C. D. (1999a). State-trait anger expression inventory–2. Odessa, FL: Psychological Assessment Resources.Google Scholar
Spielberger, C. D. (1999b). STAXI–2: State-trait anger expression inventory–2: Professional manual . Odessa, FL: Psychological Assessment Resources.Google Scholar
Spielberger, C. D., Jacobs, G., Russell, S., & Crane, R. S. (1983). Assessment of anger: The state-trait anger scale. Advances in Personality Assessment, 2, 159–187.Google Scholar
Spielberger, C. D., Krasner, S. S., & Solomon, E. P. (1988). The experience, expression, and control of anger. In Janisse, M. P. (Ed.) Individual Differences, Stress, and Health Psychology (pp. 89–108). New York, NY: Springer.CrossRefGoogle Scholar
Spielberger, C. D., & Reheiser, E. C. (2009). Assessment of emotions: Anxiety, anger, depression, and curiosity. Applied Psychology: Health and Well-Being, 1(3), 271–302. https://doi.org/10.1111/j.1758-0854.2009.01017.xGoogle Scholar
Spokas, M., Luterek, J. A., & Heimberg, R. G. (2009). Social anxiety and emotional suppression: The mediating role of beliefs. Journal of Behavior Therapy and Experimental Psychiatry, 40(2), 283–291. https://doi.org/10.1016/j.jbtep.2008.12.004CrossRefGoogle ScholarPubMed
Sprengelmeyer, R., Rausch, M., Eysel, U. T., & Przuntek, H. (1998). Neural structures associated with recognition of facial expressions of basic emotions. Proceedings of the Royal Society of London B: Biological Sciences, 265(1409), 1927–1931. https://doi.org/10.1098/rspb.1998.0522CrossRefGoogle ScholarPubMed
Subramanian, K., Suresh, S., & Babu, R. V. (2012, June). Meta-cognitive neuro-fuzzy inference system for human emotion recognition. In The 2012 International Joint Conference on Neural Networks (IJCNN) (pp. 1–7). Brisbane, Australia.Google Scholar
Tafrate, R. C., Kassinove, H., & Dundin, L. (2002). Anger episodes in high-and low-trait-anger community adults. Journal of Clinical Psychology, 58(12), 1573–1590. https://doi.org/10.1002/jclp.10076CrossRefGoogle ScholarPubMed
Teodorescu, H.-N. L., Kandel, A., & Jain, L. C. (1999). Soft computing in human-related sciences. Boca Raton, FL: CRC press.Google Scholar