Convite à Psicologia Matemática
Modelos e Benefícios da Teorização Formal
DOI:
https://doi.org/10.1590/0102.3772e39515.enPalavras-chave:
Psicologia matemática, Teorização formal, Modelagem quantitativaResumo
Na maior parte das áreas os fenômenos psicológicos tendem a ser explicados apenas por meio de construções textuais. Diversos autores, no entanto, apontam para a necessidade de teorias que tenham uma natureza mais formal, baseada em raciocínio matemático. A fim de incentivar acesso mais amplo às suas aplicações, apresentamos os modelos e vantagens da abordagem da psicologia matemática para o estudo do comportamento. Revisamos as limitações da teorização verbal, apresentando em seguida uma taxonomia, comum na psicologia matemática, que classifica os modelos de dados como descritivos, explicativos e de caracterização. Como casos bem sucedidos, examinamos a psicologia matemática da tomada de decisão, do comportamento de ajuda, da memória e dos relacionamentos românticos. Por fim, discutimos os benefícios e usos potenciais da abordagem. Bem-vindo(a) à psicologia matemática.
Downloads
Referências
Adner, R., Polos, L., Ryall, M., & Sorenson, O. (2009). The case for formal theory. Academy of Management Review, 34(2), 201-208. https://doi.org/10.5465/amr.2009.36982613
Altman, M. (2004). The Nobel Prize in behavioral and experimental economics: A contextual and critical appraisal of the contributions of Daniel Kahneman and Cernon Smith. Review of Political Economy, 16(1), 3-41. https://doi.org/10.1080/0953825032000145445
Amato, P. R. (2007). Alone together: How marriage in America is changing. Harvard University Press.
Baron, J. (2007). Thinking and deciding. Cambridge University Press.
Batchelder, W. H., Colonius, H., Dzhafarov, E. N., & Myung, J. (Eds.). (2016). New handbook of mathematical psychology: Volume 1, Foundations and Methodology. Cambridge University Press.
Box, G. E. P., & Draper, N. R. (1987). Empirical model-building and response surfaces. John Wiley & Sons.
Brown, G. D. A., Neath, I., & Chater, N. (2007). A temporal ratio model of memory. Psychological Review, 114, 539-576.
Bruza, P. D., Wang, Z., & Busemeyer, J. R. (2015). Quantum cognition: A new theoretical approach to psychology. Trends in Cognitive Sciences, 19(7), 383-393. https://doi.org/10.1016/j.tics.2015.05.001
Busemeyer, J. R., & Bruza, P. D. (2012). Quantum models of cognition and decision. Cambridge University Press.
Busemeyer, J. R., Wang, Z., Eidels, A., & Townsend, J. T. (2015). Review of basic mathematical concepts used in computational and mathematical psychology. In J.R. Busemeyer, Z. Wang, A. Eidels & J.T. Townsend (Eds.), The Oxford handbook of computational and mathematical psychology (pp. 1-10). Oxford University Press.
Coombs, C. H., Dawes, R. M., & Tversky, A. (1970). Mathematical psychology: An elementary introduction. Prentice Hall.
Dancey, C., & J. Reidy (2006). Estatística sem matemática para psicologia [Statistics without Maths for Psychology]. Bookman/Artmed.
Devlin, K. J. (2012). Introduction to mathematical thinking. Keith Devlin.
Doignon, J. P., & Falmagne, J. C. (1991). Mathematical psychology: Current developments. Springer-Verlag.
Edwards, W. (1977). How to use multiattribute utility measurement for social decisionmaking. IEEE Transactions on Systems, Man, and Cybernetics, 7(5), 326-340. https://doi.org/10.1109/TSMC.1977.4309720
Falmagne, J. C. (2005). Mathematical psychology: a perspective. Journal of Mathematical Psychology, 49(6), 436-439. https://doi.org/10.1016/j.jmp.2005.06.007
Fischhoff, B., & Broomell, S. B. (2020). Judgment and decision making. Annual Review of Psychology, 71, 331-355. https://doi.org/10.1146/annurev-psych-010419-050747
Fum, D., Del Missier, F., & Stocco, A. (2007). The cognitive modeling of human behavior: Why a model is (sometimes) better than 10,000 words. Cognitive Systems Research, 8, 135-142. https://doi.org/10.1016/j.jmp.2005.06.007
Gigerenzer, G., & Murray, D. J. (2015). Cognition as intuitive statistics. Psychology Press.
Gottman, J. M., Murray, J. D., Swanson, C. C., Tyson, R., & Swanson, K. R. (2002). The mathematics of marriage: Dynamic nonlinear models. MIT Press.
Heathcote, A., Brown, S., & Mewhort, D. J. (2000). The power law repealed: The case for an exponential law of practice. Psychonomic Bulletin & Review, 7, 185-207. https://doi.org/10.3758/BF03212979
Hunt, E. (2006). The mathematics of behavior. Cambridge University Press.
Janis, I. L., & Mann, L. (1977). Decision making: A psychological analysis of conflict, choice, and commitment. Free Press.
Kahana, M. J. (2020). Computational models of memory search. Annual Review of Psychology, 71, 107-138. https://doi.org/10.1146/annurev-psych-010418-103358
Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American Economic Review, 1449-1475.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society, 47, 263-291. https://doi.org/10.1142/9789814417358_0006
Kocher, M. G., & Sutter, M. (2005). The decision maker matters: Individual versus group behaviour in experimental beauty‐contest games. The Economic Journal, 115(500), 200-223. https://doi.org/10.1111/j.1468-0297.2004.00966.x
Lewandowsky, S., & Farrell, S. (2000). A redintegration account of the effects of speech rate, lexicality, and word frequency in immediate serial recall. Psychological Research, 63, 163-173. https://doi.org/10.1007/PL00008175
Lewandowsky, S., & Farrell, S. (2010). Computational modeling in cognition: Principles and practice. Sage.
Lieder, F., & Griffiths, T. L. (2020). Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources. Behavioral and Brain Sciences, 43, 1-85. https://doi.org/10.1017/S0140525X1900061X
Luce, R. D. (1995). Four tensions concerning mathematical modeling in psychology. Annual Review of Psychology, 46(1), 1-27. https://doi.org/10.1146/annurev.ps.46.020195.000245
Luce, R. D. (1997). Several unresolved conceptual problems of mathematical psychology. Journal of Mathematical Psychology, 41(1), 79-87. https://doi.org/10.1006/jmps.1997.1150
Luce, R. D., R. R. Bush, & E. Galanter (Eds.). (1963-1965a). Handbook of mathematical psychology (Vols. 1-2) . Wiley.
Luce, R. D., R. R. Bush, & E. Galanter (Eds.). (1963-1965b). Readings in mathematical psychology (Vols. 1-2). Wiley
Mertens, D. M. (2014). Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methods. Sage Publications.
McGrath, R. E. (2011). Quantitative models in psychology. American Psychological Association.
Millroth, P., & Collsiöö, A. (2020). Strictly Minskyian: Advancing theories of decision making under risk by carefully mapping current states of individuals. Unpublished manuscript. http://dx.doi.org/10.13140/RG.2.2.17034.49602
Nelson, L. D., Simmons, J., & Simonsohn, U. (2018). Psychology’s renaissance. Annual Review of Psychology, 69, 511-534. https://doi.org/10.1146/annurev-psych-122216-011836
Norris, D. (2005). How do computational models help us build better theories? In A. Cutler (Ed.), Twenty-first century psycholinguistics: Four cornerstones (pp. 331-346). Lawrence Erlbaum.
Pasquali, L. (2001). Técnicas de exame psicológico - TEP: Manual [Psychological exam techniques: Guide]. Casa do Psicólogo.
Regenwetter, M., Dana, J., & Davis-Stober, C. P. (2011). Transitivity of preferences. Psychological review, 118(1), 42-56. https://doi.org/10.1037/a0021150
Schweickert, R. (1993). A multinomial processing tree model for degradation and redintegration in immediate recall. Memory & Cognition, 21, 168-175. https://doi.org/10.3758/BF03202729
Simon, H. A. (1959). Theories of decision-making in economics and behavioral science. The American Economic Review, 49(3), 253-283.
Smaldino, P. E., & Epstein, J. M. (2015). Social conformity despite individual preferences for distinctiveness. Royal Society Open Science, 2(3), 140437. https://doi.org/10.1098/rsos.140437
Stanovich, K. E. (2015). Rational and irrational thought: The thinking that IQ tests miss. Scientific American Mind Special Collector’s Edition, 23(4), 12-17.
Srivastava, S. (2009, May 14). Making progress in the hardest science. https://thehardestscience.com/2009/03/14/making-progress-in-the-hardest-science/
Townsend, J. T. (2008). Mathematical psychology: Prospects for the 21st century: a guest editorial. Journal of Mathematical Psychology, 52(5), 269-280. https://doi.org/10.1016/j.jmp.2008.05.001
Turner, B. M., Forstmann, B. U., Wagenmakers, E. J., Brown, S. D., Sederberg, P. B., & Steyvers, M. (2013). A Bayesian framework for simultaneously modeling neural and behavioral data. NeuroImage, 72, 193-206. https://doi.org/10.1016/j.neuroimage.2013.01.048
Van Zandt, T., & Townsend, J. T. (2012). Mathematical psychology. In H. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA handbook of research methods in psychology, Vol. 2. Research designs: Quantitative, qualitative, neuropsychological, and biological (pp. 369-386). American Psychological Association.https://doi.org/10.1037/13620-020
Von Bertalanffy, L. (1968). Organismic psychology and systems theory. Clark University Press.
Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100-1122. https://doi.org/10.1177/1745691617693393
Yukalov, V. I., & Sornette, D. (2008). Quantum decision theory as quantum theory of measurement. Physics Letters A, 372(46), 6867-6871. https://doi.org/10.1016/j.physleta.2008.09.053
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2023 Víthor Rosa Franco, Fabio Iglesias
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.