Introduction to the practical using of Baysian inference for Psychology Research

베이지언 처리는 근래 심리학에서 유용한 도구로 자리 잡아 가고 있다. 이러한 흐름은 심리학에서 인지처리를 보는 관점의 변화를 일으키고 있으며 영가설 검증에 기반을 둔 전통적인 통계 처리 방식과 경쟁하며 뿌리를 내리고 있다. 이 논문에서 통계 처리에 관한 두 관점인 빈도 주의와 베이지언 주의의 논쟁에서 벗어나 베이지언 처리 방식을 하나의 실용적인 처리 도구라는 관점에서, 심리학 연구의 주된 측정치인 반응비율과 반응시간을 베이지언 추론에서 어떻게 분석하는지 소개하고자 한다.

keywords: 베이지언 추론, 반응 비율, 반응 시간, 신호탐지이론, 고정 시간, 반응분포 추정, Baysian inference, response rate, response time, signal detection theory, fixation duration, distribution estimation

Abstract

Becoming a useful tool in the modern psychology, Baysian inference is a recent powerful movement to new statistics in order to improve traditional statistics based on the null-hypothesis significance testing (NHST). This tendency substantially challenges the view of cognitive processing and is being widely accepted as a new area of statistics. In this study, the authors introduce Baysian inference in terms of practical tool beyond the scope of the argument between frequency view and Baysian view. In addition, the authors present several examples to indicate how to use Baysian inference for an understanding of the results consisting of response ratio and reaction time that are popular in psychology studies.

keywords: 베이지언 추론, 반응 비율, 반응 시간, 신호탐지이론, 고정 시간, 반응분포 추정, Baysian inference, response rate, response time, signal detection theory, fixation duration, distribution estimation

참고문헌

김윤현, 고성룡, Valerie Hazan (2012). 한국어 화자의 영어 양순음 /b/와 순치음 /v/ 식별에서 시각 단서의 효과. 말소리와 음성과학, 4(3), 25-30.

오성주 (2014). 빨강-초록 조합에 대한 전경-배경 조직화에서 성차. 인지과학회, 25(2), 73-90.

황지영 (2012). 낱말 빈도와 차폐가 고정시간 분포에 미치는 효과. 서울대학교 석사학위 청구 논문.

Koh, S., & Hwang, J. (투고중). A hierarchical Bayesian analysis on eye fixation durations in Korean.

Balota, D. A. & Spieler D. H. (1999). Word frequency, repetition, and lexicality effects in word recognition tasks: Beyond measures of central tendency. Journal of Experimental Psychology: General, 12, 32-55.

Balota, D. A., Yap, M. J., Cortese, M. J., & Watson, J. M. (2008). Beyond mean response latency: Response time distributional analyses of semantic priming. Journal of Memory and Language, 59, 495-523.

Cumming, G. (2014). The new statistics: Why and How. Psychological Science, 25(1), 7-29.

Cumming, G. (2012). Understanding the new statistics: Effect sizes, confidence, intervals and meta-analysis. New York: Routledge.

Cousineau, D., Brown, S., & Heathcote, A. (2004) Fitting distributions using Maximum Likelihood: Methods and packages. Behavior Research, Methods, Instruments & Computer, 36, 277-290.

10.

Gelman, A., Carlin, J., Stern, H., & Rubin, D. (2003). Bayesian data analysis, Second Edition. Chapman & HALL/CRC.

11.

Green, P. (1995). Reversible Jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711-732.

12.

Gelman, A., Carlin, J., Stern, H., & Rubin, D. (1995). Bayesian data analysis. Chapman & HALL/CRC.

13.

Heathcote A, Brown S, & Cousineau D. (2004). QMPE: estimating Lognormal, Wald, and Weibull RT distributions with a parameter- dependent lower bound. Behav Res Methods Instrum Comput, 36(2), 277-90.

14.

Kass, R., & Raftery, A. (1995). Bayes factors. Journal of the American Statistical. Association 90, 773-795.

15.

Kruschke, J. K. (2013). Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General, 142(2), 573-603.

16.

Lee, M. D. (2008). BayesSDT: Software for Bayesian inference with signal detection theory. Behavior Research Methods, 40, 450-456.

17.

Lodewyckx, T., Kim, W., Lee, M., Tuerlinckx, F., Kuppens, P. & Wagenmakers, E. (2011). A tutorial on Bayes factor estimation with the product space method. Journal of Mathematical Psychology, 55, 331-347.

18.

Lunn, D. J., Thomas, A., Best, N., & Spiegelhalter, D. (2000). WinBUGS-A Bayesian modeling framework: Concepts, structure, and extensibility. Statistics & Computing, 10, 325-337.

19.

Macmillan, N. A., & Creelman, C. D. (2005). Detection theory: A user’s guide. New York: Lawrence Erlbaum Associates.

20.

Matzke, D. & Wagenmakers, E. (2009). Psychological interpretation of the ex-Gaussian and shifted Wald parameters: A diffusion model analysis. Psychonomic Bulletin & Review, 16, 798-817.

21.

Oh. (2011). The eyeglass reversal. Attention, Perception, and Psychophysics, 73, 1336-1343.

22.

Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59-108.

23.

Rouder, J., Lu, J., Speckman, P., Sun, D., & Jiang, Y. (2005). A hierarchical model for estimating response time distributions. Psychonomic Bulletin & Review, 12, 195-223.

24.

Staub, A. (2011). The effect of lexical predictability on distributions of eye fixation duration. Psychonomic Bulletin & Review, 18, 371-376.

25.

Staub, A., White, S., Drieghe, D., Hollway, E., & Rayner, K. (2010). Distributional effects of word frequency on eye fixation durations. Journal of Experimental Psychology: Human Perception and Performance, 36(5), 1280-1293.

26.

Swets, J. A., Dawes, R. M., & Monahan, J. (2000). Better decisions through science. Scientific American, October, 82-87.

27.

Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240, 1285-1293.

28.

Tenenbaum, J., Kemp, C., Griffiths T., & Goodman N. (2011). How to grow a mind: statistics, structure, and abstraction. Science, 331, 1279-1285.

29.

Wickens, T. D. (2002). Elementary signal detection theory. New York: Oxford University Press.

30.

Yap, M., & Balota, D. (2007). Additive and interactive effects on response time distribution in visual word recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 274-296.

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논문 상세

Vol.33 No.3

심리학 연구를 위한 실용적인 베이지언 추론의 소개

Introduction to the practical using of Baysian inference for Psychology Research

초록

Abstract

참고문헌

한국심리학회지: 일반