Korean Journal of Cognitive Science (인지과학)

The Korean Society for Cognitive Science (한국인지과학회)
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Analysis Program for Diffusion Model: SNUDM

확산모형 분석도구: SNUDM

  • Koh, Sungryong (Department of Psychology, Seoul National University) ;
  • Choo, Hyeree (Cognitive Program, Seoul National University) ;
  • Lee, Dajung (Department of Psychology, Seoul National University)
  • 고성룡 (서울대학교 심리학과) ;
  • 주혜리 (서울대학교 인지과학협동과정) ;
  • 이다정 (서울대학교 심리학과)
  • Received : 2020.05.07
  • Accepted : 2020.05.07
  • Published : 2020.03.31
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Abstract

This paper introduces SNUDM, an analysis program for Ratcliff's diffusion model, which has been one of the most important models in cognitive psychology over the past 35 years and which has come to occupy an important place in cognitive neuroscience in recent years. The analysis tool is designed with the basic principles of easy comprehension and simplicity in use. A diffusion process was programmed as the limit of a simple random walk in a manner resembling Ratcliff & Tuerlinckx(2002). The response time distribution of the model was constructed by simulating the time taken by a random walk until it reaches a threshold with small steps. The optimal parameter values in the model are found to be the smallest value of the chi-square values obtained by comparing the resulting distribution and the experimental data using Simplex method. For simplicity and ease of use, the input file used here is created as a file containing the quantile of the reaction time, the trials and other information. The number of participants and the number of conditions required for such work programs are given in a way that answers the question. Using this analysis tool, the experimental data of Ratcliff, Gomez, & McKoon(2004) were analyzed. We found the very similar pattern of parameter values to Ratcliff et al.(2004) found. When comparing DMAT, fast-dm and SNUDM with the generated data, we found that when the number of trials is small, SNUDM estimates the boundary parameter to a value similar to fast-dm and less than the DMAT. In addition, when the number of trials was large, it was confirmed that all three tools estimate parameters similarly.

이 논문에서는 지난 40여 년 동안 인지심리학에서 가장 중요한 모형 가운데 하나이며 근래에는 인지신경과학에서도 중요한 자리를 차지하고 있는 Ratcliff의 확산(diffusion)모형을 분석하는 도구 SNUDM을 소개한다. SNUDM은 확산과정을 Ratcliff & Tuerlinckx(2002)에 소개된 방식으로 단순 무작위걷기(random walk)를 묘사했다. 구체적으로, 모형이 생성하는 반응시간 분포는 주어진 파라미터 값들에서 작은 걸음으로 무작위걷기를 하여 일정 수준에 다다를 때까지 걸린 시간들로 이루어졌고, 모형의 파라미터 추정치는 단순도형(Simplex) 방식을 이용하여 실험 자료와 생성된 분포를 비교하기 위해 계산된 카이제곱값을 최소화하는 파라미터의 값을 사용한다. 사용의 간편함을 위해, 입력 파일은 반응시간의 분위수(quantile), 시행수와 기타 정보를 담은 파일로 간단하게 했고, 프로그램 작동에 필요한 피험자 수와 조건 수 등은 질문에 답을 하는 방식으로 입력하도록 했으며, 조건에 따라 비교할 파라미터와 그렇지 않고 고정할 파라미터도 미리 지정하도록 했다. 분석도구 SNUDM이 파라미터 값을 제대로 찾아내는지를 알아보기 위해 Ratcliff, Gomez, & McKoon(2004)의 실험1 자료를 써서 검토한 결과, 그들이 보고한 실험 조건들 사이에서 보인 상대적인 표집율의 크기에서 동일한 패턴을 얻었다. 또한 SNUDM으로 생성된 자료를 DMAT과 fast-dm의 자료와 비교해 보았을 때 SNUDM은 시행수가 적을 경우에는 경계 파라미터를 fast-dm과는 비슷한 값을 추정하였고 DMAT보다는 작은 값으로 추정했으나 시행수가 많은 경우에는 세 도구 모두 비슷하게 파라미터를 추정하는 것을 확인하였다.

Keywords

References

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