ex-Gaussian 모형을 활용한 인지적 과제의 반응시간 분포 분석
The ex-Gaussian analysis of reaction time distributions for cognitive experiments
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초록
대부분의 인지적 과제에서 관찰되는 반응시간 자료의 분포는 정적으로 편포되어 나타남에도 불구하고, 반응시간을 종속측정치로 하는 대다수의 연구들은 표본 평균에 근거한 집중경향치 분석에 의존한다. 본 연구에서는 반응시간 자료의 분포특성에 분석의 초점을 맞추어 실험적 처치의 효과를 구체적으로 추론하는 방법을 소개하였다. 평균 반응시간의 변화는 그 분포상 가우시안 및 지수 분포가 혼합된 형태로 나타난다고 가정할 수 있으며, 최대우도 추정법에 근거한 ex-Gaussian 모형 검증을 통해 반응시간 분포 특성을 수치화된 파라미터로 산출하고 확률밀도함수를 구현할 수 있다. 분석 사례를 위해 두 가지 고전적 시각탐색과제에서 얻어진 반응시간 자료를 사용하였으며, ex-Gaussian 함수를 통해 탐색배열의 항목개수의 증가가 초래하는 평균 반응시간의 지연효과에 대한 해석을 시도하였다. 수리적 모형을 통한 반응시간 분포 분석은 고전적 집중경향치 분석의 한계를 넘어 반응시간을 활용한 다양한 이론 및 개인차 연구에서 활용될 수 있을 것으로 기대된다.
Abstract
Although most behavioral reaction times (RTs) for cognitive tasks exhibit positively skewed distributions, the majority of studies primarily rely on a measure of central tendency (e.g. mean) which can cause misinterpretations of data’s underlying property. The purpose of current study is to introduce procedures for describing characteristics of RT distributions, thereby effectively examine the influence of experimental manipulations. On the basis of assumption that RT distribution can be represented as a convolution of Gaussian and exponential variables, we fitted the ex-Gaussian function under a maximum-likelihood method. The ex-Gaussian function provides quantitative parameters of distributional properties and the probability density functions. Here we exemplified distributional analysis by using empirical RT data from two conventional visual search tasks, and attempted theoretical interpretation for setsize effect leading proportional mean RT delays. We believe that distributional RT analysis with a mathematical function beyond the central tendency estimates could provide insights into various theoretical and individual difference studies.
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