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Bayesian interpretation of eyewitness statement data

Abstract

This study deals with a Bayesian approach in interpreting eyewitness statement data. The Bayesian approach has the advantage of being able to elucidate real world investigation and trial. The main purpose of this study is to summarize a series of research trials to prove these advantages. Wells & Lindsay(1980) are first research to take this approach. They newly invented the information gain model and this Bayesian formula proves that identification failure, such as false identification or inability to identify lineup participants also has diagnosticity which reduces suspect's probability of being a culprit. Wells & Turtle(1986) used the same information gain model context and analysis methodology, and suggest using the single-suspect model instead because the likelihood of false identification errors inherent in the all suspect model is higher when compared to the single-suspect model. Wells & Olson(2002) found that among the major system variables dealt with in eyewitness psychology, the contrasting effect of sequential lineup vs. simultaneous lineup and the effect of the lineup participant selection method were explained in the information gain model. Clark & Wells(2008) attempted to analyze the effect of identification response from two or more witnesses who witnessed an one culprit in the lineup based on diagnosticity indicator. My research integrate multiple evidences of guilty or not guilty to refer to implications of this study, and then examines the functional relationship that yields the final post-guilt probabilities. Wells, Yang, & Smalarz(2015) newly devised Base-Rate Effect-Equilvalency(BREE) curves as emphasizing that prior probability(base rate) is a very important system variable as it has been confirmed based on information gain model through Bayesian approach. In addition, it suggested that a new standard of “reasonable suspicion” is required as a legal premise for the lineup structure in order to prevent misjudgement due to system variables such as lineups.

keywords
Eyewitness statement, Bayesian, Base rate, Information gain, BREE(Base-Rate Effect Equivalency, 목격자 진술, 라인업, 베이지안, 기저율, 정보획득, 기저율 효과 등가성

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