인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
How do witnesses make identification decisions when viewing a lineup? Understanding the witness decision-making process is essential for researchers to develop methods that can reduce mistaken identifications and improve lineup practices. Yet, the inclusion of fillers has posed a pivotal challenge to this task because the traditional signal detection theory is only applicable to binary decisions and cannot easily incorporate lineup fillers. This paper proposes a multi-item signal detection theory (mSDT) model to help understand the witness decision-making process. The mSDT model clarifies the importance of considering the joint distributions of suspect and filler signals. The model also visualizes the joint distributions in a multivariate decision space, which allows for the incorporation of all eyewitness responses, including suspect identifications, filler identifications, and rejections. The paper begins with a set of simple assumptions to develop the mSDT model and then explores alternative assumptions that can potentially accommodate more sophisticated considerations. The paper further discusses the implications of the mSDT model. With a mathematical modeling and visualization approach, the mSDT model provides a novel theoretical framework for understanding eyewitness identification decisions and addressing debates around eyewitness SDT and ROC applications.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.