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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 filler memory signals. This paper proposes a three-dimensional signal detection theory (3D-SDT) model to decipher the witness decision-making process. The 3D-SDT model clarifies the importance of considering the joint distributions of suspect and filler memory signals. The model also visualizes the joint memory distributions in a three-dimensional decision space, which allows for the incorporation of all eyewitness responses, including suspect identifications, filler identifications, and rejections. The paper starts with a set of simple assumptions to develop the 3D-SDT model, including equal variance, no criterion shift, and uncorrelated memory signals. The paper then develops the model under alternative assumptions that can accommodate more sophisticated considerations. Finally, the paper discusses potential applications of the 3D-SDT model for eyewitness research. With a mathematical modeling and visualization approach, the 3D-SDT model provides a transformative theoretical framework for understanding eyewitness identification decisions and analyzing how various factors affect these decisions. |
