Disentangling Different Aspects of Between-Item Similarity Unveils Evidence Against the Ensemble Model of Lineup Memory

Computational Brain & Behavior(2022)

引用 1|浏览0
暂无评分
摘要
For modeling recognition decisions in a typical eyewitness identification lineup task with multiple simultaneously presented test stimuli (also known as simultaneous detection and identification), essentially two different models based on signal detection theory are currently under consideration. These two models mainly differ with respect to their assumptions regarding the interplay between the memory signals of different stimuli presented in the same lineup. The independent observations model (IOM), on the one hand, assumes that the memory signal of each simultaneously presented test stimulus is separately assessed by the decision-maker, whereas the ensemble model (EM), on the other hand, assumes that each of these memory signals is first compared with and then assessed relative to its respective context (i.e., the memory signals of the other stimuli within the same lineup). Here, we discuss some reasons why comparing confidence ratings between trials with and without a dud (i.e., a lure with no systematic resemblance to the target) in an otherwise fair lineup—results of which have been interpreted as evidence in favor of the EM—is in fact inconclusive for differentiating between the EM and the IOM. However, the lack of diagnostic value hinges on the fact that in these experiments two aspects of between-item similarity (viz. old–new and within-lineup similarity) are perfectly confounded. Indeed, if separately manipulating old–new similarity, we demonstrate that EM and IOM make distinct predictions. Following this, we show that previously published data are inconsistent with the predictions made by the EM.
更多
查看译文
关键词
Recognition memory,Similarity,Eyewitness identification,Ensemble models,Signal detection theory,Simultaneous detection and identification
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要