A model of position effects in the sequential lineup

Dunn, John C.; Kaesler, Matthew; Semmler, Carolyn

doi:10.1016/j.jml.2021.104297

Cited by 14 publications

(46 citation statements)

References 29 publications

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“…Their results were consistent with the predictions of the DFD hypothesis. A more recent experiment reported by Dunn et al ( 2022 ) also produced consistent results.…”

Section: Sequential Lineupssupporting

confidence: 71%

“…More specifically, it allows us to test whether differences in observed responses can be attributed to (1) differences in discriminability, (2) shifts in the response criterion, (3) or both. This ability, which cannot be achieved by means of naïve summary statistics (see Rotello et al, 2015 ), has been put to use in eyewitness identification research (Dunn et al, 2022 ; Kaesler et al, 2020 ; Wilson et al, 2019 ; Wixted et al, 2017 ). Many of these SDT model implementations have focused on testing a prominent hypothesis in eyewitness research known as the diagnostic feature detection (DFD) hypothesis (Wixted & Mickes, 2014 ).…”

Section: Sequential Lineupsmentioning

confidence: 99%

“…Another hypothesis is that the positioning of the response criteria is not stable, being affected by the specific history of faces encountered by each eyewitness throughout the sequential lineup. Based on the modeling insights by Treisman and Williams ( 1984 ) and more recently Turner et al ( 2011 ), there is good reason to assume that response criteria, especially the criterion responsible for binary judgments, will increase after the rejection of the target (see also Dunn et al, 2022 ).…”

Section: General Modeling Approachmentioning

confidence: 99%

“…The different SDT models will be fit to the large-scale datasets reported by Wilson et al ( 2019 ) and Dunn et al ( 2022 ). Each of these studies will be considered in turn.…”

Section: General Modeling Approachmentioning

confidence: 99%

“…The goal of the present work is to address these issues, bridge a number of existing knowledge gaps, and raise new concerns. This general goal will be achieved by applying a large family of models based on Signal Detection Theory (SDT) to the large-scale sequential lineup data reported by Wilson et al ( 2019 ) and Dunn et al ( 2022 ). These models will allow us to test a number of hypotheses regarding people’s discriminability and response bias throughout the sequential lineup procedure.…”

mentioning

confidence: 99%

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Toward a more comprehensive modeling of sequential lineups

Kellen

McAdoo

2022

Cogn. Research

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Sequential lineups are one of the most commonly used procedures in police departments across the USA. Although this procedure has been the target of much experimental research, there has been comparatively little work formally modeling it, especially the sequential nature of the judgments that it elicits. There are also important gaps in our understanding of how informative different types of judgments can be (binary responses vs. confidence ratings), and the severity of the inferential risks incurred when relying on different aggregate data structures. Couched in a signal detection theory (SDT) framework, the present work directly addresses these issues through a reanalysis of previously published data alongside model simulations. Model comparison results show that SDT modeling can provide elegant characterizations of extant data, despite some discrepancies across studies, which we attempt to address. Additional analyses compare the merits of sequential lineups (with and without a stopping rule) relative to showups and delineate the conditions in which distinct modeling approaches can be informative. Finally, we identify critical issues with the removal of the stopping rule from sequential lineups as an approach to capture within-subject differences and sidestep the risk of aggregation biases.

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“…Their results were consistent with the predictions of the DFD hypothesis. A more recent experiment reported by Dunn et al ( 2022 ) also produced consistent results.…”

Section: Sequential Lineupssupporting

confidence: 71%

Section: Sequential Lineupsmentioning

confidence: 99%

Section: General Modeling Approachmentioning

confidence: 99%

“…The different SDT models will be fit to the large-scale datasets reported by Wilson et al ( 2019 ) and Dunn et al ( 2022 ). Each of these studies will be considered in turn.…”

Section: General Modeling Approachmentioning

confidence: 99%

mentioning

confidence: 99%

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Toward a more comprehensive modeling of sequential lineups

Kellen

McAdoo

2022

Cogn. Research

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

Meyer-Grant

Klauer

2022

Comput Brain Behav

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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.

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