Toward a More Comprehensive Modeling of Sequential Lineups

Autor/inn/en	Kellen, David; McAdoo, Ryan M.
Titel	Toward a More Comprehensive Modeling of Sequential Lineups
Quelle	In: Cognitive Research: Principles and Implications, 7 (2022), Artikel 65 (35 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2365-7464
DOI	10.1186/s41235-022-00397-3
Schlagwörter	Guidelines; Comparative Analysis; Police; Law Enforcement; Identification; Decision Making; Risk; Theories; Simulation; Models + Suchen Sie Ihr Suchwort? Richtlinien; Gesetzesvollzug; Identifikation; Identifizierung; Decision-making; Entscheidungsfindung; Risiko; Theory; Theorie; Simulation program; Simulationsprogramm; Analogiemodell
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01