Using Measurement Models to Understand Eyewitness Identification

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dc.contributor.authorKaesler, M.P.
dc.contributor.authorDunn, J.
dc.contributor.authorSemmler, C.
dc.contributor.conference39th Annual Meeting of the Cognitive Science Society (CogSci) (26 Jul 2017 - 29 Jul 2017 : London, UK)
dc.contributor.editorGunzelman, G.
dc.contributor.editorHowes, A.
dc.contributor.editorTenbrik, T.
dc.contributor.editorDavelaar, E.
dc.date.issued2017
dc.description.abstractMuch research effort has been expended improving police lineup procedures used in collecting eyewitness identification evidence. Sequential presentation of lineup members, in contrast to simultaneous presentation, has been posited to increase witness accuracy, though analyses based in Signal Detection Theory (SDT) have challenged these claims. A possible way to clarify the effect of presentation format on witness accuracy is to develop SDT-based measurement models, which characterise decision performance in terms of psychologically-relevant parameters, particularly discriminability and response bias. A model of the sequential lineup task was developed with a “first-above-criterion” decision rule, alongside a simultaneous model with a “maximum familiarity” decision rule. These models were fit to a corpus of data comparing simultaneous and sequential lineup performance. Results showed no difference in discriminability between the procedures and more conservative responding for the sequential lineup. Future work will examine criterion setting in the sequential lineup and model alternative decision rules.
dc.description.statementofresponsibilityMatthew Kaesler, John Dunn, Carolyn Semmler
dc.identifier.citationProceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition (CogSci 2017), 2017 / Gunzelman, G., Howes, A., Tenbrik, T., Davelaar, E. (ed./s), pp.625-630
dc.identifier.isbn9780991196760
dc.identifier.orcidKaesler, M.P. [0000-0003-0136-4837]
dc.identifier.orcidDunn, J. [0000-0002-3950-3460]
dc.identifier.orcidSemmler, C. [0000-0001-7912-293X]
dc.identifier.urihttps://hdl.handle.net/2440/136724
dc.language.isoen
dc.publisherCognitive Science Society
dc.publisher.placeUSA
dc.relation.granthttp://purl.org/au-research/grants/arc/DP160101048
dc.rightsCopyright status unknown
dc.source.urihttps://cogsci.mindmodeling.org/2017/papers/0126/index.html
dc.subjectEyewitness identification; Signal Detection; memory
dc.titleUsing Measurement Models to Understand Eyewitness Identification
dc.typeConference paper
pubs.publication-statusPublished

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