Experimental validation of a multinomial processing tree model for analyzing eyewitness identification decisions
To improve police protocols for lineup procedures, it is helpful to understand the processes underlying eyewitness identification performance. The two-high threshold (2-HT) eyewitness identification model is a multinomial processing tree model that measures four latent cognitive processes on which e...
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          | Published in | Scientific reports Vol. 12; no. 1; pp. 15571 - 18 | 
|---|---|
| Main Authors | , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        London
          Nature Publishing Group UK
    
        16.09.2022
     Nature Publishing Group Nature Portfolio  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 2045-2322 2045-2322  | 
| DOI | 10.1038/s41598-022-19513-w | 
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| Abstract | To improve police protocols for lineup procedures, it is helpful to understand the processes underlying eyewitness identification performance. The
two-high threshold (2-HT) eyewitness identification model
is a multinomial processing tree model that measures four latent cognitive processes on which eyewitness identification decisions are based: two detection-based processes (the detection of culprit presence and absence) and two non-detection-based processes (biased and guessing-based selection). The model takes into account the full 2 × 3 data structure of lineup procedures, that is, suspect identifications, filler identifications and rejections in both culprit-present and culprit-absent lineups. Here the model is introduced and the results of four large validation experiments are reported, one for each of the processes specified by the model. The validation experiments served to test whether the model’s parameters sensitively reflect manipulations of the processes they were designed to measure. The results show that manipulations of exposure duration of the culprit’s face at encoding, lineup fairness, pre-lineup instructions and ease of rejection of culprit-absent lineups were sensitively reflected in the parameters representing culprit-presence detection, biased suspect selection, guessing-based selection and culprit-absence detection, respectively. The results of the experiments thus validate the interpretations of the parameters of the 2-HT eyewitness identification model. | 
    
|---|---|
| AbstractList | To improve police protocols for lineup procedures, it is helpful to understand the processes underlying eyewitness identification performance. The two-high threshold (2-HT) eyewitness identification model is a multinomial processing tree model that measures four latent cognitive processes on which eyewitness identification decisions are based: two detection-based processes (the detection of culprit presence and absence) and two non-detection-based processes (biased and guessing-based selection). The model takes into account the full 2 × 3 data structure of lineup procedures, that is, suspect identifications, filler identifications and rejections in both culprit-present and culprit-absent lineups. Here the model is introduced and the results of four large validation experiments are reported, one for each of the processes specified by the model. The validation experiments served to test whether the model’s parameters sensitively reflect manipulations of the processes they were designed to measure. The results show that manipulations of exposure duration of the culprit’s face at encoding, lineup fairness, pre-lineup instructions and ease of rejection of culprit-absent lineups were sensitively reflected in the parameters representing culprit-presence detection, biased suspect selection, guessing-based selection and culprit-absence detection, respectively. The results of the experiments thus validate the interpretations of the parameters of the 2-HT eyewitness identification model. To improve police protocols for lineup procedures, it is helpful to understand the processes underlying eyewitness identification performance. The two-high threshold (2-HT) eyewitness identification model is a multinomial processing tree model that measures four latent cognitive processes on which eyewitness identification decisions are based: two detection-based processes (the detection of culprit presence and absence) and two non-detection-based processes (biased and guessing-based selection). The model takes into account the full 2 × 3 data structure of lineup procedures, that is, suspect identifications, filler identifications and rejections in both culprit-present and culprit-absent lineups. Here the model is introduced and the results of four large validation experiments are reported, one for each of the processes specified by the model. The validation experiments served to test whether the model’s parameters sensitively reflect manipulations of the processes they were designed to measure. The results show that manipulations of exposure duration of the culprit’s face at encoding, lineup fairness, pre-lineup instructions and ease of rejection of culprit-absent lineups were sensitively reflected in the parameters representing culprit-presence detection, biased suspect selection, guessing-based selection and culprit-absence detection, respectively. The results of the experiments thus validate the interpretations of the parameters of the 2-HT eyewitness identification model. To improve police protocols for lineup procedures, it is helpful to understand the processes underlying eyewitness identification performance. The two-high threshold (2-HT) eyewitness identification model is a multinomial processing tree model that measures four latent cognitive processes on which eyewitness identification decisions are based: two detection-based processes (the detection of culprit presence and absence) and two non-detection-based processes (biased and guessing-based selection). The model takes into account the full 2 × 3 data structure of lineup procedures, that is, suspect identifications, filler identifications and rejections in both culprit-present and culprit-absent lineups. Here the model is introduced and the results of four large validation experiments are reported, one for each of the processes specified by the model. The validation experiments served to test whether the model's parameters sensitively reflect manipulations of the processes they were designed to measure. The results show that manipulations of exposure duration of the culprit's face at encoding, lineup fairness, pre-lineup instructions and ease of rejection of culprit-absent lineups were sensitively reflected in the parameters representing culprit-presence detection, biased suspect selection, guessing-based selection and culprit-absence detection, respectively. The results of the experiments thus validate the interpretations of the parameters of the 2-HT eyewitness identification model.To improve police protocols for lineup procedures, it is helpful to understand the processes underlying eyewitness identification performance. The two-high threshold (2-HT) eyewitness identification model is a multinomial processing tree model that measures four latent cognitive processes on which eyewitness identification decisions are based: two detection-based processes (the detection of culprit presence and absence) and two non-detection-based processes (biased and guessing-based selection). The model takes into account the full 2 × 3 data structure of lineup procedures, that is, suspect identifications, filler identifications and rejections in both culprit-present and culprit-absent lineups. Here the model is introduced and the results of four large validation experiments are reported, one for each of the processes specified by the model. The validation experiments served to test whether the model's parameters sensitively reflect manipulations of the processes they were designed to measure. The results show that manipulations of exposure duration of the culprit's face at encoding, lineup fairness, pre-lineup instructions and ease of rejection of culprit-absent lineups were sensitively reflected in the parameters representing culprit-presence detection, biased suspect selection, guessing-based selection and culprit-absence detection, respectively. The results of the experiments thus validate the interpretations of the parameters of the 2-HT eyewitness identification model. Abstract To improve police protocols for lineup procedures, it is helpful to understand the processes underlying eyewitness identification performance. The two-high threshold (2-HT) eyewitness identification model is a multinomial processing tree model that measures four latent cognitive processes on which eyewitness identification decisions are based: two detection-based processes (the detection of culprit presence and absence) and two non-detection-based processes (biased and guessing-based selection). The model takes into account the full 2 × 3 data structure of lineup procedures, that is, suspect identifications, filler identifications and rejections in both culprit-present and culprit-absent lineups. Here the model is introduced and the results of four large validation experiments are reported, one for each of the processes specified by the model. The validation experiments served to test whether the model’s parameters sensitively reflect manipulations of the processes they were designed to measure. The results show that manipulations of exposure duration of the culprit’s face at encoding, lineup fairness, pre-lineup instructions and ease of rejection of culprit-absent lineups were sensitively reflected in the parameters representing culprit-presence detection, biased suspect selection, guessing-based selection and culprit-absence detection, respectively. The results of the experiments thus validate the interpretations of the parameters of the 2-HT eyewitness identification model.  | 
    
| ArticleNumber | 15571 | 
    
| Author | Bell, Raoul Buchner, Axel Menne, Nicola M. Winter, Kristina  | 
    
| Author_xml | – sequence: 1 givenname: Kristina orcidid: 0000-0002-2220-5889 surname: Winter fullname: Winter, Kristina email: kristina.winter@hhu.de organization: Department of Experimental Psychology, Heinrich Heine University Düsseldorf – sequence: 2 givenname: Nicola M. orcidid: 0000-0001-9421-8286 surname: Menne fullname: Menne, Nicola M. organization: Department of Experimental Psychology, Heinrich Heine University Düsseldorf – sequence: 3 givenname: Raoul orcidid: 0000-0002-0592-0362 surname: Bell fullname: Bell, Raoul organization: Department of Experimental Psychology, Heinrich Heine University Düsseldorf – sequence: 4 givenname: Axel orcidid: 0000-0003-4529-3444 surname: Buchner fullname: Buchner, Axel organization: Department of Experimental Psychology, Heinrich Heine University Düsseldorf  | 
    
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36114219$$D View this record in MEDLINE/PubMed | 
    
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| CitedBy_id | crossref_primary_10_1016_j_cognition_2024_105917 crossref_primary_10_1038_s41598_023_44003_y crossref_primary_10_1038_s41598_024_63004_z crossref_primary_10_1038_s41598_024_78936_9 crossref_primary_10_1038_s41598_023_33101_6 crossref_primary_10_1038_s41598_024_64768_0 crossref_primary_10_1038_s41598_023_33424_4 crossref_primary_10_1038_s41598_022_17400_y  | 
    
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3 Erdfelder, Buchner (CR93) 1998; 127 Zarkadi, Wade, Stewart (CR68) 2009; 20 CR29 Smith, Bayen (CR24) 2004; 30 Kellen, Singmann, Vogt, Klauer (CR97) 2015; 62 Flowe, Ebbesen (CR57) 2007; 31 Meiser, Bröder (CR22) 2002; 28 Meiser, Sattler, Von Hecker (CR65) 2007; 60 Memon, Hope, Bull (CR40) 2003; 94 Palmer, Brewer, Weber, Nagesh (CR41) 2013; 19 KL Amendola (19513_CR14) 2015; 11 JS Neuschatz (19513_CR86) 2016 GL Wells (19513_CR3) 1998; 22 RCL Lindsay (19513_CR4) 1985; 70 T Valentine (19513_CR42) 2003; 17 JG Snodgrass (19513_CR34) 1988; 117 F Faul (19513_CR69) 2007; 39 MF Colloff (19513_CR44) 2016; 27 19513_CR29 A Buchner (19513_CR70) 1995; 124 JM Lampinen (19513_CR76) 2020; 34 JM Province (19513_CR98) 2012; 109 AN Doob (19513_CR82) 1973; 1 19513_CR43 E Erdfelder (19513_CR93) 1998; 127 M Sauerland (19513_CR58) 2009; 15 A Memon (19513_CR40) 2003; 94 N Tupper (19513_CR92) 2019; 25 H Singmann (19513_CR31) 2013; 45 19513_CR2 N Brewer (19513_CR54) 2010; 15 I Sučić (19513_CR84) 2015; 21 GL Wells (19513_CR7) 2000; 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17 TM Seale-Carlisle (19513_CR88) 2019; 25 GL Wells (19513_CR5) 2014; 23 J Schütz (19513_CR95) 2011; 58 T Meiser (19513_CR22) 2002; 28 R Horry (19513_CR62) 2021; 27 MA Palmer (19513_CR41) 2013; 19 JT Wixted (19513_CR17) 2015; 4 NA Macmillan (19513_CR19) 2005 CA Carlson (19513_CR38) 2014; 3 MF Colloff (19513_CR45) 2017; 32 C Stahl (19513_CR32) 2007; 39 K Murnane (19513_CR78) 1991; 17 JT Wixted (19513_CR99) 2007; 114 DM Riefer (19513_CR67) 1994; 20 GL Wells (19513_CR6) 2015; 11 SE Clark (19513_CR71) 2005; 29 HD Flowe (19513_CR57) 2007; 31 SM Andersen (19513_CR37) 2014; 60 PN Shapiro (19513_CR80) 1986; 100 DW Heck (19513_CR90) 2018; 50 JC Brigham (19513_CR55) 1990; 11 D Kellen (19513_CR96) 2015; 122 RCL Lindsay (19513_CR73) 1991; 76 RB Saraiva (19513_CR52) 2020; 25 N Brewer (19513_CR49) 2006; 30 SA Wetmore (19513_CR81) 2015; 4 D Kellen (19513_CR97) 2015; 62 TM Seale-Carlisle (19513_CR87) 2016; 3 RE Smith (19513_CR24) 2004; 30 J Meisters (19513_CR47) 2018; 26 A Bröder (19513_CR35) 2013; 21 C Stahl (19513_CR26) 2007; 54 M Moshagen (19513_CR30) 2010; 42 19513_CR60 E Hirshman (19513_CR66) 1995; 21 JT Wixted (19513_CR12) 2014; 121 RS Malpass (19513_CR77) 1981; 66 C Unkelbach (19513_CR27) 2009; 18 CA Carlson (19513_CR63) 2016; 30 KC Klauer (19513_CR94) 2010; 17 M Minear (19513_CR59) 2004; 36 GL Wells (19513_CR8) 2015; 4 DG Dobolyi (19513_CR85) 2013; 19 SE Clark (19513_CR83) 2012; 7 AP Yonelinas (19513_CR100) 2007; 133 HD Flowe (19513_CR56) 2011; 25  | 
    
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| Snippet | To improve police protocols for lineup procedures, it is helpful to understand the processes underlying eyewitness identification performance. The
two-high... To improve police protocols for lineup procedures, it is helpful to understand the processes underlying eyewitness identification performance. The two-high... Abstract To improve police protocols for lineup procedures, it is helpful to understand the processes underlying eyewitness identification performance. The...  | 
    
| SourceID | doaj unpaywall pubmedcentral proquest pubmed crossref springer  | 
    
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher  | 
    
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| SubjectTerms | 631/477 631/477/2811 Cognitive ability Crime Excipients Experiments Humanities and Social Sciences Humans Mental Recall multidisciplinary Police Recognition, Psychology Science Science (multidisciplinary)  | 
    
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| Title | Experimental validation of a multinomial processing tree model for analyzing eyewitness identification decisions | 
    
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