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Individual differences predict eyewitness identification performance
Personality and Individual Differences 60 (2014) 36–40
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Individual differences predict eyewitness identification performance Shannon M. Andersen a,⇑, Curt A. Carlson b, Maria A. Carlson b, Scott D. Gronlund a a b
University of Oklahoma, Norman, OK 73019-2007, United States Texas A&M University-Commerce, Commerce, TX 75429, United States
a r t i c l e
i n f o
Article history: Received 26 April 2013 Received in revised form 18 November 2013 Accepted 9 December 2013 Available online 29 December 2013 Keywords: Eyewitness identification Simultaneous and sequential lineups Working memory Facial recognition ability Autism spectrum Need for Cognition
a b s t r a c t A great deal of research has focused on eyewitness identification performance as a function of sequential versus simultaneous lineup presentation methods. We examined if individual differences in cognitive ability influence eyewitness identification, and whether these factors lead to performance differences as a function of lineup presentation method. We found that individual differences in facial recognition ability, working memory capacity, and levels of autistic traits, did result in differential performance. Differences in lineup performance are due to the interaction of individual differences and presentation method, signaling that it is possible to enhance the accuracy of eyewitness identifications by tailoring a lineup presentation method to the capabilities of an individual eyewitness. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction In a perfect world, eyewitnesses would always correctly identify the guilty suspect and never identify an innocent suspect. However, it has been well documented that errors are made (see http://www.innocenceproject.org). But what if we could make it more likely that an eyewitness makes an accurate identification by tailoring a lineup to that individual’s cognitive ability? Simultaneous (all lineup members presented at once) and sequential (lineup members presented one-at-a-time) lineups have been a major focus of study (for a review see Gronlund, Andersen, & Perry, 2013). Lindsay and Wells (1985) first reported that presenting lineup members sequentially reduced innocent suspect identifications without significantly decreasing guilty suspect identifications. The resulting performance advantage came to be known as the sequential superiority effect. However, recent research has raised questions about this claim (e.g. Clark, 2012; Gronlund, Carlson, Dailey, & Goodsell, 2009). Using improved analysis tools, recent research has demonstrated that the reduction in false identifications described in the sequential superiority effect comes at the cost of a reduction in correct identifications and that the simultaneous lineup may actually lead to better performance (e.g., Gronlund, Wixted, & Mickes, in press). Is it possible that this ⇑ Corresponding author. Address: University of Oklahoma, Department of Psychology, 455 W. Lindsey Street, Dale Hall Tower, Room 705, Norman, OK 73019-2007, United States. Tel.: +1 (320) 224 8828. E-mail addresses: [email protected] (S.M. Andersen), curt.carlson@ tamuc.edu (C.A. Carlson), [email protected] (M.A. Carlson), sgronlund@ ou.edu (S.D. Gronlund). 0191-8869/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.paid.2013.12.011
disagreement may have arisen, in part, because some individuals perform better given a simultaneous lineup and others perform better given a sequential lineup? If we can identify individual difference factors that differentially affect performance as a function of lineup presentation method, it may be possible to enhance the accuracy of eyewitness identifications by tailoring a lineup presentation method to the capabilities of an individual eyewitness. In light of previous research, it is the goal of the present study to examine how individual differences in facial recognition ability, working memory capacity, Need for Cognition, and autism spectrum scores, predict sequential and simultaneous lineup performance. We begin by describing the individual difference factors we examined. We also hypothesize how each factor might affect eyewitness identification performance, and how each might differentially impact performance given simultaneous or sequential lineup presentation. 1.1. Facial recognition ability: Cambridge Face Memory Test (CFMT) Eyewitness identification performance depends on facial recognition ability. Morgan et al. (2007) assessed this ability in soldiers undergoing survival training. After 48 h of high-stress training with food and sleep deprivation, the soldiers were interrogated for about 30 min. After an 8-h recovery period, their facial recognition ability was assessed using the Wechsler Face Test, followed by a sequential lineup either containing their interrogator or not. Morgan et al. found that poorer performance on the Wechsler predicted more errors in the eyewitness identification task. We assessed facial recognition ability using the CFMT (Duchaine &
S.M. Andersen et al. / Personality and Individual Differences 60 (2014) 36–40
Nakayama, 2006). Like Morgan et al. (2007), we posited that individuals with better facial recognition ability would show improved eyewitness performance, marked by more correct identifications (of the guilty suspect) and fewer false identifications (of the innocent suspect), across both sequential and simultaneous lineups.
1.2. Working memory capacity: Automated Operation Span Task (AOSPAN) Individuals with higher working memory capacity (WMC) have more cognitive resources available for processing. Unsworth and Engle (2007) proposed that high capacity individuals can better maintain information in short-term memory, and can construct better retrieval cues resulting in superior access to information in long-term memory. Retrieval from long-term memory is critical to successful eyewitness identification. Current memory theory posits that retrieval is dependent on contributions from two processes (e.g., Yonelinas, 2002). Familiarity involves a reliance on a general sense of remembering a prior occurrence in the absence of any details. For example, one might see the neighborhood butcher on the bus, know that the person is familiar, but be unable to determine why. Recollection occurs when a person can remember details surrounding an event. For instance, determining that the man on the bus is the butcher because you remember he recommended salmon last week. Oberauer (2005) showed that WMC correlates with recollection, not familiarity. This especially is true when recollection can be used to retrieve distinctive information about the event in question (Mäntylä, 1997). Because recollection requires more WMC, it is difficult to bring to bear when capacity is scarce (Craik, Govoni, Naveh-Benjamin, & Anderson, 1996). Therefore, viewing one face at a time during a sequential lineup may allow participants to more readily use recollection. Gronlund (2005) proposed that a sequential lineup might result in superior performance when distinctive information about a perpetrator is encoded and recollection is used to retrieve this information; Carlson and Gronlund (2011) found support for this hypothesis. In consequence, individuals with greater WMC can better utilize recollection to enhance long-term memory retrieval, especially when presented with a sequential lineup. Conversely, eyewitnesses with less WMC may be distracted by the faces in a simultaneous lineup, hampering their ability to recollect a perpetrator’s face, thereby harming performance.
1.3. Autistic traits: Autism Spectrum Quotient (AQ) The facial details to which eyewitnesses pay attention during a lineup should affect their ultimate decision. For example, Darling, Martin, Hellmann, and Memon (2009) found that individual differences on a perceptual task predicted performance on a simultaneous lineup. Jones, Scullin, and Meissner (2011) used the AQ (Baron-Cohen, Wheelwright, Skinner, Martin, & Clubley, 2001) on a normal population, and found that greater levels of autistic traits predicted fewer guilty suspect identifications from a simultaneous lineup. In addition, a lower score on the attention switching subscale resulted in fewer correct identifications of the guilty suspect and worse discrimination in a sequential lineup. Higher scores on the attention to detail subscale resulted in worse discrimination and fewer guilty suspect identifications in the simultaneous lineup, but the opposite finding in the sequential lineup. Jones et al. utilized a paradigm in which participants learned multiple faces and completed multiple lineups within a single session. We sought to extend their findings to a more ecologically-valid paradigm, plus examine correlations of AQ with WMC/recollection.
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1.4. Preference for effortful cognitive activities: Need for Cognition (NFC) A lineup can be a difficult cognitive task. Therefore, individuals who enjoy effortful cognitive tasks may perform better than those who do not. Cacioppo, Petty, and Kao’s (1984) NFC scale measures an individual’s propensity to engage in effortful cognition. Individuals with high NFC use more cognitive effort in evaluating complex and difficult stimuli (see Cacioppo, Petty, Feinstein, & Jarvis, 1996, for a review). Consequently, high NFC individuals should more carefully evaluate lineup members, resulting in more accurate decisions. This might be especially true when individuals with greater NFC examine a sequential lineup because this task has been described as inherently more difficult (e.g., Carlson & Gronlund, 2011). 2. Method 2.1. Design We conducted an experiment with two independent variables: (a) lineup presentation method: simultaneous versus sequential, between-subjects, and (b) presence of the perpetrator in the lineup: perpetrator-present (PP) versus -absent (PA), within-subjects. Dependent variables assessed were correct identifications from PP lineups, innocent suspect identifications from PA lineups, and confidence. The predictor variables were CMFT, AQ, AOSPAN, and NFC. 2.2. Participants A total of 238 (170 females, 68 males; M age 19.94) students from two midwestern universities participated in this study. 2.3. Materials 2.3.1. Measures Using the upright version of the CFMT (Duchaine & Nakayama, 2006), participants learned and were tested on six male targets with neutral expressions and no visible hair or distinctive characteristics. Targets were presented with two similar distractors across three tests: recognition of the target (a) among distractors, (b) from different viewpoints, and (c) from different viewpoints with Gaussian noise (Wilmer et al., 2010). Higher scores (proportion correct out of 102 trials) correspond to better facial recognition ability. The AQ (Baron-Cohen et al., 2001) consists of five 10-item subscales: imagination, attention to detail, social skills, attention switching, and communication skills. Higher scores (range 0–50) indicate a greater preponderance of autistic traits. During the AOSPAN (Unsworth, Heitz, Schrock, & Engle, 2005), participants encounter two to seven math problem/letter iterations and then must report the letters in serial order. Recalling a high number of letters in the correct order across trials (range 0– 75) results in a high score and is indicative of a larger WMC. NFC (Cacioppo et al., 1984) measures an individual’s willingness to engage in effortful cognition. An example item is ‘‘I would prefer complex to simple problems.’’ Higher scores (range 0–72) indicate a greater NFC. 2.3.2. Videos We presented two different mock crimes to increase the generalizability of our findings. One video features a male perpetrator stealing a purse from a woman; the other involves a different male perpetrator car-jacking a woman’s car. Both perpetrators are Caucasian with dark hair, medium build, in their early-20s, and with no facial hair or other distinguishing features.
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2.3.3. Lineups Two lineups were created for each perpetrator. One lineup included a photo of the perpetrator (i.e., guilty suspect), plus five additional foils (PP lineup). In the PA lineup, the photo of the perpetrator was removed and an innocent replacement substituted, together with the same five foils. This lineup represents the case where the police have accused the wrong man. To assess lineup fairness, 10 volunteers watched the videos and provided descriptions of each perpetrator. We compiled these to get a modal description for each perpetrator. We provided another group of participants (N = 149) with the appropriate description and asked them to choose the person from each lineup who best matched the description. We computed Tredoux’s E0 (Tredoux, 1998) from this information, which approximates the number of reasonable competitors in these six-person lineups. The carjacker lineups were relatively fair (Tredoux’s E0 = 4.31 for the PP, 4.40 for the PA lineup); the purse-snatcher lineups were moderately fair (E0 = 3.07 for the PP, 2.59 for the PA lineup). 2.4. Procedure Participants were randomly assigned to either sequential or simultaneous lineups. The first lineup tested was randomly determined to be either PP or PA, and the second lineup was the converse. The experiment began with the carjacker video. Participants were instructed to pay attention to the video as they could be asked questions about it later. After the video, participants spent three minutes on a word search puzzle, and then viewed a PP or PA simultaneous or sequential lineup. They were informed that the perpetrator may or not be present in the lineup. After their decision, participants provided a confidence rating on a Likert scale ranging from 1 (not confident at all) to 7 (extremely confident). They then repeated this procedure for the purse theft video. Following the second lineup task, they completed the aforementioned measures in the order reported above. 3. Results and discussion In addition to the predictor variables (individual difference measures), the experiment examined three dependent variables: (a) correct identifications of the perpetrator from PP lineups, (b) false identifications of the innocent suspect from PA lineups, and (c) confidence in each lineup decision. Logistic regression is the appropriate analysis to address hypotheses regarding how the individual difference measures predict correct and false identifications because these data are binary (PP lineups: 1 = correct ID and 0 = not a correct ID; PA lineups: 1 = false ID, 0 = not a false ID). We begin with the overall accuracy results. Because ratio-based measures like diagnosticity (correct ID/false ID) confound accuracy and response bias (Wixted & Mickes, 2012), we compared the performance of sequential and simultaneous presentation methods using Receiver Operating Characteristic (ROC) analysis. We then present the logistic regression analyses, first for correct identifications, and then for false identifications. The descriptive statistics from these analyses are reported in Table 1. Table 2 presents the proportions of correct, false, foil identifications (IDs), and rejection rates.
Table 1 Descriptive statistics for the Autism Spectrum Quotient, Need for Cognition, Cambridge Face Memory Test, and Automated Operation Span Task. Measure
M
SD
ASQ total Attention to Detail Attention Switching Social skills Communication Imagination CFMT AOSPAN score NFC
17.21 5.37 5.12 1.98 2.25 2.50 66.80 38.00 10.17
5.61 2.13 1.95 2.03 1.81 1.55 10.74 18.61 21.24
Table 2 Traditional performance measures for simultaneous and sequential lineups. Perpetrator present
Perpetrator absent
Decision
Simultaneous
Sequential
Simultaneous
Sequential
ID Suspect ID Foil No ID Diagnosticity
.63 .11 .26 1.80
.48 .17 .36 1.26
.35 .17 .48
.38 .24 .38
Note: ID = identification; diagnosticity = correct ID rate/false ID rate.
gin utilizing signal detection based assessments like ROC analysis (Gronlund et al., 2012). An ROC is a plot of correct (perpetrator) versus false (innocent suspect) IDs across varying levels of confidence in the ID decision (see Fig. 1). The lower-left point of an ROC curve reflects the correct and false IDs made with the highest level of confidence (i.e., ‘‘extremely confident’’ that was the perpetrator). Each additional point extends the ROC curve as a cumulative record of correct and false ID rates across decreasing levels of confidence (i.e., the second point on the ROC reflects correct and false IDs made with the highest and second-highest confidence, the third point adds IDs with the third-highest confidence, and so forth). The closer an ROC curve is to the upper left-hand corner of the space, the more accurate the lineup presentation method. Statistically, we compare lineup presentation methods by computing the area under each ROC curve (AUC). The simultaneous and sequential lineup ROCs are presented in Fig. 1. Using the statistical package pROC (Robin et al., 2011), we analyzed the AUC for a false ID rate from 0 to .38, which subsumes the performance range of both curves. Although the simultaneous AUC (.15, 95%-confidence interval .11–.18) was greater than the
3.1. Overall accuracy: ROC analysis Recently it has been shown that ratio-based measures like diagnosticity are confounded with a willingness to choose (Gronlund et al., in press; Wixted & Mickes, 2012). It has been argued that researchers must follow the lead of medical diagnosticians and be-
Fig. 1. ROC curves for simultaneous (SIM) and sequential (SEQ) lineups. The solid line reflects the diagonal reflecting chance performance.
S.M. Andersen et al. / Personality and Individual Differences 60 (2014) 36–40 Table 3 Interaction terms in the logistic regression models. Included interaction terms Lineup ⁄ CFMT Lineup ⁄ AOSPAN Lineup ⁄ AQ Lineup ⁄ NFC CFMT ⁄ AOSPAN CFMT ⁄ AQ CFMT ⁄ NFC AOSPAN ⁄ AQ AOSPAN ⁄ NFC AQ ⁄ NFC Note: These terms were included, in both a model for correct identifications and a model for false identifications. Lineup = Simultaneous or Sequential; CFMT = Cambridge Face Memory Test; AOSPAN = Automated Operation Span Task; AQ = Autism Spectrum Quotient; NFC = Need for Cognition.
sequential (.11, 95%-confidence interval .08–.14), the difference was not significant (D1 = 1.45, p > .05). The effect size for this analysis (d = 0.21) can be classified as a small effect (Cohen, 1988).
3.2. Predictors of lineup accuracy: individual difference measures We next constructed two logistic regression models, one for PP lineups and one for PA lineups, because they produce mutuallyexclusive response variables: correct IDs from PP lineups and false IDs from PA lineups. Within each model, there was no difference between the two mock crime videos. Therefore, this variable was removed. There also was no difference in either correct or false IDs between simultaneous and sequential lineups, in line with the ROC results above. Finally, there were no interactions between any of the independent variables and the predictors. (See Table 3 for a list of all two-way interactions tested.) As a result, we describe the results of two different logistic regression models (one for simultaneous and one for sequential lineups) for each response variable.
3.2.1. Correct IDs of the perpetrator Correct IDs from the simultaneous lineup increased with higher CFMT (Odds Ratio (OR)2 = 1.01, X2 = 8.25, p = .004), higher AQ (OR = 1.03, X2 = 5.43, p = .02), and higher AOSPAN (OR = 1.01, X2 = 6.35, p = .01). That is, participants were more likely to ID the perpetrator if they had greater facial recognition ability, scored higher in Autism Spectrum Factors, or had higher WMC. When AQ was broken down into subscales, attention to detail (OR = 1.09, X2 = 7.04, p = .01) and attention switching (OR = 1.10, X2 = 7.00, p = .01) both positively predicted correct IDs in the simultaneous lineup. There were no significant predictors of correct IDs for the sequential lineup. This finding could be important, as it suggests a level of immunity of the sequential lineup to the individual difference variables tested in this study, at least when the perpetrator is present. However, this would not be beneficial if the sequential lineup failed to result in superior performance overall, which it did not in this study. We found no support for the prediction that higher NFC would be predictive of more correct IDs in either type of lineup. 1 D is defined as (AUC1 AUC2)/s, where s is the standard error of the difference between the two AUCs estimated by a bootstrap. 2 An OR of 1.01 for CFMT means that for every unit increase in CFMT score, a correct ID was 1% more likely. In the CFMT (0 -100), a participant who scores 10 units greater than another would improve the likelihood of a correct ID by 10%.
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3.2.2. False IDs of the innocent suspect For both simultaneous and sequential lineups, participants with higher CFMT, higher AOSPAN, and higher AQ scores, were less likely to choose the innocent suspect from PA lineups: CFMT (sequential OR = 0.99, X2 = 8.57, p = .003; simultaneous OR = 0.99, X2 = 9.12, p = .003), AOSPAN (sequential OR = 0.99, X2 = 7.85, p = .005; simultaneous OR = 0.99, X2 = 8.20, p < .001), AQ (sequential OR = 0.97, X2 = 7.61, p = .006; simultaneous OR = 0.97, X2 = 8.13, p = .004). Two AQ subscales yielded parallel effects across lineup type: attention to detail (simultaneous OR = 0.91, X2 = 7.88, p = .005; sequential OR = 0.91, X2 = 7.35, p = .006) and attention switching (simultaneous OR = 0.91, X2 = 7.85, p = .005; sequential OR = 0.91, X2 = 7.85, p = .005). In other words, higher attention to detail and attention switching led to fewer innocent suspect IDs for both simultaneous and sequential lineups. Other individual difference measures predicted performance from only simultaneous lineups, or only sequential lineups. Participants with higher NFC chose the innocent suspect less often from simultaneous lineups (OR = 0.97, X2 = 9.99, p = .001). However, those scoring higher on the other three AQ subscales selected the innocent suspect less often when presented in sequential lineups: social skills OR = 0.83, X2 = 5.69, p = 0.02, communication OR = 0.86, X2 = 4.47, p = .03; and imagination OR = 0.84, X2 = 6.88, p < .001. Jones et al. (2011) did not find that these subscales were significant predictors of lineup performance.
4. General discussion Despite the ROC analysis showing equivalent simultaneous and sequential lineup performance, certain individual differences differentially predicted lineup performance as a function of lineup type. Correct IDs from the simultaneous lineup were influenced positively by facial recognition ability, particular autistic traits, and WMC. Interestingly, the sequential lineup was immune to these individual differences when it came to PP lineups. However, this was not the case for PA lineups, as all but one of our measures predicted both simultaneous and sequential PA lineup performance. Better facial recognition ability predicted improved eyewitness performance, but it had a larger effect on simultaneous lineup performance insofar as it increased correct and decreased false IDs. This could be due to the fact that multiple faces must be considered at once, which understandably would tax one’s facial recognition ability more than viewing faces one at a time, resulting in a greater improvement for individuals with a better facial recognition ability when viewing a simultaneous lineup. Additionally, the CMFT and simultaneous lineup both require the selection of a target face among similar foils and therefore are similar tasks. Jones et al. (2011) found that increased scores on the attention to detail subscale of the AQ resulted in improved discrimination in sequential lineups due to an increase in correct IDs; they found the opposite for the simultaneous lineup. Our results partially replicated their findings, as we found that greater attention to detail improved both sequential and simultaneous discrimination per a reduction in false IDs. Jones et al. also reported that greater attention switching predicted better sequential lineup discrimination. We replicated this finding in the form of a reduction in false IDs for the sequential lineup, but found that attention switching had a stronger relationship with simultaneous lineup performance, predicting both correct and false ID rates. Jones et al. also suggested that greater attention to detail should benefit the sequential lineup because it requires focus on one face at a time. However, it is possible that it could instead lead individuals to better notice small differences among faces in a simultaneous lineup (Wixted
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& Mickes, in press), resulting in the detection of the perpetrator or rejection of an innocent suspect. Although we still lack a theory for interpreting the pattern of AQ results, there does appear to be some consistency in the results between the present study and Jones et al. Participants with higher WMC were more likely to make a correct ID from simultaneous lineups. This could be due to an increased ability to make comparisons among faces, but it is contrary to prior research proposing that the sequential lineup would free up more capacity to be deployed to utilize recollection and thereby enhance performance. However, this is exactly what happened to false IDs (see Gronlund, 2005). The extra capacity might have allowed recollection to engage a recall-to-reject strategy (Rotello, 2000) in sequential lineups to limit false IDs (Carlson & Gronlund, 2011). However, as greater capacity also predicted reduced false IDs from simultaneous lineups, more research is needed to delineate the relationship between WMC and recollection in simultaneous versus sequential lineups. This study was not without limitations. For example, we only examined normal functioning undergraduate participants using two different mock crime scenarios. To assess the robustness of our findings we need to examine a wider range of participants and materials. Although our study is more ecologically valid than Jones et al. (2011), our materials employed a mock crime procedure, which is different than the conditions an eyewitness to a live crime would experience. In a live crime, encoding is adversely affected by stress, poor lighting, weapon focus, etc. Overcoming these factors may demand more WMC, thereby making recollection difficult to deploy. This could hamper the predictive ability of the individual difference factors we examined in more realistic situations. 4.1. Conclusions and potential applications Our findings suggest that differences in lineup performance are a joint function of certain individual differences and lineup presentation method. Continued research into these factors could improve eyewitness ID accuracy by presenting eyewitnesses with a lineup tailored to their cognitive abilities. For example, a simultaneous lineup could be presented to individuals who perform well on the CFMT because they are more likely to identify a guilty suspect and less likely to identify an innocent suspect. Conversely, an eyewitness with a low CFMT score may be more likely to make an incorrect lineup decision, and it might be prudent to not present a lineup in this case., The police also should be extra cautious with individuals with lower WMC because they are more likely to make a false ID. Likewise, caution should be exercised for individuals who score low in attention switching. With this knowledge, we can improve the reliability of lineup identifications at the level of the individual and enhance public safety. Acknowledgements The authors would like to thank Dr. Russell for sharing the Cambridge Face Memory Test program. We also thank Xiaoqian Wang and Elizabeth Tucker for their help with data collection. References Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The autism-spectrum quotient (AQ): Evidence from Asperger syndrome/highfunctioning autism, males and females, scientists and mathematicians. Journal of Autism and Developmental Disorders, 31, 5–17. http://dx.doi.org/10.1023/ A:1005653411471. Cacioppo, J. T., Petty, R. E., Feinstein, J. A., & Jarvis, W. B. G. (1996). Dispositional differences in cognitive motivation: The life and times of individuals varying in
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Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid
Individual differences predict eyewitness identification performance Shannon M. Andersen a,⇑, Curt A. Carlson b, Maria A. Carlson b, Scott D. Gronlund a a b
University of Oklahoma, Norman, OK 73019-2007, United States Texas A&M University-Commerce, Commerce, TX 75429, United States
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Article history: Received 26 April 2013 Received in revised form 18 November 2013 Accepted 9 December 2013 Available online 29 December 2013 Keywords: Eyewitness identification Simultaneous and sequential lineups Working memory Facial recognition ability Autism spectrum Need for Cognition
a b s t r a c t A great deal of research has focused on eyewitness identification performance as a function of sequential versus simultaneous lineup presentation methods. We examined if individual differences in cognitive ability influence eyewitness identification, and whether these factors lead to performance differences as a function of lineup presentation method. We found that individual differences in facial recognition ability, working memory capacity, and levels of autistic traits, did result in differential performance. Differences in lineup performance are due to the interaction of individual differences and presentation method, signaling that it is possible to enhance the accuracy of eyewitness identifications by tailoring a lineup presentation method to the capabilities of an individual eyewitness. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction In a perfect world, eyewitnesses would always correctly identify the guilty suspect and never identify an innocent suspect. However, it has been well documented that errors are made (see http://www.innocenceproject.org). But what if we could make it more likely that an eyewitness makes an accurate identification by tailoring a lineup to that individual’s cognitive ability? Simultaneous (all lineup members presented at once) and sequential (lineup members presented one-at-a-time) lineups have been a major focus of study (for a review see Gronlund, Andersen, & Perry, 2013). Lindsay and Wells (1985) first reported that presenting lineup members sequentially reduced innocent suspect identifications without significantly decreasing guilty suspect identifications. The resulting performance advantage came to be known as the sequential superiority effect. However, recent research has raised questions about this claim (e.g. Clark, 2012; Gronlund, Carlson, Dailey, & Goodsell, 2009). Using improved analysis tools, recent research has demonstrated that the reduction in false identifications described in the sequential superiority effect comes at the cost of a reduction in correct identifications and that the simultaneous lineup may actually lead to better performance (e.g., Gronlund, Wixted, & Mickes, in press). Is it possible that this ⇑ Corresponding author. Address: University of Oklahoma, Department of Psychology, 455 W. Lindsey Street, Dale Hall Tower, Room 705, Norman, OK 73019-2007, United States. Tel.: +1 (320) 224 8828. E-mail addresses: [email protected] (S.M. Andersen), curt.carlson@ tamuc.edu (C.A. Carlson), [email protected] (M.A. Carlson), sgronlund@ ou.edu (S.D. Gronlund). 0191-8869/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.paid.2013.12.011
disagreement may have arisen, in part, because some individuals perform better given a simultaneous lineup and others perform better given a sequential lineup? If we can identify individual difference factors that differentially affect performance as a function of lineup presentation method, it may be possible to enhance the accuracy of eyewitness identifications by tailoring a lineup presentation method to the capabilities of an individual eyewitness. In light of previous research, it is the goal of the present study to examine how individual differences in facial recognition ability, working memory capacity, Need for Cognition, and autism spectrum scores, predict sequential and simultaneous lineup performance. We begin by describing the individual difference factors we examined. We also hypothesize how each factor might affect eyewitness identification performance, and how each might differentially impact performance given simultaneous or sequential lineup presentation. 1.1. Facial recognition ability: Cambridge Face Memory Test (CFMT) Eyewitness identification performance depends on facial recognition ability. Morgan et al. (2007) assessed this ability in soldiers undergoing survival training. After 48 h of high-stress training with food and sleep deprivation, the soldiers were interrogated for about 30 min. After an 8-h recovery period, their facial recognition ability was assessed using the Wechsler Face Test, followed by a sequential lineup either containing their interrogator or not. Morgan et al. found that poorer performance on the Wechsler predicted more errors in the eyewitness identification task. We assessed facial recognition ability using the CFMT (Duchaine &
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Nakayama, 2006). Like Morgan et al. (2007), we posited that individuals with better facial recognition ability would show improved eyewitness performance, marked by more correct identifications (of the guilty suspect) and fewer false identifications (of the innocent suspect), across both sequential and simultaneous lineups.
1.2. Working memory capacity: Automated Operation Span Task (AOSPAN) Individuals with higher working memory capacity (WMC) have more cognitive resources available for processing. Unsworth and Engle (2007) proposed that high capacity individuals can better maintain information in short-term memory, and can construct better retrieval cues resulting in superior access to information in long-term memory. Retrieval from long-term memory is critical to successful eyewitness identification. Current memory theory posits that retrieval is dependent on contributions from two processes (e.g., Yonelinas, 2002). Familiarity involves a reliance on a general sense of remembering a prior occurrence in the absence of any details. For example, one might see the neighborhood butcher on the bus, know that the person is familiar, but be unable to determine why. Recollection occurs when a person can remember details surrounding an event. For instance, determining that the man on the bus is the butcher because you remember he recommended salmon last week. Oberauer (2005) showed that WMC correlates with recollection, not familiarity. This especially is true when recollection can be used to retrieve distinctive information about the event in question (Mäntylä, 1997). Because recollection requires more WMC, it is difficult to bring to bear when capacity is scarce (Craik, Govoni, Naveh-Benjamin, & Anderson, 1996). Therefore, viewing one face at a time during a sequential lineup may allow participants to more readily use recollection. Gronlund (2005) proposed that a sequential lineup might result in superior performance when distinctive information about a perpetrator is encoded and recollection is used to retrieve this information; Carlson and Gronlund (2011) found support for this hypothesis. In consequence, individuals with greater WMC can better utilize recollection to enhance long-term memory retrieval, especially when presented with a sequential lineup. Conversely, eyewitnesses with less WMC may be distracted by the faces in a simultaneous lineup, hampering their ability to recollect a perpetrator’s face, thereby harming performance.
1.3. Autistic traits: Autism Spectrum Quotient (AQ) The facial details to which eyewitnesses pay attention during a lineup should affect their ultimate decision. For example, Darling, Martin, Hellmann, and Memon (2009) found that individual differences on a perceptual task predicted performance on a simultaneous lineup. Jones, Scullin, and Meissner (2011) used the AQ (Baron-Cohen, Wheelwright, Skinner, Martin, & Clubley, 2001) on a normal population, and found that greater levels of autistic traits predicted fewer guilty suspect identifications from a simultaneous lineup. In addition, a lower score on the attention switching subscale resulted in fewer correct identifications of the guilty suspect and worse discrimination in a sequential lineup. Higher scores on the attention to detail subscale resulted in worse discrimination and fewer guilty suspect identifications in the simultaneous lineup, but the opposite finding in the sequential lineup. Jones et al. utilized a paradigm in which participants learned multiple faces and completed multiple lineups within a single session. We sought to extend their findings to a more ecologically-valid paradigm, plus examine correlations of AQ with WMC/recollection.
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1.4. Preference for effortful cognitive activities: Need for Cognition (NFC) A lineup can be a difficult cognitive task. Therefore, individuals who enjoy effortful cognitive tasks may perform better than those who do not. Cacioppo, Petty, and Kao’s (1984) NFC scale measures an individual’s propensity to engage in effortful cognition. Individuals with high NFC use more cognitive effort in evaluating complex and difficult stimuli (see Cacioppo, Petty, Feinstein, & Jarvis, 1996, for a review). Consequently, high NFC individuals should more carefully evaluate lineup members, resulting in more accurate decisions. This might be especially true when individuals with greater NFC examine a sequential lineup because this task has been described as inherently more difficult (e.g., Carlson & Gronlund, 2011). 2. Method 2.1. Design We conducted an experiment with two independent variables: (a) lineup presentation method: simultaneous versus sequential, between-subjects, and (b) presence of the perpetrator in the lineup: perpetrator-present (PP) versus -absent (PA), within-subjects. Dependent variables assessed were correct identifications from PP lineups, innocent suspect identifications from PA lineups, and confidence. The predictor variables were CMFT, AQ, AOSPAN, and NFC. 2.2. Participants A total of 238 (170 females, 68 males; M age 19.94) students from two midwestern universities participated in this study. 2.3. Materials 2.3.1. Measures Using the upright version of the CFMT (Duchaine & Nakayama, 2006), participants learned and were tested on six male targets with neutral expressions and no visible hair or distinctive characteristics. Targets were presented with two similar distractors across three tests: recognition of the target (a) among distractors, (b) from different viewpoints, and (c) from different viewpoints with Gaussian noise (Wilmer et al., 2010). Higher scores (proportion correct out of 102 trials) correspond to better facial recognition ability. The AQ (Baron-Cohen et al., 2001) consists of five 10-item subscales: imagination, attention to detail, social skills, attention switching, and communication skills. Higher scores (range 0–50) indicate a greater preponderance of autistic traits. During the AOSPAN (Unsworth, Heitz, Schrock, & Engle, 2005), participants encounter two to seven math problem/letter iterations and then must report the letters in serial order. Recalling a high number of letters in the correct order across trials (range 0– 75) results in a high score and is indicative of a larger WMC. NFC (Cacioppo et al., 1984) measures an individual’s willingness to engage in effortful cognition. An example item is ‘‘I would prefer complex to simple problems.’’ Higher scores (range 0–72) indicate a greater NFC. 2.3.2. Videos We presented two different mock crimes to increase the generalizability of our findings. One video features a male perpetrator stealing a purse from a woman; the other involves a different male perpetrator car-jacking a woman’s car. Both perpetrators are Caucasian with dark hair, medium build, in their early-20s, and with no facial hair or other distinguishing features.
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2.3.3. Lineups Two lineups were created for each perpetrator. One lineup included a photo of the perpetrator (i.e., guilty suspect), plus five additional foils (PP lineup). In the PA lineup, the photo of the perpetrator was removed and an innocent replacement substituted, together with the same five foils. This lineup represents the case where the police have accused the wrong man. To assess lineup fairness, 10 volunteers watched the videos and provided descriptions of each perpetrator. We compiled these to get a modal description for each perpetrator. We provided another group of participants (N = 149) with the appropriate description and asked them to choose the person from each lineup who best matched the description. We computed Tredoux’s E0 (Tredoux, 1998) from this information, which approximates the number of reasonable competitors in these six-person lineups. The carjacker lineups were relatively fair (Tredoux’s E0 = 4.31 for the PP, 4.40 for the PA lineup); the purse-snatcher lineups were moderately fair (E0 = 3.07 for the PP, 2.59 for the PA lineup). 2.4. Procedure Participants were randomly assigned to either sequential or simultaneous lineups. The first lineup tested was randomly determined to be either PP or PA, and the second lineup was the converse. The experiment began with the carjacker video. Participants were instructed to pay attention to the video as they could be asked questions about it later. After the video, participants spent three minutes on a word search puzzle, and then viewed a PP or PA simultaneous or sequential lineup. They were informed that the perpetrator may or not be present in the lineup. After their decision, participants provided a confidence rating on a Likert scale ranging from 1 (not confident at all) to 7 (extremely confident). They then repeated this procedure for the purse theft video. Following the second lineup task, they completed the aforementioned measures in the order reported above. 3. Results and discussion In addition to the predictor variables (individual difference measures), the experiment examined three dependent variables: (a) correct identifications of the perpetrator from PP lineups, (b) false identifications of the innocent suspect from PA lineups, and (c) confidence in each lineup decision. Logistic regression is the appropriate analysis to address hypotheses regarding how the individual difference measures predict correct and false identifications because these data are binary (PP lineups: 1 = correct ID and 0 = not a correct ID; PA lineups: 1 = false ID, 0 = not a false ID). We begin with the overall accuracy results. Because ratio-based measures like diagnosticity (correct ID/false ID) confound accuracy and response bias (Wixted & Mickes, 2012), we compared the performance of sequential and simultaneous presentation methods using Receiver Operating Characteristic (ROC) analysis. We then present the logistic regression analyses, first for correct identifications, and then for false identifications. The descriptive statistics from these analyses are reported in Table 1. Table 2 presents the proportions of correct, false, foil identifications (IDs), and rejection rates.
Table 1 Descriptive statistics for the Autism Spectrum Quotient, Need for Cognition, Cambridge Face Memory Test, and Automated Operation Span Task. Measure
M
SD
ASQ total Attention to Detail Attention Switching Social skills Communication Imagination CFMT AOSPAN score NFC
17.21 5.37 5.12 1.98 2.25 2.50 66.80 38.00 10.17
5.61 2.13 1.95 2.03 1.81 1.55 10.74 18.61 21.24
Table 2 Traditional performance measures for simultaneous and sequential lineups. Perpetrator present
Perpetrator absent
Decision
Simultaneous
Sequential
Simultaneous
Sequential
ID Suspect ID Foil No ID Diagnosticity
.63 .11 .26 1.80
.48 .17 .36 1.26
.35 .17 .48
.38 .24 .38
Note: ID = identification; diagnosticity = correct ID rate/false ID rate.
gin utilizing signal detection based assessments like ROC analysis (Gronlund et al., 2012). An ROC is a plot of correct (perpetrator) versus false (innocent suspect) IDs across varying levels of confidence in the ID decision (see Fig. 1). The lower-left point of an ROC curve reflects the correct and false IDs made with the highest level of confidence (i.e., ‘‘extremely confident’’ that was the perpetrator). Each additional point extends the ROC curve as a cumulative record of correct and false ID rates across decreasing levels of confidence (i.e., the second point on the ROC reflects correct and false IDs made with the highest and second-highest confidence, the third point adds IDs with the third-highest confidence, and so forth). The closer an ROC curve is to the upper left-hand corner of the space, the more accurate the lineup presentation method. Statistically, we compare lineup presentation methods by computing the area under each ROC curve (AUC). The simultaneous and sequential lineup ROCs are presented in Fig. 1. Using the statistical package pROC (Robin et al., 2011), we analyzed the AUC for a false ID rate from 0 to .38, which subsumes the performance range of both curves. Although the simultaneous AUC (.15, 95%-confidence interval .11–.18) was greater than the
3.1. Overall accuracy: ROC analysis Recently it has been shown that ratio-based measures like diagnosticity are confounded with a willingness to choose (Gronlund et al., in press; Wixted & Mickes, 2012). It has been argued that researchers must follow the lead of medical diagnosticians and be-
Fig. 1. ROC curves for simultaneous (SIM) and sequential (SEQ) lineups. The solid line reflects the diagonal reflecting chance performance.
S.M. Andersen et al. / Personality and Individual Differences 60 (2014) 36–40 Table 3 Interaction terms in the logistic regression models. Included interaction terms Lineup ⁄ CFMT Lineup ⁄ AOSPAN Lineup ⁄ AQ Lineup ⁄ NFC CFMT ⁄ AOSPAN CFMT ⁄ AQ CFMT ⁄ NFC AOSPAN ⁄ AQ AOSPAN ⁄ NFC AQ ⁄ NFC Note: These terms were included, in both a model for correct identifications and a model for false identifications. Lineup = Simultaneous or Sequential; CFMT = Cambridge Face Memory Test; AOSPAN = Automated Operation Span Task; AQ = Autism Spectrum Quotient; NFC = Need for Cognition.
sequential (.11, 95%-confidence interval .08–.14), the difference was not significant (D1 = 1.45, p > .05). The effect size for this analysis (d = 0.21) can be classified as a small effect (Cohen, 1988).
3.2. Predictors of lineup accuracy: individual difference measures We next constructed two logistic regression models, one for PP lineups and one for PA lineups, because they produce mutuallyexclusive response variables: correct IDs from PP lineups and false IDs from PA lineups. Within each model, there was no difference between the two mock crime videos. Therefore, this variable was removed. There also was no difference in either correct or false IDs between simultaneous and sequential lineups, in line with the ROC results above. Finally, there were no interactions between any of the independent variables and the predictors. (See Table 3 for a list of all two-way interactions tested.) As a result, we describe the results of two different logistic regression models (one for simultaneous and one for sequential lineups) for each response variable.
3.2.1. Correct IDs of the perpetrator Correct IDs from the simultaneous lineup increased with higher CFMT (Odds Ratio (OR)2 = 1.01, X2 = 8.25, p = .004), higher AQ (OR = 1.03, X2 = 5.43, p = .02), and higher AOSPAN (OR = 1.01, X2 = 6.35, p = .01). That is, participants were more likely to ID the perpetrator if they had greater facial recognition ability, scored higher in Autism Spectrum Factors, or had higher WMC. When AQ was broken down into subscales, attention to detail (OR = 1.09, X2 = 7.04, p = .01) and attention switching (OR = 1.10, X2 = 7.00, p = .01) both positively predicted correct IDs in the simultaneous lineup. There were no significant predictors of correct IDs for the sequential lineup. This finding could be important, as it suggests a level of immunity of the sequential lineup to the individual difference variables tested in this study, at least when the perpetrator is present. However, this would not be beneficial if the sequential lineup failed to result in superior performance overall, which it did not in this study. We found no support for the prediction that higher NFC would be predictive of more correct IDs in either type of lineup. 1 D is defined as (AUC1 AUC2)/s, where s is the standard error of the difference between the two AUCs estimated by a bootstrap. 2 An OR of 1.01 for CFMT means that for every unit increase in CFMT score, a correct ID was 1% more likely. In the CFMT (0 -100), a participant who scores 10 units greater than another would improve the likelihood of a correct ID by 10%.
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3.2.2. False IDs of the innocent suspect For both simultaneous and sequential lineups, participants with higher CFMT, higher AOSPAN, and higher AQ scores, were less likely to choose the innocent suspect from PA lineups: CFMT (sequential OR = 0.99, X2 = 8.57, p = .003; simultaneous OR = 0.99, X2 = 9.12, p = .003), AOSPAN (sequential OR = 0.99, X2 = 7.85, p = .005; simultaneous OR = 0.99, X2 = 8.20, p < .001), AQ (sequential OR = 0.97, X2 = 7.61, p = .006; simultaneous OR = 0.97, X2 = 8.13, p = .004). Two AQ subscales yielded parallel effects across lineup type: attention to detail (simultaneous OR = 0.91, X2 = 7.88, p = .005; sequential OR = 0.91, X2 = 7.35, p = .006) and attention switching (simultaneous OR = 0.91, X2 = 7.85, p = .005; sequential OR = 0.91, X2 = 7.85, p = .005). In other words, higher attention to detail and attention switching led to fewer innocent suspect IDs for both simultaneous and sequential lineups. Other individual difference measures predicted performance from only simultaneous lineups, or only sequential lineups. Participants with higher NFC chose the innocent suspect less often from simultaneous lineups (OR = 0.97, X2 = 9.99, p = .001). However, those scoring higher on the other three AQ subscales selected the innocent suspect less often when presented in sequential lineups: social skills OR = 0.83, X2 = 5.69, p = 0.02, communication OR = 0.86, X2 = 4.47, p = .03; and imagination OR = 0.84, X2 = 6.88, p < .001. Jones et al. (2011) did not find that these subscales were significant predictors of lineup performance.
4. General discussion Despite the ROC analysis showing equivalent simultaneous and sequential lineup performance, certain individual differences differentially predicted lineup performance as a function of lineup type. Correct IDs from the simultaneous lineup were influenced positively by facial recognition ability, particular autistic traits, and WMC. Interestingly, the sequential lineup was immune to these individual differences when it came to PP lineups. However, this was not the case for PA lineups, as all but one of our measures predicted both simultaneous and sequential PA lineup performance. Better facial recognition ability predicted improved eyewitness performance, but it had a larger effect on simultaneous lineup performance insofar as it increased correct and decreased false IDs. This could be due to the fact that multiple faces must be considered at once, which understandably would tax one’s facial recognition ability more than viewing faces one at a time, resulting in a greater improvement for individuals with a better facial recognition ability when viewing a simultaneous lineup. Additionally, the CMFT and simultaneous lineup both require the selection of a target face among similar foils and therefore are similar tasks. Jones et al. (2011) found that increased scores on the attention to detail subscale of the AQ resulted in improved discrimination in sequential lineups due to an increase in correct IDs; they found the opposite for the simultaneous lineup. Our results partially replicated their findings, as we found that greater attention to detail improved both sequential and simultaneous discrimination per a reduction in false IDs. Jones et al. also reported that greater attention switching predicted better sequential lineup discrimination. We replicated this finding in the form of a reduction in false IDs for the sequential lineup, but found that attention switching had a stronger relationship with simultaneous lineup performance, predicting both correct and false ID rates. Jones et al. also suggested that greater attention to detail should benefit the sequential lineup because it requires focus on one face at a time. However, it is possible that it could instead lead individuals to better notice small differences among faces in a simultaneous lineup (Wixted
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& Mickes, in press), resulting in the detection of the perpetrator or rejection of an innocent suspect. Although we still lack a theory for interpreting the pattern of AQ results, there does appear to be some consistency in the results between the present study and Jones et al. Participants with higher WMC were more likely to make a correct ID from simultaneous lineups. This could be due to an increased ability to make comparisons among faces, but it is contrary to prior research proposing that the sequential lineup would free up more capacity to be deployed to utilize recollection and thereby enhance performance. However, this is exactly what happened to false IDs (see Gronlund, 2005). The extra capacity might have allowed recollection to engage a recall-to-reject strategy (Rotello, 2000) in sequential lineups to limit false IDs (Carlson & Gronlund, 2011). However, as greater capacity also predicted reduced false IDs from simultaneous lineups, more research is needed to delineate the relationship between WMC and recollection in simultaneous versus sequential lineups. This study was not without limitations. For example, we only examined normal functioning undergraduate participants using two different mock crime scenarios. To assess the robustness of our findings we need to examine a wider range of participants and materials. Although our study is more ecologically valid than Jones et al. (2011), our materials employed a mock crime procedure, which is different than the conditions an eyewitness to a live crime would experience. In a live crime, encoding is adversely affected by stress, poor lighting, weapon focus, etc. Overcoming these factors may demand more WMC, thereby making recollection difficult to deploy. This could hamper the predictive ability of the individual difference factors we examined in more realistic situations. 4.1. Conclusions and potential applications Our findings suggest that differences in lineup performance are a joint function of certain individual differences and lineup presentation method. Continued research into these factors could improve eyewitness ID accuracy by presenting eyewitnesses with a lineup tailored to their cognitive abilities. For example, a simultaneous lineup could be presented to individuals who perform well on the CFMT because they are more likely to identify a guilty suspect and less likely to identify an innocent suspect. Conversely, an eyewitness with a low CFMT score may be more likely to make an incorrect lineup decision, and it might be prudent to not present a lineup in this case., The police also should be extra cautious with individuals with lower WMC because they are more likely to make a false ID. Likewise, caution should be exercised for individuals who score low in attention switching. With this knowledge, we can improve the reliability of lineup identifications at the level of the individual and enhance public safety. Acknowledgements The authors would like to thank Dr. Russell for sharing the Cambridge Face Memory Test program. We also thank Xiaoqian Wang and Elizabeth Tucker for their help with data collection. References Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The autism-spectrum quotient (AQ): Evidence from Asperger syndrome/highfunctioning autism, males and females, scientists and mathematicians. Journal of Autism and Developmental Disorders, 31, 5–17. http://dx.doi.org/10.1023/ A:1005653411471. Cacioppo, J. T., Petty, R. E., Feinstein, J. A., & Jarvis, W. B. G. (1996). Dispositional differences in cognitive motivation: The life and times of individuals varying in
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