Executive Summary

Advances in research on factors affecting the reliability of eyewitness identification as well as on procedures for using eyewitness reports in legal proceedings have been conducted since the National Academy of Sciences (NAS) summarized the state of the scientific research in this area.1National Research Council, Identifying the Culprit: Assessing Eyewitness Identification 1 (2014).  This report describes this research primarily with respect to: (1) research syntheses; (2) experiments that vary system and estimator variables to assess their influence on accuracy; (3) statistical methods for analyzing such experiments; and, (4) procedures that have been implemented in legal proceedings.

Research syntheses

systematic overview of the literature in this area has been conducted to identify syntheses of quantitative research on eyewitness identification, to appraise their quality/rigor, and to recommend any changes needed to improve synthesis methods. Forty-five evidence syntheses have been identified, fifteen of which were completed since the publication of the NAS report. The work in appraising their methodology and in extracting data on variables examined is being concluded and will be publicly available.

scoping review has been conducted to identify and catalog all original quantitative studies of eyewitness identification accuracy and confidence. We have identified 1,246 empirical studies of eyewitness identification or facial memory, of which 265 studies have been reported since the NAS report. Extraction of data from these studies is nearing completion, and work has begun to categorize these studies.

An important development and a useful tool for researchers, police officers and other legal professionals is an online, accessible evidence-and-gap-map.  It will array both primary studies and evidence syntheses according to the independent variables on which they focus, and will provide links to the studies where available, as well as indicate which studies from the scoping review have been synthesized, where they were cited, and which have not yet been used in syntheses. Work on this evidence-and-gap-map is underway.

Finally, faculty and a PhD student in the Department of Statistics at University of Virginia are using text mining analysis to identify relationships among these studies. The analytical methods that are developed will help to indicate future directions in eyewitness identification research as well as to inform research synthesis methods elsewhere.

Experiments on system/estimator variables

Research continues on the effects of “system“ and “estimator” variables on the accuracy of eyewitness identification. “System” variables are factors that are under the control of law enforcement; e.g., the number of persons in a lineup and the lineup instructions given. “Estimator” variables reflect conditions at the scene, such as levels of light, distance between the eyewitness and perpetrator, or presence of a weapon at the time of the incident.

Some of the key results from recent research include:

  1. An eyewitness’ level of confidence in an identification and the speed in which the identification is made can be reliable predictors of accuracy under some conditions affected by system and/or estimator variables. Both are stronger predictors of accuracy when the eyewitness makes an identification from a lineup than when the eyewitness responds that the suspect is not in the lineup.
  2. Simultaneous lineups appear to elicit higher overall performance than sequential lineups, although sequential lineups orient eyewitnesses to use a more conservative decision process.
  3. Recent research on constructing lineups indicates that eyewitness identification performance appears superior when the filler faces in the lineup are dissimilar rather than similar to the suspect, as long as the faces all match the suspect’s description.

Statistical methods

While receiver operator characteristic (ROC) curves are useful for comparing two procedures in light of another variable (e.g., expressed confidence level), traditional statistical methods such as logistic regression are more powerful for comparing the accuracies of two procedures (e.g., sequential versus simultaneous lineups; “blind” versus “non-blind” lineups) particularly when multiple factors are involved (e.g., weapon presence versus absence; good versus poor lighting).

Measures and displays that consider both positive predictive value and negative predictive value provide additional metrics of performance in proposed eyewitness identification procedures. Most analyses consider only positive predictive value (probability that the ID was correct), but negative predictive value is also critical (probability that exclusion was correct) – for purposes of both low false positive rates and for recognizing that the true perpetrator remains to be identified.

“Machine learning” algorithms such as random forests can be useful for predicting the likelihood of “choosing” (versus “not choosing”) a suspect from a lineup, and for estimating the accuracy of the decisions, based on conditions of the scenario (e.g., delay, presence of weapon).

With large studies of 1000+ participants, these newer approaches can model complex interactions among system and estimator variables.

Ecological validity of online or lab experiments, versus more realistic experimental conditions, needs to be evaluated.

Use of research findings by law enforcement and the courts

The response by law enforcement agencies, lawmakers, and judges to research in the eyewitness area has been striking:

Change has been steady, and the pace of change has been more pronounced in the six years since the NAS report was released.

Several state courts have reconsidered their rulings and developed new approaches informed by eyewitness evidence research.  States have enacted laws requiring that police agencies adopt eyewitness identification policies meeting minimum criteria.  Other states have adopted model policies, adopted new jury instructions, or otherwise promoted reception of research in police policies. Policing organizations have adopted new policies.

Future work should solidify these gains, through investment in training and further dissemination of these best practices.

However, some aspects of law enforcement and judicial practice has been resistant to research recommendations, including videotaping lineups and limiting subsequent identifications and confidence statements in the courtroom. Reform efforts should focus on those challenges.

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