Research
Research in the PLEA Lab focuses on the application of social and cognitive psychology to questions and issues related to law and the legal system. PLEA Lab research projects address issues in three core areas: eyewitness memory, public attitudes toward police, and research and quantitative methods used in forensic psychological research. Explore our current research projects below.
Eyewitness Memory
Eyewitness Descriptions & Identifications of Suspects
A main area of research in the lab focuses on understanding how the process of describing a suspect influences a witness's ability to subsequently identify that suspect. Of interest is the verbal overshadowing effect, the finding that eyewitnesses who provide a verbal description of a previously seen suspect often show impaired identification accuracy compared to those who do not provide a description. Our research investigates the cognitive mechanisms that drive this effect, including how the type and content of recall instructions, the accuracy of suspect descriptions, and the timing between description and identification tasks influence eyewitness recognition performance. We are also interested in how broader theoretical accounts of verbal overshadowing, such as competing memory representations and shifts in processing strategies, can help explain why describing a face interferes with recognizing it.
AI-Generated Facial Composites of Suspects & Eyewitness Recognition Performance
Before eyewitnesses to a crime identify a suspect from a lineup, law enforcement routinely requires eyewitnesses to provide a description or facial composite of the suspect. Because eyewitness descriptions are important to investigations and in trials, many methods designed to obtain eyewitness descriptions and improve eyewitness performance have been developed. The most recent method put forth is the use of generative artificial intelligence (AI)-facial composite software to obtain descriptions of suspects from eyewitnesses and produce a facial composite of the suspect. However, 30+ years of research suggests that eyewitnesses who engage in facial recall of suspects will have impaired subsequent recognition performance of that suspect. Currently, our lab is completing a series of experiments designed to examine how AI-sketch software impacts eyewitness recognition performance and better understand the cognitive mechanisms underlying eyewitness recall of suspects using AI-facial composite systems.
Legal Stakeholder Perspectives on AI-Facial Composite Technology
The integration of artificial intelligence into criminal investigations has introduced new tools intended to support law enforcement. Of particular relevance to our lab's research is the emerging use of generative AI-based facial composite software, which allows law enforcement to generate suspect composites from eyewitness descriptions. Across a series of studies, our lab has found that creating a facial composite using this AI-driven software impairs eyewitnesses' subsequent ability to recognize the suspect. Despite these findings, no research to date has examined how key legal stakeholders perceive the use of AI-facial composite software. Judges, attorneys, and police officers play a central role in determining the admissibility, use, and interpretation of forensic evidence, while members of the public serve as jurors who must weigh the strength of that evidence in court. Building on this gap, our lab is now investigating how perceptions of AI-facial composite systems, including their perceived accuracy, utility, and potential risks, differ across judges, attorneys, police officers, and jury-eligible citizens.
Public Attitudes toward Police
Police Use of Excessive Force Under Title 18 U.S.C. § 242
Title 18 of the U.S. Code Section 242, also known as the “deprivation of rights under color of law” statute, holds public officials accountable for violations of constitutional rights, such as public officials’ abuse of authority. Although § 242 applies to a range of public or governmental officials and covers different conduct, it is most frequently raised in cases involving allegations of police misconduct, particularly the use of excessive force. To secure a conviction under § 242, the prosecution must establish four distinct statutory elements: that the officer (1) used more force than was reasonably necessary, (2) deprived the person of a right or privilege protected by the Constitution or federal law, (3) acted willfully, and (4) acted under color of law. It is unknown how jurors, the ultimate arbiters of guilt, interpret and apply the four elements when evaluating police use of force cases. Currently, the lab is examining how mock jurors' judgments of each statutory element of § 242 predict their verdict decisions.
Public Judgments of Police Use of Force
Another area of research in the lab examines public attitudes toward police and how those attitudes shape perceptions of police behavior. A central focus of this work is understanding how people make judgments about police use of force when viewing police officer-civilian confrontation videos. Our research investigates how various factors influence these judgments, including the contextual information that accompanies a video and the emotional processes involved in evaluating what they see. For example, we have examined how informational frames, such as background information about a civilian's mental health or substance use, or whether an officer was aware of a civilian's condition prior to an encounter, can change how people evaluate an officer's actions. We are also interested in the role of emotions and cognitive appraisal in this process, exploring how giving people relevant context and time to reflect can shift their initial judgments. Taken together, this line of research highlights that public evaluations of police use of force are shaped by far more than the video footage itself, and has implications for how confrontation videos are presented in the media and how communities and law enforcement can better understand one another.
Research & Quantitative Methods used in Forensic Psychological Research
Signal Detection Theory & Eyewitness Identifications
Signal detection theory (SDT) and associated methods have recently been applied to the study of eyewitness identifications. However, forensic signal detection tasks often differ in important ways from standard signal detection face recognition memory tasks. Signal detection paradigms, when used in forensic eyewitness recognition memory tasks, are designed specifically to measure memory strength (d') for a face while controlling for the fact that different research participants might be more or less predisposed to make positive face identifications, or response criterion (c). Instead of directly computing hits and false alarms as is done in traditional forensic face SDT recognition studies, we take a different analytic approach to eyewitness data: we used the eyewitness decision that each participant makes (“yes” or “no” in a 1AFC task) as the dependent variable. We use logistic regression to treat "yes/no" binary response data. The regression analysis allows us to assess main effects and interactions among factors that are of interest. Generalized multiple variate linear logistic regression can be used to estimate SDT parameters such as d’ and c, when “yes” responses are treated as the dependent variable.