About

Chad Dodson is a Professor and Chair of Psychology at the University of Virginia. His research focuses on memory, with particular emphasis on false memories, overconfidence in one’s memories, and changes in memory across the lifespan. His work includes examining factors that contribute to eyewitness identification errors, especially those made with high confidence. He investigates aspects such as face recognition ability, decision-time, and how eyewitnesses justify their identification of a face. Dodson has contributed to understanding the reliability of high confidence eyewitness identifications and has published research on these topics, including studies on the accuracy and confidence in eyewitness memory and the predictors of high confidence errors.

Research topics

• Psychology
• Social psychology
• Cognitive psychology
• Artificial Intelligence
• Sociology
• Computer Science
• Data Mining
• Machine Learning
• Natural Language Processing
• Linguistics
• Statistics
• Criminology

Selected publications

• How Does the Number and Type of Information Given Influence Accuracy in Judging Eyewitness Identifications?

  OSF Preprints (OSF Preprints) · 2026-04-16

  other

  An important part in preventing false convictions is being able to tell how confident an eyewitness is about who they select in a lineup. Studies suggest that confidence is a good predictor of accuracy in fair lineups, although people sometimes have difficulty identifying if an eyewitness was right or wrong by looking at their confidence alone (Smalarz & Wells, 2014). An important question to ask is which way of presenting confidence information helps people best discriminate between correct and incorrect identifications. It has been demonstrated in existing literature that adding different pieces of information such as decision time can increase the likelihood of accurate discrimination (Ayala et al., 2022). Also, studies have shown that people with better face recognition ability make more accurate judgments (McBryde et al. 2020). Although various studies have examined how the inclusion of different information influences accurate discrimination, there has not really been a study that compares how different combinations of confidence information influences judgment of correct or incorrect identification. In our study, we test the hypothesis that participants who are given more pieces of information about the eyewitness will more accurately discriminate between correct and incorrect eyewitness identifications. Accuracy will peak when all predictors are given to the participant (ex. Verbal, Numeric, Justification, Decision time, Face recognition Ability). We also hypothesize that participants given only a numeric confidence statement will show better discrimination than those given only a verbal certainty statement because verbal confidence is harder to interpret and more ambiguous. We use lineup decisions and confidence statements from Grabman et al. (2019). In each trial, participants view a witness’s lineup identification with the witness’s confidence. Participants then judge whether the identification was correct or incorrect and express their confidence of this decision on a 101-point scale ranging from 0% (guessing) to 100% (completely confident). Each participant evaluates six lineup decisions: three correct identifications and three incorrect identifications. Participants are randomly assigned to one of the five confidence conditions: 1. Verbal Confidence Only: Participants see the lineup decision and the eyewitness’s verbal certainty (ex. “I am positive this man was present before”). 2. Numeric Confidence Only: Participants see the lineup decision and the eyewitness’s numeric confidence on a scale from 0-100%. 3. Verbal + Numeric + Justification: Participants see the lineup decision, verbal certainty numeric confidence, and a written justification for their decision. This condition is inspired by Ayala et al. (2022). 4. Verbal + Numeric + Justification + Decision Time: Participants see all pieces from condition 3 as well as the eyewitness’s decision time. We can evaluate how temporal information influences accuracy judgments. 5. Verbal + Numeric + Justification + Decision Time + Face Recognition Ability: Participant see all information from Condition 4 plus the eyewitness’s face recognition ability. This condition is motivated by findings from (McBryde et al., 2020). Comparing these five conditions will allow us to understand how the number and combination of information given to a third party influences their judgment on whether an eyewitness identification is correct or incorrect.

  Publisher

• AI assistance improves people’s ability to distinguish correct from incorrect eyewitness lineup identifications

  Proceedings of the National Academy of Sciences · 2025-05-19 · 3 citations

  articleOpen accessSenior author

  Mistaken eyewitness identification is one of the leading causes of false convictions. Improving law enforcement’s ability to identify correct identifications could have profound implications for criminal justice. Across two experiments, we show that AI-assistance can improve people’s ability to distinguish between accurate and inaccurate eyewitness lineup identifications. Participants (Experiment 1: N = 1,092, Experiment 2: N = 1,809) saw an eyewitness’s lineup identification, accompanied by the eyewitness’s verbal confidence statement (e.g., “I’m pretty sure”) and either a featural (“I remember his eyes”), recognition (“I remember him”), or familiarity (“He looks familiar”) justification. They then judged the accuracy of the eyewitness’s identification. AI-assistance (vs. no assistance) improved people’s ability to distinguish between correct identifications and misidentifications, but only when they evaluated lineup identifications based on recognition or featural justifications. Discrimination of identifications based on familiarity justifications showed little improvement with AI-assistance. This project is a critical step in evaluating human-algorithm interactions before widespread use of AI-assistance by law enforcement.

  PublisherDOI

• New Insights on Expert Opinion About Eyewitness Memory Research

  Perspectives on Psychological Science · 2024-04-18 · 15 citations

  articleOpen access

  Experimental psychologists investigating eyewitness memory have periodically gathered their thoughts on a variety of eyewitness memory phenomena. Courts and other stakeholders of eyewitness research rely on the expert opinions reflected in these surveys to make informed decisions. However, the last survey of this sort was published more than 20 years ago, and the science of eyewitness memory has developed since that time. Stakeholders need a current database of expert opinions to make informed decisions. In this article, we provide that update. We surveyed 76 scientists for their opinions on eyewitness memory phenomena. We compared these current expert opinions to expert opinions from the past several decades. We found that experts today share many of the same opinions as experts in the past and have more nuanced thoughts about two issues. Experts in the past endorsed the idea that confidence is weakly related to accuracy, but experts today acknowledge the potential diagnostic value of initial confidence collected from a properly administered lineup. In addition, experts in the past may have favored sequential over simultaneous lineup presentation, but experts today are divided on this issue. We believe this new survey will prove useful to the court and to other stakeholders of eyewitness research.

  PublisherOA PDFDOI

• Does artificial intelligence (AI) assistance mitigate biased evaluations of eyewitness identifications?

  Journal of Applied Research in Memory and Cognition · 2024-08-15 · 1 citations

  articleOpen accessSenior author
  PublisherDOI

• Persistence of the verbal overshadowing and weapon-focus effects on lineup identification performance.

  Journal of Applied Research in Memory and Cognition · 2024-11-18 · 2 citations

  article1st authorCorresponding

  Peer reviewed

  PublisherDOI

• Aging and memory

  Elsevier eBooks · 2024-10-16

  book-chapter1st authorCorresponding
  PublisherDOI

• Comparing human evaluations of eyewitness statements to a machine learning classifier under pristine and suboptimal lineup administration procedures

  Cognition · 2024-07-14 · 5 citations

  reviewSenior author
  PublisherDOI

• Probing the origins of subjective confidence in source memory decisions in young and older adults: A sequential sampling account.

  Journal of Experimental Psychology General · 2024-12-12

  articleOpen access

  Subjective confidence is an important factor in our decision making, but how confidence arises is a matter of debate. A number of computational models have been proposed that integrate confidence into sequential sampling models of decision making, in which evidence accumulates across time to a threshold. An influential example of this approach is the relative balance of evidence hypothesis, in which confidence is determined by the amount of evidence for the choice that was made compared to the evidence for all possible choices. Here, we modify this approach by mapping distance from a decision threshold to confidence via a sigmoid function. This allows for individual differences in bias toward lower or higher levels of confidence, as well as sensitivity to differences in evidence between choices. We apply several variants of the model to assess potential age differences between young and older adults in source memory decision making in an existing data set (Dodson, Bawa, & Slotnick, 2007). We compare our model to the relative balance of evidence approach, and the results indicate that the sigmoidal method substantially improves model fit. We also consider models in which memory errors can arise from a misrecollection process that involves associating items with the incorrect source, a process that has been proposed to account for age differences in source memory confidence and accuracy, but find no evidence that misrecollection is necessary to account for the results. This work provides a viable model of subjective confidence that is integrated with well-established models of decision making and provides insights into effects of aging on source memory decisions. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

  PublisherDOI

• A comparison between numeric confidence ratings and verbal confidence statements.

  Journal of Experimental Psychology Applied · 2024-10-15 · 6 citations

  articleSenior author

  Is confidence most diagnostic of accuracy when expressed in numbers or when expressed in words? This question bears immense importance in many real-world contexts especially within the confines of eyewitness identification. In an eyewitness identification task, we compared the diagnostic value of numeric confidence across rating scales that varied in grain size (3-point vs. 6-point vs. 21-point vs. 101-point rating scales). We also compared the diagnostic value of numeric confidence to verbal confidence statements using several machine-learning algorithms. We found that fine-grain ratings are more diagnostic of identification accuracy than coarse-grain ratings, which suggests that the former provides a closer correspondence to memory strength than the latter. Moreover, we found that verbal confidence statements capture diagnostic information about the likely accuracy of an identification that numeric confidence ratings do not capture. This suggests that verbal confidence statements and numeric confidence ratings reflect partially independent, nonoverlapping sources of information. These results shed light on the processes that provide diagnostic value to confidence. From an applied standpoint, these results suggest that verbal confidence statements and numeric confidence ratings ought to be collected from eyewitnesses after an identification decision. Collecting both captures more diagnostic information than either can capture in isolation. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

  PublisherDOI

• Unskilled, underperforming, or unaware? Testing three accounts of individual differences in metacognitive monitoring

  Cognition · 2023-11-06 · 11 citations

  articleSenior author
  PublisherDOI

Recent grants

• High Confidence Eyewitness Memory Errors in Older Adults

  NSF · $324k · 2009–2013

• NIH Grant F32MH011135

  NIH · $59k

• Understanding Confidence: Eyewitness Testimony as a Model Case

  NSF · $241k · 2016–2019

• Using AI to improve our understanding of verbal confidence and to aid decision-making: Eyewitness lineup identification as a model case

  NSF · $367k · 2023–2026

Frequent coauthors

• Daniel L. Schacter

  19 shared

• Jesse H. Grabman

  16 shared

• David G. Dobolyi

  University of Colorado System

  14 shared

• Brandon L. Garrett

  Duke University

  14 shared

• Andrew E. Budson

  VA Boston Healthcare System

  12 shared

• Joanne Yaffe

  10 shared

• Travis M. Seale‐Carlisle

  University of Aberdeen

  10 shared

• Karen Kafadar

  University of Virginia

  10 shared