Andrew Smith

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Assistant Professor

Iowa State University

United States

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About

Andrew Smith is an Assistant Professor at Iowa State University, USA. His research focuses on eyewitness identification accuracy and forensic methodologies, as evidenced by his recent articles exploring the implications of task fluency, the use of artificial intelligence in assessments, and the challenges within forensic evidence reporting. He aims to enhance the understanding of memory and judgment processes in the context of legal and forensic settings.

Recent Grants

Grant: Close

Video-Recordings of Eyewitness Identification in Actual Cases: The Postdictive Value of Eyewitness Behaviors

Open Date: 2020-09-01

Close Date: 2023-08-01

Grant: Close

Signal Detection Theory in Single-Trial Human-Decision Tasks

Open Date: 2018-04-01

Close Date: 2023-03-01

Grant: Close

Signal Detection Theory in Single-Trial Human-Decision Tasks

Open Date: 2018-04-01

Close Date: 2019-03-01

Grant: Close

Collaborative proposal on eyewitness identification: Modeling, new discoveries, negative predictive value, and ecologically-valid field tests

Open Date: 2017-01-01

Close Date: 2021-04-01

Grant: Close

Multiple perpetrator crimes and multiple suspect showups

Open Date: 2015-04-01

Close Date: 2017-03-31

Articles (17)

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UNITED STATES

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Andrew Smith

Research Interests

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About

Recent Grants

Articles (17)

Perceptions of task fluency mislead judgments of eyewitness identification accuracy.

Beyond the confidence-accuracy relation: A multiple-reflector-variable approach to postdicting accuracy on eyewitness lineups.

Does the Forensic Filler-Control Method Reduce Examiner Overconfidence? An Experimental Investigation Using Mock Fingerprint Examiners

Predicting and postdicting eyewitness identification accuracy on forensic-object lineups.

Cartridge-case examiners’ aversion to true rejections: A shocking problem with use of the “inconclusive” category.

Telling us less than what they know: Expert inconclusive reports conceal exculpatory evidence in forensic cartridge-case comparisons.

Using artificial intelligence to assess eyewitness identification accuracy.

The untapped potential of lineups: using eyewitness memory to rule out innocent suspects

Exploring the relationship between prior knowledge and metacognitive monitoring accuracy

The rule out procedure: A signal-detection-informed approach to the collection of eyewitness identification evidence.

Fair lineups improve outside observers’ discriminability, not eyewitnesses’ discriminability: Evidence for differential filler-siphoning using empirical data and the WITNESS computer-simulation architecture.

The rule-out procedure: Increasing the potential for police investigators to detect suspect innocence from eyewitness lineup procedures.

fullROC: An R package for generating and analyzing eyewitness-lineup ROC curves

Evaluating the claim that high confidence implies high accuracy in eyewitness identification.

The distinction between discriminability and reliability in forensic science

Providing witnesses with an option to say “I’m not sure” to a showup neither improves classification performance nor the reliability of suspect identifications.

Do Traditional Lineups Undermine the Capacity for Eyewitness Memory to Rule Out Innocent Suspects?

Collaborators (6)

Tess Neal

Amber Witherby

Shana Carpenter

Yueran Yang

Ryan Ditchfield

Rebecca Ying

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