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Predicting correctness of eyewitness statements using the Semantic Evaluation Method (SEM)

Journal article
Authors Farhan Sarwar
Sverker Sikström
Carl Martin Allwood
Åse Innes-Ker
Published in Quality & Quantity
Volume 49
Issue 4
Pages 1735-1745
ISSN 0033-5177
Publication year 2015
Published at Department of Psychology
Pages 1735-1745
Language en
Links dx.doi.org/10.1007/s11135-014-9997-...
Keywords Eyewitnesses’ correctness · Semantic evaluation method · Semantic spaces · Consistency
Subject categories Psychology

Abstract

Evaluating the correctness of eyewitness statements is one of the biggest challenges for the legal system, and this task is currently typically performed by human evaluations. Here we study whether a computational method could be applied to discriminate between correct and incorrect statements. The semantic evaluation method (SEM) is based on latent semantic analysis (Landauer and Dumais Psychol Rev 104: 211–240, 1997),—a method for automatically generating high dimensional semantic representations of words and sentences. The verbal data was extracted from the recorded narratives from a prior eyewitness study investigating the role of repeated retellings on subsequent recall accuracy and confidence (Sarwar et al. Cognit Psychol 25(5):782–791, 2011). Participants watched a film of a kidnapping and then either retold the events to a single listener, or discussed the content with a confederate at five separate times over a 20-day period. Their subsequent written recall was analyzed using the SEM. The results show that accuracy can be predicted from quantification of the semantic content of eyewitness memory reports using SEM. This result also held true when data was separated into three distinct categories and the SEM was trained and tested on different categories of data.

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