Predicting correctness of eyewitness statements using the semantic evaluation method (SEM)

Predicting correctness of eyewitness statements using the semantic evaluation method (SEM) 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Predicting correctness of eyewitness statements using the semantic evaluation method (SEM)

Loading next page...
 
/lp/springer_journal/predicting-correctness-of-eyewitness-statements-using-the-semantic-oQvUGWEHqM
Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer Science+Business Media Dordrecht
Subject
Social Sciences, general; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-014-9997-7
Publisher site
See Article on Publisher Site

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.

Journal

Quality & QuantitySpringer Journals

Published: Feb 6, 2014

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off