No practice effect on the classification accuracy of the response time concealed information test.
Published In: Journal of Forensic Sciences, 2025, v. 70, n. 1. P. 215 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Lukács, Gáspár; Matsuda, Izumi 3 of 3
Abstract
The Response Time Concealed Information Test can reveal that a person recognizes a relevant item (probe, e.g., a murder weapon) among other, irrelevant items (controls), based on slower responses to the probe compared to the controls. A previous study (Lukács, 2022, JARMAC) analyzed the data of 14 experiments and demonstrated that classification accuracy is increased by increased test length (i.e., increased number of trials included in the analysis). However, that study left the important question open whether prior practice (whose trials are not included in the analysis) influences the classification accuracy of subsequent testing (i.e., subsequent trials included in the analysis). Reanalyzing the same data from the 14 experiments (comprising 2223 individual tests), we show that different sections of the test (each with the same number of trials), such as the first and second half of each examined test, do not differ substantially in their classification accuracy. The main implications for real‐life application are that, at least up to about 600 trials, prior practice does not affect subsequent tests' results, and the number of examined relevant items or their order of presentation may be freely chosen without compromising the method's validity. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Forensic Sciences. 2025/01, Vol. 70, Issue 1, p215
- Document Type:Article
- Subject Area:Anatomy and Physiology
- Publication Date:2025
- ISSN:0022-1198
- DOI:10.1111/1556-4029.15656
- Accession Number:181984240
- Copyright Statement:Copyright of Journal of Forensic Sciences is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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