No practice effect on the classification accuracy of the response time concealed information test

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.

Authors

Institutions

Publication Details

Journal
Journal of Forensic Sciences
Published
2024-11-08
DOI
https://doi.org/10.1111/1556-4029.15656
Citations
1
Primary Topic
Deception detection and forensic psychology
Type
article
Field-Weighted Citation Impact
0.64

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

No practice effect on the classification accuracy of the response time concealed information test

Izumi Matsuda, Gáspár Lukács
1 citations
Journal of Forensic Sciences
Deception detection and forensic psychology
0.64
article

No practice effect on the classification accuracy of the response time concealed information test

Izumi Matsuda, Gáspár Lukács
article en
1 citations

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.

Journal of Forensic SciencesVol. 70(1)
University of Vienna (AT), Aoyama Gakuin University (JP), Japan Society for the Promotion of Science (JP)
Japan Society for the Promotion of Science
Peace, Justice and strong institutions
Openalex Percentile: Top 26%
Deception detection and forensic psychology
0.64
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.