Importance-related fillers improve the classification accuracy of the response time concealed information test in a crime scenario
Purpose The Response Time Concealed Information Test (RT-CIT) can reveal when a person recognizes a relevant item among other irrelevant items, based on comparatively slower responses. Therefore, if a person is concealing knowledge about the relevance of this item (e.g., recognizing it as a murder w...
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| Format: | Electronic Article |
| Language: | English |
| Published: |
2022
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| In: |
Legal and criminological psychology
Year: 2022, Volume: 27, Issue: 1, Pages: 82-100 |
| Online Access: |
Volltext (lizenzpflichtig) Volltext (lizenzpflichtig) |
| Journals Online & Print: | Drawer...
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| Check availability: | HBZ Gateway |
| Keywords: |
| Summary: | Purpose The Response Time Concealed Information Test (RT-CIT) can reveal when a person recognizes a relevant item among other irrelevant items, based on comparatively slower responses. Therefore, if a person is concealing knowledge about the relevance of this item (e.g., recognizing it as a murder weapon), this deception can be revealed. A recent study introduced additional ‘familiarity-related fillers’, and these items substantially enhanced diagnostic efficiency in detecting autobiographical data. However, the generalizability of the efficiency of fillers to other scenarios remains an open question. We empirically investigated whether new importance-related fillers enhanced diagnostic efficiency in an imaginary crime scenario. Methods Two hundred and thirty-nine volunteers participated in an independent samples experiment. Participants were asked to imagine either committing a crime (‘guilty’ group) or to imagine visiting a museum (‘innocent’ group). Then, all participants underwent RT-CIT testing using either a standard single probe or an enhanced single probe (with importance-related fillers) protocol. Results The enhanced RT-CIT (with importance-related fillers) showed high diagnostic efficiency (AUC = .810), and significantly outperformed the standard version (AUC = .562). Neither dropout rates nor exclusion criteria influenced this enhancement. Conclusions Importance-related fillers improve diagnostic efficiency when detecting episodic information using the RT-CIT and seem to be useful in detecting knowledge in a wide range of scenarios. |
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| ISSN: | 2044-8333 |
| DOI: | 10.1111/lcrp.12198 |