It is important to ascertain whether a statement is true or not in field of neurolaw
and forensic psychology. For lie detection research, the concealed information test
(CIT) is most widely used with electroencephalography (EEG) recording which has
been focused on P300 event related potential (ERP) component. And mock crime
scenarios were commonly adopted for providing materials for lie. Mock crime
scenarios have relatively higher ecological validity than other methods like
autobiographical information or list of learning. However, the mock crime scenarios
also have limitations because of ethical issues (e.g., impossible to ask participants to
beat someone), resource issues (e.g., difficulty in securing space), and experimental
controllability. Virtual reality (VR) is an alternative to overcome these disadvantages.
Nonetheless, few studies using VR for mock crime. Even they used low-fidelity virtual
environment. Furthermore, there is no research on the comparison between ‘actual’
mock crime and ‘virtual’ mock crime. In other words, the effect of virtual experience
on brain activities during lie detection test has remained unknown. The purpose of this
study is to learn about the effect, using theft scenario and P300-based CIT which are
both the most popular in lie detection research field.
A high-fidelity virtual environment was developed by capturing a place where
the actual mock crime would be done. Participants performed either ‘guilty’ or
‘innocent’ scenarios with ‘actual’ or ‘virtual’ conditions. That is, 2 by 2 full factorial
design expriement was conducted. After the scenario stage, participants were tested by P300-based CIT with EEG recording. The measured EEG signals were analyzed in
various ways include conventional ERP, time-frequency, source localization, and
connectivity analysis. This is to explore various aspects of the effect using virtual mock
crime on EEG during concealed information test.
All results support that there is no significant difference by Mock crime type
(‘actual’ or ‘virtual’). Conventional P300 ERP component has a significant difference
between the guilty and the innocent only. Individual classification by bootstrapping
method indicate the same accuracy regardless of the mock crime type. Source
localization statistics also has no significant difference. Furthermore, connectivity
analysis indicate significant differences between crime status (guilty or innocent) solely.
These results suggest the possibility that the virtual mock crime can be an alternative
mock crime method for lie detection research. Virtual mock crime will enable to use
other mock crime scenarios that were impossible previously, for example, assault or
robbery. And it could be to help lie detection researches aimed to high ecological
validity