4.4 Individual analysis
In the current fCIT, we successfully identified a distinct conflict-monitoring N200 component by employing PCA analysis to separate it from the overlapped ERPs. The analysis revealed a fine AUC value of 0.77. This finding reaffirmed the role of N200 in lie detection, which is consistent with previous studies (Hu et al., 2013; Ganis et al., 2016).
In addition, recognition P300 can effectively distinguish guilty participants from innocent ones with an AUC of 0.91. However, it should be noted that this result is not consistent with the low AUCs (0.70 - 0.82) found in previous fCIT studies (Sai et al. 2016, 2020; Zheng et al., 2022). One possible reason is that we used autobiographical information for the current fCIT, whereas those studies used incidentally-acquired information as the concealed information. Autobiographical information is more meaningful than information incidentally acquired, which may, in turn, lead to more accurate lie detection (Gronau, Ben-Shakhar, & Cohen, 2005; Noordraven & Verschuere, 2013; Rosenfeld et al., 2006).
Consistent with previous fCIT studies (Sai et al., 2016, 2020; Zheng et al., 2022), our ROC results show that the feedback P300 can effectively distinguish between guilty and innocent participants, with a high AUC of 0.96. Taken together, our findings across these studies demonstrate that the feedback P300 shows promise in detecting both concealed autobiographical and incidentally-acquired information. Unfortunately, combining the conflict-monitoring N200 and recognition P300 with feedback P300 couldn’t achieve a higher deception detection efficiency. Nonetheless, when incorporating the P200 component from the discovery analysis, the combined AUC of multiple indicators (P200, N200, P300, and feedback P300) could achieve a value of 0.99 (see thesupplementary materials ).