The trajectory of crime: Integrating mouse-tracking into concealed memory detection.

Abstract

The autobiographical implicit association test (aIAT) is an approach of memory detection that can be used to identify true autobiographical memories. This study incorporates mouse-tracking (MT) into aIAT, which offers a more robust technique of memory detection. Participants were assigned to mock crime and then performed the aIAT with MT. Results showed that mouse metrics exhibited IAT effects that correlated with the IAT effect of RT and showed differences in autobiographical and irrelevant events while RT did not. Our findings suggest the validity of MT in offering measurement of the IAT effect. We also observed different patterns in mouse trajectories and velocity for autobiographical and irrelevant events. Lastly, utilizing MT metric, we identified that the Past Negative Score was positively correlated with IAT effect. Integrating the Past Negative Score and AUC into computational models improved the simulation results. Our model captured the ubiquitous implicit association between autobiographical events and the attribute True, and offered a mechanistic account for implicit bias. Across the traditional IAT and the MT results, we provide evidence that MT-aIAT can better capture the memory identification and with implications in crime detection.