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Terrorist attacks carried out by individuals or single cells have significantly accelerated over the last 20 years. This type of terrorism, defined as lone-actor (LA) terrorism, stands as one of the greatest security threats of our time. Research on LA behavior and characteristics has emerged and accelerated over the last decade. While these studies have produced valuable information on demographics, behavior, classifications, and warning signs, the relationship among these characters are yet to be addressed. Moreover, the means of radicalization and attacking have changed over decades. This study first identifies 25 binary behavioral characteristics of LAs and analyzes 192 LAs recorded on three different databases. Next, the classification is carried out according to first ideology, then to incident scene behavior via a virtual attacker-defender game, and, finally, according to the clusters obtained from the data. In addition, within each class, statistically significant associations and temporal relations are extracted using the A-priori algorithm. These associations would be instrumental in identifying the attacker type and intervene at the right time. The results indicate that while pre-9/11 LAs were mostly radicalized by the people in their environment, post-9/11 LAs are more diverse. Furthermore, the association chains for different LA types present unique characteristic pathways to violence and after-attack behavior. 

Previous research evaluating the influence of category knowledge on memory found that children, like adults, rely on category information to facilitate recall (Duffy, Huttenlocher, & Crawford, 2006). A model that combines category and target information (Integrative) provides a superior fit to preschoolers recall data compared to a category only (Prototype) and target only (Target) model (Macias, Persaud, Hemmer, & Bonawitz, in revision). Utilizing data and computational approaches from Macias et al., (in revision), we explore whether individual and age-related differences persist in the model fits. Results revealed that a greater proportion of preschoolers recall was best fit by the Prototype model and trials where children displayed individuating behaviors, such as spontaneously labeling, were also best fit by the Prototype model. Furthermore, the best fitting model varied by age. This work demonstrates a rich complexity and variation in recall between developmental groups that can be illuminated by computationally evaluating individual differences.