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Neurobiologically inspired algorithms for exploiting track data to learn normal patterns of motion behavior, detect deviations from normalcy, and predict future behavior are presented. These capabilities contribute to higher-level fusion situational awareness and assessment objectives. They also provide essential elements for automated scene understanding to shift operator focus from sensor monitoring and activity detection to behavior assessment and response decision-making. Our learning algorithms construct models of normal activity patterns at a variety of conceptual, spatial, and temporal levels to reduce a massive amount of track data to a rich set of information regarding the current status of active entities within an operator's field of regard. Continuous incremental learning enables the models of normal behavior to adapt well to evolving situations while maintaining high levels of performance. Deviations from normalcy result in notification reports that can be published directly to operator displays. Deviation tolerance levels are user settable during system operation to tune alerting sensitivity. Operator responses to anomaly alerts can be fed back into the algorithms to further enhance and refine learned models. These algorithms have been successfully demonstrated to learn vessel behaviors across the maritime domain and to learn vehicle and dismount behavior in land-based settings.