are forced to rely more on operators' observable actions and verbalizations in order to infer their level of SA. In this case, such actions and verbalizations are best assessed using performance and behavioral measures of SA, as described next. Performance and behavioral measures Performance measures infer SA from the end result (i.e., task performance outcomes), based on the assumption that better performance indicates better SA. Common performance metrics include quantity of output or productivity level, time to perform the task or respond to an event, and the accuracy of the response or, conversely, the number of errors committed. The main advantage of performance measures is that these can be collected objectively and without disrupting task performance. However, although evidence exists to suggest a positive relation between SA and performance, this connection is probabilistic and not always direct and unequivocal. In other words, good SA does not always lead to good performance and poor SA does not always lead to poor performance. Thus, performance measures should be used in conjunction with others measures of SA that directly assess this construct. Behavioral measures also infer SA from the actions that individuals choose to take, based on the assumption that good actions will follow from good SA and vice versa. Behavioral measures rely primarily on observer ratings, and are, thus, somewhat subjective in nature. To address this limitation, observers can be asked to evaluate the degree to which individuals are carrying out actions and exhibiting behaviors that would be expected to promote the achievement of higher levels of SA. This approach removes some of the subjectivity associated with making judgments about an individual's internal state of knowledge by allowing them to make judgments about SA indicators that are more readily observable. Process indices Process indices examine how individuals process information in their environment, such as by analyzing communication patterns between team members or using eye tracking devices. Team communication (particularly verbal communication) supports the knowledge building and information processing that leads to SA construction. Thus, since SA may be distributed via communication, computational linguistics and machine learning techniques can be combined with natural language analytical techniques (e.g., Latent semantic analysis) to create models that draw on the verbal expressions of the team to predict SA and task performance. Although evidence exists to support the utility of communication analysis for predicting team SA, time constraints and technological limitations (e.g., cost and availability of speech