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Intelligence analysts are faced with the demanding tasks of identifying suspicious patterns in large volumes of complex, disparate information sources and predicting numerous possible outcomes based on perceived patterns. The challenge is in transforming information from these unstructured and massive collections into small and precise chunks suitable for reasoning. Scanning immense quantities of data can be tedious and takes time away from the goal of using this information to draw a conclusion. In fact, analysts often spend the majority of their time finding the correct information to answer their research question, leaving little time for analyzing and projecting possible outcomes. The underlying purpose of analyzing information is to gain a better understanding of the current state of the environment or situation under investigation and to predict future activities/events based on current status. Under operational conditions, where time and accuracy are essential, critical information may not be consciously perceived or be incorrectly perceived or interpreted (e.g., relevance or importance explained away).
Due to the nature of the tasks performed by intelligence analysts’, individual biases greatly influence analysis outcomes. Biases result from individual mental models and are influenced by structural (e.g., past experience, cultural differences, education and personality) and situational factors (e.g., stress, fatigue, complexity etc). Biases can lead to (1) information being discarded too quickly without the necessary evidence or (2) important information simply not being attended to at all. The Advanced Neurophysiology for Intelligence Text Analysis (ANITA) project, a collaboration between Design Interactive and ABM, is designed to develop a system using eye-tracking with EEG signatures to highlight words or phrases of interest to the reader allowing them to review text more efficiently by eliminating the need to stop reading in order to highlight a particular section of interest.