When making sense of intelligence data, analysts rely on rich repertoires of conceptual knowledge to resolve ambiguities, make inferences, and draw conclusions. Conceptual knowledge refers to knowledge about the properties of an entity (e.g., an apple is edible) as well as its relationships to other entities (e.g., an apple is associated with orchards, grocery stores, etc.). Understanding how the human brain represents conceptual knowledge is a step toward building new analysis tools that acquire, organize and wield knowledge with unprecedented proficiency. Moreover, such understanding may lead to the development of novel techniques for training intelligence analysts and linguists.
Although decades of neuroscience research have shed light on how the brain represents various types of sensory and motor information, far less is known about the neural basis of conceptual knowledge. Studies to date have focused on a limited number of coarsely defined concept classes (e.g., faces and places), but a general predictive theory of the neural basis of conceptual knowledge remains elusive.
The KRNS Program seeks to develop and rigorously assess novel theories that explain how the human brain represents diverse types of conceptual knowledge within spatial and temporal (dynamic) patterns of neural activity. To demonstrate the power of their theories, KRNS performers will develop systems that aim to predict patterns of neural activity associated with particular concepts and that can interpret which concepts are represented within measured patterns of neural activity. All neural activity data in KRNS will be obtained using non-invasive methods such as (but not limited to) functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG).
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