Knowledge Representation in Neural Systems (KRNS)
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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 general 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.
The goal of the KRNS Program is to develop and rigorously evaluate theories that explain how the human brain represents conceptual knowledge. In part the evaluation will rest on how well concepts can be interpreted from neural activity patterns using algorithms derived from the theories. In addition to new theories and algorithms, KRNS seeks the development of innovative protocols for evoking and measuring concept-related neural activity using neural imaging methods such as (but not limited to) functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG).
Whereas previous research has examined the neural representations of single concepts in isolation, the KRNS Program seeks to greatly expand our understanding of how the brain represents combinations of concepts (e.g., how the neural representation of "the student was bored with the book" differs from the neural representations of the individual concepts "student," "bored," and "book"). A wide range of concept types is of interest in KRNS, from the concrete to the abstract, including: animate and inanimate objects; actions; physical and temporal settings; events; social roles; social interactions; emotions; properties; conditions.
Performers (Prime Contractors)
Carnegie Mellon University; HRL Laboratories, LLC; Siemens Corporation Corporate Technology; Teledyne Scientific and Imaging
- Knowledge discovery
- Artificial Intelligence
- Cognitive bias
- Decision making
- Behavioral science
- Human factors
- Data sense-making
To access KRNS program-related publications, please visit Google Scholar.
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