From Information to Knowledge: A Role for Knowledge Networks in Decision Making and Action Selection

Abstract

The brain receives information via sensory inputs through the peripheral nervous system and stores a small subset as memories within the central nervous system. Short-term, working memory is present in the hippocampus whereas long-term memories are distributed within neural networks throughout the brain. Elegant studies on the mechanisms for memory storage and the neuroeconomic formulation of human decision making have been recognized with Nobel Prizes in Physiology or Medicine and in Economics, respectively. There is a wide gap, however, in our understanding of how memories of disparate bits of information translate into “knowledge”, and the neural mechanisms by which knowledge is used to make decisions. I propose that the conceptualization of a “knowledge network” for the creation, storage and recall of knowledge is critical to start bridging this gap. Knowledge creation involves value-driven contextualization of memories through cross-validation via certainty-seeking behaviors, including rumination or reflection. Knowledge recall, like memory, may occur via oscillatory activity that dynamically links multiple networks. These networks may show correlated activity and interactivity despite their presence within widely separated regions of the nervous system, including the brainstem, spinal cord and gut. The hippocampal–amygdala complex together with the entorhinal and prefrontal cortices are likely components of multiple knowledge networks since they participate in the contextual recall of memories and action selection. Sleep and reflection processes and attentional mechanisms mediated by the habenula are expected to play a key role in knowledge creation and consolidation. Unlike a straightforward test of memory, determining the loci and mechanisms for the storage and recall of knowledge requires the implementation of a naturalistic decision-making paradigm. By formalizing a neuroscientific concept of knowledge networks, we can experimentally test their functionality by recording large-scale neural activity during decision making in awake, naturally behaving animals. These types of studies are difficult but important also for advancing knowledge-driven as opposed to big data-driven models of artificial intelligence. A knowledge network-driven understanding of brain function may have practical implications in other spheres, such as education and the treatment of mental disorders.