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The hierarchical network model is part of the scale-free model family sharing their main property of having proportionally more hubs among the nodes than by random generation; however, it significantly differs from the other similar models (Barabási–Albert, Watts–Strogatz) in the distribution of the nodes' clustering coefficients: as other models would predict a constant clustering ...
Psi-theory suggests hierarchical networks of nodes as a universal mode of representation for declarative, procedural and tacit knowledge. These nodes may encode localist and distributed representations. The activity of the system is modeled using modulated and directional spreading of activation within these networks.
The model of hierarchical complexity (MHC), developed by Commons, is a way of measuring the complexity of a behavior. The MHC uses mathematical principles to quantify behavioral characteristics, assigning individuals to stages based on properly completed tasks.
Hierarchical network models are, by design, scale free and have high clustering of nodes. [33] The iterative construction leads to a hierarchical network. Starting from a fully connected cluster of five nodes, we create four identical replicas connecting the peripheral nodes of each cluster to the central node of the original cluster.
The model of hierarchical complexity (MHC) is a formal theory and a mathematical psychology framework for scoring how complex a behavior is. [4] Developed by Michael Lamport Commons and colleagues, [3] it quantifies the order of hierarchical complexity of a task based on mathematical principles of how the information is organized, [5] in terms of information science.
In neural networks, short pathlength between nodes and high clustering at network hubs supports efficient communication between brain regions at the lowest energetic cost. [36] The brain is constantly processing and adapting to new information and small-world network model supports the intense communication demands of neural networks. [37]
As early as the 1860s, with the work of Hermann Helmholtz in experimental psychology, the brain's ability to extract perceptual information from sensory data was modeled in terms of probabilistic estimation. [5] [6] The basic idea is that the nervous system needs to organize sensory data into an accurate internal model of the outside world.
Andrew J. Elliot (born 1962) is a professor of psychology at the University of Rochester.His research on the hierarchical model of approach and avoidance motivation focuses on combining classic and contemporary methods to test various theories. [1]