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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 ...
Next to the motivational and emotional system, Psi-theory suggests a neuro-symbolic model of representation, which encodes semantic relationships in a hierarchical spreading activation network. The representations are grounded in sensors and actuators, and are acquired by autonomous exploration.
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.
Networked individualism represents the shift of the classical model of social arrangements formed around hierarchical bureaucracies or social groups that are tightly-knit, like households and work groups, to connected individuals, using the means provided by the evolution of Information and communications technology. Although the turn to ...
A 'second wave' connectionist (ANN) model with a hidden layer. Connectionism is an approach to the study of human mental processes and cognition that utilizes mathematical models known as connectionist networks or artificial neural networks. [1] Connectionism has had many "waves" since its beginnings.
This makes predictive coding similar to some other models of hierarchical learning, such as Helmholtz machines and Deep belief networks, which however employ different learning algorithms. Thus, the dual use of prediction errors for both inference and learning is one of the defining features of predictive coding.
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.
Hierarchical network models of memory were largely discarded due to some findings related to mental chronometry. The Teachable Language Comprehender (TLC) model proposed by Collins and Quillian (1969) had a hierarchical structure indicating that recall speed in memory should be based on the number of levels in memory traversed in order to find ...