Networks

Author(s):  
Stephen K. Reed

Networks provide organization through nodes that are connected by links. Characteristics of networks that matter include clusters, path lengths, link weights, and hubs. A semantic network displays connections among concepts in which shorter links represent stronger associations between two concepts. A spreading activation model is a theory of how related concepts become activated. Variations of the theory enable predictions, such as spreading activation, is partitioned among the links at a node. This assumption leads to the testable prediction that the strength of activation along each link diminishes as the number of links increases. Brain imaging has revealed that information transfer depends not only on the direct path between nodes but also on the availability of alternative detour paths. This hyperconnectivity following a lesion lowers efficiency and is reduced with recovery from brain injury.

2019 ◽  
Author(s):  
Simon De Deyne ◽  
Danielle Navarro ◽  
Amy Perfors ◽  
Gert Storms

Similarity plays an important role in organizing the semantic system. However, given that similarity cannot be defined on purely logical grounds, it is important to understand how people perceive similarities between different entities. Despite this, the vast majority of studies focus on measuring similarity between very closely related items. When considering concepts that are very weakly related, little is known. In this article, we present 4 experiments showing that there are reliable and systematic patterns in how people evaluate the similarities between very dissimilar entities. We present a semantic network account of these similarities showing that a spreading activation mechanism defined over a word association network naturally makes correct predictions about weak similarities, whereas, though simpler, models based on direct neighbors between word pairs derived using the same network cannot.


2018 ◽  
Author(s):  
Dirk U. Wulff ◽  
Thomas Hills ◽  
Rui Mata

Cognitive science invokes semantic networks to explain diverse phenomena from reasoning to memory retrieval and creativity. While diverse approaches are available, researchers commonly assume a single underlying semantic network that is shared across individuals. Yet, semantic networks are considered the product of experience implying that individuals who make different experiences should possess different semantic networks. By studying differences between younger and older adults, we demonstrate that this is the case. Using a network analytic approach and diverse empirical data, we present converging evidence of age-related differences in semantic networks of groups and, for the first time, individuals. Specifically, semantic networks of older adults exhibited larger degrees, less clustering, and longer path lengths. Furthermore, the edge weight distributions of older adults individual networks exhibited significantly more skew and higher entropy across node pairs and, except for unrelated node pairs, less inter-individual agreement, suggesting that older adults networks are generally more distinct than younger adults networks. Our results challenge the common conception of a single semantic network shared by individuals and highlight the importance of individual differences in cognitive modeling. They also present valuable benchmarks to discern between theories of age-related changes in cognitive performance.


Brain Injury ◽  
2006 ◽  
Vol 20 (5) ◽  
pp. 485-497 ◽  
Author(s):  
Craig B. Roberts ◽  
Robert Rafal ◽  
B. Rudi Coetzer

2018 ◽  
Author(s):  
Natalia A. Goriounova ◽  
Djai B. Heyer ◽  
René Wilbers ◽  
Matthijs B. Verhoog ◽  
Michele Giugliano ◽  
...  

AbstractIt is generally assumed that human intelligence relies on efficient processing by neurons in our brain. Behavioral and brain-imaging studies robustly show that higher intelligence associates with faster reaction times and thicker gray matter in temporal and frontal cortical areas. However, no direct evidence exists that links individual neuron activity and structure to human intelligence. Since a large part of cortical grey matter consists of dendrites, these structures likely determine cortical architecture. In addition, dendrites strongly affect functional properties of neurons, including action potential speed. Thereby, dendritic size and action potential firing may constitute variation in cortical thickness, processing speed, and ultimately IQ.To investigate this, we took advantage of brain tissue available from neurosurgery and recorded from pyramidal neurons in the medial temporal cortex, an area showing high association between cortical thickness, cortical activity and intelligence. Next, we reconstructed full dendritic structures of recorded neurons and combined these with brain-imaging data and IQ scores from the same subjects. We find that high IQ scores and large temporal cortical thickness associate with larger, more complex dendrites of human pyramidal neurons. We show in silico that larger dendrites enable pyramidal neurons to track activity of synaptic inputs with higher temporal precision, due to fast action potential initiation. Finally, we find that human pyramidal neurons of individuals with higher IQ scores sustain faster action potentials during repeated firing. These findings provide first evidence that human intelligence is associated with neuronal complexity, action potential speed and efficient information transfer in cortical neurons.


2021 ◽  
Vol 15 ◽  
Author(s):  
Emery Schubert

Creativity is commonly defined as a process that leads to a novel and useful outcome (an idea, product, or expression). However, two dilemmas about this definition remain unresolved: (1) A strict application of usefulness is difficult to apply to artistic works: who decides what artwork is useful, and how it is useful? (2) The implied boundary conditions of novelty are problematic: The default perspective is that novelty has a monotonic increasing relationship with creativity, or it is categorical—i.e., novel or not. To address these dilemmas, this paper proposes a spreading activation model of creativity (SAMOC), a model built on a brain-architecture-inspired vast interconnected network of nodes, each node representing information, and assigned meanings through interaction with the environment. Nodes are linked to each other according to principles of temporal contiguity (linking objects/events in time) and similarity (linking objects/events by shared features). A node activated by attention spreads through the network through previously linked nodes. Nodes that are well connected activate each other easily, while those that are weakly connected do not. Net total activation corresponds to positive affect (e.g., pleasure), and this is proposed as an essential criteria for a creative work of art, instead of usefulness. SAMOC also predicts that creativity will be optimized at an intermediate, not extreme, level of novelty. Too much activation will occur with the activation of preexisting ideas (hence reproduction rather than creativity), and too much novelty will not produce spread of activation. The two functions (spreading activation and the novelty curve) are superposed to demonstrate this optimal novelty hypothesis. Early evidence of the hypothesis comes from the data that some great works of art were critically rejected at premiers (suggesting excessive novelty), but after sufficient repetition (and therefore linking) became suitably associated and commenced generating activation. The hypothesis has important implications for future empirical research programs on creativity, and for the definition of creativity itself.


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