scholarly journals A neural network model of the effect of prior experience with regularities on subsequent category learning

Cognition ◽  
2022 ◽  
Vol 222 ◽  
pp. 104997
Author(s):  
Casey L. Roark ◽  
David C. Plaut ◽  
Lori L. Holt
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Category Learning ◽  
Prior Experience

scholarly journals A neural network model of the effect of prior experience with regularities on subsequent category learning

2020 ◽  
Author(s):  
Casey L Roark ◽  
David C. Plaut ◽  
Lori L. Holt
Keyword(s):  
Neural Network ◽  
Linear Relationship ◽  
Systems Theory ◽  
Network Model ◽  
Neural Network Model ◽  
Category Learning ◽  
Prior Experience ◽  
Critical Aspect ◽  
Dual Systems ◽  
Category Exemplars

Categories are often structured by the similarities of instances within the category. A popular dual systems theory of category learning argues that the structure of exemplars forming categories determines the mechanisms that drive learning. Category distributions are necessarily defined by dimensions or features. Researchers typically assume that there is a direct, linear relationship between the physical input dimensions across which category exemplars are defined and the psychological representation of these dimensions, but this assumption is not always warranted. Through a set of simulations, we demonstrate that the psychological representations of input dimensions developed through prior experience can place drastic constraints on category learning. We compare the model’s behavior to auditory, visual, and cross-modal human category learning and make conclusions regarding the nature of the psychological representations of the dimensions in those studies. These simulations support the conclusion that the nature of psychological representations is a critical aspect to understanding the mechanisms underlying category learning.

scholarly journals A neural network model of hippocampal contributions to category learning

2022 ◽  
Author(s):  
Jelena Sucevic ◽  
Anna C. Schapiro
Keyword(s):  
Neural Network ◽  
Dentate Gyrus ◽  
Entorhinal Cortex ◽  
Network Model ◽  
Neural Network Model ◽  
Category Learning ◽  
Critical Role ◽  
Pattern Separation ◽  
Rapid Binding ◽  
Separation Functions

In addition to its critical role in encoding individual episodes, the hippocampus is capable of extracting regularities across experiences. This ability is central to category learning, and a growing literature indicates that the hippocampus indeed makes important contributions to this kind of learning. Using a neural network model that mirrors the anatomy of the hippocampus, we investigated the mechanisms by which the hippocampus may support novel category learning. We simulated three category learning paradigms and evaluated the network's ability to categorize and to recognize specific exemplars in each. We found that the trisynaptic pathway within the hippocampus-connecting entorhinal cortex to dentate gyrus, CA3, and CA1-was critical for remembering individual exemplars, reflecting the rapid binding and pattern separation functions of this circuit. The monosynaptic pathway from entorhinal cortex to CA1, in contrast, was responsible for detecting the regularities that define category structure, made possible by the use of distributed representations and a slower learning rate. Together, the simulations provide an account of how the hippocampus and its constituent pathways support novel category learning.

A neural network model of entropy discrimination in pigeons

2003 ◽  
Author(s):  
Christopher Silansky ◽  
Anthony Chemero
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model

A neural network model of pre-pulse inhibition

2003 ◽  
Author(s):  
Nestor Schmajuk ◽  
Roger Smith
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model

Neural Network Model to Calculate the Creep Data Using Size

2017 ◽  
Vol 7 (8) ◽  
pp. 257
Author(s):  
Seetharam .K ◽  
Sharana Basava Gowda ◽  
. Varadaraj
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Software Engineering ◽  
Network Model ◽  
Neural Network Model ◽  
Software Metrics ◽  
Creep Data ◽  
Engineering Software ◽  
Function Points ◽  
Different Levels

In Software engineering software metrics play wide and deeper scope. Many projects fail because of risks in software engineering development[1]t. Among various risk factors creeping is also one factor. The paper discusses approximate volume of creeping requirements that occur after the completion of the nominal requirements phase. This is using software size measured in function points at four different levels. The major risk factors are depending both directly and indirectly associated with software size of development. Hence It is possible to predict risk due to creeping cause using size.

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