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For example, a neural network may be more effective than a linear regression model for some types of data. [14] Increase the amount of training data: If the model is underfitting due to a lack of data, increasing the amount of training data may help. This will allow the model to better capture the underlying patterns in the data. [14]
In 1974, Baddeley and Hitch [5] introduced and made popular the multicomponent model of working memory.This theory proposes a central executive that, among other things, is responsible for directing attention to relevant information, suppressing irrelevant information and inappropriate actions, and for coordinating cognitive processes when more than one task must be done at the same time.
The Atkinson–Shiffrin memory model was proposed in 1968 by Richard C. Atkinson and Richard Shiffrin. This model illustrates their theory of the human memory. These two theorists used this model to show that the human memory can be broken in to three sub-sections: Sensory Memory, short-term memory and long-term memory. [9]
The Atkinson–Shiffrin model (also known as the multi-store model or modal model) is a model of memory proposed in 1968 by Richard Atkinson and Richard Shiffrin. [1] The model asserts that human memory has three separate components: a sensory register, where sensory information enters memory,
The working memory model. In 1974 Baddeley and Hitch proposed a "working memory model" that replaced the general concept of short-term memory with active maintenance of information in short-term storage. In this model, working memory consists of three basic stores: the central executive, the phonological loop, and the visuo-spatial sketchpad.
Memory is a complex system that relies on interactions between many distinct parts of the brain. In order to fully understand memory, researchers must cumulate evidence from human, animal, and developmental research in order to make broad theories about how memory works. Intraspecies comparisons are key.
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The Cerebellar Model Articulation Controller (CMAC) is a type of neural network based on a model of the mammalian cerebellum. It is a type of associative memory . [ 11 ] The CMAC was first proposed as a function modeler for robotic controllers by James Albus in 1975 and has been extensively used in reinforcement learning and also as for ...