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A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, [1] [2] [3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.
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]
This model quantified the nature of retrieval from long-term memory and characterized recall as a memory search with cycles of sampling and recovery. [8] In 1984, another quantum step forward occurred, when the theory was extended to recognition memory, in which a decision is based on summed activation of related memory traces. [ 9 ]
The free energy principle is a theoretical framework suggesting that the brain reduces surprise or uncertainty by making predictions based on internal models and updating them using sensory input. It highlights the brain's objective of aligning its internal model and the external world to enhance prediction accuracy.
To model emotion, we need a cognitive system that can be modulated to adapt its use of processing resources and behavior tendencies. In the Psi-theory, emotions are interpreted as a configurational setting of the cognitive modulators along with the pleasure/distress dimension and the assessment of the cognitive urges.
The generation effect is typically achieved in cognitive psychology experiments by asking participants to generate words from word fragments. [2] This effect has also been demonstrated using a variety of other materials, such as when generating a word after being presented with its antonym, [3] synonym, [1] picture, [4] arithmetic problems, [2] [5] or keyword in a paragraph. [6]
The Levels of Processing model, created by Fergus I. M. Craik and Robert S. Lockhart in 1972, describes memory recall of stimuli as a function of the depth of mental processing. More analysis produce more elaborate and stronger memory than lower levels of processing. Depth of processing falls on a shallow to deep continuum.
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,