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Decay theory. The Decay theory is a theory that proposes that memory fades due to the mere passage of time. Information is therefore less available for later retrieval as time passes and memory, as well as memory strength, wears away. [1] When an individual learns something new, a neurochemical "memory trace" is created.
Forgetting curve. The forgetting curve hypothesizes the decline of memory retention in time. This curve shows how information is lost over time when there is no attempt to retain it. [1] A related concept is the strength of memory that refers to the durability that memory traces in the brain. The stronger the memory, the longer period of time ...
Hopfield network. A Hopfield network (or associative memory) is a form of recurrent neural network, or a spin glass system, that can serve as a content-addressable memory. The Hopfield network, named for John Hopfield, consists of a single layer of neurons, where each neuron is connected to every other neuron except itself.
The black hole information paradox[1] is a paradox that appears when the predictions of quantum mechanics and general relativity are combined. The theory of general relativity predicts the existence of black holes that are regions of spacetime from which nothing—not even light—can escape. In the 1970s, Stephen Hawking applied the ...
Random number generation is a process by which, often by means of a random number generator (RNG), a sequence of numbers or symbols that cannot be reasonably predicted better than by random chance is generated.
This theory was tested by Anderson and Green using the Think/No-Think paradigm. [13] The decay theory is another theory of forgetting which refers to the loss of memory over time. When information enters memory, neurons are activated. These memories are retained as long as the neurons remain active.
James Nairne proposed one of the first unitary theories, which criticized Alan Baddeley's working memory model, [2] which is the dominant theory of the functions of short-term memory. Other theories since Nairne have been proposed; they highlight alternative mechanisms that the working memory model initially overlooked.
Autoregressive model. In statistics, econometrics, and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it can be used to describe certain time-varying processes in nature, economics, behavior, etc. The autoregressive model specifies that the output variable depends linearly on its own ...