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Though this theory represented an important leap forward in motor learning research, [1] one weakness in Adams’ closed-loop theory was the requirement of 1-to-1 mapping between stored states (motor programs) and movements to be made. This presented an issue related to the storage capacity of the central nervous system; a vast array of ...
Motor learning research often considers variables that contribute to motor program formation (i.e., underlying skilled motor behaviour), sensitivity of error-detection processes, [1] [2] and strength of movement schemas (see motor program). Motor learning is "relatively permanent", as the capability to respond appropriately is acquired and ...
Central pattern generators (CPGs) are self-organizing biological neural circuits [1] [2] that produce rhythmic outputs in the absence of rhythmic input. [3] [4] [5] They are the source of the tightly-coupled patterns of neural activity that drive rhythmic and stereotyped motor behaviors like walking, swimming, breathing, or chewing.
These difficulties have led to a more nuanced notion of motor programs known as generalized motor programs. [30]: 240–257 A generalized motor program is a program for a particular class of action, rather than a specific movement. This program is parameterized by the context of the environment and the current state of the organism.
The learner uses generalized patterns, principles, and other similarities between past experiences and novel experiences to more efficiently navigate the world. [2] For example, if a person has learned in the past that every time they eat an apple, their throat becomes itchy and swollen, they might assume they are allergic to all fruit.
Psychomotor learning is the relationship between cognitive functions and physical movement.Psychomotor learning is demonstrated by physical skills such as movement, coordination, manipulation, dexterity, grace, strength, speed—actions which demonstrate the fine or gross motor skills, such as use of precision instruments or tools, and walking.
The generalized Hebbian algorithm is an iterative algorithm to find the highest principal component vectors, in an algorithmic form that resembles unsupervised Hebbian learning in neural networks. Consider a one-layered neural network with n {\displaystyle n} input neurons and m {\displaystyle m} output neurons y 1 , … , y m {\displaystyle y ...
In neuroscience and motor control, the degrees of freedom problem or motor equivalence problem states that there are multiple ways for humans or animals to perform a movement in order to achieve the same goal. In other words, under normal circumstances, no simple one-to-one correspondence exists between a motor problem (or task) and a motor ...