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Bayesian Inference In Motor Learning
Bayesian inference is a statistical tool that can be applied to motor learning, specifically to adaptation. Adaptation is a short-term learning process involving gradual improvement in performance in response to a change in sensory information. Bayesian inference is used to describe the way the nervous system combines this sensory information with prior knowledge to estimate the position or other characteristics of something in the environment. Bayesian inference can also be used to show how information from multiple senses (e.g. visual and proprioception) can be combined for the same purpose. In either case, Bayesian inference dictates that the estimate is most influenced by whichever information is most certain. Example: integrating prior knowledge with sensory information in tennis A person uses Bayesian inference to create an estimate that is a weighted combination of his current sensory information and his previous knowledge, or prior. This can be illustrated by decisions m ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayesian Inference
Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law. In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability". Introduction to Bayes' rule Formal explanation Bayesian inference derives the posterior probability as a consequence of two antecedents: a prior probability and a "likelihood function" derived from a statistical model for the observed data. Bayesian inference computes the posterior probability according to Bayes' theorem: ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Motor Learning
Motor learning refers broadly to changes in an organism's movements that reflect changes in the structure and function of the nervous system. Motor learning occurs over varying timescales and degrees of complexity: humans learn to walk or talk over the course of years, but continue to adjust to changes in height, weight, strength etc. over their lifetimes. Motor learning enables animals to gain new skills, and improves the smoothness and accuracy of movements, in some cases by calibrating simple movements like reflexes. Motor learning research often considers variables that contribute to motor program formation (i.e., underlying skilled motor behaviour), sensitivity of error-detection processes, and strength of movement schemas (see motor program). Motor learning is "relatively permanent", as the capability to respond appropriately is acquired and retained. Temporary gains in performance during practice or in response to some perturbation are often termed motor adaptation, a transi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neural Adaptation
Neural adaptation or sensory adaptation is a gradual decrease over time in the responsiveness of the sensory system to a constant stimulus. It is usually experienced as a change in the stimulus. For example, if a hand is rested on a table, the table's surface is immediately felt against the skin. Subsequently, however, the sensation of the table surface against the skin gradually diminishes until it is virtually unnoticeable. The sensory neurons that initially respond are no longer stimulated to respond; this is an example of neural adaptation. All sensory and neural systems have a form of adaptation to constantly detect changes in the environment. Neural receptor cells that process and receive stimulation go through constant changes for mammals and other living organisms to sense vital changes in their environment. Some key players in several neural systems include Ca2+ions (see Calcium in biology) that send negative feedback in second messenger pathways that allow the neural recep ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Proprioception
Proprioception ( ), also referred to as kinaesthesia (or kinesthesia), is the sense of self-movement, force, and body position. It is sometimes described as the "sixth sense". Proprioception is mediated by proprioceptors, mechanosensory neurons located within muscles, tendons, and joints. Most animals possess multiple subtypes of proprioceptors, which detect distinct kinematic parameters, such as joint position, movement, and load. Although all mobile animals possess proprioceptors, the structure of the sensory organs can vary across species. Proprioceptive signals are transmitted to the central nervous system, where they are integrated with information from other sensory systems, such as the visual system and the vestibular system, to create an overall representation of body position, movement, and acceleration. In many animals, sensory feedback from proprioceptors is essential for stabilizing body posture and coordinating body movement. System overview In vertebrates, limb ve ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayesian Tennis Court
Thomas Bayes (/beɪz/; c. 1701 – 1761) was an English statistician, philosopher, and Presbyterian minister. Bayesian () refers either to a range of concepts and approaches that relate to statistical methods based on Bayes' theorem, or a follower of these methods. A number of things have been named after Thomas Bayes, including: Bayes *Bayes action * Bayes Business School *Bayes classifier * Bayes discriminability index * Bayes error rate *Bayes estimator * Bayes factor * Bayes Impact * Bayes linear statistics *Bayes prior * Bayes' theorem / Bayes-Price theorem -- sometimes called Bayes' rule or Bayesian updating. *Empirical Bayes method *Evidence under Bayes theorem * Hierarchical Bayes model *Laplace–Bayes estimator *Naive Bayes classifier * Random naive Bayes Bayesian * Approximate Bayesian computation *Bayesian average * Bayesian Analysis (journal) *Bayesian approaches to brain function * Bayesian bootstrap *Bayesian control rule *Bayesian cognitive science *Bayesi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayes' Theorem
In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule), named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if the risk of developing health problems is known to increase with age, Bayes' theorem allows the risk to an individual of a known age to be assessed more accurately (by conditioning it on their age) than simply assuming that the individual is typical of the population as a whole. One of the many applications of Bayes' theorem is Bayesian inference, a particular approach to statistical inference. When applied, the probabilities involved in the theorem may have different probability interpretations. With Bayesian probability interpretation, the theorem expresses how a degree of belief, expressed as a probability, should rationally change to account for the availability of related evidence. Bayesian inference is fundamental to Bayesia ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayes' Theorem
In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule), named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if the risk of developing health problems is known to increase with age, Bayes' theorem allows the risk to an individual of a known age to be assessed more accurately (by conditioning it on their age) than simply assuming that the individual is typical of the population as a whole. One of the many applications of Bayes' theorem is Bayesian inference, a particular approach to statistical inference. When applied, the probabilities involved in the theorem may have different probability interpretations. With Bayesian probability interpretation, the theorem expresses how a degree of belief, expressed as a probability, should rationally change to account for the availability of related evidence. Bayesian inference is fundamental to Bayesia ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
One Exact Data Point (A), Versus Data Drawn Randomly From A Normal Distribution (B)
1 (one, unit, unity) is a number representing a single or the only entity. 1 is also a numerical digit and represents a single unit of counting or measurement. For example, a line segment of ''unit length'' is a line segment of length 1. In conventions of sign where zero is considered neither positive nor negative, 1 is the first and smallest positive integer. It is also sometimes considered the first of the infinite sequence of natural numbers, followed by 2, although by other definitions 1 is the second natural number, following 0. The fundamental mathematical property of 1 is to be a multiplicative identity, meaning that any number multiplied by 1 equals the same number. Most if not all properties of 1 can be deduced from this. In advanced mathematics, a multiplicative identity is often denoted 1, even if it is not a number. 1 is by convention not considered a prime number; this was not universally accepted until the mid-20th century. Additionally, 1 is the s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Center Of Pressure (terrestrial Locomotion)
In biomechanics, center of pressure (CoP) is the term given to the point of application of the ground reaction force vector. The ground reaction force vector represents the sum of all forces acting between a physical object and its supporting surface. Analysis of the center of pressure is common in studies on human postural control and gait. It is thought that changes in motor control may be reflected in changes in the center of pressure. In biomechanical studies, the effect of some experimental condition on movement execution will regularly be quantified by alterations in the center of pressure. The center of pressure is not a static outcome measure. For instance, during human walking, the center of pressure is near the heel at the time of heelstrike and moves anteriorly throughout the step, being located near the toes at toe-off. For this reason, analysis of the center of pressure will need to take into account the dynamic nature of the signal. In the scientific literature variou ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ground Reaction Force
In physics, and in particular in biomechanics, the ground reaction force (GRF) is the force exerted by the ground on a body in contact with it. For example, a person standing motionless on the ground exerts a contact force on it (equal to the person's weight) and at the same time an equal and opposite ground reaction force is exerted by the ground on the person. In the above example, the ground reaction force coincides with the notion of a normal force. However, in a more general case, the GRF will also have a component parallel to the ground, for example when the person is walking – a motion that requires the exchange of horizontal (frictional) forces with the ground. The use of the word reaction derives from Newton's third law Newton's laws of motion are three basic laws of classical mechanics that describe the relationship between the motion of an object and the forces acting on it. These laws can be paraphrased as follows: # A body remains at rest, or in moti ..., ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayesian Approaches To Brain Function
Bayesian approaches to brain function investigate the capacity of the nervous system to operate in situations of uncertainty in a fashion that is close to the optimal prescribed by Bayesian statistics. This term is used in behavioural sciences and neuroscience and studies associated with this term often strive to explain the brain's cognitive abilities based on statistical principles. It is frequently assumed that the nervous system maintains internal probabilistic models that are updated by neural processing of sensory information using methods approximating those of Bayesian probability. Origins This field of study has its historical roots in numerous disciplines including machine learning, experimental psychology and Bayesian statistics. As early as the 1860s, with the work of Hermann Helmholtz in experimental psychology the brain's ability to extract perceptual information from sensory data was modeled in terms of probabilistic estimation. The basic idea is that the nervous sys ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Perception
Perception () is the organization, identification, and interpretation of sensory information in order to represent and understand the presented information or environment. All perception involves signals that go through the nervous system, which in turn result from physical or chemical stimulation of the sensory system.Goldstein (2009) pp. 5–7 Vision involves light striking the retina of the eye; smell is mediated by odor molecules; and hearing involves pressure waves. Perception is not only the passive receipt of these signals, but it is also shaped by the recipient's learning, memory, expectation, and attention. Gregory, Richard. "Perception" in Gregory, Zangwill (1987) pp. 598–601. Sensory input is a process that transforms this low-level information to higher-level information (e.g., extracts shapes for object recognition). The process that follows connects a person's concepts and expectations (or knowledge), restorative and selective mechanisms (such as ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
