Social learning theorySocial learning is a theory of learning process social behavior which proposes that new behaviors can be acquired by observing and imitating others. It states that learning is a cognitive process that takes place in a social context and can occur purely through observation or direct instruction, even in the absence of motor reproduction or direct reinforcement. In addition to the observation of behavior, learning also occurs through the observation of rewards and punishments, a process known as vicarious reinforcement.
Decision tree learningDecision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels.
Observational learningObservational learning is learning that occurs through observing the behavior of others. It is a form of social learning which takes various forms, based on various processes. In humans, this form of learning seems to not need reinforcement to occur, but instead, requires a social model such as a parent, sibling, friend, or teacher with surroundings. Particularly in childhood, a model is someone of authority or higher status in an environment.
Decision treeA decision tree is a decision support hierarchical model that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning.
Q-learningQ-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations. For any finite Markov decision process (FMDP), Q-learning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current state.
Intraoperative neurophysiological monitoringIntraoperative neurophysiological monitoring (IONM) or intraoperative neuromonitoring is the use of electrophysiological methods such as electroencephalography (EEG), electromyography (EMG), and evoked potentials to monitor the functional integrity of certain neural structures (e.g., nerves, spinal cord and parts of the brain) during surgery. The purpose of IONM is to reduce the risk to the patient of iatrogenic damage to the nervous system, and/or to provide functional guidance to the surgeon and anesthesiologist.
ReinforcementIn reinforcement theory, it is argued that human behavior is a result of "contingent consequences" to human actions The publication pushes forward the idea that "you get what you reinforce" This means that behavior when given the right types of reinforcers can change employee behavior for the better and negative behavior can be weeded out. The model of self-regulation has three main aspects of human behavior, which are self-awareness, self-reflection, and self-regulation. Reinforcements traditionally align with self-regulation.
Behavioral addictionBehavioral addiction is a form of addiction that involves a compulsion to engage in a rewarding non-substance-related behavior – sometimes called a natural reward – despite any negative consequences to the person's physical, mental, social or financial well-being. Addiction canonically refers to substance abuse; however, the term's connotation has been expanded to include behaviors that may lead to a reward (such as gambling, eating, or shopping) since the 1990s.
Decision theoryDecision theory (or the theory of choice; not to be confused with choice theory) is a branch of applied probability theory and analytic philosophy concerned with the theory of making decisions based on assigning probabilities to various factors and assigning numerical consequences to the outcome. There are three branches of decision theory: Normative decision theory: Concerned with the identification of optimal decisions, where optimality is often determined by considering an ideal decision-maker who is able to calculate with perfect accuracy and is in some sense fully rational.
Heuristic (psychology)Heuristics is the process by which humans use mental short cuts to arrive at decisions. Heuristics are simple strategies that humans, animals, organizations, and even machines use to quickly form judgments, make decisions, and find solutions to complex problems. Often this involves focusing on the most relevant aspects of a problem or situation to formulate a solution. While heuristic processes are used to find the answers and solutions that are most likely to work or be correct, they are not always right or the most accurate.
