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Reinforcement theory is a limited effects media model applicable within the realm of communication.The theory generally states that people seek out and remember information that provides cognitive support for their pre-existing attitudes and beliefs.
Other theories may focus on additional factors such as whether the person expected a behavior to produce a given outcome, but in the behavioral theory, reinforcement is defined by an increased probability of a response. The study of reinforcement has produced an enormous body of reproducible experimental results.
Just as "reward" was commonly used to alter behavior long before "reinforcement" was studied experimentally, the Premack principle has long been informally understood and used in a wide variety of circumstances. An example is a mother who says, "You have to finish your vegetables (low frequency) before you can eat any ice cream (high frequency)."
Reinforcement sensitivity theory (RST) proposes three brain-behavioral systems that underlie individual differences in sensitivity to reward, punishment, and motivation. While not originally defined as a theory of personality , the RST has been used to study and predict anxiety , impulsivity , and extraversion . [ 1 ]
Mathematical principles of reinforcement describe how incentives fuel behavior, how time constrains it, and how contingencies direct it. It is a general theory of reinforcement that combines both contiguity and correlation as explanatory processes of behavior.
Reinforcement, a key concept of behaviorism, is the primary process that shapes and controls behavior, and occurs in two ways: positive and negative. In The Behavior of Organisms (1938), Skinner defines negative reinforcement to be synonymous with punishment, i.e. the presentation of an aversive stimulus
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised ...
Edward Thorndike had a powerful impact on reinforcement theory and behavior analysis, providing the basic framework for empirical laws in behavior psychology with his law of effect. Through his contributions to the behavioral psychology field came his major impacts on education, where the law of effect has great influence in the classroom.