The Emergence of Grammar through Reinforcement Learning

Reinforcement learning in psychology (as opposed to machine learning) refers to a family of mathematical models of how animals and humans learn. It has its origins with Thorndike's Law of Effect: behavior with positive outcomes is reinforced and likely to be repeated (learned). Reinforcement learning is part of a larger family of stochastic learning models where behavior is probabilistic (Bush and Mosteller 1951, 1953, 1955). The key ideas are that the STATE OF LEARNING of a SUBJECT (person or animal) is represented by a vector in a STATE SPACE. The subject's behavior (or RESPONSE) given a STIMULUS is not deterministic, but depends on probabilities determined by the state of learning. The OUTCOME(or PAYOFF) changes the state of learning. In reinforcement learning, the relative size of the payoff determines how strongly (if at all) the behavior is reinforced.