How Psychology Drives Machine Learning
Reinforcement Learning and Conditioning
People have continuously wanted to create machines that can think, learn, and reason. The research within the field of artificial intelligence (AI) leads us to believe that we should look at algorithms, thinking they are comparable to our human ways of thinking and reasoning. A good example is the following definition of AI as “any machine that does things a brain can do.”
This consideration also applies to reinforcement learning: It’s a machine learning field that accroding to Prakriteswar Santikary is concerned with how software agents should take actions in an environment to maximize rewards. It’s one of the three paradigms in machine learning and builds on a well-known educational/psychological concept: conditioning.
Conditioning Methods
Pavlov’s behavioral learning theory (also known as classical conditioning) states that a new, conditional reaction can be added to a natural, mostly innate, so-called unconditional response through learning. A well-known example is a Pavlovian dog: every time the dog detected food (the biological stimulus), a bell would ring (neutral stimulus). After a few such feedings, the dog would react to the bell’s sound in the same way it would respond to food: its salvia began to flow. Ivan…
