Did Pavlov’s experiment also condition reinforcement learning?
Do you know what reinforcement learning and Pavlov’s classical conditioning have in common? We present the real explanation.
Did Pavlov’s experiment also condition reinforcement learning?
Do you know what reinforcement learning and Pavlov’s classical conditioning have in common? We present the real explanation
Do you know what reinforcement learning and Pavlov’s classical conditioning have in common? We present the real explanation.
Human and AI learning process are connected
Artificial intelligence often takes inspiration from human psychology. Indeed, we have already seen how algorithms learn and assimilate data over time to make predictions similar to the human mind.
Today, however, we will focus on the learning process and how it can be modified. We will talk first about how this works in humans and later in the AI world.
Undoubtedly one of the most important capabilities of humans is to learn and improve on past attempts at the same task.
When we mention learning, we immediately think of the education we receive as children and adolescents. We can also connect it to studying and examinations, which are the foundations of the process of acquiring new knowledge.
However, learning is not an exclusive human ability. Moreover, it is not only the result of academic training or a certain period of a person’s life.
The ability to learn is innate in all living beings and is an ongoing process. In addition, it cannot be observed directly, but it is noticeable from the behavior of the individual.
Over time, psychologists have proposed several theories to explain how the learning process takes place. Among the most importants we find the one developed by Russian physician and physiologist Ivan Pavlov.
Pavlov’s classical conditioning: what it is
Pavlov conducted an experiment with one of his dogs and came to discover the now famous “classical conditioning”.
What connection do his dogs and this theory have?
The physiologist discovered this phenomenon while studying digestion in dogs. Normally, when food was brought in, dogs would salivate. This is an involuntary biological response to food.
The scientist experimented more deeply with psychic secretion. He rang a bell a few times, which caused no reaction in the dog. Then Pavlov rang the bell and subsequently provided the animal with the slice of meat. So, the dog associated the sound with the arrival of the meat.
Repeating the stimulus several times, finally, the scientist saw the salivation level rise when the bell rang, without presenting meat.
Basically, the ringing of the bell had become a conditioned stimulus. This initially does not generate any response in the subject. Only by associating it with the stimulus is it able to generate salivation.
The resulting increase in salivation to the sound of the bell was a conditioned response to the conditioned stimulus.
In the image below there are the steps of this experiment.
Thanks to this conditioning, the dog began to salivate whenever it heard the bell ring. Thus, it had learned that the ringing of the bell meant that food was coming.
Humans and animals learn to associate one stimulus with another. In classical (or Pavlovian) conditioning, reflexes, i.e., uncontrollable responses, are the reaction to stimuli. So it is something each of us does without even realizing it.
You are probably wondering about the link between this experiment and AI.
After explaining classical conditioning, we can finally explain this association. Are you curious?
Reinforcement learning: what it is and its correlation with Pavlov’s classical conditioning
There is a class of machine learning (ML) that borrows from psychology. This is reinforcement learning. It is based on the concept of conditioning in psychology and applies it to facilitate learning.
Having explained how classical conditioning operates, surely you can imagine how reinforcement learning will work.
Do not worry, we now present it in detail.
In a reinforcement learning system an agent makes observations in an environment, where it performs actions and receives rewards in return.
The agent’s goal is to maximize reward over the long term.
Reinforcement learning differs from supervised learning in that in the latter: the training data have the response key with them. So the model is trained with the correct response itself. In contrast, in reinforcement learning there is no response but the agent decides what to do to perform the assigned task.
We find the basic aspects of classical conditioning, namely, continuous learning and conditioned responses on the basis of new conditioned stimuli.
Let us look at the latter aspect in more detail.
Reinforcement learning uses two forms of reinforcement. These are positive reinforcement and negative reinforcement. Positive reinforcement is when a reward is given to encourage positive behavior. This reinforcement increases the strength and frequency of the behavior.
Negative reinforcement is when a punishment is given to discourage unwanted behavior.
In this type of learning, these concepts are used to ensure that the system continues on its path of self-improvement. This creates a conditioning within the algorithm, i.e., that the most effective solutions offer a greater chance of obtaining rewards. Thus, this leads the agent to try to choose the solution that offers the maximum amount of rewards.
Due to its nature, reinforcement learning is used in systems where many small decisions need to be made without human guidance. Here are some examples:
- This type of machine learning can provide robots with the ability to learn tasks that a human teacher cannot demonstrate. It also allows them to adapt a learned skill to a new task.
- Play is the most common field of use for reinforcement learning. It is able to achieve powerful performance in multiple games. For example, playing chess online after making your move, it will be the software’s turn. This completed action is an informed choice, both planning and anticipating possible replication and counter-response.
This is just one of many psychological concepts applied in AI. Machines are expected to be able to apply them as much as possible in the future. This may come from a deeper psychological understanding of human consciousness. Who knows what the next discoveries will be.
© Copyright 2012 – 2023 | All Rights Reserved
Author: Niccolò Cacciotti, Head of AI Department
Do you know what reinforcement learning and Pavlov’s classical conditioning have in common? We present the real explanation.
Human and AI learning process are connected
Artificial intelligence often takes inspiration from human psychology. Indeed, we have already seen how algorithms learn and assimilate data over time to make predictions similar to the human mind.
Today, however, we will focus on the learning process and how it can be modified. We will talk first about how this works in humans and later in the AI world.
Undoubtedly one of the most important capabilities of humans is to learn and improve on past attempts at the same task.
When we mention learning, we immediately think of the education we receive as children and adolescents. We can also connect it to studying and examinations, which are the foundations of the process of acquiring new knowledge.
However, learning is not an exclusive human ability. Moreover, it is not only the result of academic training or a certain period of a person’s life.
The ability to learn is innate in all living beings and is an ongoing process. In addition, it cannot be observed directly, but it is noticeable from the behavior of the individual.
Over time, psychologists have proposed several theories to explain how the learning process takes place. Among the most importants we find the one developed by Russian physician and physiologist Ivan Pavlov.
Pavlov’s classical conditioning: what it is
Pavlov conducted an experiment with one of his dogs and came to discover the now famous “classical conditioning”.
What connection do his dogs and this theory have?
The physiologist discovered this phenomenon while studying digestion in dogs. Normally, when food was brought in, dogs would salivate. This is an involuntary biological response to food.
The scientist experimented more deeply with psychic secretion. He rang a bell a few times, which caused no reaction in the dog. Then Pavlov rang the bell and subsequently provided the animal with the slice of meat. So, the dog associated the sound with the arrival of the meat.
Repeating the stimulus several times, finally, the scientist saw the salivation level rise when the bell rang, without presenting meat.
Basically, the ringing of the bell had become a conditioned stimulus. This initially does not generate any response in the subject. Only by associating it with the stimulus is it able to generate salivation.
The resulting increase in salivation to the sound of the bell was a conditioned response to the conditioned stimulus.
In the image below there are the steps of this experiment.
Thanks to this conditioning, the dog began to salivate whenever it heard the bell ring. Thus, it had learned that the ringing of the bell meant that food was coming.
Humans and animals learn to associate one stimulus with another. In classical (or Pavlovian) conditioning, reflexes, i.e., uncontrollable responses, are the reaction to stimuli. So it is something each of us does without even realizing it.
You are probably wondering about the link between this experiment and AI.
After explaining classical conditioning, we can finally explain this association. Are you curious?
Reinforcement learning: what it is and its correlation with Pavlov’s classical conditioning
There is a class of machine learning (ML) that borrows from psychology. This is reinforcement learning. It is based on the concept of conditioning in psychology and applies it to facilitate learning.
Having explained how classical conditioning operates, surely you can imagine how reinforcement learning will work.
Do not worry, we now present it in detail.
In a reinforcement learning system an agent makes observations in an environment, where it performs actions and receives rewards in return.
The agent’s goal is to maximize reward over the long term.
Reinforcement learning differs from supervised learning in that in the latter: the training data have the response key with them. So the model is trained with the correct response itself. In contrast, in reinforcement learning there is no response but the agent decides what to do to perform the assigned task.
We find the basic aspects of classical conditioning, namely, continuous learning and conditioned responses on the basis of new conditioned stimuli.
Let us look at the latter aspect in more detail.
Reinforcement learning uses two forms of reinforcement. These are positive reinforcement and negative reinforcement. Positive reinforcement is when a reward is given to encourage positive behavior. This reinforcement increases the strength and frequency of the behavior.
Negative reinforcement is when a punishment is given to discourage unwanted behavior.
In this type of learning, these concepts are used to ensure that the system continues on its path of self-improvement. This creates a conditioning within the algorithm, i.e., that the most effective solutions offer a greater chance of obtaining rewards. Thus, this leads the agent to try to choose the solution that offers the maximum amount of rewards.
Due to its nature, reinforcement learning is used in systems where many small decisions need to be made without human guidance. Here are some examples:
- This type of machine learning can provide robots with the ability to learn tasks that a human teacher cannot demonstrate. It also allows them to adapt a learned skill to a new task.
- Play is the most common field of use for reinforcement learning. It is able to achieve powerful performance in multiple games. For example, playing chess online after making your move, it will be the software’s turn. This completed action is an informed choice, both planning and anticipating possible replication and counter-response.
This is just one of many psychological concepts applied in AI. Machines are expected to be able to apply them as much as possible in the future. This may come from a deeper psychological understanding of human consciousness. Who knows what the next discoveries will be.
© Copyright 2012 – 2023 | All Rights Reserved
Author: Niccolò Cacciotti, Head of AI Department