skinner operant conditioning experiment procedure

Skinner’s Box Experiment (Behaviorism Study)

We receive rewards and punishments for many behaviors. More importantly, once we experience that reward or punishment, we are likely to perform (or not perform) that behavior again in anticipation of the result.

Psychologists in the late 1800s and early 1900s believed that rewards and punishments were crucial to shaping and encouraging voluntary behavior. But they needed a way to test it. And they needed a name for how rewards and punishments shaped voluntary behaviors. Along came Burrhus Frederic Skinner , the creator of Skinner's Box, and the rest is history.

What Is Skinner's Box?

The "Skinner box" is a setup used in animal experiments. An animal is isolated in a box equipped with levers or other devices in this environment. The animal learns that pressing a lever or displaying specific behaviors can lead to rewards or punishments.

This setup was crucial for behavioral psychologist B.F. Skinner developed his theories on operant conditioning. It also aided in understanding the concept of reinforcement schedules.

Here, "schedules" refer to the timing and frequency of rewards or punishments, which play a key role in shaping behavior. Skinner's research showed how different schedules impact how animals learn and respond to stimuli.

Who is B.F. Skinner?

Burrhus Frederic Skinner, also known as B.F. Skinner is considered the “father of Operant Conditioning.” His experiments, conducted in what is known as “Skinner’s box,” are some of the most well-known experiments in psychology. They helped shape the ideas of operant conditioning in behaviorism.

Law of Effect (Thorndike vs. Skinner)

At the time, classical conditioning was the top theory in behaviorism. However, Skinner knew that research showed that voluntary behaviors could be part of the conditioning process. In the late 1800s, a psychologist named Edward Thorndike wrote about “The Law of Effect.” He said, “Responses that produce a satisfying effect in a particular situation become more likely to occur again in that situation, and responses that produce a discomforting effect become less likely to occur again in that situation.”

Thorndike tested out The Law of Effect with a box of his own. The box contained a maze and a lever. He placed a cat inside the box and a fish outside the box. He then recorded how the cats got out of the box and ate the fish.

Thorndike noticed that the cats would explore the maze and eventually found the lever. The level would let them out of the box, leading them to the fish faster. Once discovering this, the cats were more likely to use the lever when they wanted to get fish.

Skinner took this idea and ran with it. We call the box where animal experiments are performed "Skinner's box."

Why Do We Call This Box the "Skinner Box?"

Edward Thorndike used a box to train animals to perform behaviors for rewards. Later, psychologists like Martin Seligman used this apparatus to observe "learned helplessness." So why is this setup called a "Skinner Box?" Skinner not only used Skinner box experiments to show the existence of operant conditioning, but he also showed schedules in which operant conditioning was more or less effective, depending on your goals. And that is why he is called The Father of Operant Conditioning.

How Skinner's Box Worked

Inspired by Thorndike, Skinner created a box to test his theory of Operant Conditioning. (This box is also known as an “operant conditioning chamber.”)

The box was typically very simple. Skinner would place the rats in a Skinner box with neutral stimulants (that produced neither reinforcement nor punishment) and a lever that would dispense food. As the rats started to explore the box, they would stumble upon the level, activate it, and get food. Skinner observed that they were likely to engage in this behavior again, anticipating food. In some boxes, punishments would also be administered. Martin Seligman's learned helplessness experiments are a great example of using punishments to observe or shape an animal's behavior. Skinner usually worked with animals like rats or pigeons. And he took his research beyond what Thorndike did. He looked at how reinforcements and schedules of reinforcement would influence behavior.

About Reinforcements

Reinforcements are the rewards that satisfy your needs. The fish that cats received outside of Thorndike’s box was positive reinforcement. In Skinner box experiments, pigeons or rats also received food. But positive reinforcements can be anything added after a behavior is performed: money, praise, candy, you name it. Operant conditioning certainly becomes more complicated when it comes to human reinforcements.

Positive vs. Negative Reinforcements

Skinner also looked at negative reinforcements. Whereas positive reinforcements are given to subjects, negative reinforcements are rewards in the form of things taken away from subjects. In some experiments in the Skinner box, he would send an electric current through the box that would shock the rats. If the rats pushed the lever, the shocks would stop. The removal of that terrible pain was a negative reinforcement. The rats still sought the reinforcement but were not gaining anything when the shocks ended. Skinner saw that the rats quickly learned to turn off the shocks by pushing the lever.

About Punishments

Skinner's Box also experimented with positive or negative punishments, in which harmful or unsatisfying things were taken away or given due to "bad behavior." For now, let's focus on the schedules of reinforcement.

Schedules of Reinforcement

We know that not every behavior has the same reinforcement every single time. Think about tipping as a rideshare driver or a barista at a coffee shop. You may have a string of customers who tip you generously after conversing with them. At this point, you’re likely to converse with your next customer. But what happens if they don’t tip you after you have a conversation with them? What happens if you stay silent for one ride and get a big tip?

Psychologists like Skinner wanted to know how quickly someone makes a behavior a habit after receiving reinforcement. Aka, how many trips will it take for you to converse with passengers every time? They also wanted to know how fast a subject would stop conversing with passengers if you stopped getting tips. If the rat pulls the lever and doesn't get food, will they stop pulling the lever altogether?

Skinner attempted to answer these questions by looking at different schedules of reinforcement. He would offer positive reinforcements on different schedules, like offering it every time the behavior was performed (continuous reinforcement) or at random (variable ratio reinforcement.) Based on his experiments, he would measure the following:

Response rate (how quickly the behavior was performed)
Extinction rate (how quickly the behavior would stop)

He found that there are multiple schedules of reinforcement, and they all yield different results. These schedules explain why your dog may not be responding to the treats you sometimes give him or why gambling can be so addictive. Not all of these schedules are possible, and that's okay, too.

Continuous Reinforcement

If you reinforce a behavior repeatedly, the response rate is medium, and the extinction rate is fast. The behavior will be performed only when reinforcement is needed. As soon as you stop reinforcing a behavior on this schedule, the behavior will not be performed.

Fixed-Ratio Reinforcement

Let’s say you reinforce the behavior every fourth or fifth time. The response rate is fast, and the extinction rate is medium. The behavior will be performed quickly to reach the reinforcement.

Fixed-Interval Reinforcement

In the above cases, the reinforcement was given immediately after the behavior was performed. But what if the reinforcement was given at a fixed interval, provided that the behavior was performed at some point? Skinner found that the response rate is medium, and the extinction rate is medium.

Variable-Ratio Reinforcement

Here's how gambling becomes so unpredictable and addictive. In gambling, you experience occasional wins, but you often face losses. This uncertainty keeps you hooked, not knowing when the next big win, or dopamine hit, will come. The behavior gets reinforced randomly. When gambling, your response is quick, but it takes a long time to stop wanting to gamble. This randomness is a key reason why gambling is highly addictive.

Variable-Interval Reinforcement

Last, the reinforcement is given out at random intervals, provided that the behavior is performed. Health inspectors or secret shoppers are commonly used examples of variable-interval reinforcement. The reinforcement could be administered five minutes after the behavior is performed or seven hours after the behavior is performed. Skinner found that the response rate for this schedule is fast, and the extinction rate is slow.

Skinner's Box and Pigeon Pilots in World War II

Yes, you read that right. Skinner's work with pigeons and other animals in Skinner's box had real-life effects. After some time training pigeons in his boxes, B.F. Skinner got an idea. Pigeons were easy to train. They can see very well as they fly through the sky. They're also quite calm creatures and don't panic in intense situations. Their skills could be applied to the war that was raging on around him.

B.F. Skinner decided to create a missile that pigeons would operate. That's right. The U.S. military was having trouble accurately targeting missiles, and B.F. Skinner believed pigeons could help. He believed he could train the pigeons to recognize a target and peck when they saw it. As the pigeons pecked, Skinner's specially designed cockpit would navigate appropriately. Pigeons could be pilots in World War II missions, fighting Nazi Germany.

When Skinner proposed this idea to the military, he was met with skepticism. Yet, he received $25,000 to start his work on "Project Pigeon." The device worked! Operant conditioning trained pigeons to navigate missiles appropriately and hit their targets. Unfortunately, there was one problem. The mission killed the pigeons once the missiles were dropped. It would require a lot of pigeons! The military eventually passed on the project, but cockpit prototypes are on display at the American History Museum. Pretty cool, huh?

Examples of Operant Conditioning in Everyday Life

Not every example of operant conditioning has to end in dropping missiles. Nor does it have to happen in a box in a laboratory! You might find that you have used operant conditioning on yourself, a pet, or a child whose behavior changes with rewards and punishments. These operant conditioning examples will look into what this process can do for behavior and personality.

Hot Stove: If you put your hand on a hot stove, you will get burned. More importantly, you are very unlikely to put your hand on that hot stove again. Even though no one has made that stove hot as a punishment, the process still works.

Tips: If you converse with a passenger while driving for Uber, you might get an extra tip at the end of your ride. That's certainly a great reward! You will likely keep conversing with passengers as you drive for Uber. The same type of behavior applies to any service worker who gets tips!

Training a Dog: If your dog sits when you say “sit,” you might treat him. More importantly, they are likely to sit when you say, “sit.” (This is a form of variable-ratio reinforcement. Likely, you only treat your dog 50-90% of the time they sit. If you gave a dog a treat every time they sat, they probably wouldn't have room for breakfast or dinner!)

Operant Conditioning Is Everywhere!

We see operant conditioning training us everywhere, intentionally or unintentionally! Game makers and app developers design their products based on the "rewards" our brains feel when seeing notifications or checking into the app. Schoolteachers use rewards to control their unruly classes. Dog training doesn't always look different from training your child to do chores. We know why this happens, thanks to experiments like the ones performed in Skinner's box.

Operant Conditioning (Examples + Research)
Edward Thorndike (Psychologist Biography)
Schedules of Reinforcement (Examples)
B.F. Skinner (Psychologist Biography)
Fixed Ratio Reinforcement Schedule (Examples)

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Operant Conditioning: What It Is, How It Works, and Examples

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Operant conditioning, or instrumental conditioning, is a theory of learning where behavior is influenced by its consequences. Behavior that is reinforced (rewarded) will likely be repeated, and behavior that is punished will occur less frequently.

By the 1920s, John B. Watson had left academic psychology, and other behaviorists were becoming influential, proposing new forms of learning other than classical conditioning . Perhaps the most important of these was Burrhus Frederic Skinner. Although, for obvious reasons, he is more commonly known as B.F. Skinner.

Skinner’s views were slightly less extreme than Watson’s (1913). Skinner believed that we do have such a thing as a mind, but that it is simply more productive to study observable behavior rather than internal mental events.

Skinner’s work was rooted in the view that classical conditioning was far too simplistic to fully explain complex human behavior. He believed that the best way to understand behavior is to examine its causes and consequences. He called this approach operant conditioning.

How It Works

Skinner is regarded as the father of Operant Conditioning, but his work was based on Thorndike’s (1898) Law of Effect . According to this principle, behavior that is followed by pleasant consequences is likely to be repeated, and behavior followed by unpleasant consequences is less likely to be repeated.

Skinner introduced a new term into the Law of Effect – Reinforcement. Behavior that is reinforced tends to be repeated (i.e., strengthened); behavior that is not reinforced tends to die out or be extinguished (i.e., weakened).

Skinner (1948) studied operant conditioning by conducting experiments using animals, which he placed in a “ Skinner Box, ” which was similar to Thorndike’s puzzle box.

Skinner box or operant conditioning chamber experiment outline diagram. Labeled educational laboratory apparatus structure for mouse or rat experiment to understand animal behavior vector illustration

A Skinner box, also known as an operant conditioning chamber, is a device used to objectively record an animal’s behavior in a compressed time frame. An animal can be rewarded or punished for engaging in certain behaviors, such as lever pressing (for rats) or key pecking (for pigeons).

Skinner identified three types of responses, or operant, that can follow behavior.

Neutral operants : Responses from the environment that neither increase nor decrease the probability of a behavior being repeated.
Reinforcers : Responses from the environment that increase the probability of a behavior being repeated. Reinforcers can be either positive or negative.
Punishers : Responses from the environment that decrease the likelihood of a behavior being repeated. Punishment weakens behavior.

We can all think of examples of how reinforcers and punishers have affected our behavior. As a child, you probably tried out a number of behaviors and learned from their consequences.

For example, when you were younger, if you tried smoking at school, and the chief consequence was that you got in with the crowd you always wanted to hang out with, you would have been positively reinforced (i.e., rewarded) and would be likely to repeat the behavior.

If, however, the main consequence was that you were caught, caned, suspended from school, and your parents became involved, you would most certainly have been punished, and you would consequently be much less likely to smoke now.

Positive Reinforcement

B. F. Skinner’s theory of operant conditioning describes positive reinforcement. In positive reinforcement, a response or behavior is strengthened by rewards, leading to the repetition of the desired behavior. The reward is a reinforcing stimulus.

Primary reinforcers are stimuli that are naturally reinforcing because they are not learned and directly satisfy a need, such as food or water.

Secondary reinforcers are stimuli that are reinforced through their association with a primary reinforcer, such as money, school grades. They do not directly satisfy an innate need but may be the means. So a secondary reinforcer can be just as powerful a motivator as a primary reinforcer.

Skinner showed how positive reinforcement worked by placing a hungry rat in his Skinner box. The box contained a lever on the side, and as the rat moved about the box, it would accidentally knock the lever. Immediately, it did so that a food pellet would drop into a container next to the lever.

After being put in the box a few times, the rats quickly learned to go straight to the lever. The consequence of receiving food if they pressed the lever ensured that they would repeat the action again and again.

Positive reinforcement strengthens a behavior by providing a consequence an individual finds rewarding. For example, if your teacher gives you £5 each time you complete your homework (i.e., a reward), you will be more likely to repeat this behavior in the future, thus strengthening the behavior of completing your homework.

The Premack principle is a form of positive reinforcement in operant conditioning. It suggests using a preferred activity (high-probability behavior) as a reward for completing a less preferred one (low-probability behavior).

This method incentivizes the less desirable behavior by associating it with a desirable outcome, thus strengthening the less favored behavior.

Negative Reinforcement

Negative reinforcement is the termination of an unpleasant state following a response.

This is known as negative reinforcement because it is the removal of an adverse stimulus which is ‘rewarding’ to the animal or person. Negative reinforcement strengthens behavior because it stops or removes an unpleasant experience.

For example, if you do not complete your homework, you give your teacher £5. You will complete your homework to avoid paying £5, thus strengthening the behavior of completing your homework.

Skinner showed how negative reinforcement worked by placing a rat in his Skinner box and then subjecting it to an unpleasant electric current which caused it some discomfort. As the rat moved about the box it would accidentally knock the lever.

Immediately, it did so the electric current would be switched off. The rats quickly learned to go straight to the lever after being put in the box a few times. The consequence of escaping the electric current ensured that they would repeat the action again and again.

In fact, Skinner even taught the rats to avoid the electric current by turning on a light just before the electric current came on. The rats soon learned to press the lever when the light came on because they knew that this would stop the electric current from being switched on.

These two learned responses are known as Escape Learning and Avoidance Learning .

Punishment is the opposite of reinforcement since it is designed to weaken or eliminate a response rather than increase it. It is an aversive event that decreases the behavior that it follows.

Like reinforcement, punishment can work either by directly applying an unpleasant stimulus like a shock after a response or by removing a potentially rewarding stimulus, for instance, deducting someone’s pocket money to punish undesirable behavior.

Note : It is not always easy to distinguish between punishment and negative reinforcement.

They are two distinct methods of punishment used to decrease the likelihood of a specific behavior occurring again, but they involve different types of consequences:

Positive Punishment :

Positive punishment involves adding an aversive stimulus or something unpleasant immediately following a behavior to decrease the likelihood of that behavior happening in the future.
It aims to weaken the target behavior by associating it with an undesirable consequence.
Example : A child receives a scolding (an aversive stimulus) from their parent immediately after hitting their sibling. This is intended to decrease the likelihood of the child hitting their sibling again.

Negative Punishment :

Negative punishment involves removing a desirable stimulus or something rewarding immediately following a behavior to decrease the likelihood of that behavior happening in the future.
It aims to weaken the target behavior by taking away something the individual values or enjoys.
Example : A teenager loses their video game privileges (a desirable stimulus) for not completing their chores. This is intended to decrease the likelihood of the teenager neglecting their chores in the future.

There are many problems with using punishment, such as:

Punished behavior is not forgotten, it’s suppressed – behavior returns when punishment is no longer present.
Causes increased aggression – shows that aggression is a way to cope with problems.
Creates fear that can generalize to undesirable behaviors, e.g., fear of school.
Does not necessarily guide you toward desired behavior – reinforcement tells you what to do, and punishment only tells you what not to do.

Examples of Operant Conditioning

Positive Reinforcement : Suppose you are a coach and want your team to improve their passing accuracy in soccer. When the players execute accurate passes during training, you praise their technique. This positive feedback encourages them to repeat the correct passing behavior.

Negative Reinforcement : If you notice your team working together effectively and exhibiting excellent team spirit during a tough training session, you might end the training session earlier than planned, which the team perceives as a relief. They understand that teamwork leads to positive outcomes, reinforcing team behavior.

Negative Punishment : If an office worker continually arrives late, their manager might revoke the privilege of flexible working hours. This removal of a positive stimulus encourages the employee to be punctual.

Positive Reinforcement : Training a cat to use a litter box can be achieved by giving it a treat each time it uses it correctly. The cat will associate the behavior with the reward and will likely repeat it.

Negative Punishment : If teenagers stay out past their curfew, their parents might take away their gaming console for a week. This makes the teenager more likely to respect their curfew in the future to avoid losing something they value.

Ineffective Punishment : Your child refuses to finish their vegetables at dinner. You punish them by not allowing dessert, but the child still refuses to eat vegetables next time. The punishment seems ineffective.

Premack Principle Application : You could motivate your child to eat vegetables by offering an activity they love after they finish their meal. For instance, for every vegetable eaten, they get an extra five minutes of video game time. They value video game time, which might encourage them to eat vegetables.

Other Premack Principle Examples :

A student who dislikes history but loves art might earn extra time in the art studio for each history chapter reviewed.
For every 10 minutes a person spends on household chores, they can spend 5 minutes on a favorite hobby.
For each successful day of healthy eating, an individual allows themselves a small piece of dark chocolate at the end of the day.
A child can choose between taking out the trash or washing the dishes. Giving them the choice makes them more likely to complete the chore willingly.

Skinner’s Pigeon Experiment

B.F. Skinner conducted several experiments with pigeons to demonstrate the principles of operant conditioning.

One of the most famous of these experiments is often colloquially referred to as “ Superstition in the Pigeon .”

This experiment was conducted to explore the effects of non-contingent reinforcement on pigeons, leading to some fascinating observations that can be likened to human superstitions.

Non-contingent reinforcement (NCR) refers to a method in which rewards (or reinforcements) are delivered independently of the individual’s behavior. In other words, the reinforcement is given at set times or intervals, regardless of what the individual is doing.

The Experiment:

Pigeons were brought to a state of hunger, reduced to 75% of their well-fed weight.
They were placed in a cage with a food hopper that could be presented for five seconds at a time.
Instead of the food being given as a result of any specific action by the pigeon, it was presented at regular intervals, regardless of the pigeon’s behavior.

Observation:

Over time, Skinner observed that the pigeons began to associate whatever random action they were doing when food was delivered with the delivery of the food itself.
This led the pigeons to repeat these actions, believing (in anthropomorphic terms) that their behavior was causing the food to appear.
In most cases, pigeons developed different “superstitious” behaviors or rituals. For instance, one pigeon would turn counter-clockwise between food presentations, while another would thrust its head into a cage corner.
These behaviors did not appear until the food hopper was introduced and presented periodically.
These behaviors were not initially related to the food delivery but became linked in the pigeon’s mind due to the coincidental timing of the food dispensing.
The behaviors seemed to be associated with the environment, suggesting the pigeons were responding to certain aspects of their surroundings.
The rate of reinforcement (how often the food was presented) played a significant role. Shorter intervals between food presentations led to more rapid and defined conditioning.
Once a behavior was established, the interval between reinforcements could be increased without diminishing the behavior.

Superstitious Behavior:

The pigeons began to act as if their behaviors had a direct effect on the presentation of food, even though there was no such connection. This is likened to human superstitions, where rituals are believed to change outcomes, even if they have no real effect.

For example, a card player might have rituals to change their luck, or a bowler might make gestures believing they can influence a ball already in motion.

Conclusion:

This experiment demonstrates that behaviors can be conditioned even without a direct cause-and-effect relationship. Just like humans, pigeons can develop “superstitious” behaviors based on coincidental occurrences.

This study not only illuminates the intricacies of operant conditioning but also draws parallels between animal and human behaviors in the face of random reinforcements.

Schedules of Reinforcement

Imagine a rat in a “Skinner box.” In operant conditioning, if no food pellet is delivered immediately after the lever is pressed, then after several attempts, the rat stops pressing the lever (how long would someone continue to go to work if their employer stopped paying them?). The behavior has been extinguished.

Behaviorists discovered that different patterns (or schedules) of reinforcement had different effects on the speed of learning and extinction. Ferster and Skinner (1957) devised different ways of delivering reinforcement and found that this had effects on

1. The Response Rate – The rate at which the rat pressed the lever (i.e., how hard the rat worked).

2. The Extinction Rate – The rate at which lever pressing dies out (i.e., how soon the rat gave up).

Skinner found that variable-ratio reinforcement produces the slowest rate of extinction (i.e., people will continue repeating the behavior for the longest time without reinforcement). The type of reinforcement with the quickest rate of extinction is continuous reinforcement.

(A) Continuous Reinforcement

An animal or human is positively reinforced every time a specific behavior occurs, e.g., every time a lever is pressed, a pellet is delivered, and then food delivery is shut off.

Response rate is SLOW
Extinction rate is FAST

(B) Fixed Ratio Reinforcement

Behavior is reinforced only after the behavior occurs a specified number of times. e.g., one reinforcement is given after every so many correct responses, e.g., after every 5th response. For example, a child receives a star for every five words spelled correctly.

Response rate is FAST
Extinction rate is MEDIUM

(C) Fixed Interval Reinforcement

One reinforcement is given after a fixed time interval providing at least one correct response has been made. An example is being paid by the hour. Another example would be every 15 minutes (half hour, hour, etc.) a pellet is delivered (providing at least one lever press has been made) then food delivery is shut off.

Response rate is MEDIUM

(D) Variable Ratio Reinforcement

behavior is reinforced after an unpredictable number of times. For example, gambling or fishing.

Extinction rate is SLOW (very hard to extinguish because of unpredictability)

(E) Variable Interval Reinforcement

Providing one correct response has been made, reinforcement is given after an unpredictable amount of time has passed, e.g., on average every 5 minutes. An example is a self-employed person being paid at unpredictable times.

Extinction rate is SLOW

Applications In Psychology

1. behavior modification therapy.

Behavior modification is a set of therapeutic techniques based on operant conditioning (Skinner, 1938, 1953). The main principle comprises changing environmental events that are related to a person’s behavior. For example, the reinforcement of desired behaviors and ignoring or punishing undesired ones.

This is not as simple as it sounds — always reinforcing desired behavior, for example, is basically bribery.

There are different types of positive reinforcements. Primary reinforcement is when a reward strengths a behavior by itself. Secondary reinforcement is when something strengthens a behavior because it leads to a primary reinforcer.

Examples of behavior modification therapy include token economy and behavior shaping.

Token Economy

Token economy is a system in which targeted behaviors are reinforced with tokens (secondary reinforcers) and later exchanged for rewards (primary reinforcers).

Tokens can be in the form of fake money, buttons, poker chips, stickers, etc. While the rewards can range anywhere from snacks to privileges or activities. For example, teachers use token economy at primary school by giving young children stickers to reward good behavior.

Token economy has been found to be very effective in managing psychiatric patients . However, the patients can become over-reliant on the tokens, making it difficult for them to adjust to society once they leave prison, hospital, etc.

Staff implementing a token economy program have a lot of power. It is important that staff do not favor or ignore certain individuals if the program is to work. Therefore, staff need to be trained to give tokens fairly and consistently even when there are shift changes such as in prisons or in a psychiatric hospital.

Behavior Shaping

A further important contribution made by Skinner (1951) is the notion of behavior shaping through successive approximation.

Skinner argues that the principles of operant conditioning can be used to produce extremely complex behavior if rewards and punishments are delivered in such a way as to encourage move an organism closer and closer to the desired behavior each time.

In shaping, the form of an existing response is gradually changed across successive trials towards a desired target behavior by rewarding exact segments of behavior.

To do this, the conditions (or contingencies) required to receive the reward should shift each time the organism moves a step closer to the desired behavior.

According to Skinner, most animal and human behavior (including language) can be explained as a product of this type of successive approximation.

2. Educational Applications

In the conventional learning situation, operant conditioning applies largely to issues of class and student management, rather than to learning content. It is very relevant to shaping skill performance.

A simple way to shape behavior is to provide feedback on learner performance, e.g., compliments, approval, encouragement, and affirmation.

A variable-ratio produces the highest response rate for students learning a new task, whereby initial reinforcement (e.g., praise) occurs at frequent intervals, and as the performance improves reinforcement occurs less frequently, until eventually only exceptional outcomes are reinforced.

For example, if a teacher wanted to encourage students to answer questions in class they should praise them for every attempt (regardless of whether their answer is correct). Gradually the teacher will only praise the students when their answer is correct, and over time only exceptional answers will be praised.

Unwanted behaviors, such as tardiness and dominating class discussion can be extinguished through being ignored by the teacher (rather than being reinforced by having attention drawn to them). This is not an easy task, as the teacher may appear insincere if he/she thinks too much about the way to behave.

Knowledge of success is also important as it motivates future learning. However, it is important to vary the type of reinforcement given so that the behavior is maintained.

This is not an easy task, as the teacher may appear insincere if he/she thinks too much about the way to behave.

Operant Conditioning vs. Classical Conditioning

Learning type.

While both types of conditioning involve learning, classical conditioning is passive (automatic response to stimuli), while operant conditioning is active (behavior is influenced by consequences).

Classical conditioning links an involuntary response with a stimulus. It happens passively on the part of the learner, without rewards or punishments. An example is a dog salivating at the sound of a bell associated with food.
Operant conditioning connects voluntary behavior with a consequence. Operant conditioning requires the learner to actively participate and perform some type of action to be rewarded or punished. It’s active, with the learner’s behavior influenced by rewards or punishments. An example is a dog sitting on command to get a treat.

Learning Process

Classical conditioning involves learning through associating stimuli resulting in involuntary responses, while operant conditioning focuses on learning through consequences, shaping voluntary behaviors.

Over time, the person responds to the neutral stimulus as if it were the unconditioned stimulus, even when presented alone. The response is involuntary and automatic.

An example is a dog salivating (response) at the sound of a bell (neutral stimulus) after it has been repeatedly paired with food (unconditioned stimulus).

Behavior followed by pleasant consequences (rewards) is more likely to be repeated, while behavior followed by unpleasant consequences (punishments) is less likely to be repeated.

For instance, if a child gets praised (pleasant consequence) for cleaning their room (behavior), they’re more likely to clean their room in the future.

Conversely, if they get scolded (unpleasant consequence) for not doing their homework, they’re more likely to complete it next time to avoid the scolding.

Timing of Stimulus & Response

The timing of the response relative to the stimulus differs between classical and operant conditioning:

Classical Conditioning (response after the stimulus) : In this form of conditioning, the response occurs after the stimulus. The behavior (response) is determined by what precedes it (stimulus).

For example, in Pavlov’s classic experiment, the dogs started to salivate (response) after they heard the bell (stimulus) because they associated it with food.

The anticipated consequence influences the behavior or what follows it. It is a more active form of learning, where behaviors are reinforced or punished, thus influencing their likelihood of repetition.

For example, a child might behave well (behavior) in anticipation of a reward (consequence), or avoid a certain behavior to prevent a potential punishment.

Looking at Skinner’s classic studies on pigeons’ and rats’ behavior, we can identify some of the major assumptions of the behaviorist approach .

• Psychology should be seen as a science , to be studied in a scientific manner. Skinner’s study of behavior in rats was conducted under carefully controlled laboratory conditions . • Behaviorism is primarily concerned with observable behavior, as opposed to internal events like thinking and emotion. Note that Skinner did not say that the rats learned to press a lever because they wanted food. He instead concentrated on describing the easily observed behavior that the rats acquired. • The major influence on human behavior is learning from our environment. In the Skinner study, because food followed a particular behavior the rats learned to repeat that behavior, e.g., operant conditioning. • There is little difference between the learning that takes place in humans and that in other animals. Therefore research (e.g., operant conditioning) can be carried out on animals (Rats / Pigeons) as well as on humans. Skinner proposed that the way humans learn behavior is much the same as the way the rats learned to press a lever.

So, if your layperson’s idea of psychology has always been of people in laboratories wearing white coats and watching hapless rats try to negotiate mazes to get to their dinner, then you are probably thinking of behavioral psychology.

Behaviorism and its offshoots tend to be among the most scientific of the psychological perspectives . The emphasis of behavioral psychology is on how we learn to behave in certain ways.

We are all constantly learning new behaviors and how to modify our existing behavior. Behavioral psychology is the psychological approach that focuses on how this learning takes place.

Critical Evaluation

Operant conditioning can explain a wide variety of behaviors, from the learning process to addiction and language acquisition . It also has practical applications (such as token economy) that can be used in classrooms, prisons, and psychiatric hospitals.

Researchers have found innovative ways to apply operant conditioning principles to promote health and habit change in humans.

In a recent study, operant conditioning using virtual reality (VR) helped stroke patients use their weakened limb more often during rehabilitation. Patients shifted their weight in VR games by maneuvering a virtual object. When they increased weight on their weakened side, they received rewards like stars. This positive reinforcement conditioned greater paretic limb use (Kumar et al., 2019).

Another study utilized operant conditioning to assist smoking cessation. Participants earned vouchers exchangeable for goods and services for reducing smoking. This reward system reinforced decreasing cigarette use. Many participants achieved long-term abstinence (Dallery et al., 2017).

Through repeated reinforcement, operant conditioning can facilitate forming exercise and eating habits. A person trying to exercise more might earn TV time for every 10 minutes spent working out. An individual aiming to eat healthier may allow themselves a daily dark chocolate square for sticking to nutritious meals. Providing consistent rewards for desired actions can instill new habits (Michie et al., 2009).

Apps like Habitica apply operant conditioning by gamifying habit tracking. Users earn points and collect rewards in a fantasy game for completing real-life habits. This virtual reinforcement helps ingrain positive behaviors (Eckerstorfer et al., 2019).

Operant conditioning also shows promise for managing ADHD and OCD. Rewarding concentration and focus in ADHD children, for example, can strengthen their attention skills (Rosén et al., 2018). Similarly, reinforcing OCD patients for resisting compulsions may diminish obsessive behaviors (Twohig et al., 2018).

However, operant conditioning fails to take into account the role of inherited and cognitive factors in learning, and thus is an incomplete explanation of the learning process in humans and animals.

For example, Kohler (1924) found that primates often seem to solve problems in a flash of insight rather than be trial and error learning. Also, social learning theory (Bandura, 1977) suggests that humans can learn automatically through observation rather than through personal experience.

The use of animal research in operant conditioning studies also raises the issue of extrapolation. Some psychologists argue we cannot generalize from studies on animals to humans as their anatomy and physiology are different from humans, and they cannot think about their experiences and invoke reason, patience, memory or self-comfort.

Frequently Asked Questions

Who discovered operant conditioning.

Operant conditioning was discovered by B.F. Skinner, an American psychologist, in the mid-20th century. Skinner is often regarded as the father of operant conditioning, and his work extensively dealt with the mechanism of reward and punishment for behaviors, with the concept being that behaviors followed by positive outcomes are reinforced, while those followed by negative outcomes are discouraged.

How does operant conditioning differ from classical conditioning?

Operant conditioning differs from classical conditioning, focusing on how voluntary behavior is shaped and maintained by consequences, such as rewards and punishments.

In operant conditioning, a behavior is strengthened or weakened based on the consequences that follow it. In contrast, classical conditioning involves the association of a neutral stimulus with a natural response, creating a new learned response.

While both types of conditioning involve learning and behavior modification, operant conditioning emphasizes the role of reinforcement and punishment in shaping voluntary behavior.

How does operant conditioning relate to social learning theory?

Operant conditioning is a core component of social learning theory , which emphasizes the importance of observational learning and modeling in acquiring and modifying behavior.

Social learning theory suggests that individuals can learn new behaviors by observing others and the consequences of their actions, which is similar to the reinforcement and punishment processes in operant conditioning.

By observing and imitating models, individuals can acquire new skills and behaviors and modify their own behavior based on the outcomes they observe in others.

Overall, both operant conditioning and social learning theory highlight the importance of environmental factors in shaping behavior and learning.

What are the downsides of operant conditioning?

The downsides of using operant conditioning on individuals include the potential for unintended negative consequences, particularly with the use of punishment. Punishment may lead to increased aggression or avoidance behaviors.

Additionally, some behaviors may be difficult to shape or modify using operant conditioning techniques, particularly when they are highly ingrained or tied to complex internal states.

Furthermore, individuals may resist changing their behaviors to meet the expectations of others, particularly if they perceive the demands or consequences of the reinforcement or punishment to be undesirable or unjust.

What is an application of bf skinner’s operant conditioning theory?

An application of B.F. Skinner’s operant conditioning theory is seen in education and classroom management. Teachers use positive reinforcement (rewards) to encourage good behavior and academic achievement, and negative reinforcement or punishment to discourage disruptive behavior.

For example, a student may earn extra recess time (positive reinforcement) for completing homework on time, or lose the privilege to use class computers (negative punishment) for misbehavior.

O perant C onditioning

J e r staddon, d t cerutti.

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Issue date 2003.

Operant behavior is behavior “controlled” by its consequences. In practice, operant conditioning is the study of reversible behavior maintained by reinforcement schedules. We review empirical studies and theoretical approaches to two large classes of operant behavior: interval timing and choice. We discuss cognitive versus behavioral approaches to timing, the “gap” experiment and its implications, proportional timing and Weber's law, temporal dynamics and linear waiting, and the problem of simple chain-interval schedules. We review the long history of research on operant choice: the matching law, its extensions and problems, concurrent chain schedules, and self-control. We point out how linear waiting may be involved in timing, choice, and reinforcement schedules generally. There are prospects for a unified approach to all these areas.

Keywords: interval timing, choice, concurrent schedules, matching law, self-control

INTRODUCTION

The term operant conditioning 1 was coined by B. F. Skinner in 1937 in the context of reflex physiology, to differentiate what he was interested in—behavior that affects the environment—from the reflex-related subject matter of the Pavlovians. The term was novel, but its referent was not entirely new. Operant behavior , though defined by Skinner as behavior “controlled by its consequences” is in practice little different from what had previously been termed “instrumental learning” and what most people would call habit. Any well-trained “operant” is in effect a habit. What was truly new was Skinner's method of automated training with intermittent reinforcement and the subject matter of reinforcement schedules to which it led. Skinner and his colleagues and students discovered in the ensuing decades a completely unsuspected range of powerful and orderly schedule effects that provided new tools for understanding learning processes and new phenomena to challenge theory.

A reinforcement schedule is any procedure that delivers a reinforcer to an organism according to some well-defined rule. The usual reinforcer is food for a hungry rat or pigeon; the usual schedule is one that delivers the reinforcer for a switch closure caused by a peck or lever press. Reinforcement schedules have also been used with human subjects, and the results are broadly similar to the results with animals. However, for ethical and practical reasons, relatively weak reinforcers must be used—and the range of behavioral strategies people can adopt is of course greater than in the case of animals. This review is restricted to work with animals.

Two types of reinforcement schedule have excited the most interest. Most popular are time-based schedules such as fixed and variable interval, in which the reinforcer is delivered after a fixed or variable time period after a time marker (usually the preceding reinforcer). Ratio schedules require a fixed or variable number of responses before a reinforcer is delivered.

Trial-by-trial versions of all these free-operant procedures exist. For example, a version of the fixed-interval schedule specifically adapted to the study of interval timing is the peak-interval procedure, which adds to the fixed interval an intertrial interval (ITI) preceding each trial and a percentage of extra-long “empty” trials in which no food is given.

For theoretical reasons, Skinner believed that operant behavior ought to involve a response that can easily be repeated, such as pressing a lever, for rats, or pecking an illuminated disk (key) for pigeons. The rate of such behavior was thought to be important as a measure of response strength ( Skinner 1938 , 1966 , 1986 ; Killeen & Hall 2001 ). The current status of this assumption is one of the topics of this review. True or not, the emphasis on response rate has resulted in a dearth of experimental work by operant conditioners on nonrecurrent behavior such as movement in space.

Operant conditioning differs from other kinds of learning research in one important respect. The focus has been almost exclusively on what is called reversible behavior, that is, behavior in which the steady-state pattern under a given schedule is stable, meaning that in a sequence of conditions, XAXBXC…, where each condition is maintained for enough days that the pattern of behavior is locally stable, behavior under schedule X shows a pattern after one or two repetitions of X that is always the same. For example, the first time an animal is exposed to a fixed-interval schedule, after several daily sessions most animals show a “scalloped” pattern of responding (call it pattern A): a pause after each food delivery—also called wait time or latency —followed by responding at an accelerated rate until the next food delivery. However, some animals show negligible wait time and a steady rate (pattern B). If all are now trained on some other procedure—a variable-interval schedule, for example—and then after several sessions are returned to the fixed-interval schedule, almost all the animals will revert to pattern A. Thus, pattern A is the stable pattern. Pattern B, which may persist under unchanging conditions but does not recur after one or more intervening conditions, is sometimes termed metastable ( Staddon 1965 ). The vast majority of published studies in operant conditioning are on behavior that is stable in this sense.

Although the theoretical issue is not a difficult one, there has been some confusion about what the idea of stability (reversibility) in behavior means. It should be obvious that the animal that shows pattern A after the second exposure to procedure X is not the same animal as when it showed pattern A on the first exposure. Its experimental history is different after the second exposure than after the first. If the animal has any kind of memory, therefore, its internal state 2 following the second exposure is likely to be different than after the first exposure, even though the observed behavior is the same. The behavior is reversible; the organism's internal state in general is not. The problems involved in studying nonreversible phenomena in individual organisms have been spelled out elsewhere (e.g., Staddon 2001a , Ch. 1); this review is mainly concerned with the reversible aspects of behavior.

Once the microscope was invented, microorganisms became a new field of investigation. Once automated operant conditioning was invented, reinforcement schedules became an independent subject of inquiry. In addition to being of great interest in their own right, schedules have also been used to study topics defined in more abstract ways such as timing and choice. These two areas constitute the majority of experimental papers in operant conditioning with animal subjects during the past two decades. Great progress has been made in understanding free-operant choice behavior and interval timing. Yet several theories of choice still compete for consensus, and much the same is true of interval timing. In this review we attempt to summarize the current state of knowledge in these two areas, to suggest how common principles may apply in both, and to show how these principles may also apply to reinforcement schedule behavior considered as a topic in its own right.

INTERVAL TIMING

Interval timing is defined in several ways. The simplest is to define it as covariation between a dependent measure such as wait time and an independent measure such as interreinforcement interval (on fixed interval) or trial time-to-reinforcement (on the peak procedure). When interreinforcement interval is doubled, then after a learning period wait time also approximately doubles ( proportional timing ). This is an example of what is sometimes called a time production procedure: The organism produces an approximation to the to-be-timed interval. There are also explicit time discrimination procedures in which on each trial the subject is exposed to a stimulus and is then required to respond differentially depending on its absolute ( Church & Deluty 1977 , Stubbs 1968 ) or even relative ( Fetterman et al. 1989 ) duration. For example, in temporal bisection , the subject (e.g., a rat) experiences either a 10-s or a 2-s stimulus, L or S . After the stimulus goes off, the subject is confronted with two choices. If the stimulus was L , a press on the left lever yields food; if S , a right press gives food; errors produce a brief time-out. Once the animal has learned, stimuli of intermediate duration are presented in lieu of S and L on test trials. The question is, how will the subject distribute its responses? In particular, at what intermediate duration will it be indifferent between the two choices? [Answer: typically in the vicinity of the geometric mean, i.e., √( L.S ) − 4.47 for 2 and 10.]

Wait time is a latency; hence (it might be objected) it may vary on time-production procedures like fixed interval because of factors other than timing—such as degree of hunger (food deprivation). Using a time-discrimination procedure avoids this problem. It can also be mitigated by using the peak procedure and looking at performance during “empty” trials. “Filled” trials terminate with food reinforcement after (say) T s. “Empty” trials, typically 3 T s long, contain no food and end with the onset of the ITI. During empty trials the animal therefore learns to wait, then respond, then stop (more or less) until the end of the trial ( Catania 1970 ). The mean of the distribution of response rates averaged over empty trials ( peak time ) is then perhaps a better measure of timing than wait time because motivational variables are assumed to affect only the height and spread of the response-rate distribution, not its mean. This assumption is only partially true ( Grace & Nevin 2000 , MacEwen & Killeen 1991 , Plowright et al. 2000 ).

There is still some debate about the actual pattern of behavior on the peak procedure in each individual trial. Is it just wait, respond at a constant rate, then wait again? Or is there some residual responding after the “stop” [yes, usually (e.g., Church et al. 1991 )]? Is the response rate between start and stop really constant or are there two or more identifiable rates ( Cheng & Westwood 1993 , Meck et al. 1984 )? Nevertheless, the method is still widely used, particularly by researchers in the cognitive/psychophysical tradition. The idea behind this approach is that interval timing is akin to sensory processes such as the perception of sound intensity (loudness) or luminance (brightness). As there is an ear for hearing and an eye for seeing, so (it is assumed) there must be a (real, physiological) clock for timing. Treisman (1963) proposed the idea of an internal pacemaker-driven clock in the context of human psychophysics. Gibbon (1977) further developed the approach and applied it to animal interval-timing experiments.

WEBER'S LAW, PROPORTIONAL TIMING AND TIMESCALE INVARIANCE

The major similarity between acknowledged sensory processes, such as brightness perception, and interval timing is Weber's law . Peak time on the peak procedure is not only proportional to time-to-food ( T ), its coefficient of variation (standard deviation divided by mean) is approximately constant, a result similar to Weber's law obeyed by most sensory dimensions. This property has been called scalar timing ( Gibbon 1977 ). Most recently, Gallistel & Gibbon (2000) have proposed a grand principle of timescale invariance , the idea that the frequency distribution of any given temporal measure (the idea is assumed to apply generally, though in fact most experimental tests have used peak time) scales with the to-be-timed-interval. Thus, given the normalized peak-time distribution for T =60 s, say; if the x -axis is divided by 2, it will match the distribution for T = 30 s. In other words, the frequency distribution for the temporal dependent variable, normalized on both axes, is asserted to be invariant.

Timescale invariance is in effect a combination of Weber's law and proportional timing. Like those principles, it is only approximately true. There are three kinds of evidence that limit its generality. The simplest is the steady-state pattern of responding (key-pecking or lever-pressing) observed on fixed-interval reinforcement schedules. This pattern should be the same at all fixed-interval values, but it is not. Gallistel & Gibbon wrote, “When responding on such a schedule, animals pause after each reinforcement and then resume responding after some interval has elapsed. It was generally supposed that the animals' rate of responding accelerated throughout the remainder of the interval leading up to reinforcement. In fact, however, conditioned responding in this paradigm … is a two-state variable (slow, sporadic pecking vs. rapid, steady pecking), with one transition per interreinforcement interval ( Schneider 1969 )” (p. 293).

This conclusion over-generalizes Schneider's result. Reacting to reports of “break-and-run” fixed-interval performance under some conditions, Schneider sought to characterize this feature more objectively than the simple inspection of cumulative records. He found a way to identify the point of maximum acceleration in the fixed-interval “scallop” by using an iterative technique analogous to attaching an elastic band to the beginning of an interval and the end point of the cumulative record, then pushing a pin, representing the break point, against the middle of the band until the two resulting straight-line segments best fit the cumulative record (there are other ways to achieve the same result that do not fix the end points of the two line-segments). The postreinforcement time ( x -coordinate) of the pin then gives the break point for that interval. Schneider showed that the break point is an orderly dependent measure: Break point is roughly 0.67 of interval duration, with standard deviation proportional to the mean (the Weber-law or scalar property).

This finding is by no means the same as the idea that the fixed-interval scallop is “a two-state variable” ( Hanson & Killeen 1981 ). Schneider showed that a two-state model is an adequate approximation; he did not show that it is the best or truest approximation. A three- or four-line approximation (i.e., two or more pins) might well have fit significantly better than the two-line version. To show that the process is two-state, Schneider would have had to show that adding additional segments produced negligibly better fit to the data.

The frequent assertion that the fixed-interval scallop is always an artifact of averaging flies in the face of raw cumulative-record data“the many nonaveraged individual fixed-interval cumulative records in Ferster & Skinner (1957 , e.g., pp. 159, 160, 162), which show clear curvature, particularly at longer fixed-interval values (> ∼2 min). The issue for timescale invariance, therefore, is whether the shape, or relative frequency of different-shaped records, is the same at different absolute intervals.

The evidence is that there is more, and more frequent, curvature at longer intervals. Schneider's data show this effect. In Schneider's Figure 3, for example, the time to shift from low to high rate is clearly longer at longer intervals than shorter ones. On fixed-interval schedules, apparently, absolute duration does affect the pattern of responding. (A possible reason for this dependence of the scallop on fixed-interval value is described in Staddon 2001a , p. 317. The basic idea is that greater curvature at longer fixed-interval values follows from two things: a linear increase in response probability across the interval, combined with a nonlinear, negatively accelerated, relation between overall response rate and reinforcement rate.) If there is a reliable difference in the shape, or distribution of shapes, of cumulative records at long and short fixed-interval values, the timescale-invariance principle is violated.

A second dataset that does not agree with timescale invariance is an extensive set of studies on the peak procedure by Zeiler & Powell (1994 ; see also Hanson & Killeen 1981) , who looked explicitly at the effect of interval duration on various measures of interval timing. They conclude, “Quantitative properties of temporal control depended on whether the aspect of behavior considered was initial pause duration, the point of maximum acceleration in responding [break point], the point of maximum deceleration, the point at which responding stopped, or several different statistical derivations of a point of maximum responding … . Existing theory does not explain why Weber's law [the scalar property] so rarely fit the results …” (p. 1; see also Lowe et al. 1979 , Wearden 1985 for other exceptions to proportionality between temporal measures of behavior and interval duration). Like Schneider (1969) and Hanson & Killeen (1981) , Zeiler & Powell found that the break point measure was proportional to interval duration, with scalar variance (constant coefficient of variation), and thus consistent with timescale invariance, but no other measure fit the rule.

Moreover, the fit of the breakpoint measure is problematic because it is not a direct measure of behavior but is itself the result of a statistical fitting procedure. It is possible, therefore, that the fit of breakpoint to timescale invariance owes as much to the statistical method used to arrive at it as to the intrinsic properties of temporal control. Even if this caveat turns out to be false, the fact that every other measure studied by Zeiler & Powell failed to conform to timescale invariance surely rules it out as a general principle of interval timing.

The third and most direct test of the timescale invariance idea is an extensive series of time-discrimination experiments carried out by Dreyfus et al. (1988) and Stubbs et al. (1994) . The usual procedure in these experiments was for pigeons to peck a center response key to produce a red light of one duration that is followed immediately by a green light of another duration. When the green center-key light goes off, two yellow side-keys light up. The animals are reinforced with food for pecking the left side-key if the red light was longer, the right side-key if the green light was longer.

The experimental question is, how does discrimination accuracy depend on relative and absolute duration of the two stimuli? Timescale invariance predicts that accuracy depends only on the ratio of red and green durations: For example, accuracy should be the same following the sequence red:10, green:20 as the sequence red:30, green:60, but it is not. Pigeons are better able to discriminate between the two short durations than the two long ones, even though their ratio is the same. Dreyfus et al. and Stubbs et al. present a plethora of quantitative data of the same sort, all showing that time discrimination depends on absolute as well as relative duration.

Timescale invariance is empirically indistinguishable from Weber's law as it applies to time, combined with the idea of proportional timing: The mean of a temporal dependent variable is proportional to the temporal independent variable. But Weber's law and proportional timing are dissociable—it is possible to have proportional timing without conforming to Weber's law and vice versa (cf. Hanson & Killeen 1981 , Zeiler & Powell 1994 ), and in any case both are only approximately true. Timescale invariance therefore does not qualify as a principle in its own right.

Cognitive and Behavioral Approaches to Timing

The cognitive approach to timing dates from the late 1970s. It emphasizes the psychophysical properties of the timing process and the use of temporal dependent variables as measures of (for example) drug effects and the effects of physiological interventions. It de-emphasizes proximal environmental causes. Yet when timing (then called temporal control; see Zeiler 1977 for an early review) was first discovered by operant conditioners (Pavlov had studied essentially the same phenomenon— delay conditioning —many years earlier), the focus was on the time marker , the stimulus that triggered the temporally correlated behavior. (That is one virtue of the term control : It emphasizes the fact that interval timing behavior is usually not free-running. It must be cued by some aspect of the environment.) On so-called spaced-responding schedules, for example, the response is the time marker: The subject must learn to space its responses more than T s apart to get food. On fixed-interval schedules the time marker is reinforcer delivery; on the peak procedure it is the stimulus events associated with trial onset. This dependence on a time marker is especially obvious on time-production procedures, but on time-discrimination procedures the subject's choice behavior must also be under the control of stimuli associated with the onset and offset of the sample duration.

Not all stimuli are equally effective as time markers. For example, an early study by Staddon & Innis (1966a ; see also 1969) showed that if, on alternate fixed intervals, 50% of reinforcers (F) are omitted and replaced by a neutral stimulus (N) of the same duration, wait time following N is much shorter than after F (the reinforcement-omission effect ). Moreover, this difference persists indefinitely. Despite the fact that F and N have the same temporal relationship to the reinforcer, F is much more effective as a time marker than N. No exactly comparable experiment has been done using the peak procedure, partly because the time marker there involves ITI offset/trial onset rather than the reinforcer delivery, so that there is no simple manipulation equivalent to reinforcement omission.

These effects do not depend on the type of behavior controlled by the time marker. On fixed-interval schedules the time marker is in effect inhibitory: Responding is suppressed during the wait time and then occurs at an accelerating rate. Other experiments ( Staddon 1970 , 1972 ), however, showed that given the appropriate schedule, the time marker can control a burst of responding (rather than a wait) of a duration proportional to the schedule parameters ( temporal go–no-go schedules) and later experiments have shown that the place of responding can be controlled by time since trial onset in the so-called tri-peak procedure ( Matell & Meck 1999 ).

A theoretical review ( Staddon 1974 ) concluded, “Temporal control by a given time marker depends on the properties of recall and attention, that is, on the same variables that affect attention to compound stimuli and recall in memory experiments such as delayed matching-to-sample.” By far the most important variable seems to be “the value of the time-marker stimulus—Stimuli of high value … are more salient …” (p. 389), although the full range of properties that determine time-marker effectiveness is yet to be explored.

Reinforcement omission experiments are transfer tests , that is, tests to identify the effective stimulus. They pinpoint the stimulus property controlling interval timing—the effective time marker—by selectively eliminating candidate properties. For example, in a definitive experiment, Kello (1972) showed that on fixed interval the wait time is longest following standard reinforcer delivery (food hopper activated with food, hopper light on, house light off, etc.). Omission of any of those elements caused the wait time to decrease, a result consistent with the hypothesis that reinforcer delivery acquires inhibitory temporal control over the wait time. The only thing that makes this situation different from the usual generalization experiment is that the effects of reinforcement omission are relatively permanent. In the usual generalization experiment, delivery of the reinforcer according to the same schedule in the presence of both the training stimulus and the test stimuli would soon lead all to be responded to in the same way. Not so with temporal control: As we just saw, even though N and F events have the same temporal relationship to the next food delivery, animals never learn to respond similarly after both. The only exception is when the fixed-interval is relatively short, on the order of 20 s or less ( Starr & Staddon 1974 ). Under these conditions pigeons are able to use a brief neutral stimulus as a time marker on fixed interval.

The Gap Experiment

The closest equivalent to fixed-interval reinforcement–omission using the peak procedure is the so-called gap experiment ( Roberts 1981 ). In the standard gap paradigm the sequence of stimuli in a training trial (no gap stimulus) consists of three successive stimuli: the intertrial interval stimulus (ITI), the fixed-duration trial stimulus (S), and food reinforcement (F), which ends each training trial. The sequence is thus ITI, S, F, ITI. Training trials are typically interspersed with empty probe trials that last longer than reinforced trials but end with an ITI only and no reinforcement. The stimulus sequence on such trials is ITI, S, ITI, but the S is two or three times longer than on training trials. After performance has stabilized, gap trials are introduced into some or all of the probe trials. On gap trials the ITI stimulus reappears for a while in the middle of the trial stimulus. The sequence on gap trials is therefore ITI, S, ITI, S, ITI. Gap trials do not end in reinforcement.

What is the effective time marker (i.e., the stimulus that exerts temporal control) in such an experiment? ITI offset/trial onset is the best temporal predictor of reinforcement: Its time to food is shorter and less variable than any other experimental event. Most but not all ITIs follow reinforcement, and the ITI itself is often variable in duration and relatively long. So reinforcer delivery is a poor temporal predictor. The time marker therefore has something to do with the transition between ITI and trial onset, between ITI and S. Gap trials also involve presentation of the ITI stimulus, albeit with a different duration and within-trial location than the usual ITI, but the similarities to a regular trial are obvious. The gap experiment is therefore a sort of generalization (of temporal control) experiment. Buhusi & Meck (2000) presented gap stimuli more or less similar to the ITI stimulus during probe trials and found results resembling generalization decrement, in agreement with this analysis.

However, the gap procedure was not originally thought of as a generalization test, nor is it particularly well designed for that purpose. The gap procedure arose directly from the cognitive idea that interval timing behavior is driven by an internal clock ( Church 1978 ). From this point of view it is perfectly natural to inquire about the conditions under which the clock can be started or stopped. If the to-be-timed interval is interrupted—a gap—will the clock restart when the trial stimulus returns (reset)? Will it continue running during the gap and afterwards? Or will it stop and then restart (stop)?

“Reset” corresponds to the maximum rightward shift (from trial onset) of the response-rate peak from its usual position t s after trial onset to t + G E , where G E is the offset time (end) of the gap stimulus. Conversely, no effect (clock keeps running) leaves the peak unchanged at t , and “stop and restart” is an intermediate result, a peak shift to G E − G B + t , where G B is the time of onset (beginning) of the gap stimulus.

Both gap duration and placement within a trial have been varied. The results that have been obtained so far are rather complex (cf. Buhusi & Meck 2000 , Cabeza de Vaca et al. 1994 , Matell & Meck 1999 ). In general, the longer the gap and the later it appears in the trial, the greater the rightward peak shift. All these effects can be interpreted in clock terms, but the clock view provides no real explanation for them, because it does not specify which one will occur under a given set of conditions. The results of gap experiments can be understood in a qualitative way in terms of the similarity of the gap presentation to events associated with trial onset; the more similar, the closer the effect will be to reset, i.e., the onset of a new trial. Another resemblance between gap results and the results of reinforcement-omission experiments is that the effects of the gap are also permanent: Behavior on later trials usually does not differ from behavior on the first few ( Roberts 1981 ). These effects have been successfully simulated quantitatively by a neural network timing model ( Hopson 1999 , 2002 ) that includes the assumption that the effects of time-marker presentation decay with time ( Cabeza de Vaca et al. 1994 ).

The original temporal control studies were strictly empirical but tacitly accepted something like the psychophysical view of timing. Time was assumed to be a sensory modality like any other, so the experimental task was simply to explore the different kinds of effect, excitatory, inhibitory, discriminatory, that could come under temporal control. The psychophysical view was formalized by Gibbon (1977) in the context of animal studies, and this led to a static information-processing model, scalar expectancy theory (SET: Gibbon & Church 1984 , Meck 1983 , Roberts 1983 ), which comprised a pacemaker-driven clock, working and reference memories, a comparator, and various thresholds. A later dynamic version added memory for individual trials (see Gallistel 1990 for a review). This approach led to a long series of experimental studies exploring the clocklike properties of interval timing (see Gallistel & Gibbon 2000 , Staddon & Higa 1999 for reviews), but none of these studies attempted to test the assumptions of the SET approach in a direct way.

SET was for many years the dominant theoretical approach to interval timing. In recent years, however, its limitations, of parsimony and predictive range, have become apparent and there are now a number of competitors such as the behavioral theory of timing ( Killeen & Fetterman 1988 , MacEwen & Killeen 1991 , Machado 1997 ), spectral timing theory ( Grossberg & Schmajuk 1989 ), neural network models ( Church & Broadbent 1990 , Hopson 1999 , Dragoi et al. 2002 ), and the habituation-based multiple time scale theory (MTS: Staddon & Higa 1999 , Staddon et al. 2002 ). There is as yet no consensus on the best theory.

Temporal Dynamics: Linear Waiting

A separate series of experiments in the temporal-control tradition, beginning in the late 1980s, studied the real-time dynamics of interval timing (e.g., Higa et al. 1991 , Lejeune et al. 1997 , Wynne & Staddon 1988 ; see Staddon 2001a for a review). These experiments have led to a simple empirical principle that may have wide application. Most of these experiments used the simplest possible timing schedule, a response-initiated delay (RID) schedule 3 . In this schedule the animal (e.g., a pigeon) can respond at any time, t , after food. The response changes the key color and food is delivered after a further T s. Time t is under the control of the animal; time T is determined by the experimenter. These experiments have shown that wait time on these and similar schedules (such as fixed interval) is strongly determined by the duration of the previous interfood interval (IFI). For example, wait time will track a cyclic sequence of IFIs, intercalated at a random point in a sequence of fixed ( t + T =constant) intervals, with a lag of one interval; a single short IFI is followed by a short wait time in the next interval (the effect of a single long interval is smaller), and so on (see Staddon et al. 2002 for a review and other examples of temporal tracking). To a first approximation, these results are consistent with a linear relation between wait time in IFI N + 1 and the duration of IFI N :

where I is the IFI, a is a constant less than one, and b is usually negligible. This relation has been termed linear waiting ( Wynne & Staddon 1988 ). The principle is an approximation: an expanded model, incorporating the multiple time scale theory, allows the principle to account for the slower effects of increases as opposed to decreases in IFI (see Staddon et al. 2002 ).

Most importantly for this discussion, the linear waiting principle appears to be obligatory. That is, organisms seem to follow the linear waiting rule even if they delay or even prevent reinforcer delivery by doing so. The simplest example is the RID schedule itself. Wynne & Staddon (1988) showed that it makes no difference whether the experimenter holds delay time T constant or the sum of t + T constant ( t + T = K ): Equation 1 holds in both cases, even though the optimal (reinforcement-rate-maximizing) strategy in the first case is for the animal to set t equal to zero, whereas in the second case reinforcement rate is maximized so long as t < K . Using a version of RID in which T in interval N + 1 depended on the value of t in the preceding interval, Wynne & Staddon also demonstrated two kinds of instability predicted by linear waiting.

The fact that linear waiting is obligatory allows us to look for its effects on schedules other than the simple RID schedule. The most obvious application is to ratio schedules. The time to emit a fixed number of responses is approximately constant; hence the delay to food after the first response in each interval is also approximately constant on fixed ratio (FR), as on fixed- T RID ( Powell 1968 ). Thus, the optimal strategy on FR, as on fixed- T RID, is to respond immediately after food. However, in both cases animals wait before responding and, as one might expect based on the assumption of a roughly constant interresponse time on all ratio schedules, the duration of the wait on FR is proportional to the ratio requirement ( Powell 1968 ), although longer than on a comparable chain-type schedule with the same interreinforcement time ( Crossman et al. 1974 ). The phenomenon of ratio strain —the appearance of long pauses and even extinction on high ratio schedules ( Ferster & Skinner 1957 )—may also have something to do with obligatory linear waiting.

Chain Schedules

A chain schedule is one in which a stimulus change, rather than primary reinforcement, is scheduled. Thus, a chain fixed-interval–fixed-interval schedule is one in which, for example, food reinforcement is followed by the onset of a red key light in the presence of which, after a fixed interval, a response produces a change to green. In the presence of green, food delivery is scheduled according to another fixed interval. RID schedules resemble two-link chain schedules. The first link is time t , before the animal responds; the second link is time T , after a response. We may expect, therefore, that waiting time in the first link of a two-link schedule will depend on the duration of the second link. We describe two results consistent with this conjecture and then discuss some exceptions.

Davison (1974) studied a two-link chain fixed-interval–fixed-interval schedule. Each cycle of the schedule began with a red key. Responding was reinforced, on fixed-interval I 1 s, by a change in key color from red to white. In the presence of white, food reinforcement was delivered according to fixed-interval I 2 s, followed by reappearance of the red key. Davison varied I 1 and I 2 and collected steady-state rate, pause, and link-duration data. He reported that when programmed second-link duration was long in relation to the first-link duration, pause in the first link sometimes exceeded the programmed link duration. The linear waiting predictions for this procedure can therefore be most easily derived for those conditions where the second link is held constant and the first link duration is varied (because under these conditions, the first-link pause was always less than the programmed first-link duration). The prediction for the terminal link is

where a is the proportionality constant, I 2 is the duration of the terminal-link fixed-interval, and t 2 is the pause in the terminal link. Because I 2 is constant in this phase, t 2 is also constant. The pause in the initial link is given by

where I 1 is the duration of the first link. Because I 2 is constant, Equation 3 is a straight line with slope a and positive y-intercept aI 2 .

Linear waiting theory can be tested with Davison's data by plotting, for every condition, t 1 and t 2 versus time-to-reinforcement (TTR); that is, plot pause in each link against TTR for that link in every condition. Linear waiting makes a straightforward prediction: All the data points for both links should lie on the same straight line through the origin (assuming that b → 0). We show this plot in Figure 1 . There is some variability, because the data points are individual subjects, not averages, but points from first and second links fit the same line, and the deviations do not seem to be systematic.

Steady-state pause duration plotted against actual time to reinforcement in the first and second links of a two-link chain schedule. Each data point is from a single pigeon in one experimental condition (three data points from an incomplete condition are omitted). (From Davison 1974 , Table 1)

A study by Innis et al. (1993) provides a dynamic test of the linear waiting hypothesis as applied to chain schedules. Innis et al. studied two-link chain schedules with one link of fixed duration and the other varying from reinforcer to reinforcer according to a triangular cycle. The dependent measure was pause in each link. Their Figure 3, for example, shows the programmed and actual values of the second link of the constant-cycle procedure (i.e., the first link was a constant 20 s; the second link varied from 5 to 35 s according to the triangular cycle) as well as the average pause, which clearly tracks the change in second-link duration with a lag of one interval. They found similar results for the reverse procedure, cycle-constant , in which the first link varied cyclically and the second link was constant. The tracking was a little better in the first procedure than in the second, but in both cases first-link pause was determined primarily by TTR.

There are some data suggesting that linear waiting is not the only factor that determines responding on simple chain schedules. In the four conditions of Davison's experiment in which the programmed durations of the first and second links added to a constant (120 s)—which implies a constant first-link pause according to linear waiting—pause in the first link covaried with first-link duration, although the data are noisy.

The alternative to the linear waiting account of responding on chain schedules is an account in terms of conditioned reinforcement (also called secondary reinforcement)—the idea that a stimulus paired with a primary reinforcer acquires some independent reinforcing power. This idea is also the organizing principle behind most theories of free-operant choice. There are some data that seem to imply a response-strengthening effect quite apart from the linear waiting effect, but they do not always hold up under closer inspection. Catania et al. (1980) reported that “higher rates of pecking were maintained by pigeons in the middle component of three-component chained fixed-interval schedules than in that component of the corresponding multiple schedule (two extinction components followed by a fixed-interval component)” (p. 213), but the effect was surprisingly small, given that no responding at all was required in the first two components. Moreover, results of a more critical control condition, chain versus tandem (rather than multiple) schedule, were the opposite: Rate was generally higher in the middle tandem component than in the second link of the chain. (A tandem schedule is one with the same response contingencies as a chain but with the same stimulus present throughout.)

Royalty et al. (1987) introduced a delay into the peck-stimulus-change contingency of a three-link variable-interval chain schedule and found large decreases in response rate [wait time (WT) was not reported] in both first and second links. They concluded that “because the effect of delaying stimulus change was comparable to the effect of delaying primary reinforcement in a simple variable-interval schedule … the results provide strong evidence for the concept of conditioned reinforcement” (p. 41). The implications of the Royalty et al. data for linear waiting are unclear, however, ( a ) because the linear waiting hypothesis does not deal with the assignment-of-credit problem, that is, the selection of the appropriate response by the schedule. Linear waiting makes predictions about response timing—when the operant response occurs—but not about which response will occur. Response-reinforcer contiguity may be essential for the selection of the operant response in each chain link (as it clearly is during “shaping”), and diminishing contiguity may reduce response rate, but contiguity may play little or no role in the timing of the response. The idea of conditioned reinforcement may well apply to the first function but not to the second. ( b ) Moreover, Royalty et al. did not report obtained time-to-reinforcement data; the effect of the imposed delay may therefore have been via an increase in component duration rather than directly on response rate.

Williams & Royalty (1990) explicitly compared conditioned reinforcement and time to reinforcement as explanations for chain schedule performance in three-link chains and concluded “that time to reinforcement itself accounts for little if any variance in initial-link responding” (p. 381) but not timing, which was not measured. However, these data are from chain schedules with both variable-interval and fixed-interval links, rather than fixed-interval only, and with respect to response rate rather than pause measures. In a later paper Williams qualified this claim: “The effects of stimuli in a chain schedule are due partly to the time to food correlated with the stimuli and partly to the time to the next conditioned reinforcer in the sequence” (1997, p. 145).

The conclusion seems to be that linear waiting plays a relatively major, and conditioned reinforcement (however defined) a relatively minor, role in the determination of response timing on chain fixed-interval schedules. Linear waiting also provides the best available account of a striking, unsolved problem with chain schedules: the fact that in chains with several links, pigeon subjects may respond at a low level or even quit completely in early links ( Catania 1979 , Gollub 1977 ). On fixed-interval chain schedules with five or more links, responding in the early links begins to extinguish and the overall reinforcement rate falls well below the maximum possible—even if the programmed interreinforcement interval is relatively short (e.g., 6×15=90 s). If the same stimulus is present in all links (tandem schedule), or if the six different stimuli are presented in random order (scrambled-stimuli chains), performance is maintained in all links and the overall reinforcement rate is close to the maximum possible (6 I , where I is the interval length). Other studies have reported very weak responding in early components of a simple chain fixed-interval schedule (e.g., Catania et al. 1980 , Davison 1974 , Williams 1994 ; review in Kelleher & Gollub 1962 ). These studies found that chains with as few as three fixed-interval 60-s links ( Kelleher & Fry 1962 ) occasionally produce extreme pausing in the first link. No formal theory of the kind that has proliferated to explain behavior on concurrent chain schedules (discussed below) has been offered to account for these strange results, even though they have been well known for many years.

The informal suggestion is that the low or zero response rates maintained by early components of a multi-link chain are a consequence of the same discrimination process that leads to extinction in the absence of primary reinforcement. Conversely, the stimulus at the end of the chain that is actually paired with primary reinforcement is assumed to be a conditioned reinforcer; stimuli in the middle sustain responding because they lead to production of a conditioned reinforcer ( Catania et al. 1980 , Kelleher & Gollub 1962 ). Pairing also explains why behavior is maintained on tandem and scrambled-stimuli chains ( Kelleher & Fry 1962 ). In both cases the stimuli early in the chain are either invariably (tandem) or occasionally (scrambled-stimulus) paired with primary reinforcement.

There are problems with the conditioned-reinforcement approach, however. It can explain responding in link two of a three-link chain but not in link one, which should be an extinction stimulus. The explanatory problem gets worse when more links are added. There is no well-defined principle to tell us when a stimulus changes from being a conditioned reinforcer, to a stimulus in whose presence responding is maintained by a conditioned reinforcer, to an extinction stimulus. What determines the stimulus property? Is it stimulus number, stimulus duration or the durations of stimuli later in the chain? Perhaps there is some balance between contrast/extinction, which depresses responding in early links, and conditioned reinforcement, which is supposed to (but sometimes does not) elevate responding in later links? No well-defined compound theory has been offered, even though there are several quantitative theories for multiple-schedule contrast (e.g., Herrnstein 1970 , Nevin 1974 , Staddon 1982 ; see review in Williams 1988 ). There are also data that cast doubt even on the idea that late-link stimuli have a rate-enhancing effect. In the Catania et al. (1980) study, for example, four of five pigeons responded faster in the middle link of a three-link tandem schedule than the comparable chain.

The lack of formal theories for performance on simple chains is matched by a dearth of data. Some pause data are presented in the study by Davison (1974) on pigeons in a two-link fixed-interval chain. The paper attempted to fit Herrnstein's (1970) matching law between response rates and link duration. The match was poor: The pigeon's rates fell more than predicted when the terminal links (contiguous with primary reinforcement) of the chain were long, but Davison did find that “the terminal link schedule clearly changes the pause in the initial link, longer terminal-link intervals giving longer initial-link pauses” (1974, p. 326). Davison's abstract concludes, “Data on pauses during the interval schedules showed that, in most conditions, the pause duration was a linear function of the interval length, and greater in the initial link than in the terminal link” (p. 323). In short, the pause (time-to-first-response) data were more lawful than response-rate data.

Linear waiting provides a simple explanation for excessive pausing on multi-link chain fixed-interval schedules. Suppose the chief function of the link stimuli on chain schedules is simply to signal changing times to primary reinforcement 4 . Thus, in a three-link fixed-interval chain, with link duration I , the TTR signaled by the end of reinforcement (or by the onset of the first link) is 3 I . The onset of the next link signals a TTR of 2 I and the terminal, third, link signals a TTR of I . The assumptions of linear waiting as applied to this situation are that pausing (time to first response) in each link is determined entirely by TTR and that the wait time in interval N +1 is a linear function of the TTR in the preceding interval.

To see the implications of this process, consider again a three-link chain schedule with I =1 (arbitrary time units). The performance to be expected depends entirely on the value of the proportionality constant, a , that sets the fraction of time-to-primary-reinforcement that the animal waits (for simplicity we can neglect b ; the logic of the argument is unaffected). All is well so long as a is less than one-third. If a is exactly 0.333, then for unit link duration the pause in the third link is 0.33, in the second link 0.67, and in the first link 1.0 However, if a is larger, for instance 0.5, the three pauses become 0.5, 1.0, and 1.5; that is, the pause in the first link is now longer than the programmed interval, which means the TTR in the first link will be longer than 3 the next time around, so the pause will increase further, and so on until the process stabilizes (which it always does: First-link pause never goes to ∞).

The steady-state wait times in each link predicted for a five-link chain, with unit-duration links, for two values of a are shown in Figure 2 . In both cases wait times in the early links are very much longer than the programmed link duration. Clearly, this process has the potential to produce very large pauses in the early links of multilink-chain fixed-interval schedules and so may account for the data Catania (1979) and others have reported.

Wait time (pause, time to first response) in each equal-duration link of a five-link chain schedule (as a multiple of the programmed link duration) as predicted by the linear-waiting hypothesis. The two curves are for two values of parameter a in Equation 1 ( b =0). Note the very long pauses predicted in early links—almost two orders of magnitude greater than the programmed interval in the first link for a =0.67. (From Mazur 2001 )

Gollub in his dissertation research (1958) noticed the additivity of this sequential pausing. Kelleher & Gollub (1962) in their subsequent review wrote, “No two pauses in [simple fixed interval] can both postpone food-delivery; however, pauses in different components of [a] five-component chain will postpone food-delivery additively” (p. 566). However, this additivity was only one of a number of processes suggested to account for the long pauses in early chain fixed-interval links, and its quantitative implications were never explored.

Note that the linear waiting hypothesis also accounts for the relative stability of tandem schedules and chain schedules with scrambled components. In the tandem schedule, reinforcement constitutes the only available time marker. Given that responding after the pause continues at a relatively high rate until the next time marker, Equation 1 (with b assumed negligible) and a little algebra shows that the steady-state postreinforcement pause for a tandem schedule with unit links will be

where N is the number of links and a is the pause fraction. In the absence of any time markers, pauses in links after the first are necessarily short, so the experienced link duration equals the programmed duration. Thus, the total interfood-reinforcement interval will be t + N − 1 ( t ≥ 1): the pause in the first link (which will be longer than the programmed link duration for N > 1/ a ) plus the programmed durations of the succeeding links. For the case of a = 0.67 and unit link duration, which yielded a steady-state interfood interval (IFI) of 84 for the five-link chain schedule, the tandem yields 12. For a = 0.5, the two values are approximately 16 and 8.

The long waits in early links shown in Figure 2 depend critically on the value of a . If, as experience suggests (there has been no formal study), a tends to increase slowly with training, we might expect the long pausing in initial links to take some time to develop, which apparently it does ( Gollub 1958 ).

On the scrambled-stimuli chain each stimulus occasionally ends in reinforcement, so each signals a time-to-reinforcement (TTR) 5 of I , and pause in each link should be less than the link duration—yielding a total IFI of approximately N , i.e., 5 for the example in the figure. These predictions yield the order IFI in the chain > tandem > scrambled, but parametric data are not available for precise comparison. We do not know whether an N -link scrambled schedule typically stabilizes at a shorter IFI than the comparable tandem schedule, for example. Nor do we know whether steady-state pause in successive links of a multilink chain falls off in the exponential fashion shown in Figure 2 .

In the final section we explore the implications of linear waiting for studies of free-operant choice behavior.

Although we can devote only limited space to it, choice is one of the major research topics in operant conditioning (see Mazur 2001 , p. 96 for recent statistics). Choice is not something that can be directly observed. The subject does this or that and, in consequence, is said to choose. The term has unfortunate overtones of conscious deliberation and weighing of alternatives for which the behavior itself—response A or response B—provides no direct evidence. One result has been the assumption that the proper framework for all so-called choice studies is in terms of response strength and the value of the choice alternatives. Another is the assumption that procedures that are very different are nevertheless studying the same thing.

For example, in a classic series of experiments, Kahneman & Tversky (e.g., 1979) asked a number of human subjects to make a single choice of the following sort: between $400 for sure and a 50% chance of $1000. Most went for the sure thing, even though the expected value of the gamble is higher. This is termed risk aversion , and the same term has been applied to free-operant “choice” experiments. In one such experiment an animal subject must choose repeatedly between a response leading to a fixed amount of food and one leading equiprobably to either a large or a small amount with the same average value. Here the animals tend to be either indifferent or risk averse, preferring the fixed alternative ( Staddon & Innis 1966b , Bateson & Kacelnik 1995 , Kacelnik & Bateson 1996 ).

In a second example pigeons responded repeatedly to two keys associated with equal variable-interval schedules. A successful response on the left key, for example, is reinforced by a change in the color of the pecked key (the other key light goes off). In the presence of this second stimulus, food is delivered according to a fixed-interval schedule (fixed-interval X ). The first stimulus, which is usually the same on both keys, is termed the initial link ; the second stimulus is the terminal link . Pecks on the right key lead in the same way to food reinforcement on variable-interval X . (This is termed a concurrent-chain schedule.) In this case subjects overwhelmingly prefer the initial-link choice leading to the variable-interval terminal link; that is, they are apparently risk seeking rather than risk averse ( Killeen 1968 ).

The fact that these three experiments (Kahneman & Tversky and the two free-operant studies) all produce different results is sometimes thought to pose a serious research problem, but, we contend, the problem is only in the use of the term choice for all three. The procedures (not to mention the subjects) are in fact very different, and in operant conditioning the devil is very much in the details. Apparently trivial procedural differences can sometimes lead to wildly different behavioral outcomes. Use of the term choice as if it denoted a unitary subject matter is therefore highly misleading. We also question the idea that the results of choice experiments are always best explained in terms of response strength and stimulus value.

Concurrent Schedules

Bearing these caveats in mind, let's look briefly at the extensive history of free-operant choice research. In Herrnstein's seminal experiment (1961 ; see Davison & McCarthy 1988 , Williams 1988 for reviews; for collected papers see Rachlin & Laibson 1997 ) hungry pigeons pecked at two side-by-side response keys, one associated with variable-interval v 1 s and the other with variable-interval v 2 s ( concurrent variable-interval–variable-interval schedule). After several experimental sessions and a range of v 1 and v 2 values chosen so that the overall programmed reinforcement rate was constant (1/ v 1 + 1/ v 2 = constant), the result was matching between steady-state relative response rates and relative obtained reinforcement rates:

where x and y are the response rates on the two alternatives and R ( x ) and R ( y ) are the rates of obtained reinforcement for them. This relation has become known as Herrnstein's matching law. Although the obtained reinforcement rates are dependent on the response rates that produce them, the matching relation is not forced, because x and y can vary over quite a wide range without much effect on R ( x ) and R ( y ).

Because of the negative feedback relation intrinsic to variable-interval schedules (the less you respond, the higher the probability of payoff), the matching law on concurrent variable-interval–variable-interval is consistent with reinforcement maximization ( Staddon & Motheral 1978 ), although the maximum of the function relating overall payoff, R ( x ) + R ( y ), to relative responding, x /( x + y ), is pretty flat. However, little else on these schedules fits the maximization idea. As noted above, even responding on simple fixed- T response-initiated delay (RID) schedules violates maximization. Matching is also highly overdetermined, in the sense that almost any learning rule consistent with the law of effect—an increase in reinforcement probability causes an increase in response probability—will yield either simple matching ( Equation 5 ) or its power-law generalization ( Baum 1974 , Hinson & Staddon 1983 , Lander & Irwin 1968 , Staddon 1968 ). Matching by itself therefore reveals relatively little about the dynamic processes operating in the responding subject (but see Davison & Baum 2000 ). Despite this limitation, the strikingly regular functional relations characteristic of free-operant choice studies have attracted a great deal of experimental and theoretical attention.

Herrnstein (1970) proposed that Equation 5 can be derived from the function relating steady-state response rate, x , and reinforcement rate, R ( x ), to each response key considered separately. This function is negatively accelerated and well approximated by a hyperbola:

where k is a constant and R 0 represents the effects of all other reinforcers in the situation. The denominator and parameter k cancel in the ratio x / y , yielding Equation 5 for the choice situation.

There are numerous empirical details that are not accounted for by this formulation: systematic deviations from matching [undermatching and overmatching ( Baum 1974 )] as a function of different types of variable-interval schedules, dependence of simple matching on use of a changeover delay , extensions to concurrent-chain schedules, and so on. For example, if animals are pretrained with two alternatives presented separately, so that they do not learn to switch between them, when given the opportunity to respond to both, they fixate on the richer one rather than matching [extreme overmatching ( Donahoe & Palmer 1994 , pp. 112–113; Gallistel & Gibbon 2000 , pp. 321–322)]. (Fixation—extreme overmatching—is, trivially, matching, of course but if only fixation were observed, the idea of matching would never have arisen. Matching implies partial, not exclusive, preference.) Conversely, in the absence of a changeover delay, pigeons will often just alternate between two unequal variable-interval choices [extreme undermatching ( Shull & Pliskoff 1967 )]. In short, matching requires exactly the right amount of switching. Nevertheless, Herrnstein's idea of deriving behavior in choice experiments from the laws that govern responding to the choice alternatives in isolation is clearly worth pursuing.

In any event, Herrnstein's approach—molar data, predominantly variable-interval schedules, rate measures—set the basic pattern for subsequent operant choice research. It fits the basic presuppositions of the field: that choice is about response strength , that response strength is equivalent to response probability, and that response rate is a valid proxy for probability (e.g., Skinner 1938 , 1966 , 1986 ; Killeen & Hall 2001 ). (For typical studies in this tradition see, e.g., Fantino 1981 ; Grace 1994 ; Herrnstein 1961 , 1964 , 1970 ; Rachlin et al. 1976 ; see also Shimp 1969 , 2001 .)

We can also look at concurrent schedules in terms of linear waiting. Although published evidence is skimpy, recent unpublished data ( Cerutti & Staddon 2002 ) show that even on variable-interval schedules (which necessarily always contain a few very short interfood intervals), postfood wait time and changeover time covary with mean interfood time. It has also long been known that Equation 6 can be derived from two time-based assumptions: that the number of responses emitted is proportional to the number of reinforcers received multiplied by the available time and that available time is limited by the time taken up by each response ( Staddon 1977 , Equations 23–25). Moreover, if we define mean interresponse time as the reciprocal of mean response rate, 6 x , and mean interfood interval is the reciprocal of obtained reinforcement rate, R ( x ), then linear waiting yields

where a and b are linear waiting constants. Rearranging yields

where 1/ b = k and a / b = R 0 in Equation 6 . Both these derivations of the hyperbola in Equation 6 from a linear relation in the time domain imply a correlation between parameters k and R 0 in Equation 6 under parametric experimental variation of parameter b by (for example) varying response effort or, possibly, hunger motivation. Such covariation has been occasionally but not universally reported ( Dallery et al. 2000 , Heyman & Monaghan 1987 , McDowell & Dallery 1999 ).

Concurrent-Chain Schedules

Organisms can be trained to choose between sources of primary reinforcement (concurrent schedules) or between stimuli that signal the occurrence of primary reinforcement ( conditioned reinforcement : concurrent chain schedules). Many experimental and theoretical papers on conditioned reinforcement in pigeons and rats have been published since the early 1960s using some version of the concurrent chains procedure of Autor (1960 , 1969) . These studies have demonstrated a number of functional relations between rate measures and have led to several closely related theoretical proposals such as a version of the matching law, incentive theory, delay-reduction theory, and hyperbolic value-addition (e.g., Fantino 1969a , b ; Grace 1994 ; Herrnstein 1964 ; Killeen 1982 ; Killeen & Fantino 1990 ; Mazur 1997 , 2001 ; Williams 1988 , 1994 , 1997 ). Nevertheless, there is as yet no theoretical consensus on how best to describe choice between sources of conditioned reinforcement, and no one has proposed an integrated theoretical account of simple chain and concurrent chain schedules.

Molar response rate does not capture the essential feature of behavior on fixed-interval schedules: the systematic pattern of rate-change in each interfood interval, the “scallop.” Hence, the emphasis on molar response rate as a dependent variable has meant that work on concurrent schedules has emphasized variable or random intervals over fixed intervals. We lack any theoretical account of concurrent fixed-interval–fixed-interval and fixed-interval–variable-interval schedules. However, a recent study by Shull et al. (2001 ; see also Shull 1979) suggests that response rate may not capture what is going on even on simple variable-interval schedules, where the time to initiate bouts of relatively fixed-rate responding seems to be a more sensitive dependent measure than overall response rate. More attention to the role of temporal variables in choice is called for.

We conclude with a brief account of how linear waiting may be involved in several well-established phenomena of concurrent-chain schedules: preference for variable-interval versus fixed-interval terminal links, effect of initial-link duration, and finally, so-called self-control experiments.

preference for variable-interval versus fixed-interval terminal links On concurrent-chain schedules with equal variable-interval initial links, animals show a strong preference for the initial link leading to a variable-interval terminal link over the terminal-link alternative with an equal arithmetic-mean fixed interval. This result is usually interpreted as a manifestation of nonarithmetic (e.g., harmonic) reinforcement-rate averaging ( Killeen 1968 ), but it can also be interpreted as linear waiting. Minimum TTR is necessarily much less on the variable-interval than on the fixed-interval side, because some variable intervals are short. If wait time is determined by minimum TTR—hence shorter wait times on the variable-interval side—and ratios of wait times and overall response rates are (inversely) correlated ( Cerutti & Staddon 2002 ), the result will be an apparent bias in favor of the variable-interval choice.

effect of initial-link duration Preference for a given pair of terminal-link schedules depends on initial link duration. For example, pigeons may approximately match initial-link relative response rates to terminal-link relative reinforcement rates when the initial links are 60 s and the terminal links range from 15 to 45 s ( Herrnstein 1964 ), but they will undermatch when the initial-link schedule is increased to, for example, 180 s. This effect is what led to Fantino's delay-reduction modification of Herrnstein's matching law (see Fantino et al. 1993 for a review). However, the same qualitative prediction follows from linear waiting: Increasing initial-link duration reduces the proportional TTR difference between the two choices. Hence the ratio of WTs or of initial-link response rates for the two choices should also approach unity, which is undermatching. Several other well-studied theories of concurrent choice, such as delay reduction and hyperbolic value addition, also explain these results.

Self-Control

The prototypical self-control experiment has a subject choosing between two outcomes: not-so-good cookie now or a good cookie after some delay ( Rachlin & Green 1972 ; see Logue 1988 for a review; Mischel et al. 1989 reviewed human studies). Typically, the subject chooses the immediate, small reward, but if both delays are increased by the same amount, D , he will learn to choose the larger reward, providing D is long enough. Why? The standard answer is derived from Herrnstein's matching analysis ( Herrnstein 1981 ) and is called hyperbolic discounting (see Mazur 2001 for a review and Ainslie 1992 and Rachlin 2000 for longer accounts). The idea is that the expected value of each reward is inversely related to the time at which it is expected according to a hyperbolic function:

where A i is the undiscounted value of the reward, D i is the delay until reward is received, i denotes the large or small reward, and k is a fitted constant.

Now suppose we set D L and D S to values such that the animal shows a preference for the shorter, sooner reward. This would be the case ( k =1) if A L =6, A S =2, D L = 6 s, and D S = 1 s: V L =0.86 and V S =1—preference for the small, less-delayed reward. If 10 s is added to both delays, so that D L = 16 s and D S =11 s, the values are V L =0.35 and V S =0.17—preference for the larger reward. Thus, Equation 8 predicts that added delay—sometimes awkwardly termed pre-commitment— should enhance self-control, which it does.

The most dramatic prediction from this analysis was made and confirmed by Mazur (1987 , 2001) in an experiment that used an adjusting-delay procedure (also termed titration ). “A response on the center key started each trial, and then a pigeon chose either a standard alternative (by pecking the red key) or an adjusting alternative (by pecking the green key) … the standard alternative delivered 2 s of access to grain after a 10-s delay, and the adjusting alternative delivered 6 s of access to grain after an adjusting delay” (2001, p. 97). The adjusting delay increased (on the next trial) when it was chosen and decreased when the standard alternative was chosen. (See Mazur 2001 for other procedural details.) The relevant independent variable is TTR. The discounted value of each choice is given by Equation 8 . When the subject is indifferent does not discriminate between the two choices, V L = V S . Equating Equation 8 for the large and small choices yields

that is, an indifference curve that is a linear function relating D L and D S , with slope A L / A S > 1 and a positive intercept. The data ( Mazur 1987 ; 2001 , Figure 2 ) are consistent with this prediction, but the intercept is small.

It is also possible to look at this situation in terms of linear waiting. One assumption is necessary: that the waiting fraction, a , in Equation 1 is smaller when the upcoming reinforcer is large than when it is small ( Powell 1969 and Perone & Courtney 1992 showed this for fixed-ratio schedules; Howerton & Meltzer 1983 , for fixed-interval). Given this assumption, the linear waiting analysis is even simpler than hyperbolic discounting. The idea is that the subject will appear to be indifferent when the wait times to the two alternatives are equal. According to linear waiting, the wait time for the small alternative is given by

where b S is a small positive intercept and a S > a L . Equating the wait times for small and large alternatives yields

which is also a linear function with slope > 1 and a small positive intercept.

Equations 9 and 11 are identical in form. Thus, the linear waiting and hyperbolic discounting models are almost indistinguishable in terms of these data. However, the linear waiting approach has three potential advantages: Parameters a and b can be independently measured by making appropriate measurements in a control study that retains the reinforcement-delay properties of the self-control experiments without the choice contingency; the linear waiting approach lacks the fitted parameter k in Equation 9 ; and linear waiting also applies to a wide range of time-production experiments not covered by the hyperbolic discounting approach.

Temporal control may be involved in unsuspected ways in a wide variety of operant conditioning procedures. A renewed emphasis on the causal factors operating in reinforcement schedules may help to unify research that has hitherto been defined in terms of more abstract topics like timing and choice.

ACKNOWLEDGMENTS

We thank Catalin Buhusi and Jim Mazur for comments on an earlier version and the NIMH for research support over many years.

The first and only previous Annual Review contribution on this topic was as part of a 1965 article, “Learning, Operant Conditioning and Verbal Learning” by Blough & Millward. Since then there have been (by our estimate) seven articles on learning or learning theory in animals, six on the neurobiology of learning, and three on human learning and memory, but this is the first full Annual Review article on operant conditioning. We therefore include rather more old citations than is customary (for more on the history and philosophy of Skinnerian behaviorism, both pro and con, see Baum 1994 , Rachlin 1991 , Sidman 1960 , Staddon 2001b , and Zuriff 1985 ).

By “internal” we mean not “physiological” but “hidden.” The idea is simply that the organism's future behavior depends on variables not all of which are revealed in its current behavior (cf. Staddon 2001b , Ch. 7).

When there is no response-produced stimulus change, this procedure is also called a conjunctive fixed-ratio fixed-time schedule ( Shull 1970 ).

This idea surfaced very early in the history of research on equal-link chain fixed-interval schedules, but because of the presumed importance of conditioned reinforcement, it was the time to reinforcement from link stimulus offset, rather than onset that was thought to be important. Thus, Gollub (1977) , echoing his 1958 Ph.D. dissertation in the subsequent Kelleher & Gollub (1962) review, wrote, “In chained schedules with more than two components … the extent to which responding is sustained in the initial components … depends on the time that elapses from the end of the components to food reinforcement” (p. 291).

Interpreted as time to the first reinforcement opportunity.

It is not of course: The reciprocal of the mean IRT is the harmonic mean rate. In practice, “mean response rate” usually means arithmetic mean, but note that harmonic mean rate usually works better for choice data than the arithmetic mean (cf. Killeen 1968 ).

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Where operant conditioning went wrong, why did skinner's innovations stall.

Posted July 15, 2016 | Reviewed by Ekua Hagan

Operant conditioning is B.F. Skinner’s name for instrumental learning: learning by consequences. Not a new idea, of course. Humanity has always known how to teach children and animals by means of reward and punishment . What gave Skinner’s label the edge was his invention of a brilliant method of studying this kind of learning in individual organisms. The Skinner box and the cumulative recorder were an unbeatable duo.

Operant conditioning advanced rapidly at first. The discovery of schedules of reinforcement revealed unsuspected regularities. Each new reinforcement schedule yielded a new pattern of cumulative record: the fixed-interval “scallop" steady responding on variable interval and break-and-run on fixed-ratio schedules. The patterns were reliable and could be recovered after the organism was switched to a new procedure. The data allowed full exploitation of the within-organism experimental method: comparing the behavior of a single animal reversibly exposed to two different procedures, rather than comparing two groups of animals. Group results apply to groups; they may or may not apply to the individuals that make up a group. In 2016, 52% of Britons approved of Brexit; but each individual was either 100% for or 100% against. All too often researchers assumed that group data showing a smooth learning curve meant that individual subjects also learn gradually. They do not.

The natural next step would have been to unravel the processes behind the order revealed by cumulative records. What is going on in this interaction between the schedule procedure and the individual organism that gives rise to these striking regularities? In other words, what is the organism learning and how is it learning? What is the process?

The field did not take this step. In this note, I will try and explain why.

Three things have prevented operant conditioning from developing as a science: a limitation of the method, over-valuing order, and distrust of theory.

The cumulative record was a fantastic breakthrough in one respect: It allowed the study of the behavior of a single animal to be studied in real time. Until Skinner, the data of animal psychology consisted largely of group averages—how many animals in group X or Y turned left vs. right in the maze, for example. Not only were individual animals lost in the group, so were the actual times—how long did the rat in the maze take to decide, how fast did it run? What did it explore before deciding?

But the Skinner-box setup is also limited—to one or a few pre-defined responses and to changes in their rate of occurrence. Operant conditioning in fact involves selection from a repertoire of activities: the trial bit of trial-and-error. The Skinner-box method encourages the study of just one or two already-learned responses. Of the repertoire, that set of possible responses emitted (in Skinner’s words) “for other reasons”—of all those possible modes of behavior lurking below the threshold but available to be selected—of those covert responses, so essential to instrumental learning, there is no mention.

Too much order?

The second problem is unexamined respect for orderly data: smooth curves that might measure simple, atheoretical properties of behavior. Fred Skinner frequently quoted Pavlov: “control your conditions and you will see order.” But order in what? Is just any order worth getting? Or are some orderly results perhaps more informative than others?

The easiest way to get order, to reduce variation, is to take an average . Skinnerian experiments involve single animals, so the method discourages averaging across animals. But why not average all those pecks or lever presses? Skinner himself seemed to provide a rationale. In one of his few theoretical excursions, he proposed that responses have a strength equivalent to probability of response . He never really justified the idea, but it is so plausible that little justification seems to be required.

The next step was crucial: how to measure response probability? Rate of response is an obvious candidate. But cumulative records show that response rate varies from moment to moment on most schedules of reinforcement. On fixed-interval, for example, subjects quit responding right after each reinforcement and then slowly accelerate to a maximum as the time for the next reinforcement approaches. A fixed-interval schedule (FI) arranges that the first response after a fixed time, call it I , is reinforced. Post-reinforcement time is a reliable cue to when the next reward will be available. Organisms adapt accordingly, waiting a fixed fraction of time I before beginning to respond.

But on another schedule, variable-interval (VI), the time is variable. If it is completely random from moment to moment and the organism responds at a steady rate, post-reinforcement time gives no information about the likelihood that the next response will be rewarded. Organisms adapt to the lack of information by responding at an unvarying rate on variable-interval schedules. This property of VI made it an obvious tool. The steady response rate it produces seemed to provide a simple way to measure Skinner’s response strength. Hence, the most widely used datum in operant psychology is the response rate sustained by a VI schedule. Rate is usually measured by the number of responses that occur over a time period of minutes or hours.

skinner operant conditioning experiment procedure

Another way to reduce variability is negative feedback. A thermostatically controlled HVAC system heats when the inside temperature falls below a preset level and cools when it rises above. In this way, it reduces the variation in house temperature that would otherwise occur as outside temperature varies. Any kind of negative feedback will reduce variation in the controlled variable. Unfortunately, the more effective the feedback, the less the variation in the dependent variable and the less we can learn about the feedback mechanism itself. A perfect negative feedback process is invisible.

Operant conditioning, by definition, involves feedback since the reward received depends on responses made. The more the organism responds, the more reward it gets—subject to the constraints of whatever reinforcement schedule is in effect. This is positive feedback. But the most-studied operant choice procedure— concurrent variable-interval schedule—also involves negative feedback . When the choice is between two variable-interval schedules, the more time is spent on one choice the higher the payoff probability for switching to the other. So no matter the difference in payoff rates for the choices, the organism will never just fixate on one. The result is a very regular relation between choice preference and relative payoff—the matching law . (For the full technical story, check out Adaptive Behavior and Learning, 2016 .)

As technology advanced, these two things converged: the desire for order, enabled by averaging and negative feedback, and Skinner’s idea that response probability is an appropriate—the appropriate—dependent variable. Variable-interval schedules either singly or in two-choice situations became a kind of measuring device. The response rate on VI is steady—no waits, pauses, or sudden spikes. It seemed to offer a simple and direct way to measure response probability. From response rate as response probability to the theoretical idea of rate as somehow equivalent to response strength was but a short step. The matching law thus came to be regarded as a general principle. Researchers began to see it as underlying not just animal choice but the choice behavior of human beings in real-life situations.

Response strength is a theoretical construct. It goes well beyond response rate or indeed any other directly measurable quantity. Unfortunately, most people think they know what they mean by “strength." The Skinnerian tradition made it difficult to see that more is needed.

A landmark 1961 study by George Reynolds illustrates the problem (although George never saw it in this way). Here is a simplified version: Imagine two experimental conditions and two identical pigeons. Each condition runs for several daily sessions. In Condition A, pigeon A pecks a red key for food reward delivered on a VI 30-s schedule. In Condition B, pigeon B pecks a green key for food reward delivered on a VI 15-s schedule. Because both food rates are relatively high, after lengthy exposure to the procedure, the pigeons will be pecking at a high rate in both cases: response rates—hence "strengths" – will be roughly the same. Now change the procedure for both pigeons. Instead of a single schedule, two schedules alternate, for a minute or so each, across a one-hour experimental session. The added second schedule is the same for both pigeons: VI 15 s, signaled by a yellow key (alternating two signaled schedules in this way is called a multiple schedule). Thus, pigeon A is on a mult VI 30 VI 15 (red and yellow stimuli) and pigeon B on a mult VI 15 VI 15 (green and yellow stimuli). In summary, the two experimental conditions are (stimulus colors in ()):

Experiment A: VI 30 (Red); mult VI 30 (Red) VI 15 (Yellow)

Experiment B: VI 15 (Green); mult VI 15 (Green) VI 15 (Yellow)

Now, look at the second condition for each pigeon. Unsurprisingly, B’s response rate in green will not change. All that has changed for him is the key color—from green all the time to green and yellow alternating, both with the same payoff. But A’s response rate in red, the VI 30 stimulus, will be much depressed , and response rate in yellow for A will be considerably higher than B’s yellow response rate, even though the VI 15-s schedule is the same in both. The effect on responding in the yellow stimulus by pigeon A, an increase in response rate when a given schedule is alternated with a leaner one, is called positive behavioral contrast and the rate decrease in the leaner schedule for pigeon A is negative contrast.

Responding by And B in the presence of the red and green stimuli in the first condition is much same and so should be the strength of the two responses. But the very different effect of adding the alternative yellow stimulus, paid off on the richer schedule, on the two animals in the second condition shows that it is not.

The consensus that response rate is an adequate measure of the "strength" of an operant response is wrong. The steady rate maintained by VI schedules is misleading. It looks like a simple measure of strength. Because of Skinner’s emphasis on order, because the averaged-response and feedback-rich concurrent variable-interval schedule seemed to provide it, and because it was easy to equate response probability with response rate, the idea took root. Yet even in the 1950s, it was well known that response rate can itself be manipulated—by so-called differential-reinforcement-of-low-rate (DRL) schedules, for example.

Two factors—Skinner's single-organism method and the desire for order—conspired to give response rate a primary role in operant conditioning. Rate was assumed to be a measure of response strength. But a third factor, disdain for theory, meant that this linkage was never much scrutinized. It is of course false: response rate does not equal response strength. Indeed, the strength concept is itself ill-defined. Hence, the field’s emphasis on response rate as the dependent variable is probably a mistake. If the strength idea is to survive the demise of rate as its best measure, something more is needed: a theory about the factors that control an operant response. But because Skinner had successfully proclaimed that theories of learning are not necessary , an adequate theory was not forthcoming for many years (see The New Behaviorism, 2014, for more on the history of Skinnerian theory).

John Staddon, Ph.D. , is James B. Duke Professor of Psychology, and Professor of Biology and Neurobiology, Emeritus at Duke University.

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Skinner’s theory on Operant Conditioning

After the retirement of John B. Watson from the world of Academic psychology, psychologists and behaviorists were eager to propose new forms of learning other than the classical conditioning. The most important among these theories was Operant Conditioning proposed by Burrhus Frederic Skinner, commonly known as B.F. Skinner.

Skinner based his theory in the simple fact that the study of observable behavior is much simpler than trying to study internal mental events. Skinner’s works concluded a study far less extreme than those of Watson (1913), and it deemed classical conditioning as too simplistic of a theory to be a complete explanation of complex human behavior.

B.F. Skinner is famous for his pioneering research in the field of learning and behavior. He proposed the theory to study complex human behavior by studying the voluntary responses shown by an organism when placed in the certain environment. He named these behaviors or responses as operant. He is also called the father of Operant Conditioning Learning, but he based his theory known as “Law of Effect”, discovered by Edward Thorndike in 1905.

Operant Conditioning Learning

B.F. Skinner proposed his theory on operant conditioning by conducting various experiments on animals. He used a special box known as “Skinner Box” for his experiment on rats.

As the first step to his experiment, he placed a hungry rat inside the Skinner box. The rat was initially inactive inside the box, but gradually as it began to adapt to the environment of the box, it began to explore around. Eventually, the rat discovered a lever, upon pressing which; food was released inside the box. After it filled its hunger, it started exploring the box again, and after a while it pressed the lever for the second time as it grew hungry again. This phenomenon continued for the third, fourth and the fifth time, and after a while, the hungry rat immediately pressed the lever once it was placed in the box. Then the conditioning was deemed to be complete.

Here, the action of pressing the lever is an operant response/behavior, and the food released inside the chamber is the reward. The experiment is also known as Instrumental Conditioning Learning as the response is instrumental in getting food.

This experiment also deals with and explains the effects of positive reinforcement. Upon pressing the lever, the hungry rat was served with food, which filled its hunger; hence, it’s a positive reinforcement.

B.F. Skinner’s Second Experiment

B.F. Skinner also conducted an experiment that explained negative reinforcement. Skinner placed a rat in a chamber in the similar manner, but instead of keeping it hungry, he subjected the chamber to an unpleasant electric current. The rat having experienced the discomfort started to desperately move around the box and accidentally knocked the lever. Pressing of the lever immediately seized the flow of unpleasant current. After a few times, the rat had smartened enough to go directly to the lever in order to prevent itself from the discomfort.

The electric current reacted as the negative reinforcement, and the consequence of escaping the electric current made sure that the rat repeated the action again and again. Here too, the pressing of the lever is an operant response, and the complete stop of the electric current flow is its reward.

Both the experiment clearly explains the working of operant conditioning. The important part in any operant conditioning learning is to recognize the operant behavior and the consequence resulted in that particular environment.

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Operant conditioning occurs when an association is made between a particular behavior and a consequence for that behavior. This association is built upon the use of reinforcement and/or punishment to encourage or discourage behavior. Operant conditioning was first defined and studied by behavioral psychologist B.F. Skinner, who conducted several well-known operant conditioning experiments with animal subjects.

Key Takeaways: Operant Conditioning

Operant conditioning is the process of learning through reinforcement and punishment.
In operant conditioning, behaviors are strengthened or weakened based on the consequences of that behavior.
Operant conditioning was defined and studied by behavioral psychologist B.F. Skinner.

B.F. Skinner was a behaviorist , which means he believed that psychology should be limited to the study of observable behaviors. While other behaviorists, like John B. Watson, focused on classical conditioning, Skinner was more interested in the learning that happened through operant conditioning.

He observed that in classical conditioning responses tend to be triggered by innate reflexes that occur automatically. He called this kind of behavior respondent . He distinguished respondent behavior from operant behavior . Operant behavior was the term Skinner used to describe a behavior that is reinforced by the consequences that follow it. Those consequences play an important role in whether or not a behavior is performed again.

Skinner’s ideas were based on Edward Thorndike’s law of effect, which stated that behavior that elicits positive consequences will probably be repeated, while behavior that elicits negative consequences will probably not be repeated. Skinner introduced the concept of reinforcement into Thorndike’s ideas, specifying that behavior that is reinforced will probably be repeated (or strengthened).

To study operant conditioning, Skinner conducted experiments using a “Skinner Box,” a small box that had a lever at one end that would provide food or water when pressed. An animal, like a pigeon or rat, was placed in the box where it was free to move around. Eventually the animal would press the lever and be rewarded. Skinner found that this process resulted in the animal pressing the lever more frequently. Skinner would measure learning by tracking the rate of the animal’s responses when those responses were reinforced.

Reinforcement and Punishment

Through his experiments, Skinner identified the different kinds of reinforcement and punishment that encourage or discourage behavior.

Reinforcement

Reinforcement that closely follows a behavior will encourage and strengthen that behavior. There are two types of reinforcement:

Positive reinforcement occurs when a behavior results in a favorable outcome, e.g. a dog receiving a treat after obeying a command, or a student receiving a compliment from the teacher after behaving well in class. These techniques increase the likelihood that the individual will repeat the desired behavior in order to receive the reward again.
Negative reinforcement occurs when a behavior results in the removal of an unfavorable experience, e.g. an experimenter ceasing to give a monkey electric shocks when the monkey presses a certain lever. In this case, the lever-pressing behavior is reinforced because the monkey will want to remove the unfavorable electric shocks again.

In addition, Skinner identified two different kinds of reinforcers.

Primary reinforcers naturally reinforce behavior because they are innately desirable, e.g. food.
Conditioned reinforcers reinforce behavior not because they are innately desirable, but because we learn to associate them with primary reinforcers. For example, Paper money is not innately desirable, but it can be used to acquire innately desirable goods, such as food and shelter.

Punishment is the opposite of reinforcement. When punishment follows a behavior, it discourages and weakens that behavior. There are two kinds of punishment.

Positive punishment (or punishment by application) occurs when a behavior is followed by an unfavorable outcome, e.g. a parent spanking a child after the child uses a curse word.
Negative punishment (or punishment by removal) occurs when a behavior leads to the removal of something favorable, e.g. a parent who denies a child their weekly allowance because the child has misbehaved.

Although punishment is still widely used, Skinner and many other researchers found that punishment is not always effective. Punishment can suppress a behavior for a time, but the undesired behavior tends to come back in the long run. Punishment can also have unwanted side effects. For example, a child who is punished by a teacher may become uncertain and fearful because they don’t know exactly what to do to avoid future punishments.

Instead of punishment, Skinner and others suggested reinforcing desired behaviors and ignoring unwanted behaviors. Reinforcement tells an individual what behavior is desired, while punishment only tells the individual what behavior isn’t desired.

Behavior Shaping

Operant conditioning can lead to increasingly complex behaviors through shaping , also referred to as the “method of approximations.” Shaping happens in a step-by-step fashion as each part of a more intricate behavior is reinforced. Shaping starts by reinforcing the first part of the behavior. Once that piece of the behavior is mastered, reinforcement only happens when the second part of the behavior occurs. This pattern of reinforcement is continued until the entire behavior is mastered.

For example, when a child is taught to swim, she may initially be praised just for getting in the water. She is praised again when she learns to kick, and again when she learns specific arm strokes. Finally, she is praised for propelling herself through the water by performing a specific stroke and kicking at the same time. Through this process, an entire behavior has been shaped.

Schedules of Reinforcement

In the real world, behavior is not constantly reinforced. Skinner found that the frequency of reinforcement can impact how quickly and how successfully one learns a new behavior. He specified several reinforcement schedules, each with different timing and frequencies.

Continuous reinforcement occurs when a particular response follows each and every performance of a given behavior. Learning happens rapidly with continuous reinforcement. However, if reinforcement is stopped, the behavior will quickly decline and ultimately stop altogether, which is referred to as extinction.
Fixed-ratio schedules reward behavior after a specified number of responses. For example, a child may get a star after every fifth chore they complete. On this schedule, the response rate slows right after the reward is delivered.
Variable-ratio schedules vary the number of behaviors required to get a reward. This schedule leads to a high rate of responses and is also hard to extinguish because its variability maintains the behavior. Slot machines use this kind of reinforcement schedule.
Fixed-interval schedules provide a reward after a specific amount of time passes. Getting paid by the hour is one example of this kind of reinforcement schedule. Much like the fixed-ratio schedule, the response rate increases as the reward approaches but slows down right after the reward is received.
Variable-interval schedules vary the amount of time between rewards. For example, a child who receives an allowance at various times during the week as long as they’ve exhibited some positive behaviors is on a variable-interval schedule. The child will continue to exhibit positive behavior in anticipation of eventually receiving their allowance.

Examples of Operant Conditioning

If you’ve ever trained a pet or taught a child, you have likely used operant conditioning in your own life. Operant conditioning is still frequently used in various real-world circumstances, including in the classroom and in therapeutic settings.

For example, a teacher might reinforce students doing their homework regularly by periodically giving pop quizzes that ask questions similar to recent homework assignments. Also, if a child throws a temper tantrum to get attention, the parent can ignore the behavior and then acknowledge the child again once the tantrum has ended.

Operant conditioning is also used in behavior modification , an approach to the treatment of numerous issues in adults and children, including phobias, anxiety, bedwetting, and many others. One way behavior modification can be implemented is through a token economy , in which desired behaviors are reinforced by tokens in the form of digital badges, buttons, chips, stickers, or other objects. Eventually these tokens can be exchanged for real rewards.

While operant conditioning can explain many behaviors and is still widely used, there are several criticisms of the process. First, operant conditioning is accused of being an incomplete explanation for learning because it neglects the role of biological and cognitive elements.

In addition, operant conditioning is reliant upon an authority figure to reinforce behavior and ignores the role of curiosity and an individual's ability to make his or her own discoveries. Critics object to operant conditioning's emphasis on controlling and manipulating behavior, arguing that they can lead to authoritarian practices. Skinner believed that environments naturally control behavior, however, and that people can choose to use that knowledge for good or ill.

Finally, because Skinner’s observations about operant conditioning relied on experiments with animals, he is criticized for extrapolating from his animal studies to make predictions about human behavior. Some psychologists believe this kind of generalization is flawed because humans and non-human animals are physically and cognitively different.

Cherry, Kendra. “What is Operant Conditioning and How Does it Work?” Verywell Mind , 2 October 2018. https://www.verywellmind.com/operant-conditioning-a2-2794863
Crain, William. Theories of Development: Concepts and Applications. 5th ed., Pearson Prentice Hall. 2005.
Goldman, Jason G. “What is Operant Conditioning? (And How Does It Explain Driving Dogs?)” Scientific American , 13 December 2012. https://blogs.scientificamerican.com/thoughtful-animal/what-is-operant-conditioning-and-how-does-it-explain-driving-dogs/
McLeod, Saul. “Skinner – Operant Conditioning.” Simply Psychology , 21 January 2018. https://www.simplypsychology.org/operant-conditioning.html#class
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Operant Conditioning – 4 Interesting Experiments by B.F. Skinner

Operant conditioning might sound like something out of a dystopian novel. But it’s not. It’s a very real thing that was forged by a brilliant, yet quirky, psychologist. Today, we will take a quick look at his work as we as a few odd experiments that went with it…

There are few names in psychology more well-known than B. F. Skinner. First-year psychology students scribble endless lecture notes on him. Doctoral candidates cite his work in their dissertations as they test whether a rat’s behavior can be used to predict behavior in humans.

Skinner is one of the most well-known psychologists of our time that was famous for his experiments on operant conditioning. But how did he become such a central figure of these Intro to Psych courses? And, how did he develop his theories and methodologies cited by those sleep-deprived Ph.D. students?

THE FATHER OF OPERANT CONDITIONING

Skinner spent his life studying the way we behave and act. But, more importantly, how this behavior can be modified.

He viewed Ivan Pavlov’s classical model of behavioral conditioning as being “too simplistic a solution” to fully explain the complexities of human (and animal) behavior and learning. It was because of this, that Skinner started to look for a better way to explain why we do things.

His early work was based on Edward Thorndike’s 1989 Law of Effect . Skinner went on to expand on the idea that most of our behavior is directly related to the consequences of said behavior. His expanded model of behavioral learning would be called operant conditioning. This centered around two things…

The concepts of behaviors – the actions an organism or test subject exhibits
The operants – the environmental response/consequences directly following the behavior

But, it’s important to note that the term “consequences” can be misleading. This is because there doesn’t need to be a causal relationship between the behavior and the operant. Skinner broke these responses down into three parts.

1. REINFORCERS – These give the organism a desirable stimulus and serve to increase the frequency of the behavior.

2. PUNISHERS – These are environmental responses that present an undesirable stimulus and serve to reduce the frequency of the behavior.

3. NEUTRAL OPERANTS – As the name suggests, these present stimuli that neither increase nor decrease the tested behavior.

Throughout his long and storied career, Skinner performed a number of strange experiments trying to test the limits of how punishment and reinforcement affect behavior.

4 INTERESTING OPERANT EXPERIMENTS

Though Skinner was a professional through and through, he was also quite a quirky person. And, his unique ways of thinking are very clear in the strange and interesting experiments he performed while researching the properties of operant conditioning.

Experiment #1: The Operant Conditioning Chamber

The Operant Conditioning Chamber, better known as the Skinner Box , is a device that B.F. Skinner used in many of his experiments. At its most basic, the Skinner Box is a chamber where a test subject, such as a rat or a pigeon, must ‘learn’ the desired behavior through trial and error.

B.F. Skinner used this device for several different experiments. One such experiment involves placing a hungry rat into a chamber with a lever and a slot where food is dispensed when the lever is pressed. Another variation involves placing a rat into an enclosure that is wired with a slight electric current on the floor. When the current is turned on, the rat must turn a wheel in order to turn off the current.

Though this is the most basic experiment in operant conditioning research, there is an infinite number of variations that can be created based on this simple idea.

Experiment #2: A Pigeon That Can Read

Building on the basic ideas from his work with the Operant Conditioning Chamber, B. F. Skinner eventually began designing more and more complex experiments.

One of these experiments involved teaching a pigeon to read words presented to it in order to receive food. Skinner began by teaching the pigeon a simple task, namely, pecking a colored disk, in order to receive a reward. He then began adding additional environmental cues (in this case, they were words), which were paired with a specific behavior that was required in order to receive the reward.

Through this evolving process, Skinner was able to teach the pigeon to ‘read’ and respond to several unique commands.

Though the pigeon can’t actually read English, the fact that he was able to teach a bird multiple behaviors, each one linked to a specific stimulus, by using operant conditioning shows us that this form of behavioral learning can be a powerful tool for teaching both animals and humans complex behaviors based on environmental cues.

Experiment #3: Pigeon Ping-Pong

But Skinner wasn’t only concerned with teaching pigeons how to read. It seems he also made sure they had time to play games as well. In one of his more whimsical experiments , B. F. Skinner taught a pair of common pigeons how to play a simplified version of table tennis.

The pigeons in this experiment were placed on either side of a box and were taught to peck the ball to the other bird’s side. If a pigeon was able to peck the ball across the table and past their opponent, they were rewarded with a small amount of food. This reward served to reinforce the behavior of pecking the ball past their opponent.

Though this may seem like a silly task to teach a bird, the ping-pong experiment shows that operant conditioning can be used not only for a specific, robot-like action but also to teach dynamic, goal-based behaviors.

Experiment #4: Pigeon-Guided Missiles

Thought pigeons playing ping-pong was as strange as things could get? Skinner pushed the envelope even further with his work on pigeon-guided missiles.

While this may sound like the crazy experiment of a deluded mad scientist, B. F. Skinner did actually do work to train pigeons to control the flight paths of missiles for the U.S. Army during the second world war.

Skinner began by training the pigeons to peck at shapes on a screen. Once the pigeons reliably tracked these shapes, Skinner was able to use sensors to track whether the pigeon’s beak was in the center of the screen, to one side or the other, or towards the top or bottom of the screen. Based on the relative location of the pigeon’s beak, the tracking system could direct the missile towards the target location.

Though the system was never used in the field due in part to advances in other scientific areas, it highlights the unique applications that can be created using operant training for animal behaviors.

THE CONTINUED IMPACT OF OPERANT CONDITIONING

B. F. Skinner is one of the most recognizable names in modern psychology, and with good reason. Though many of his experiments seem outlandish, the science behind them continues to impact us in ways we rarely think about.

The most prominent example is in the way we train animals for tasks such as search and rescue, companion services for the blind and disabled, and even how we train our furry friends at home—but the benefits of his research go far beyond teaching Fido how to roll over.

Operant conditioning research has found its way into the way schools motivate and discipline students, how prisons rehabilitate inmates, and even in how governments handle geopolitical relationships .

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Operant Conditioning in Psychology

Why being rewarded or punished affects how you behave

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Behavior Types
Reinforcement
Reinforcement Schedules

We all engage in behaviors that we know will lead to good outcomes. We also tend to avoid behaviors that we know will have negative consequences. So it makes sense that being rewarded for something makes you want to do it more often (and being punished makes you want to do it less). This type of learning is what the behavioral psychologist B.F. Skinner dubbed it 'operant conditioning,' and it can have a powerful effect on your everyday behavior.

Operant conditioning, sometimes referred to as instrumental conditioning , is a learning method that employs rewards and punishments for behavior. Through operant conditioning, an association is made between a behavior and a consequence (whether negative or positive) for that behavior.

For example, when lab rats press a lever when a green light is on, they receive a food pellet as a reward. When they press the lever when a red light is on, they receive a mild electric shock. As a result, they learn to press the lever when the green light is on and avoid the red light.

However, operant conditioning is not just something that takes place in experimental settings while training lab animals. It also plays a powerful role in everyday learning. Reinforcement and punishment take place in natural settings all the time, as well as in more structured settings such as classrooms or therapy sessions.

Keep reading to learn more about the origins of operant conditioning, how the process works, and examples of how it can be used to teach, modify, and shape your actions.

The History of Operant Conditioning

Operant conditioning was first described by behaviorist B.F. Skinner , which is why you may occasionally hear it referred to as Skinnerian conditioning. As a behaviorist, Skinner believed that it was not really necessary to look at internal thoughts and motivations in order to explain behavior. Instead, he suggested, we should look only at the external, observable causes of human behavior.

Watson's Influence

Through the first part of the 20th century, behaviorism became a major force within psychology. The ideas of John B. Watson dominated this school of thought early on. Watson focused on the principles of classical conditioning , once famously suggesting that he could take any person regardless of their background and train them to be anything he chose.

Early behaviorists focused their interests on associative learning. Skinner was more interested in how the consequences of people's actions influenced their behavior.

Skinner used the term operant to refer to any "active behavior that operates upon the environment to generate consequences." Skinner's theory explained how we acquire the range of learned behaviors we exhibit every day.

Thorndike's Influence

His theory was heavily influenced by the work of psychologist Edward Thorndike , who had proposed what he called the law of effect . According to this principle, actions that are followed by desirable outcomes are more likely to be repeated while those followed by undesirable outcomes are less likely to be repeated.

Operant conditioning relies on a fairly simple premise: Actions that are followed by reinforcement will be strengthened and more likely to occur again in the future. If you tell a funny story in class and everybody laughs, you will probably be more likely to tell that story again in the future.

How It Works

If you raise your hand to ask a question and your teacher praises your polite behavior, you will be more likely to raise your hand the next time you have a question or comment. Because the behavior was followed by reinforcement, or a desirable outcome, the preceding action is strengthened.

Conversely, actions that result in punishment or undesirable consequences will be weakened and less likely to occur again in the future. If you tell the same story again in another class but nobody laughs this time, you will be less likely to repeat the story again in the future. If you shout out an answer in class and your teacher scolds you, then you might be less likely to interrupt the class again.

Respondent vs. Operant Behaviors

Skinner distinguished between two different types of behaviors

Respondent behaviors are those that occur automatically and reflexively, such as pulling your hand back from a hot stove or jerking your leg when the doctor taps on your knee. You don't have to learn these behaviors. They simply occur automatically and involuntarily.
Operant behaviors , on the other hand, are those under our conscious control. Some may occur spontaneously and others purposely, but it is the consequences of these actions that then influence whether or not they occur again in the future. Our actions on the environment and the consequences of that action make up an important part of the learning process .

While classical conditioning could account for respondent behaviors, Skinner realized that it could not account for a great deal of learning. Instead, Skinner suggested that operant conditioning held far greater importance.

Skinner invented different devices during his boyhood and he put these skills to work during his studies on operant conditioning. He created a device known as an operant conditioning chamber, often referred to today as a Skinner box . The chamber could hold a small animal, such as a rat or pigeon. The box also contained a bar or key that the animal could press in order to receive a reward.

In order to track responses, Skinner also developed a device known as a cumulative recorder. The device recorded responses as an upward movement of a line so that response rates could be read by looking at the slope of the line.

Components of Operant Conditioning

Several key concepts in operant conditioning exist. The type of reinforcement or punishment used can affect how the individual responds and the effect of conditioning. Four types of operant conditioning can be utilized to change behavior: positive reinforcement, negative reinforcement, positive punishment, and negative punishment.

Reinforcement in Operant Conditioning

Reinforcement is any event that strengthens or increases the behavior it follows. There are two kinds of reinforcers. In both of these cases of reinforcement, the behavior increases.

Positive Reinforcement

Positive reinforcers are favorable events or outcomes that are presented after the behavior. In positive reinforcement situations, a response or behavior is strengthened by the addition of praise or a direct reward. If you do a good job at work and your manager gives you a bonus, that bonus is a positive reinforcer.

Negative Reinforcement

Negative reinforcers involve the removal of an unfavorable events or outcomes after the display of a behavior. In these situations, a response is strengthened by the removal of something considered unpleasant. For example, if your child starts to scream in the middle of a restaurant, but stops once you hand them a treat, your action led to the removal of the unpleasant condition, negatively reinforcing your behavior (not your child's).

Punishment in Operant Conditioning

Punishment is the presentation of an adverse event or outcome that causes a decrease in the behavior it follows. There are two kinds of punishment. In both of these cases, the behavior decreases.

Positive Punishment

Positive punishment , sometimes called punishment by application, presents an unfavorable event or outcome to weaken the response it elicits.

It sounds like an oxymoron, but in this instance, positive doesn't mean 'good.' Instead, it suggests that something is added to the situation to act as a punisher. Spanking for misbehavior is an example of punishment by application.

Negative Punishment

Negative punishment , also known as punishment by removal, occurs when a favorable event or outcome is removed after a behavior occurs. Taking away a child's video game following misbehavior is an example of negative punishment.

The five principles of operant conditioning are positive reinforcement, negative reinforcement, positive punishment, negative punishment, and extinction. Extinction occurs when a response is no longer reinforced or punished, which can lead to the fading and disappearance of the behavior.

Operant Conditioning Reinforcement Schedules

Reinforcement is not necessarily a straightforward process, and there are a number of factors that can influence how quickly and how well new things are learned. Skinner found that when and how often behaviors were reinforced played a role in the speed and strength of acquisition .

In other words, the timing and frequency of reinforcement influenced how new behaviors were learned and how old behaviors were modified.

Skinner identified several different schedules of reinforcement that impact the operant conditioning process:

Continuous Reinforcement

Continuous reinforcement involves delivering a reinforcement every time a response occurs. Learning tends to occur relatively quickly, yet the response rate is quite low. Extinction also occurs very quickly once reinforcement is halted.

Partial Reinforcement

Once a behavior has been established, it is usually best to transition to a partial reinforcement schedule. In this type of schedule, behaviors are only reinforced sometimes. This can be based on the number of responses that have occurred or how much time has elapsed.

Fixed-ratio schedules are a type of partial reinforcement. Responses are reinforced only after a specific number of responses have occurred, typically leading to a fairly steady response rate.
Fixed-interval schedules are another form of partial reinforcement. Reinforcement occurs only after a certain interval of time has elapsed. Response rates remain fairly steady and increase as the reinforcement time draws near but slow immediately after the reinforcement has been delivered.
Variable-ratio schedules are also a type of partial reinforcement that involves reinforcing behavior after a varied number of responses. This leads to both a high response rate and slow extinction rates.
Variable-interval schedules are the final form of partial reinforcement Skinner described. This schedule involves delivering reinforcement after a variable amount of time has elapsed. This also tends to lead to a fast response rate and slow extinction rate.

Examples of Operant Conditioning

Whether you were aware of it or not, you have learned something through operant conditioning. You may have even used it yourself without even being aware of it.

We can find examples of operant conditioning at work all around us. Consider the case of children completing homework to earn a reward from a parent or teacher, or employees finishing projects to receive praise or promotions. More examples of operant conditioning in action include:

After performing in a community theater play, you receive applause from the audience. This acts as a positive reinforcer , inspiring you to try out for more performance roles.
You train your dog to fetch by offering him praise and a pat on the head whenever he performs the behavior correctly. This is another positive reinforcer .
A professor tells students that if they have perfect attendance all semester, then they do not have to take the final comprehensive exam. By removing an unpleasant stimulus (the final test), students are negatively reinforced to attend class regularly.
If you fail to hand in a project on time, your boss becomes angry and berates your performance in front of your co-workers. This acts as a positive punisher , making it less likely that you will finish projects late in the future.
A teen girl does not clean up her room as she was asked, so her parents take away her phone for the rest of the day. This is an example of a negative punishment in which a positive stimulus is taken away.

In some of these examples, the promise or possibility of rewards causes an increase in behavior.

Operant conditioning can also be used to decrease a behavior via the removal of a desirable outcome or the application of a negative outcome. For example, a child may be told they will lose recess privileges if they talk out of turn in class. This potential for punishment may lead to a decrease in disruptive behaviors.

While behaviorism may have lost much of the dominance it held during the early part of the 20th century, operant conditioning remains an important and often used tool in the learning and behavior modification process. Sometimes, natural consequences lead to changes in our behavior. In other instances, rewards and punishments may be consciously doled out to create a change.

Operant conditioning is something you may immediately recognize in your own life, whether in your approach to teaching your children good behavior or training the family dog.

Remember that any type of learning takes time. Consider the type of reinforcement or punishment that may work best for your unique situation and assess which type of reinforcement schedule might lead to the best results.

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Athalye VR, Santos FJ, Carmena JM, Costa RM. Evidence for a neural law of effect . Science . 2018;359(6379):1024-1029. doi:10.1126/science.aao6058

Johnson AR, Christensen BA, Kelly SJ, Calipari ES. The influence of reinforcement schedule on experience-dependent changes in motivation . J Exp Anal Behav . 2022;117(3):320-330. doi:10.1002/jeab.755

Hulac D, Benson N, Nesmith M, Wollersheim S. Using variable interval reinforcement schedules to support students in the classroom: An introduction with illustrative examples . J Educ Res Prac. 2016;16(1):90-96. doi:10.5590/JERAP.2016.06.1.06

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Domjan, M. The Principles of Learning and Behavior, Seventh Edition. Cengage Learning, 2015.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

IMAGES

Skinner box or operant conditioning chamber experiment outline diagram
How a Skinner Box Is Used to Research Operant Conditioning
Skinner's Box Experiment
Skinner Coined the Term Operant to Describe
Operant conditioning approach by Skinner
Skinner's Operant Conditioning| Skinner's Rat Experiment |Reinforcement Schedules|

VIDEO

Principles of Skinner Operant Conditioning Learning Theory 09-04-2024 Part-2 by K.S.Jameel for CTET
Skinner operant conditioning theory|Ctet|Net
Operant Conditioning with B. F Skinner #operantconditioning #skinner #psychology
Skinner's Operant Conditioning Theory/Instrumental Conditioning/For all Teaching Exams
B. F. Skinner: Behavioral Psychology and Operant Conditioning
Understanding Operant Conditioning: The Skinner Box #facts #science #experiment

COMMENTS

Skinner's Box Experiment (Behaviorism Study)
Inspired by Thorndike, Skinner created a box to test his theory of Operant Conditioning. (This box is also known as an "operant conditioning chamber.") The box was typically very simple. Skinner would place the rats in a Skinner box with neutral stimulants (that produced neither reinforcement nor punishment) and a lever that would dispense ...
Operant Conditioning In Psychology: B.F. Skinner Theory
Operant conditioning is a method of learning that occurs through rewards and punishments for behavior. Through operant conditioning, an individual makes an association between a particular behavior and a consequence. B.F Skinner is regarded as the father of operant conditioning and introduced a new term to behavioral psychology, reinforcement.
Operant Conditioning
The term operant conditioning 1 was coined by B. F. Skinner in 1937 in the context of reflex physiology, to differentiate what he was interested in—behavior that affects the environment—from the reflex-related subject matter of the Pavlovians. The term was novel, but its referent was not entirely new.
Where Operant Conditioning Went Wrong
Two factors—Skinner's single-organism method and the desire for order—conspired to give response rate a primary role in operant conditioning. Rate was assumed to be a measure of response strength.
Skinner's theory on Operant Conditioning
He named these behaviors or responses as operant. He is also called the father of Operant Conditioning Learning, but he based his theory known as "Law of Effect", discovered by Edward Thorndike in 1905. Operant Conditioning Learning. B.F. Skinner proposed his theory on operant conditioning by conducting various experiments on animals.
What Is Operant Conditioning? Definition and Examples
To study operant conditioning, Skinner conducted experiments using a "Skinner Box," a small box that had a lever at one end that would provide food or water when pressed. An animal, like a pigeon or rat, was placed in the box where it was free to move around. Eventually the animal would press the lever and be rewarded.
Operant Conditioning
And, his unique ways of thinking are very clear in the strange and interesting experiments he performed while researching the properties of operant conditioning. Experiment #1: The Operant Conditioning Chamber. The Operant Conditioning Chamber, better known as the Skinner Box, is a device that B.F. Skinner used in many of his experiments. At ...
PDF Skinner's Operant Conditioning: Lesson Plan
Skinner's Operant Conditioning: Lesson Plan Topic Operant conditioning is a process of learning that encourages some behaviors and discourages others depending on whether rewards or punishments are given for that behavior. Here are the four main ways in which operant conditioning is used: 1) positive
(PDF) Operant Conditioning
Operant conditioning, a term coined by B. F. Skinner (1937), has several shades of meaning. It is both an experimental procedure and a behavioral process.
Operant Conditioning: What It Is, How It Works, and Examples
The History of Operant Conditioning . Operant conditioning was first described by behaviorist B.F. Skinner, which is why you may occasionally hear it referred to as Skinnerian conditioning. As a behaviorist, Skinner believed that it was not really necessary to look at internal thoughts and motivations in order to explain behavior.

Skinner’s Box Experiment (Behaviorism Study)

What Is Skinner's Box?

Who is B.F. Skinner?

Law of Effect (Thorndike vs. Skinner)

Why Do We Call This Box the "Skinner Box?"

How Skinner's Box Worked

About Reinforcements

Positive vs. Negative Reinforcements

About Punishments

Schedules of Reinforcement

Continuous Reinforcement

Fixed-Ratio Reinforcement

Fixed-Interval Reinforcement

Variable-Ratio Reinforcement

Variable-Interval Reinforcement

Skinner's Box and Pigeon Pilots in World War II

Examples of Operant Conditioning in Everyday Life

Operant Conditioning Is Everywhere!

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Operant Conditioning: What It Is, How It Works, and Examples

How It Works

Positive Reinforcement

Negative Reinforcement

Examples of Operant Conditioning

Skinner’s Pigeon Experiment

The Experiment:

Observation:

Superstitious Behavior:

Conclusion:

Schedules of Reinforcement

(A) Continuous Reinforcement

(B) Fixed Ratio Reinforcement

(C) Fixed Interval Reinforcement

(D) Variable Ratio Reinforcement

(E) Variable Interval Reinforcement

Applications In Psychology

Token Economy

Behavior Shaping

2. Educational Applications

Operant Conditioning vs. Classical Conditioning

Learning Process

Timing of Stimulus & Response

Critical Evaluation

Frequently Asked Questions

How does operant conditioning differ from classical conditioning?

How does operant conditioning relate to social learning theory?

What are the downsides of operant conditioning?

What is an application of bf skinner’s operant conditioning theory?

Further Reading

O perant C onditioning

INTRODUCTION

INTERVAL TIMING

WEBER'S LAW, PROPORTIONAL TIMING AND TIMESCALE INVARIANCE

Cognitive and Behavioral Approaches to Timing

The Gap Experiment

Temporal Dynamics: Linear Waiting

Chain Schedules

Concurrent Schedules

Concurrent-Chain Schedules

Self-Control

ACKNOWLEDGMENTS

LITERATURE CITED

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