Where is operant conditioning used

Skinner's study of behavior in rats was conducted under carefully controlled laboratory conditions. 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. In the Skinner study, because food followed a particular behavior the rats learned to repeat that behavior, e.

Therefore research e. 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 in order 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. Operant conditioning can be used to explain a wide variety of behaviors, from the process of learning, to addiction and language acquisition.

It also has practical application such as token economy which can be applied in classrooms, prisons and psychiatric hospitals. 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 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, 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 is different from humans, and they cannot think about their experiences and invoke reason, patience, memory or self-comfort.

McLeod, S. Skinner - operant conditioning. Simply Psychology. Bandura, A. Social learning theory. Ferster, C. Schedules of reinforcement. New York: Appleton-Century-Crofts.

Kohler, W. The mentality of apes. Skinner, B. The behavior of organisms: An experimental analysis. New York: Appleton-Century. Superstition' in the pigeon. Journal of Experimental Psychology, 38 , How to teach animals. Science and human behavior.

Thorndike, E. Animal intelligence: An experimental study of the associative processes in animals. Psychological Monographs: General and Applied, 2 4 , i Watson, J. Psychology as the behaviorist views it. Psychological Review, 20 , — Presenting the subject with something that it likes.

Toggle navigation. Note: It is not always easy to distinguish between punishment and negative reinforcement. Download this article as a PDF. How to reference this article: How to reference this article: McLeod, S. The psychiatric nurse as a behavioral engineer.

Journal of the Experimental Analysis of behavior, 2 4 , This operant conditioning example is more relevant to adults. Notice how adults are motivated by their paychecks to go to work every day.

The prospect of getting rewards motivate employees to perform their best. The same technique is used to teach young children how to behave and tidy up their rooms.

While most children are not eager to chores, giving them an incentive or allowance may encourage them to do so. Most parents unintentionally use operant conditioning while raising kids. Offering a young child an incentive to mow the lawn or tidy up their toys each night might help inculcate positive values. On the other hand, punishing children by limiting their TV time or taking away their video games might also help achieve the same results.

Most pet owners train their canine pals by offering them treats to encourage positive behavior. Doggie treats and toys are all excellent ways of enforcing positive behavior. On the other hand, pet owners can also use other methods to discourage dogs from bad behavior if they fail to follow instructions.

The same analogy may also be used to toilet train pets at home, making it one of the most common operant conditioning examples.

Ever wonder why some doctors keep a candy jar in their office for kids? Dentists too offer kids a lollipop in exchange for their good behavior. This is a classic example of positive reinforcement and how giving rewards can help us gain desired results. A class teacher may punish a child by giving them a time-out for hitting other students or for misbehaving in class.

Similarly, some teachers also punish children by giving them detention or extra homework. Doing so prevents the child from misbehaving in class for fear of being punished. Moreover, automation meant that the same animal could be run for many days, an hour or two a day, on the same procedure until the pattern of behavior stabilized.

The reinforcement schedules most frequently used today are ratio schedules and interval schedules. In interval schedules the first response after an unsignaled predetermined interval has elapsed, is rewarded. The interval duration can be fixed say, 30 seconds; FI30 or randomly drawn from a distribution with a given mean or the sequence of intervals can be determined by a rule -- ascending, descending or varying periodically, for example. If the generating distribution is the memoryless exponential distribution, the schedule is called a random interval RI , otherwise it is a variable interval VI schedule.

The first interval in an experimental session is timed from the start of the session, and subsequent intervals are timed from the previous reward. In ratio schedules reinforcement is given after a predefined number of actions have been emitted. The required number of responses can be fixed FR or drawn randomly from some distribution VR; or RR if drawn from a Geometric distribution. Schedules are often labeled by their type and the schedule parameter the mean length of the interval or the mean ratio requirement.

For instance, an RI30 schedule is a random interval schedule with the exponential waiting time having a mean of 30 seconds, and an FR5 schedule is a ratio schedule requiring a fixed number of five responses per reward. Researchers soon found that stable or steady-state behavior under a given schedule is reversible; that is, the animal can be trained successively on a series of procedures — FR5, FI10, FI20, FR5,… — and, usually, behavior on the second exposure to FR5 will be the same as on the first.

The apparently lawful relations to be found between steady-state response rates and reinforcement rates soon led to the dominance of the so-called molar approach to operant conditioning. Molar independent and dependent variables are rates, measured over intervals of a few minutes to hours the time denominator varies.

In contrast, the molecular approach — looking at behavior as it occurs in real time, has been rather neglected, even though the ability to store and analyze any quantity of anything and everything that can be recorded makes this approach much more feasible now than it was 40 years ago. The most well-known molar relationship is the matching law, first stated by Richard Herrnstein in For instance when one lever is reinforced on an RI30 schedule, while the other is reinforced on an RI15 schedule, rats will press the latter lever roughly twice as fast as they will press the first lever.

Although postulated as a general law relating response rate and reinforcement rate, it turned out that the matching relationship is actually far from being universally true. In fact, the matching relationship can be seen as a result of the negative-feedback properties of the choice situation concurrent variable-interval schedule in which it is measured. Because the probability a given response will be reinforced on a VI schedule declines the more responses are made — and increases with time away from the schedule — almost any reward-following process yields matching on concurrent VI VI schedules.

Hence matching by itself tells us little about what process is actually operating and controlling behavior. And indeed, molecular details matter. If pigeons are first trained to each choice separately then allowed to choose, they do not match, they pick the richer schedule exclusively.

Conversely, a pigeon trained from the start with two choices will match poorly or not at all i. Moreover, the degree of matching depends to some extent on the size of penalty. The pigeon on second exposure to FR5 is not the same as on first exposure, as can readily be shown by between-group experiments where for example the effects of extinction of the operant response or transfer of learning to a new task are measured.

Animals with little training first exposure behave very differently from animals with more and more varied training second exposure.

There are limits, therefore, to what can be learned simply by studying supposedly reversible steady-state behavior in individual organisms. This approach must be supplemented by between-group experiments, or by sophisticated theory that can take account of the effect on the individual animal of its own particular history.

There are also well-documented limits to what can be learned about processes operating in the individual via the between-group method that necessarily requires averaging across individuals. And sophisticated theory is hard to come by. In short, there is no royal road, no algorithmic method, that shows the way to understanding how learning works. Most theories of steady-state operant behavior are molar and are derived from the matching law. These tend to restrict themselves to descriptive accounts of experimental regularities including mathematical accounts, such as those suggested by Peter Killeen.

The reason can be traced back to B. Associative theories of operant conditioning, concerned with underlying associations and how they drive behavior, are not as limited by the legacy of Skinner. These theoretical treatments of operant learning are interested in the question: What associative structure underlies the box-opening sequence performed by the cat in Figure 1?

One option, espoused by Thorndike and Skinner , is that the cat has learned to associate this particular box with this sequence of actions. A different option, advocated by Tolman and later demonstrated by Dickinson and colleagues , is that the cat has learned that this sequence of actions leads to the opening of the door, that is, an action-outcome A-O association.

The critical difference between these two views is the role of the reinforcer: in the former it only has a role in learning, but once learned, the behavior is rather independent of the outcome or its value; in the latter the outcome is directly represented in the association controlling behavior, and thus behavior should be sensitive to changes in the value of the outcome.

For instance, if a dog is waiting outside the box, such that opening the door is no longer a desirable outcome to the cat, according the S-R theory the cat will nevertheless perform the sequence of actions that will lead to the door opening, while A-O theory deems that the cat will refrain from this behavior.

Research in the last two decades has convincingly shown that both types of control structures exist. In fact, operant behavior can be subdivided into two sub-classes, goal directed and habitual behavior, based exactly on this distinction. If it was me, what did I do? Historically, interest in assignment of credit arrived rather late on the scene. But there is a growing realization that assignment of credit is the question an operant conditioning process must answer.

There are now a few theories of credit assignment notably, those from the field of reinforcement learning. Most assume a set of pre-defined competing, emitted operant responses that compete in winner-take-all fashion. Most generally, current theories of operant learning can be divided into three main types -- those that attempt to accurately describe behavior descriptive theories , those that are concerned with how the operant learning is realized in the brain biologically inspired theories , and those that ask what is the optimal way to solve problems like that of assigning credit to actions, and whether such optimal solutions are indeed similar to what is seen in animal behavior normative theories.

Many of the theories in recent years are computational theories, in that they are accompanied by rigorous definitions in terms of equations for acquisition and response, and can make quantitative predictions. The computational field of reinforcement learning has provided a normative framework within which both Pavlovian and operant conditioned behavior can be understood. In this, optimal action selection is based on predictions of long-run future consequences, such that decision making is aimed at maximizing rewards and minimizing punishment.

Neuroscientific evidence from lesion studies, pharmacological manipulations and electrophysiological recordings in behaving animals have further provided tentative links to neural structures underlying key computational constructs in these models.

Most notably, much evidence suggests that the neuromodulator dopamine provides basal ganglia target structures with a reward prediction error that can influence learning and action selection, particularly in stimulus-driven instrumental behavior. In all these theories, however, nothing is said about the shaping of the response itself, or response topography.

Yet a pigeon pecking a response key on a ratio schedule soon develops a different topography than the one it shows on VI. Solving this problem requires a theory the elements of which are neural or hypothetically linked to overt behavior.

Different topographies then correspond to different patterns of such elements.