Natural and Artificial
In his 1967 paper, Arbitrary and Natural Reinforcement, Charles Ferster proposed classifying reinforcement into events that increase frequency of an operant as a natural consequence of the behavior itself, and events that are presumed to affect frequency by their requirement of human mediation, such as in a token economy where subjects are "rewarded" for certain behavior with an arbitrary token of a negotiable value. In 1970, Baer and Wolf created a name for the use of natural reinforcers called "behavior traps". A behavior trap requires only a simple response to enter the trap, yet once entered, the trap cannot be resisted in creating general behavior change. It is the use of a behavioral trap that increases a person's repertoire, by exposing them to the naturally occurring reinforcement of that behavior. Behavior traps have four characteristics:
- They are "baited" with virtually irresistible reinforcers that "lure" the student to the trap
- Only a low-effort response already in the repertoire is necessary to enter the trap
- Interrelated contingencies of reinforcement inside the trap motivate the person to acquire, extend, and maintain targeted academic/social skills
- They can remain effective for long periods of time because the person shows few, if any, satiation effects
As can be seen from the above, artificial reinforcement is in fact created to build or develop skills, and to generalize, it is important that either a behavior trap is introduced to "capture" the skill and utilize naturally occurring reinforcement to maintain or increase it. This behavior trap may simply be a social situation that will generally result from a specific behavior once it has met a certain criterion (e.g., if you use edible reinforcers to train a person to say hello and smile at people when they meet them, after that skill has been built up, the natural reinforcer of other people smiling, and having more friendly interactions will naturally reinforce the skill and the edibles can be faded).
Read more about this topic: Reinforcement
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