Discussion Board 4 Applied Behavioral Analysis 2

Table of Contents

Applied Behavioral Analysis

Antecedent control strategies: Misapplications and generalization

Part 1: After reading the chapter on antecedent control procedures, turn to page 351 in the Miltenberger digital text, and select one of the six “misapplications” cases. In your main post, briefly recap the case and discuss the reason the antecedent control procedure is not being used effectively. Provide an alternative plan that includes at least one antecedent control strategy from your reading.

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Part 2: Using the alternative plan that you have created, select one of the methods of promoting generalization discussed in Chapter 28 of Cooper, Heron, and Heward, and discuss how you would incorporate this method of generalization into your corrected behavior management plan.

Socially important behavior can be changed deliberately. The preceding chapters describe basic principles of behavior and how practitioners can use behavior change tactics derived from those principles to increase appropriate behaviors, achieve desired stimulus controls, teach new behaviors, and decrease problem be- haviors. Although achieving initial behavior changes often requires procedures that are intrusive or costly, or for a variety of other reasons cannot or should not be continued indefinitely, it is almost always important that the newly wrought behavior changes continue. Similarly, in many instances the intervention needed to produce new patterns of responding cannot be implemented in all of the envi- ronments in which the new behavior would benefit the learner. Nor is it possible in certain skill areas to teach directly all of the specific forms of the target behavior the learner may need. Practitioners face no more challenging or important task than that of designing, implementing, and evaluating interventions that produce behavior changes that continue after the intervention is terminated, appear in rele- vant settings and stimulus situations other than those in which the intervention was conducted, and/or spread to other related behaviors that were not taught di- rectly. Chapter 28 defines the major types of generalized behavior change and de- scribes the strategies and tactics applied behavior analysts use to achieve them.

P A R T 1 2

Promoting Generalized Behavior Change

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C H A P T E R 2 8

Generalization and Maintenance of Behavior Change

Key Terms

behavior trap contrived contingency contrived mediating stimulus general case analysis generalization generalization across subjects generalization probe

generalization setting indiscriminable contingency instructional setting lag reinforcement schedule multiple exemplar training naturally existing contingency

programming common stimuli response generalization response maintenance setting/situation generalization teaching sufficient examples teaching loosely

Behavior Analyst Certification Board® BCBA® & BCABA® Behavior Analyst Task List©,Third Edition

Content Area 3: Principles, Processes, and Concepts

3-12 Define and provide examples of generalization and discrimination.

9-28 Use behavior change procedures to promote stimulus and response generalization.

9-29 Use behavior change procedures to promote maintenance.

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Sherry’s teacher implemented an intervention that helped Sherry to complete each part of multiple-part, in- school assignments before submitting them and begin- ning another activity. Now, three weeks after the program ended, most of the work Sherry submits as “finished” is incomplete and her stick-with-a-task-until- it’s-finished behavior is as poor as it was before the in- tervention began.

Ricardo has just begun his first competitive job working as a copy machine operator in a downtown business of- fice. In spite of his long history of distractibility and poor endurance, Ricardo had learned to work indepen- dently for several hours at a time in the copy room at the vocational training center. His employer, however, is complaining that Ricardo frequently stops working after a few minutes to seek attention from others. Ricardo may soon lose his job.

Brian is a 10-year-old boy diagnosed with autism. In an effort to meet an objective on his individualized educa- tion program that targets functional language and com- munication skills, Brian’s teacher taught him to say, “Hello, how are you?” as a greeting. Now, whenever Brian meets anyone, he invariably responds with, “Hello, how are you?” Brian’s parents are concerned that their son’s language seems stilted and parrot-like.

Each of these three situations illustrates a com- mon type of teaching failure insofar as the most socially significant behavior changes are those

that last over time, are used by the learner in all relevant settings and situations, and are accompanied by changes in other relevant responses. The student who learns to count money and make change in the classroom today must be able to count and make change at the conve- nience store tomorrow and at the supermarket next month. The beginning writer who has been taught to write a few good sentences in school must be able to write many more meaningful sentences when writing notes or letters to family or friends. To perform below this stan- dard is more than just regrettable; it is a clear indication that the initial instruction was not entirely successful.

In the first scenario, the mere passage of time re- sulted in Sherry losing her ability to complete assign- ments. A change of scenery threw Ricardo off his game; the excellent work habits he had acquired at the voca- tional training center disappeared completely when he arrived at the community job site. Although Brian used his new greeting skill, its restricted form was not serving him well in the real world. In a very real sense, the in- struction they received failed all three of these people.

Applied behavior analysts face no more challeng- ing or important task than that of designing, imple- menting, and evaluating interventions that produce generalized outcomes. This chapter defines the major

types of generalized behavior change and describes the strategies and tactics researchers and practitioners use most often to promote them.

Generalized Behavior Change: Definitions and Key Concepts When Baer, Wolf, and Risley (1968) described the emerg- ing field of applied behavior analysis, they included gen- erality of behavior change as one of the discipline’s seven defining characteristics.

A behavior change may be said to have generality if it proves durable over time, if it appears in a wide variety of possible environments, or if it spreads to a wide vari- ety of related behaviors. (p. 96)

In their seminal review paper, “An Implicit Technol- ogy of Generalization,” Stokes and Baer (1977) also stressed those three facets of generalized behavior change—across time, settings, and behaviors—when they defined generalization as

the occurrence of relevant behavior under different, non- training conditions (i.e., across subjects, settings, peo- ple, behaviors, and/or time) without the scheduling of the same events in those conditions. Thus, generaliza- tion may be claimed when no extratraining manipula- tions are needed for extratraining changes; or may be claimed when some extra manipulations are necessary, but their cost is clearly less than that of the direct inter- vention. Generalization will not be claimed when simi- lar events are necessary for similar effects across conditions. (p. 350)

Stokes and Baer’s pragmatic orientation toward gen- eralized behavior change has proven useful for applied behavior analysis. They stated simply that if a trained behavior occurs at other times or in other places without it having to be retrained completely at those times or in those places, or if functionally related behaviors occur that were not taught directly, then generalized behavior change has occurred. The following sections provide def- initions and examples of the three basic forms of gener- alized behavior change: response maintenance, setting/ situation generalization, and response generalization. Box 28.1, “Perspectives on the Sometimes Confusing and Misleading Terminology of Generalization,” dis- cusses the many and varied terms applied behavior ana- lysts use to describe these outcomes.

Response Maintenance

Response maintenance refers to the extent to which a learner continues to perform the target behavior after a portion or all of the intervention responsible for the

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*Response maintenance can be measured under extinction conditions, in which case the relative frequency of continued responding is de- scribed correctly in terms of resistance to extinction. However, using resistance to extinction to describe response maintenance in most applied situations is incorrect because reinforcement typically follows some oc- currences of the target behavior in the post-treatment environment.

Applied behavior analysts have used many terms to de- scribe behavior changes that appear as adjuncts or by- products of direct intervention. Unfortunately, the overlapping and multiple meanings of some terms can lead to confusion and misunderstanding. For example, maintenance, the most frequently used term for behavior changes that persist after an intervention has been with- drawn or terminated, is also the most common name for a condition in which treatment has been discontinued or partially withdrawn. Applied behavior analysts should distinguish between response maintenance as a measure of behavior (i.e., a dependent variable) and maintenance as the name for an environmental condition (i.e., an in- dependent variable). Other terms found in the behavior analysis literature for continued responding after pro- grammed contingencies are no longer in effect include durability, behavioral persistence, and (incorrectly) re- sistance to extinction.*

Terms used in the applied behavior analysis literature for behavior changes that occur in nontraining settings or stimulus conditions include stimulus generalization, set- ting generalization, transfer of training, or simply, gen- eralization. It is technically incorrect to use stimulus generalization to refer to the generalized behavior change achieved by many applied interventions. Stimulus gen- eralization refers to the phenomenon in which a response that has been reinforced in the presence of a given stim- ulus occurs with an increased frequency in the presence of different but similar stimuli under extinction condi- tions (Guttman & Kalish, 1956; see Chapter 17). Stimulus generalization is a technical term referring to a specific behavioral process, and its use should be restricted to those instances (Cuvo, 2003; Johnston, 1979).

Terms such as collateral or side effects, response variability, induction, and concomitant behavior change are often used to indicate the occurrence of behaviors that have not been trained directly. To further complicate matters, generalization is often used as a catchall term to refer to all three types of generalized behavior change.

Johnston (1979) discussed some problems caused by using generalization (the term for a specific behavioral process) to describe any desirable behavior change in a generalization setting.

This kind of usage is misleading in that it suggests that a single phenomenon is at work when actually a number of different phenomena need to be described, explained, and controlled. . . . Carefully designing procedures to opti- mize the contributions of stimulus and response general- ization would hardly exhaust our repertoire of tactics for getting the subject to behave in a desirable way in non- instructional settings. Our successes will be more frequent when we realize that maximizing behavioral influence in such settings requires careful consideration of all behav- ioral principles and processes. (pp. 1–2)

Inconsistent use of the “terminology of generaliza- tion” can lead researchers and practitioners to incorrect assumptions and conclusions regarding the principles and processes responsible for the presence or absence of generalized outcomes. Nevertheless, applied behavior analysts will probably continue to use generalization as a dual-purpose term, referring sometimes to types of be- havior change and sometimes to behavioral processes that can bring such changes about. Stokes and Baer (1977) clearly indicated their awareness of the differ- ences in definitions.

The notion of generalization developed here is an essen- tially pragmatic one; it does not closely follow the tradi- tional conceptualizations (Keller & Schoenfeld, 1950; Skinner, 1953). In many ways, this discussion will sidestep much of the controversy concerning terminology. (p. 350)

While discussing the use of naturally existing con- tingencies of reinforcement to maintain and extend pro- grammed behavior changes, Baer (1999) explained his preference for using the term generalization:

It is the best of the techniques described here and, inter- estingly, it does not deserve the textbook definition of “generalization.” It is a reinforcement technique, and the textbook definition of generalization refers to unreinforced behavior changes resulting from other directly reinforced behavior changes. . . . [But] we are dealing with the prag- matic use of the word generalization, not the textbook meaning. We reinforce each other for using the word prag- matically, and it serves us well enough so far, so we shall probably maintain this imprecise usage. (p. 30, emphasis in original)

In an effort to promote the precise use of the tech- nical terminology of behavior analysis and as a reminder that the phenomena of interest are usually products of multiple behavior principles and procedures, we use terms for generalized behavior change that focus on the type of behavior change rather than the principles or processes that bring it about.

Box 28.1 Perspectives on the Sometimes Confusing

and Misleading Terminology of Generalization

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behavior’s initial appearance in the learner’s repertoire has been terminated. For example:

• Sayaka was having difficulty identifying the low- est common denominator (LCD) when adding and subtracting fractions. Her teacher had Sayaka write the steps for finding the LCD on an index card and told her to refer to the card when needed. Sayaka began using the LCD cue card, and the ac- curacy of her math assignments improved. After using the cue card for a week, Sayaka said she no longer needed it and returned it to her teacher. The next day Sayaka correctly computed the LCD for every problem on a quiz on adding and subtracting fractions.

• On Loraine’s first day on the job with a residential landscaping company, a coworker taught her how to use a long-handled tool to extract dandelions, root and all. Without further instruction, Loraine continues to use the tool correctly a month later.

• When he was in the seventh grade, one of Derek’s teachers taught him how to write down his assign- ments and keep materials for each class in sepa- rate folders. As a college sophomore, Derek continues to apply those organizational skills to his academic work.

These examples illustrate the relative nature of gen- eralized behavior change. Response maintenance was ev- ident in Sayaka’s performance on a math quiz one day after the cue card intervention ended and also in Derek’s continued use of the organizational skills he had learned years earlier. How long a newly learned behavior needs to be maintained depends on the importance of that be- havior in the person’s life. If covertly reciting a telephone number three times after hearing it enables a person to remember the number long enough to dial it correctly when he locates a telephone a few minutes later, suffi- cient response maintenance has been achieved. Other be- haviors, such as self-care and social skills, must be maintained in a person’s repertoire for a lifetime.

Setting/Situation Generalization

Setting/situation generalization occurs when a target be- havior is emitted in the presence of stimulus conditions other than those in which it was trained directly. We de- fine setting/situation generalization as the extent to which a learner emits the target behavior in a setting or stimulus situation that is different from the instructional setting. For example:

• While waiting for his new motorized wheelchair to arrive from the factory, Chaz used a computer sim- ulation program and a joystick to learn how to op-

erate his soon-to-arrive chair. When the new chair arrived, Chaz grabbed the joystick and immedi- ately began zipping up and down the hall and spin- ning perfectly executed donuts.

• Loraine had been taught to pull weeds from flower- beds and mulched areas. Although she had never been instructed to do so, Loraine has begun remov- ing dandelions and other large weeds from lawns as she crosses on her way to the flowerbeds.

• After Brandy’s teacher taught her to read 10 differ- ent C-V-C-E words (e.g., bike, cute, made), Brandy could read C-V-C-E words for which she had not received any instruction (e.g., cake, bite, mute).

A study by van den Pol and colleagues (1981) pro- vides an excellent example of setting/situation general- ization. They taught three young adults with multiple disabilities to eat independently in fast-food restaurants. All three students had previously eaten in restaurants but could not order or pay for a meal without assistance. The researchers began by constructing a task analysis of the steps required to order, pay for, and eat a meal appropri- ately in a fast-food restaurant. Instruction took place in the students’ classroom and consisted of role-playing each of the steps during simulated customer–cashier interac- tions and responding to questions about photographic slides showing customers at a fast-food restaurant per- forming the various steps in the sequence. The 22 steps in the task analysis were divided into four major compo- nents: locating, ordering, paying, and eating and exiting. After a student had mastered the steps in each compo- nent in the classroom, he was given “a randomly deter- mined number of bills equaling two to five dollars and instructed to go eat lunch” at a local restaurant (p. 64). Observers stationed inside the restaurant recorded each student’s performance of each step in the task analysis. The results of these generalization probes, which were also conducted before training (baseline) and after train- ing (follow-up) are shown in Figure 28.1. In addition to assessing the degree of generalization from the class- room, which was based on the specific McDonald’s restaurant used for most of the probes, the researchers conducted follow-up probes in a Burger King restaurant (also a measure of maintenance).

This study is indicative of the pragmatic approach to assessing and promoting generalized behavior change used by most applied behavior analysts. The setting in which generalized responding is desired can contain one or more components of the intervention that was imple- mented in the instructional environment, but not all of the components. If the complete intervention program is required to produce behavior change in a novel environ- ment, then no setting/situation generalization can be claimed. However, if some component(s) of the training

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Covert Novel Restaurant

Overt Novel Restaurant

Covert 1-Year Follow-Up

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2.0 3.0 4.0

5 10 15 20 25 30 35 Probe Sessions

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Figure 28.1 Percentage of steps necessary to order a meal at a fast-food restaurant correctly performed by three students with disabilities before, during, and after instruction in the classroom. During follow-up, the closed triangles represent probes conducted at a Burger King restaurant using typical observation proce- dures, open triangles represent Burger King probes during which students did not know they were being observed, and open circles represent covert probes conducted in a different McDonald’s 1 year after training. From “Teaching the Handicapped to Eat in Public Places: Acquisition, Generalization and Maintenance of Restaurant Skills” by R. A. van den Pol, B. A. Iwata, M. T. Ivanic, T. J. Page, N. A. Neef, and F. P. Whitley, 1981, Journal of Applied Behavior Analysis, 14, p. 66. Copyright 1981 by the Society for the Experimental Analysis of Behavior, Inc. Reprinted by permission.

program results in meaningful behavior change in a gen- eralization setting, then setting/situation generalization can be claimed, provided it can be shown that the com- ponent(s) used in the generalization setting was insuffi- cient to produce the behavior change alone in the training environment.

For example, van den Pol and colleagues taught Stu- dent 3, who was deaf, how to use a prosthetic ordering device in the classroom. The device, a plastic laminated sheet of cardboard with a wax pencil, had preprinted ques- tions (e.g., “How much is . . . ?”), generic item names (e.g., large hamburger), and spaces where the cashier could write responses. Simply giving the student some money and the prosthetic ordering card would not have enabled him to order, purchase, and eat a meal indepen- dently. However, after classroom instruction that included guided practice, role playing, social reinforcement (“Good job! You remembered to ask for your change” [p. 64]),

1Because the majority of the examples in this chapter are school based, we have used the language of education. For our purposes here, instruction can be a synonym for treatment, intervention, or therapy, and instructional setting can be a synonym for clinical setting or therapy setting.

corrective feedback, and review sessions with the pros- thetic ordering card produced the desired behaviors in the instructional setting, Student 3 was able to order, pay for, and eat meals in a restaurant aided only by the card.

Distinguishing Between Instructional and Generalization Settings

We use instructional setting to denote the total envi- ronment where instruction occurs, including any aspects of the environment, planned or unplanned, that may in- fluence the learner’s acquisition and generalization of the target behavior.1 Planned elements are the stimuli and

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Figure 28.2 Examples of an instructional setting and a generalization setting for six target behaviors.

Instructional Setting 1. Raising hand when special education

teacher asks a question in the resource room.

2. Practicing conversational skills with speech therapist at school.

3. Passing basketball during a team scrimmage on home court.

4. Answering addition problems in vertical format at desk at school.

5. Solving word problems with no distracter numbers on homework assignment.

6. Operating package sealer at community job site in presence of supervisor.

Generalization Setting 1. Raising hand when general education

teacher asks a question in the regular classroom.

2. Talking with peers in town. 3. Passing basketball during a game on the

opponent’s court. 4. Answering addition problems in

horizontal format at desk at school. 5. Solving word problems with distracter

numbers on homework assignment. 6. Operating package sealer at community

job site in absence of supervisor.

events the teacher has programmed in an effort to achieve initial behavior change and promote generalization. Planned elements of an instructional setting for a math lesson, for example, would include the specific math problems to be presented during the lesson and the for- mat and sequencing of those problems. Unplanned as- pects of the instructional setting are elements the teacher is not aware of or has not considered that might affect the acquisition and generalization of the target behavior. For example, the phrase, how much in a word problem may acquire stimulus control over a student’s use of ad- dition, even when the correct solution to the problem re- quires a different arithmetic operation. Or, perhaps a student always uses subtraction for the first problem on each page of word problems because a subtraction prob- lem has always been presented first during instruction.

A generalization setting is any place or stimulus situation that differs in some meaningful way from the in- structional setting and in which performance of the tar- get behavior is desired. There are multiple generalization settings for many important target behaviors. The student who learns to solve addition and subtraction word prob- lems in the classroom should be able to solve similar problems at home, at the store, and on the ball diamond with his friends.

Examples of instructional and generalization settings for six target behaviors are shown in Figure 28.2. When a person uses a skill in an environment physically re- moved from the setting where he learned it—as with Behaviors 1 through 3 in Figure 28.2—it is easy to un- derstand that event as an example of generalization across settings. However, many important generalized outcomes occur across more subtle differences between the in- structional setting and generalization setting. It is a mis- take to think that a generalization setting must be somewhere different from the place where instruction is

provided. Students often receive instruction in the same place where they will need to maintain and generalize what they have learned. In other words, the instructional setting and generalization setting can, and often do, share the same physical location (as with Behaviors 4 through 6 in Figure 28.2).

Distinguishing between Setting/Situation Generalization and Response Maintenance

Because any measure of setting/situation generalization is conducted after some instruction has taken place, it might be argued that setting/situation generalization and response maintenance are the same, or are inseparable phenomena at least. Most measures of setting/situation generalization do provide information on response main- tenance, and vice versa. For example, the post-training generalization probes conducted by van den Pol and col- leagues (1981) at the Burger King restaurant and at the second McDonald’s provided data on setting/situation generalization (i.e., to novel restaurants) and on response maintenance of up to 1 year. However, a functional dis- tinction exists between setting/situation generalization and response maintenance, with each outcome presenting a somewhat different set of challenges for programming and ensuring enduring behavior change. When a behavior change produced in the classroom or clinic is not observed in the generalization environment, a lack of setting/ situation generalization is evident. When a behavior change produced in the classroom or clinic has occurred at least once in the generalization setting and then ceases to occur, a lack of response maintenance is evident.

An experiment by Koegel and Rincover (1977) illus- trated the functional difference between setting/situation generalization and response maintenance. Participants

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Figure 28.3 Correct responding by three children on alternating blocks of 10 trials in the instructional setting and 10 trials in the generalization setting. From “Research on the Differences Between Generalization and Maintenance in Extra-Therapy Responding” by R. L. Koegel and A. Rincover, 1977, Journal of Applied Behavior Analysis, 10, p. 4. Copyright 1977 by the Society for the Experimental Analysis of Behavior, Inc. Reprinted by permission.

were three young boys with autism; each was mute, echolalic, or displayed no appropriate contextual speech. One-to-one instructional sessions were conducted in a small room with the trainer and child seated across from each other at a table. Each child was taught a series of imitative responses (e.g., the trainer said, “Touch your [nose, ear]” or “Do this” and [raised his arm, clapped his hands]). Each 40-minute session consisted of blocks of 10 training trials in the instructional setting alternated with blocks of 10 trials conducted by an unfamiliar adult stand- ing outside, surrounded by trees. All correct responses in the instructional setting were followed by candy and so- cial praise. During the generalization trials the children received the same instructions and model prompts as in the classroom, but no reinforcement or other conse- quences were provided for correct responses in the gen- eralization setting.

Figure 28.3 shows the percentage of trials in which each child responded correctly in the instructional set- ting and in the generalization setting. All three children learned to respond to the imitative models in the instruc- tional setting. All three children showed 0% correct re- sponding in the generalization setting at the end of the experiment, but for different reasons. Child 1 and Child 3 began emitting correct responses in the generalization

setting as their performances improved in the instruc- tional setting, but their generalized responding was not maintained (most likely the result of the extinction con- ditions in effect in the generalization setting). The imita- tive responding acquired by Child 2 in the instructional setting never generalized to the outside setting. There- fore, the 0% correct responding at the experiment’s con- clusion represents a lack of response maintenance for Child 1 and Child 3, but for Child 2 it represents a fail- ure of setting generalization.

Response Generalization

We define response generalization as the extent to which a learner emits untrained responses that are functionally equivalent to the trained target behavior. In other words, in response generalization forms of behavior for which no programmed contingencies have been applied appear as a function of the contingencies that have been applied to other responses. For example:

• Traci wanted to earn some extra money by helping her older brother with his lawn mowing business. Her brother taught Traci to walk the mower up and down parallel rows that moved progressively from

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one side of a lawn to the other. Traci discovered that she could mow some lawns just as quickly by first cutting around the perimeter of the lawn and then walking the mower in concentric patterns in- ward toward the center of the lawn.

• Loraine was taught to remove weeds with a long weed-removal tool. Although she has never been taught or asked to do so, sometimes Loraine removes weeds with a hand trowel or with her bare hands.

• Michael’s mother taught him how to take phone messages by using the pencil and notepaper next to the phone to write the caller’s name, phone num- ber, and message. One day, Michael’s mother came home and saw her son’s tape recorder next to the phone. She pushed the play button and heard Michael’s voice say, “Grandma called. She wants to know what you’d like her to cook for dinner Wednesday. Mr. Stone called. His number is 555- 1234, and he said the insurance payment is due.”

The study by Goetz and Baer (1973) described in Chapter 8 of the block building by three preschool girls provides a good example of response generalization. Dur- ing baseline the teacher sat by each girl as she played with the blocks, watching closely but quietly, and displaying neither enthusiasm nor criticism for any particular use of the blocks. During the next phase of the experiment, each time the child placed or rearranged the blocks to create a new form that had not appeared previously in that ses- sion’s constructions, the teacher commented with enthu- siasm and interest (e.g., “Oh, that’s very nice—that’s different!”). Another phase followed in which each re- peated construction of a given form within the session was praised (e.g., “How nice—another arch!”). The study ended with a phase in which descriptive praise was again contingent on the construction of different block forms. All three children constructed more new forms with the blocks when form diversity was reinforced than they did under baseline or under the reinforcement-for-the-same- forms condition (see Figure 8.7).

Even though specific responses produced reinforce- ment (i.e., the actual block forms that preceded each in- stance of teacher praise), other responses sharing that functional characteristic (i.e., being different from block forms constructed previously by the child) increased in frequency as a function of the teacher’s praise. As a re- sult, during reinforcement for different forms, the children constructed new forms with the blocks even though each new form itself had never before appeared and therefore could not have been reinforced previously. Reinforcing a few members of the response class of new forms in- creased the frequency of other members of the same re- sponse class.

Generalized Behavior Change: A Relative and Intermixed Concept

As the examples presented previously show, generalized behavior change is a relative concept. We might think of it as existing along a continuum. At one end of the con- tinuum are interventions that might produce a great deal of generalized behavior change; that is, after all compo- nents of an intervention have been terminated, the learner may emit the newly acquired target behavior, as well as several functionally related behaviors not observed pre- viously in his repertoire, at every appropriate opportu- nity in all relevant settings, and he may do so indefinitely. At the other end of the continuum of generalized out- comes are interventions that yield only a small amount of generalized behavior change—the learner uses the new skill only in a limited range of nontraining settings and situations, and only after some contrived response prompts or consequences are applied.

We have presented each of the three primary forms of generalized behavior change individually to isolate its defining features, but they often overlap and occur in combination. Although it is possible to obtain response maintenance without generalization across settings/ situations or behaviors (i.e., the target behavior continues to occur in the same setting in which it was trained after the training contingencies have been terminated), any meaningful measure of setting generalization will entail some degree of response maintenance. And it is common for all three forms of generalized behavior change to be represented in the same instance. For example, during a relatively quiet shift at the widget factory on Monday, Joyce’s supervisor taught her to obtain assistance by call- ing out, “Ms. Johnson, I need some help.” Later that week (response maintenance) when it was very noisy on the factory floor (setting/situation generalization), Joyce sig- naled her supervisor by waving her hand back and forth (response generalization).

Generalized Behavior Change Is Not Always Desirable

It is hard to imagine any behavior that is important enough to target for systematic instruction for which re- sponse maintenance would be undesirable. However, un- wanted setting/situation generalization and response generalization occur often, and practitioners should de- sign intervention plans to prevent or minimize such unwanted outcomes. Undesirable setting/situation gen- eralization takes two common forms: overgeneralization and faulty stimulus control.

Overgeneralization, a nontechnical but effectively descriptive term, refers to an outcome in which the

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behavior has come under the control of a stimulus class that is too broad. That is, the learner emits the target be- havior in the presence of stimuli that, although similar in some way to the instructional examples or situation, are inappropriate occasions for the behavior. For example, a student learns to spell division, mission, and fusion with the grapheme, –sion. When asked to spell fraction, the student writes f-r-a-c-s-i-o-n.

With faulty stimulus control, the target behavior comes under the restricted control of an irrelevant an- tecedent stimulus. For example, after learning to solve word problems such as, “Natalie has 3 books. Amy has 5 books. How many books do they have in total?” by adding the numerals in the problem, the student adds the numerals in any problem that includes the words “in total” (e.g., “Corinne has 3 candies. Amanda and Corinne have 8 candies in total. How many candies does Amanda have?”).2

Undesired response generalization occurs when any of a learner’s untrained but functionally equivalent re- sponses results in poor performance or undesirable out- comes. For example, although Jack’s supervisor at the widget factory taught him to operate the drill press with two hands because that is the safest method, sometimes Jack operates the press with one hand. One-handed re- sponses are functionally equivalent to two-handed re- sponses because both topographies cause the drill press to stamp out a widget, but one-handed responses com- promise Jack’s health and the factory’s safety record. Or, perhaps some of her brother’s customers do not like how their lawns look after Traci has mowed them in concen- tric rectangles.

Other Types of Generalized Outcomes

Other types of generalized outcomes that do not fit eas- ily into categories of response maintenance, setting/situ- ation generalization, and response generalization have been reported in the behavior analysis literature. For ex- ample, complex members of a person’s repertoire some- times appear quickly with little or no apparent direct conditioning, such as the stimulus equivalence relations described in Chapter 17 (Sidman, 1994). Another type of such rapid learning that appears to be a generalized outcome of other events has been called contingency ad- duction, a process whereby a behavior that was initially selected and shaped under one set of conditions is re- cruited by a different set of contingencies and takes on a

2Examples of faulty stimulus control caused by flaws in the design of in- structional materials, and suggestions for detecting and correcting those flaws, can be found in J. S. Vargas (1984).

new function in a person’s repertoire (Adronis, 1983; Johnson & Layng, 1992).

Sometimes an intervention applied to one or more people results in behavior changes in other people who were not directly treated by the contingencies. Genera- lization across subjects refers to changes in the behav- ior of people not directly treated by an intervention as a function of treatment contingencies applied to other peo- ple. This phenomenon, which has been described with a variety of related or synonymous terms—vicarious rein- forcement (Bandura, 1971; Kazdin, 1973), ripple effect (Kounin, 1970), and spillover effect (Strain, Shores, & Kerr, 1976)—provides another dimension for assessing the generalization of treatment effects. For example, Fan- tuzzo and Clement (1981) examined the degree to which behavior changes would generalize from one child who received teacher-administered or self-administered token reinforcement during a math activity to a peer seated next to the child.

Drabman, Hammer, and Rosenbaum (1979) combined four basic types of generalized treatment effects— (a) across time (i.e., response maintenance), (b) across set- tings (i.e., setting/situation generalization), (c) across be- haviors (i.e., response generalization), and (d) across subjects—into a conceptual framework they called the generalization map. By viewing each type of generalized outcome as dichotomous (i.e., either present or absent) and by combining all possible permutations of the four cate- gories, Drabman and colleagues arrived at 16 categories of generalized behavior change ranging from maintenance (Class 1) to subject-behavior-setting-time generalization (Class 16). Class 1 generalization is evident if the target be- havior of the target subject(s) continues in the treatment setting after any “experiment-controlled contingencies” have been discontinued. Class 16 generalization, which Drabman and colleagues (1979) called the “ultimate form” of generalization, is evidenced by “a change in a nontar- get subject’s nontarget behavior which endures in a dif- ferent setting after the contingencies have been withdrawn in the treatment setting” (p. 213).

Although Drabman and colleagues recognized that “with any heuristic technique the classifications may prove arbitrary” (p. 204), they provided objectively stated rules for determining whether a given behavioral event fits the requirements of each of their 16 classifications. Regardless of whether generalized behavior change con- sists of such distinctly separate and wide-ranging phe- nomena as detailed by Drabman and colleagues, their generalization map provided an objective framework by which the extended effects of behavioral interventions can be described and communicated. For example, Stevenson and Fantuzzo (1984) measured 15 of the 16 generalization map categories in a study of the effects of teaching a fifth-grade boy to use self-management

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techniques. They not only measured the effects of the in- tervention on the target behavior (math performance) in the instructional setting (school), but they also assessed effects on the student’s math behavior at home, disruptive behavior at home and at school, both behaviors for a non- treated peer in both settings, and maintenance of all of the above.

Planning for Generalized Behavior Change

In general, generalization should be programmed, rather than expected or lamented.

—Baer, Wolf, and Risley (1968, p. 97)

In their review of 270 published studies relevant to generalized behavior change, Stokes and Baer (1977) concluded that practitioners should always “assume that generalization does not occur except through some form of programming . . . and act as if there were no such an- imal as ‘free’ generalization—as if generalization never occurs ‘naturally,’ but always requires programming” (p. 365). Of course, generalization of some type and de- gree does usually occur, whether or not it is planned. Such unplanned and unprogrammed generalization may be suf- ficient, but often it is not, particularly for many learners served by applied behavior analysts (e.g., children and adults with learning problems and developmental dis- abilities). And if left unchecked, unplanned generalized outcomes may be undesirable outcomes.

Achieving optimal generalized outcomes requires thoughtful, systematic planning. This planning begins with two major steps: (1) selecting target behaviors that will meet natural contingencies of reinforcement, and (2) specifying all desired variations of the target behav- ior and the settings/situations in which it should (and should not) occur after instruction has ended.

Selecting Target Behaviors That Will Meet Naturally Existing Contingencies of Reinforcement

The everyday environment is full of steady, dependable, hardworking sources of reinforcement for almost all of the behaviors that seem natural to us. That is why they seem natural to us.

—Donald M. Baer (1999, p. 15)

Numerous criteria have been suggested for determining whether a proposed teaching objective is relevant or functional for the learner. For example, the age- appropriateness of a skill and the degree to which it rep- resents normalization are often cited as important criteria for choosing target behaviors for students with

disabilities (e.g., Snell & Brown, 2006). Each of these criteria was discussed in Chapter 3, along with numer- ous other issues that should be considered when select- ing and prioritizing target behaviors. In the end, however, there is just one ultimate criterion of func- tionality: A behavior is functional only to the extent that it produces reinforcement for the learner. This criterion holds no matter how important the behavior may be to the person’s health or welfare, or no matter how much teachers, family, friends, or the learner himself consid- ers the behavior to be desirable. To repeat: A behavior is not functional if it does not produce reinforcement for the learner. Said another way: Behaviors that are not followed by reinforcers on at least some occasions will not be maintained.

Ayllon and Azrin (1968) recognized this fundamen- tal truth when they recommended that practitioners fol- low the relevance-of-behavior rule when selecting target behaviors. The rule: Choose only those behaviors to change that will produce reinforcers in the postinterven- tion environment. Baer (1999) believed so strongly in the importance of this criterion that he recommended that practitioners heed a similar rule:

A good rule is to not make any deliberate behavior changes that will not meet natural communities of rein- forcement. Breaking this rule commits you to maintain and extend the behavior changes that you want, by your- self, indefinitely. If you break this rule, do so knowingly. Be sure that you are willing and able to do what will be necessary. (p. 16, emphasis in original)

Programming for the generalization and maintenance of any behavior for which a natural contingency of rein- forcement exists, no matter the specific tactics employed, consists of getting the learner to emit the behavior in the generalization setting just often enough to contact the oc- curring contingencies of reinforcement. Generalization and maintenance of the behavior from that point forward, while not assured, is a very good bet. For example, after receiving some basic instruction on how to operate the steering wheel, gas pedal, and brakes on a car, the natu- rally existing reinforcement and punishment contingen- cies involving moving automobiles and the road will select and maintain effective steering, acceleration, and braking. Very few drivers need booster training sessions on the basic operation of the steering wheel, gas pedal, and brakes.

We define a naturally existing contingency as any contingency of reinforcement (or punishment) that oper- ates independent of the behavior analyst’s or practi- tioner’s efforts. This is a pragmatic, functional conception of a naturally existing contingency defined by the ab- sence of the behavior analyst’s efforts. Naturally exist- ing contingencies include contingencies that operate

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without social mediation (e.g., walking fast on an icy sidewalk is often punished by a slip and fall) and socially mediated contingencies contrived and implemented by other people in the generalization setting. From the per- spective of a special educator who is teaching a set of targeted social and academic skills to students for whom the general education classroom represents the general- ization setting, a token economy operated by the general education classroom teacher is an example of the latter type of naturally existing contingency.3 Even though the token economy was contrived by the teacher in the gen- eral education classroom, it is a naturally existing con- tingency because it already operates in the generalization setting.

We define a contrived contingency as any contin- gency of reinforcement (or punishment) designed and implemented by a behavior analyst or practitioner to achieve the acquisition, maintenance, and/or generaliza- tion of a targeted behavior change. From the perspective of the teacher in the general education classroom who designed and implemented it, the token economy in the previous example is a contrived contingency.

In reality, practitioners are often charged with the difficult task of teaching important skills for which there are no dependable naturally existing contingencies of re- inforcement. In such cases, practitioners should realize and plan for the fact that the generalization and mainte- nance of target behaviors will have to be supported, per- haps indefinitely, with contrived contingencies.

Specifying All Desired Variations of the Behavior and the Settings/ Situations Where It Should (and Should Not) Occur

This stage of planning for generalized outcomes includes identifying all the desired behavior changes that need to be made and all the environments and stimulus condi- tions in which the learner should emit the target behav- ior(s) after direct training has ceased (Baer, 1999). For some target behaviors, the most important stimulus con- trol for each response variation is clearly defined (e.g., reading C-V-C-E words) and restricted in number (e.g., solving multiplication facts). For many important target behaviors, however, the learner is likely to encounter a multitude of settings and stimulus conditions where the behavior, in a wide variety of response forms, is desired. Only by considering these possibilities prior to instruction can the behavior analyst design an intervention with the best chance of preparing the learner for them.

In one sense, this component of planning for gener- alized outcomes is similar to preparing a student for a fu-

3Token economies are described in Chapter 26.

ture test without knowing the content or the format of all of the questions that will be on the test. The stimulus con- ditions and contingencies of reinforcement that exist in the generalization setting(s) will provide that test to the learner. Planning involves trying to determine what the final exam will cover (type and form of questions), whether there will be any trick questions (e.g., confus- ing stimuli that might evoke the target response when it should not occur), and whether the learner will need to use his new knowledge or skill in different ways (re- sponse generalization).

List All the Behaviors That Need to Be Changed

A list should be made of all the forms of the target be- havior that need to be changed. This is not an easy task, but a necessary one to obtain a complete picture of the teaching task ahead. For example, if the target behavior is teaching Brian, the young boy with autism, to greet people, he should learn a variety of greetings in addition to “Hello, how are you?” Brian may also need many other behaviors to initiate and participate in conversa- tions, such as responding to questions, taking turns, stay- ing on topic, and so forth. He may also need to be taught when and with whom to introduce himself. Only by hav- ing a complete list of all the desired forms of the behav- ior can the practitioner make meaningful decisions about which behaviors to teach directly and which to leave to generalization.

The practitioner should determine whether and to what extent response generalization is desirable for all of the behavior changes listed, and then, make a priori- tized list of the variations of the target behavior he would like to see as generalized outcomes.

List All the Settings and Situations in Which the Target Behavior Should Occur

A list should be made of all the desired settings and sit- uations in which the learner will emit the target behavior if optimal generalization is achieved. Will Brian need to introduce himself and talk with children his own age, to adults, to males and females? Will he need to talk with others at home, at school, in the lunchroom, on the play- ground? Will he be confronted with situations that may appear to be appropriate opportunities to converse but are not (e.g., an unknown adult approaches and offers candy) and for which an alternative response is needed (e.g., walking away, seeking out a known adult). (This kind of analysis often adds additional behaviors to the list of skills to be taught.)

When all of the possible situations and settings have been identified, they should be prioritized according to

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their importance and the client’s likelihood of encoun- tering them. Further analysis of the prioritized environ- ments should then be conducted. What discriminative stimuli usually set the occasion for the target behavior in these various settings and situations? What schedules of reinforcement for the target behavior are typical in these nontraining environments? What kinds of reinforcers are likely to be contingent on the emission of the target be- havior in each of the settings? Only when she has an- swered all of these questions, if not by objective observation then at least by considered estimation, can the behavior analyst begin to have a full picture of the teaching task ahead.

Is the Pre-Intervention Planning Worth It?

Obtaining all of the information just described requires considerable time and effort. Given limited resources, why not design an intervention and immediately begin trying to change the target behavior? It is true that many behavior changes do show generalization, even though the extension of the trained behavior across time, set- tings, and other behaviors was unplanned and unpro- grammed. When target behaviors have been chosen that are truly functional for the subject and when those be- haviors have been brought to a high level of proficiency under discriminative stimuli relevant to generalization settings, the chances of generalization are good. But what constitutes a high level of proficiency for certain behav- iors in various settings? What are all of the relevant dis- criminative stimuli in all of the relevant settings? What are all the relevant settings?

Without a systematic plan, a practitioner will usu- ally be ignorant of the answers to these vital questions. Few behaviors that are important enough to target have such limited needs for generalized outcomes that the an- swers to such questions are obvious. Just a cursory con- sideration of the behaviors, settings, and people related to a child introducing himself revealed numerous factors that may need to be incorporated into an instructional plan. A more thorough analysis would produce many more. In fact, a complete analysis will invariably reveal more behaviors to be taught, to one person or another, than time or resources would ever allow. And Brian—the 10-year-old who is learning to greet people and introduce himself—in all likelihood needs to learn many other skills also, such as self-help, academic, and recreation and leisure skills, to name just a few. Why then create all the lists in the first place when everything cannot be taught anyway? Why not just train and hope?4

4Teaching a new behavior without developing and implementing a plan to facilitate its maintenance and generalization is done so often that Stokes and Baer 1977) called it the “train and hope” approach to generalization.

Baer (1999) described six possible benefits of list- ing all the forms of behavior change and all the situations in which these behavior changes should occur.

1. You now see the full scope of the problem ahead of you, and thus see the corresponding scope that your teaching program needs to have.

2. If you teach less than the full scope of the problem, you do so by choice rather than by forgetting that some forms of the behavior could be important, or that there were some other situations in which the behavior change should or should not occur.

3. If less than a complete teaching program results in less than a complete set of behavior changes, you will not be surprised.

4. You can decide to teach less than there is to learn, perhaps because that is all that is practical or pos- sible for you to do.

5. You can decide what is most important to teach. You can also decide to teach the behavior in a way that encourages the indirect development of some of the other forms of the desired behavior, as well as the indirect occurrence of the behavior in some other desired situations, that you will not or cannot teach directly.

6. But if you choose the option discussed in number 5 above, rather than the complete program implicit in number 1, you will do so knowing that the de- sired outcome would have been more certain had you taught every desirable behavior change di- rectly. The best that you can do is to encourage the behavior changes that you do not cause directly. So, you will have chosen the option in number 5 either of necessity or else as a well-considered gamble after a thoughtful consideration of possibil- ities, costs, and benefits. (pp. 10–11)

After determining which behaviors to teach directly and in which situations and settings to teach those be- haviors, the behavior analyst is ready to consider strate- gies and tactics for achieving generalization to untrained behaviors and settings.

Strategies and Tactics for Promoting Generalized Behavior Change Various authors have described conceptual schemes and taxonomies of methods for promoting generalized be- havior change (e.g., Egel, 1982; Horner, Dunlap, & Koegel, 1988; Osnes & Lieblein, 2003; Stokes & Baer,

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Figure 28.4 Strategies and tactics for promoting generalized behavior change.

Teach the Full-Range of Relevant Stimulus Conditions and Response Requirements 1. Teach sufficient stimulus examples 2. Teach sufficient response examples

Make the Instructional Setting Similar to the Generalization Setting 3. Program common stimuli 4. Teach loosely

Maximize Contact with Reinforcement in the Generalization Setting 5. Teach the target behavior to levels of performance required by naturally existing

contingencies of reinforcement 6. Program indiscriminable contingencies 7. Set behavior traps 8. Ask people in the generalization setting to reinforce the target behavior 9. Teach the learner to recruit reinforcement

Mediate Generalization 10. Contrive a mediating stimulus 11. Teach self-management skills

Train to Generalize 12. Reinforce response variability 13. Instruct the learner to generalize

1977; Stokes & Osnes, 1989). The conceptual scheme presented here is informed by the work of those authors and others, and by our own experiences in designing, implementing, and evaluating procedures for promot- ing generalized outcomes and in teaching practitioners to use them. Although numerous methods and tech- niques have been demonstrated and given a variety of names, most tactics that effectively promote general- ized behavior change can be classified under five strate- gic approaches:

• Teach the full range of relevant stimulus conditions and response requirements.

• Make the instructional setting similar to the gener- alization setting.

• Maximize the target behavior’s contact with rein- forcement in the generalization setting.

• Mediate generalization.

• Train to generalize.

In the following sections we describe and provide examples of 13 tactics applied behavior analysts have used to accomplish these five strategies (see Figure 28.4). Although each tactic is described individually, most efforts to promote generalized behavior change entail a combination of these tactics (e.g., Ducharme & Holborn, 1997; Grossi, Kimball, & Heward, 1994; Hughes, Harmer, Killina, & Niarhos, 1995; Ninness, Fuerst, & Rutherford, 1991; Trask-Tyler, Grossi, & Heward, 1994).

Teach the Full Range of Relevant Stimulus Conditions and Response Requirements

The most common mistake that teachers make, when they want to establish a generalized behavior change, is to teach one good example of it and expect the student to generalize from that example.

— Donald M. Baer (1999, p. 15)

To be most useful, most important behaviors must be per- formed in various ways across a wide range of stimulus conditions. Consider a person skilled in reading, math, conversing with others, and cooking. That person can read thousands of different words; add, subtract, multiply, and divide any combination of numbers; make a multi- tude of relevant and appropriate comments when talking with others; and measure, combine, and prepare numer- ous ingredients in hundreds of recipes. Helping learners achieve such wide-ranging performances presents an enormous challenge to the practitioner.

One approach to this challenge would be to teach every desired form of a target behavior in every setting/ situation in which the learner may need that behavior in the future. Although this approach would eliminate the need to program for response generalization and setting/ situation generalization (response maintenance would re- main the only problem), it is seldom possible and never practical. A teacher cannot provide direct instruction on every printed word a student may encounter in the fu- ture, or teach a student every measuring, pouring, stir- ring, and sautéing movement needed to make every dish

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he may want to make in the future. Even for most skill areas for which it would be possible to teach every pos- sible example (e.g., instruction could be provided on all 900 different single-digit-times-two-digit multiplication problems), to do so would be impractical for many rea- sons, not the least of which is that the student needs to learn not only many other types of math problems but also skills in other curriculum areas.

A general strategy called teaching sufficient exam- ples consists of teaching the student to respond to a sub- set of all of the possible stimulus and response examples and then assessing the student’s performance on untrained examples.5 For example, the generalization of a student’s ability to solve two-digit-minus-two-digit arithmetic prob- lems with regrouping can be assessed by asking the stu- dent to solve several problems of the same type for which no instruction or guided practice has been provided. If the results of this generalization probe show that the student responds correctly to untaught examples, then instruction can be halted on this class of problems. If the student per- forms poorly on the generalization probe, the practitioner teaches additional examples before again assessing the student’s performance on a new set of untaught examples. This cycle of teaching new examples and probing with untaught examples continues until the learner consistently responds correctly to untrained examples representing the full range of stimulus conditions and response require- ments found in the generalization setting.

Teach Sufficient Stimulus Examples

The tactic for promoting setting/situation generalization called teaching sufficient stimulus examples involves teaching the learner to respond correctly to more than one example of antecedent stimulus conditions and prob- ing for generalization to untaught stimulus examples. A different stimulus example is incorporated into the teach- ing program each time a change is made in any dimen- sion of the instructional item itself or the environmental context in which the item is taught. Examples of four di- mensions by which different instructional examples can be identified and programmed are the following:

• The specific item taught (e.g., multiplication facts: 7 � 2, 4 � 5; letter sounds: a, t)

• The stimulus context in which the item is taught (e.g., multiplication facts presented in vertical for- mat, in horizontal format, in word problems; say- ing the sound of t when it appears at the beginning and end of words: tab, bat)

5Other terms commonly used for this strategy for promoting generalized be- havior change are training sufficient exemplars (Stokes & Baer, 1977) and training diversely (Stokes & Osnes, 1989).

• The setting where instruction occurs (e.g., large- group instruction at school, collaborative learning group, home)

• The person doing the teaching (e.g., classroom teacher, peer, parent)

As a general rule, the more examples the practitioner uses during instruction, the more likely the learner will re- spond correctly to untrained examples or situations. The actual number of examples that must be taught before sufficient generalization occurs will vary as a function of factors such as the complexity of the target behavior being taught, the teaching procedures employed, the student’s opportunities to emit the target behavior under the vari- ous conditions, the naturally existing contingencies of reinforcement, and the learner’s history of reinforcement for generalized responding.

Sometimes teaching as few as two examples will pro- duce significant generalization to untaught examples. Stokes, Baer, and Jackson (1974) taught a greeting re- sponse to four children with severe mental retardation who seldom acknowledged or greeted other people. The senior author, working as a dormitory assistant, used un- conditioned reinforcers (potato chips and M&Ms) and praise to shape the greeting response (at least two back- and-forth waves of a raised hand). Then this initial trainer maintained the newly learned hand wave by contriving three to six contacts per day with each of the children in various settings (e.g., playroom, corridor, dormitory, courtyard). Throughout the study as many as 23 different staff members systematically approached the children during different times of the day in different settings and recorded whether the children greeted them with a hand waving response. If a child greeted a prober with the wav- ing response, the prober responded with “Hello, (name).” Approximately 20 such generalization probes were con- ducted each day with each child.

Immediately after learning the greeting response with just one trainer, one of the children (Kerry) showed good setting/situation generalization by using it appropriately in most of her contacts with other staff members. How- ever, the other three children usually failed to greet staff members most of the time, even though they continued to greet the original trainer on virtually every occasion. A second staff member then began to reinforce and main- tain the greeting responses of these three children. As a result of adding the second trainer, the children’s greet- ing behavior showed widespread generalization to the other staff members. Stokes and colleagues’ (1974) study is important for at least two reasons. First, it demonstrated an effective method for continual assessment of setting/ situation generalization across numerous examples (in this case, people). Second, the study showed that it is

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sometimes possible to produce widespread generaliza- tion by programming only two examples.

Teach Sufficient Response Examples

Instruction that provides practice with a variety of re- sponse topographies helps to ensure the acquisition of desired response forms and also promotes response gen- eralization in the form of untrained topographies. Often called multiple exemplar training, this tactic typically incorporates both stimulus and response variations. Mul- tiple exemplar training was used to achieve the acquisi- tion and generalization of affective behavior by children with autism (Gena, Krantz, McClannahan, & Poulson, 1996); cooking skills by young adults with disabilities (Trask-Tyler, Grossi, & Heward, 1994); domestic skills (Neef, Lensbower, Hockersmith, DePalma, & Gray, 1990); vocational skills (Horner, Eberhard, & Sheehan, 1986); daily living skills (Horner, Williams, & Steveley, 1987); and requests for assistance (Chadsey-Rusch, Dras- gow, Reinoehl, Halle, & Collet-Klingenberg, 1993).

Four female high school students with moderate mental retardation participated in a study by Hughes and colleagues (1995) that assessed the effects of an inter- vention they called multiple-exemplar self-instructional training on the acquisition and generalization of the stu- dents’ conversational interactions with peers. The young women were recommended for the study because they initiated conversations and responded to peers efforts to talk with them at “low or nonexistent rates” and main- tained little eye contact. One of the students, Tanya, had recently been refused a job at a restaurant because of her “reticence and lack of eye contact during her job inter- view” (p. 202).

A key element of Hughes and colleagues’ interven- tion was practicing a wide variety of conversation starters and statements with different peer teachers. Ten volunteer peer teachers recruited from general education class- rooms helped teach conversation skills to the participants. The peer teachers were male and female, ranged in grade level from freshmen to seniors, and represented African American, Asian American, and Euro-American ethnic groups. Instead of learning a few scripted conversation openers, the participants practiced using multiple exam- ples of conversation starters selected from a pooled list of conversation openers used by general education students. Additionally, participants were encouraged to develop individual adaptations of statements, which further pro- moted response generalization by increasing the number and range of conversational statements that were likely to be used in subsequent conversations.

Before, during, and after multiple exemplar training, generalization probes were conducted of each partici- pant’s use of self-instructions, eye contact, and initiating

6Siegfried Engelmann and Douglas Carnine’s (1982) Theory of Instruc- tion: Principles and Applications is one of the most thorough and sophis- ticated treatments of the selection and sequencing of teaching examples for effective and efficient curriculum design.

and responding to conversation partners. The 23 to 32 different students who served as conversation partners for each participant represented the full range of student characteristics in the school population (e.g., gender, age, ethnicity, students with and without disabilities) and in- cluded students who were known and unknown to the participants prior to the study. The rate of conversation initiations by all four participants increased during mul- tiple exemplar training to levels approximating those of general education students and was maintained at those rates after the intervention was terminated completely (see Figure 28.5).

General Case Analysis

Teaching a learner to respond correctly to multiple ex- amples will not automatically produce generalized re- sponding to untaught examples. To achieve an optimal degree of generalization and discrimination, the behavior analyst must pay close attention to the specific examples used during instruction; not just any examples will do. Optimally effective instructional design requires select- ing teaching examples that represent the full range of stimulus situations and response requirements in the nat- ural environment.6 General case analysis (also called general case strategy) is a systematic method for select- ing teaching examples that represent the full range of stimulus variations and response requirements in the gen- eralization setting (Albin & Horner, 1988; Becker & En- gelmann, 1978; Engelmann & Carnine, 1982).

A series of studies by Horner and colleagues demon- strated the importance of teaching examples that sys- tematically sample the range of stimulus variations and response requirements the learner will encounter in the generalization setting (e.g., Horner, Eberhard, & Shee- han, 1986; Horner & McDonald, 1982; Horner, Williams, & Steveley, 1987). In a classic example of this line of re- search, Sprague and Horner (1984) evaluated the effects of general case instruction on the generalized use of vend- ing machines by six high school students with moderate to severe mental retardation. The dependent variable was the number of vending machines each student operated correctly during generalization probes of 10 different ma- chines located within the community. For a probe trial to be scored as correct, a student had to correctly perform a chain of five responses (i.e., insert the proper number of coins, activate the machine for the desired item, and so on). The researchers selected the 10 vending machines used to assess generalization because each student’s per- formance on those machines would serve as an index of ISB

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Figure 28.5 Conversation initiations per minute by four high school students with disabili- ties to conversation partners with and without disabilities during generalization sessions. The shaded bands represent typical performance by general education students. From “The Effects of Multiple-Exemplar Training on High-School Students’ Generalized Conversational Interactions” by C. Hughes, M. L. Harmer, D. J. Killina, and F. Niarhos, 1995. Journal of Applied Behavior Analysis, 28, p. 210. Copyright 1995 by the Society for the Experimental Analysis of Behavior, Inc. Reprinted by permission.

his performance “across all vending machines dispensing food and beverage items costing between $.20 and $.75 in Eugene, Oregon” (p. 274). None of the vending ma- chines used in the generalization probes was identical to any of the vending machines used during instruction.

After a single-baseline probe verified each student’s inability to use the 10 vending machines in the commu- nity, a condition the researchers called “single-instance instruction” began. Under this condition each student re- ceived individual training on a single vending machine located in the school until he used the machine in- dependently for three consecutive correct trials on each

of two consecutive days. Even though each student had learned to operate the training machine without errors, the generalization probe following single-instance in- struction revealed little or no success with the vending machines in the community (see Probe Session 2 in Figure 28.6). The continued poor performance of Stu- dents 2, 3, 5, and 6 on successive generalization probes that followed additional instruction with the single- instance training machine shows that overlearning on a single example does not necessarily aid generalization. Further evidence of the limited generalization obtained from single-instance instruction is the fact that seven of

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Figure 28.6 The number of nontrained probe machines operated correctly by students across phases and probe sessions. From “The Effects of Single Instance, Multiple Instance, and General Case Training on Generalized Vending Machine Use by Moderately and Severely Handicapped Students” by J. R. Sprague and R. H. Horner, 1984, Journal of Applied Behavior Analysis, 17, p. 276. Copyright 1984 by the Society for the Experimental Analysis of Behavior, Inc. Reprinted by permission.

the eight total probe trials performed correctly by all stu- dents after single-instance instruction were on Probe Ma- chine 1, the vending machine that most closely resembled the machine on which the students had been trained.

Next, multiple-instance training was implemented with Students 4, 5, and 6. The teaching procedures and performance criteria for multiple-instance training were the same as those used in the single-instance condition ex- cept that each student received instruction until he reached criterion on all three new machines. Sprague and Horner (1984) purposely selected vending machines to use in the multiple-instance instruction that were similar

to one another and that did not sample the range of stim- ulus variations and response requirements that defined the vending machines in the community. After reaching training criterion on three additional machines, Students 4, 5, and 6 were still unable to operate the machines in the community. During the six probe sessions that followed multiple-instance instruction, these students correctly completed only 9 of the 60 total trials.

The researchers then introduced general case in- struction in multiple-baseline fashion across students. This condition was the same as multiple-instance in- struction except that the three different vending machines

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used in general case training, when combined with the single-instance machine, provided the students with prac- tice across the total range of stimulus conditions and re- sponse variations found in vending machines in the community. None of the training machines, however, were exactly the same as any machine used in the gen- eralization probes. After reaching training criterion on the general case machines, all six students showed sub- stantial improvements in their performance on the 10 un- trained machines. Sprague and Horner (1984) speculated that Student 3’s poor performance on the first general- ization probe following general case instruction was caused by a ritualistic pattern of inserting coins that he had developed during previous probe sessions. After re- ceiving repeated practice on the coin insertion step dur- ing a training session between Probe Sessions 5 and 6, Student 3’s performance on the generalization machines improved greatly.

Negative, or “Don’t Do It,” Teaching Examples

Generalization across every possible condition or situation is rarely desirable. Teaching a student where and when to use a new skill or bit of knowledge does not mean that he also knows where and when not to use this newly learned behavior. Brian, for example, needs to learn that he should not say, “Hello, how are you?” to people that he greeted within the past hour or so. Learners must be taught to discriminate the stimulus conditions that signal when responding is appropriate from stimulus conditions that signal when responding is inappropriate.

Instruction that includes “don’t do it” teaching ex- amples intermixed with positive examples provides learn- ers with practice discriminating stimulus situations in which the target behavior should not be emitted (i.e., S�s) from those conditions when the behavior is appropriate. This sharpens the stimulus control necessary to master many concepts and skills (Engelmann & Carnine, 1982).7

Horner, Eberhard, and Sheehan (1986) incorporated “don’t do it” examples into training programs to teach four high school students with moderate to severe mental re- tardation how to bus tables in cafeteria-style restaurants. To correctly bus a table, the student had to remove all dishes, silverware, and garbage from tabletop, chairs, and the floor

7Teaching examples used to help students discriminate when not to re- spond (i.e., S�s) are sometimes called negative examples and contrasted with positive examples (i.e., SDs). However, in our work in teaching this concept, practitioners have told us that the term negative teaching exam- ple suggests that the teacher is modeling or showing the learner how not to perform the target behavior. Instruction on the desired topography for some behaviors may be aided by providing students with models on how not to perform certain behavior (i.e., negative examples), but the function of “don’t do it” examples is to help the learner discriminate antecedent conditions that signal an inappropriate occasion for responding.

under and around the table; wipe the tabletop; straighten chairs; and place dirty dishes and garbage in appropriate re- ceptacles. In addition, the students were taught to inquire, through the use of cards, if a customer was finished with empty dishes. The three settings, one for training and two for generalization probes, differed in terms of size and fur- niture characteristics and configurations.

Each training trial required the student to attend to the following stimulus features of a table: (a) the pres- ence or absence of people at a table, (b) whether people were eating at the table, (c) the amount and/or status of food on dishes, (d) the presence or absence of garbage at a table, and (e) the location of garbage and/or dirty dishes at a table. Training consisted of 30-minute sessions in- volving six table types that represented the range of con- ditions likely to be encountered in a cafeteria-style restaurant. A trainer modeled correct table bussing, ver- bally prompted correct responding and stopped the stu- dent when errors occurred, recreated the situation, and provided additional modeling and assistance. The six training examples consisted of four to-be-bussed tables and two not-to-be-bussed tables (see Table 28.1).

During generalization probe sessions, which were conducted in two restaurants not used for training, each student was presented with 15 tables selected to repre- sent the range of table types the students could be ex- pected to encounter if employed in a cafeteria-style restaurant. The 15 probe tables consisted of 10 to-be- bussed tables and 5 not-to-be-bussed tables. The results showed a functional relation between general case in- struction that included not-to-be-bussed tables and “im- mediate and pronounced improvement in the percentage of probe tables responded to correctly” (p. 467).

Negative teaching examples are necessary when dis- criminations must be made between appropriate and inappropriate conditions for a particular response. Prac- titioners should ask this question: Is responding always appropriate in the generalization setting(s)? If the an- swer is no, then “don’t do it now” teaching examples should be part of instruction.

Does the teaching setting or situation naturally or au- tomatically include a sufficient number and range of neg- ative examples? The teaching situation must be analyzed to answer this important question. Practitioners may need to contrive some negative teaching examples. Practition- ers should not assume the natural environment will read- ily reveal sufficient negative examples. Conducting training in the natural environment is no guarantee that learners will be exposed to stimulus situations that they are likely to encounter in the generalization environment after training. For example, in the study on teaching table bussing described earlier, Horner and colleagues (1986) noted that, “on some days the trainer needed actively to

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Table 28.1 Six Training Examples Used to Teach Students with Disabilities How to Bus Tables in Cafeteria-Style Restaurants

Training Presence Garbage: Location Examples of People and People Eating Dishes: Empty/ Present or of Garbage Correct

Possessions or Not Eating Part/New Food Not Present and Dishes Response

1 0 People + N/A Partial Present Table Don’t Bus possessions Chairs

2 0 People N/A Partial Present Table, Floor, Chair Bus

3 2 People Eating New food Present Table, Chair, Floor Don’t Bus

4 0 People N/A Empty Present Table, Floor Bus

5 1 Person Not eating Empty Present Chair, Floor Bus

6 2 People Not eating Empty Present Table Bus

From “Teaching Generalized Table Bussing: The Importance of Negative Teaching Examples” by R. H. Horner, J. M. Eberhard, and M. R. Sheehan, 1986, Behavior Modification, 10, p. 465. Copyright 1986 by the Sage Publications, Inc. Used by permission.

set up one or more table types to ensure that a student had access to a table type that was not ‘naturally’ avail- able” (p. 464).

“Don’t do it” teaching examples should be selected and sequenced according to the degree to which they dif- fer from positive examples (i.e., SDs). The most effective negative teaching examples will share many of the rele- vant characteristics of the positive teaching examples (Horner, Dunlap, & Koegel, 1988). Such minimum dif- ference negative teaching examples help the learner to perform the target behavior with the precision required by the natural environment. Minimum difference teaching examples help eliminate “generalization errors” due to overgeneralization and faulty stimulus control. For ex- ample, the “don’t bus” tables used by Horner and col- leagues (1986) shared many features with the “bus” tables (see Table 28.1).

Make the Instructional Setting Similar to the Generalization Setting

Fresno State coach Pat Hill expects Ohio Stadium to be a new experience for the Bulldogs, who will visit for the first time. For a practice last week in FSU’s stadium, Hill hired a production company to blast the Ohio State fight song—at about 90 decibels—throughout the two- hour session. “We created some noise and atmosphere to give us a feel of a live game,” Hill said.

—Columbus Dispatch (August 27, 2000)

A basic strategy for promoting generalization is to incor- porate into the instructional setting stimuli that the learner is likely to encounter in the generalization setting. The greater the similarity between the instructional environ- ment and the generalization environment, the more likely the target behavior will be emitted in the generalization setting. The principle of stimulus generalization states that

a behavior is likely to be emitted in the presence of a stim- ulus very similar to the stimulus conditions in which the behavior was reinforced previously, but the behavior will likely not be emitted under stimulus conditions that differ significantly from the training stimulus.

Stimulus generalization is a relative phenomenon: The more a given stimulus configuration resembles the stimulus conditions present during instruction, the greater the probability that the trained response will be emitted, and vice versa. A generalization setting that differs sig- nificantly from the instructional setting may not provide sufficient stimulus control over the target behavior. Such a setting may also contain stimuli that impede the target behavior because their novelty confuses or startles the learner. Exposing the learner to stimuli during instruc- tion that are commonly found in the generalization set- ting increases the likelihood that those stimuli will acquire some stimulus control over the target behavior and also prepares the learner for the presence of stimuli in the generalization setting that have the potential of impeding performance. Two tactics used by applied be- havior analysts to implement this basic strategy are pro- gramming common stimuli and teaching loosely.

Program Common Stimuli

Programming common stimuli means including typical features of the generalization setting into the instructional setting. Although behavior analysts have attached a spe- cial term to this tactic, successful practitioners in many fields have long used this technique for promoting gen- eralized behavior change. For example, coaches, music teachers, and theater directors hold scrimmages, mock auditions, and dress rehearsals to prepare their athletes, musicians, and actors to perform important skills in set- tings that include the sights, sounds, materials, people,

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and procedures that simulate as closely as possible those in the “real world.”

Van den Pol and colleagues (1981) programmed common stimuli when they taught three young adults with disabilities how to order and eat in fast-food restau- rants. The researchers used numerous items and photos from actual restaurants to make the classroom simulate the conditions found in actual restaurants. Plastic signs with pictures and names of various McDonald’s sand- wiches were posted on the classroom wall, a table was transformed into a mock “counter” for role-playing trans- actions, and the students practiced responding to 60 pho- tographic slides taken in actual restaurants showing both positive and negative (“don’t do it”) examples of situa- tions customers are likely to encounter.

Why go to all the trouble of simulating the gener- alization setting? Why not just conduct instruction in the generalization setting itself to ensure that the learner experiences all of the relevant aspects of the setting? First, conducting instruction in natural settings is not al- ways possible or practical. Lots of resources and time may be necessary to transport students to community- based settings.

Second, community-based training may not expose students to the full range of examples they are likely to encounter later in the same setting. For example, students who receive in situ instruction for grocery shopping or street crossing during school hours may not experience the long lines at the checkout counters or heavy traffic patterns typical of evening hours.

Third, instruction in natural settings may be less ef- fective and efficient than classroom instruction because the trainer cannot halt the natural flow of events to con- trive an optimal number and sequence of training trials needed (e.g., Neef, Lensbower, Hockersmith, DePalma, & Gray, 1990).

Fourth, instruction in simulated settings can be safer, particularly with target behaviors that must be performed in potentially dangerous environments or that have se- vere consequences if performed incorrectly (e.g., Mil- tenberger et al., 2005), or when children or people with learning problems must perform complex procedures. If the procedures involve invading the body or errors dur- ing practice are potentially hazardous, simulation train- ing should be used. For example, Neef, Parrish, Hannigan, Page, and Iwata (1990) had children with neu- rogenic bladder complications practice performing self- catheterization skills on dolls.

Programming common stimuli is a straightforward two-step process of (a) identifying salient stimuli that characterize the generalization setting(s) and (b) incor- porating those stimuli into the instructional setting. A practitioner can identify possible stimuli in the general- ization setting to make common by direct observation or

by asking people familiar with the setting. Practitioners should conduct observations in the generalization set- ting(s) and write down prominent features of the envi- ronment that might be important to include during training. When direct observation is not feasible, practi- tioners can obtain secondhand knowledge of the setting by interviewing or giving checklists to people who have firsthand knowledge of the generalization setting—those who live, work in, or are otherwise familiar with the gen- eralization setting(s) in question.

If a generalization setting includes important stimuli that cannot be recreated or simulated in the instructional setting, then at least some training trials must be con- ducted in the generalization setting. However, as pointed out previously, practitioners should not assume that community-based instruction will guarantee students’ ex- posure to all of the important stimuli common to the gen- eralization setting.

Teach Loosely

Applied behavior analysts control and standardize inter- vention procedures to maximize their direct effects, and so the effects of their interventions can be interpreted and replicated by others. Yet restricting teaching procedures to a “precisely repetitive handful of stimuli or formats may, in fact, correspondingly restrict generalization of the lessons being learned” (Stokes & Baer, 1977, p. 358). To the extent that generalized behavior change can be viewed as the opposite of strict stimulus control and dis- crimination, one technique for facilitating generalization is to vary as many of the noncritical dimensions of the an- tecedent stimuli as possible during instruction.

Teaching loosely, randomly varying noncritical as- pects of the instructional setting within and across teach- ing sessions, has two advantages or rationales for promoting generalization. First, teaching loosely reduces the likelihood that a single or small group of noncritical stimuli will acquire exclusive control over the target be- havior. A target behavior that inadvertently comes under the control of a stimulus present in the instructional set- ting but not always present in the generalization setting may not be emitted in the generalization setting. Here are two examples of this type of faulty stimulus control:

• Following teachers’ directions: A student with a history of receiving reinforcement for complying with teachers’ directions when they are given in a loud voice and accompanied by a stern facial ex- pression may not follow directions that do not con- tain one or both of those noncritical variables. The discriminative stimulus for the student’s compli- ance with teacher directions should be the content of the teacher’s statements.

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• Assembling bicycle sprocket sets: A new employee at the bicycle factory inadvertently learns to assem- ble rear sprocket sets by putting a red sprocket on top of a green sprocket and a green sprocket on top of a blue sprocket because the sprocket sets on a particular bicycle model in production on the day she was trained were colored in that fashion. How- ever, proper assembly of a sprocket set has nothing to do with the colors of the individual sprockets; the relevant variable is the relative size of the sprockets (i.e., the biggest sprocket goes on the bottom, the next biggest on top of that one, and so on).

Systematically varying the presence and absence of noncritical stimuli during instruction greatly decreases the chance that a functionally irrelevant factor, such as a teacher’s tone of voice or sprocket color in these two ex- amples, will acquire control of the target behavior (Kirby & Bickel, 1988).

A second rationale for loose teaching is that including a wide variety of noncritical stimuli during instruction in- creases the probability that the generalization setting will in- clude at least some of the stimuli that were present during instruction. In this sense, loose teaching acts as a kind of catchall effort at programming common stimuli and makes it less likely that a student’s performance will be impeded or “thrown off” by the presence of a “strange” stimulus.

Loose teaching applied to the previous two exam- ples might entail the following:

• Following teachers’ directions: During instruction the teacher varies all of the factors mentioned ear- lier (e.g., tone of voice, facial expression), plus gives directions while standing, while sitting, from different places within the classroom, at different times of the day, while the student is alone and in groups, while looking away from the student, and so on. In each instance, reinforcement is contin- gent on the student’s compliance with the content of the teacher’s direction irrespective of the pres- ence or absence of any of the noncritical features.

• Assembling bicycle sprocket sets: During training the new employee assembles sprocket sets contain- ing sprockets of widely varying colors, after receiv- ing the component sprockets in varied sequences, when the factory floor is busy, at different times during a work shift, with and without music play- ing, and so forth. Irrespective of the presence, ab- sence, or values of any of these noncritical factors, reinforcement would be contingent on correct as- sembly of sprockets by relative size.

Seldom used as a stand-alone tactic, loose teaching is often a recognizable component of interventions when

generalization to highly variable and diverse settings or situations is desired. For example, Horner and colleagues (1986) incorporated loose teaching into their training pro- gram for table busing by systematically but randomly varying the location of the tables, the number of people at the tables, whether food was completely or partially eaten, the amount and location of garbage, and so forth. Hughes and colleagues (1995) incorporated loose teach- ing by varying the peer teachers and varying the loca- tions of training sessions. Loose teaching is often a recognizable feature of language training programs that use milieu, incidental, and naturalistic teaching methods (e.g., Charlop-Christy & Carpenter, 2000; McGee, Mor- rier, & Daly, 1999; Warner, 1992).

Few studies evaluating the effects of using loose teaching in isolation have been reported. One exception is an experiment by Campbell and Stremel-Campbell (1982), who evaluated the effectiveness of loose teaching as a tactic for facilitating the generalization of newly ac- quired language by two students with moderate mental re- tardation. The students were taught the correct use of the words is and are in “wh” questions (e.g., “What are you doing?”), yes/no reversal questions (e.g., “Is this mine?”), and statements (e.g., “These are mine?”). Each student re- ceived two 15-minute language training sessions con- ducted within the context of other instructional activities that were part of each child’s individualized education program, one during an academic task and the second during a self-help task. The student could initiate a lan- guage interaction based on the wide variety of naturally occurring stimuli, and the teacher could try to evoke a statement or question from the student by intentionally misplacing instructional materials or offering indirect prompts. Generalization probes of the students’ language use during two daily 15-minute free-play periods revealed substantial generalization of the language structures ac- quired during the loose teaching sessions.

The learner’s performance of the target behavior should be established under fairly restricted, simplified, and consistent conditions, before much “looseness” is in- troduced. This is particularly important when teaching complex and difficult skills. Only noncritical (i.e., func- tionally irrelevant) stimuli should be “loosened.” Practi- tioners should not inadvertently loosen stimuli that reliably function in the generalization setting as discrim- inative stimuli (SDs) or as “don’t do it” examples (S�s). Stimuli known to play important roles in signaling when and when not to respond should be systematically incor- porated into instructional programs as teaching examples. A stimulus condition that may be functionally irrelevant for one skill may be a critical SD for another skill.

Taking the notion of varying noncritical aspects of the instructional setting and procedures to its logical

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limit, Baer (1999) offered the following advice for loose teaching:

• Use two or more teachers.

• Teach in two or more places.

• Teach from a variety of positions.

• Vary your tone of voice.

• Vary your choice of words.

• Show the stimuli from a variety of angles, using sometimes one hand and sometimes the other.

• Have other persons present sometimes and not other times.

• Dress quite differently on different days.

• Vary the reinforcers.

• Teach sometimes in bright light, sometimes in dim light.

• Teach sometimes in noisy settings, sometimes in quiet ones.

• In any setting, vary the decorations, vary the furni- ture, and vary their locations.

• Vary the times of day when you and everyone else teach.

• Vary the temperature in the teaching settings.

• Vary the smells in the teaching settings.

• Within the limits possible vary the content of what’s being taught.

• Do all of this as often and as unpredictably as pos- sible. (p. 24)

Of course, Baer (1999) was not suggesting that a teacher needs to vary all of these factors for every be- havior taught. But building a reasonable degree of ”loose- ness” into teaching is an important element of a teacher’s overall effort to program for generalization rather than train and hope.

Maximize Contact with Reinforcement in the Generalization Setting

Even though a practitioner is successful in getting the learner to emit a newly acquired target behavior in a gen- eralization setting with a naturally existing contingency of reinforcement, generalization and maintenance may be short-lived if the behavior makes insufficient contact with reinforcement. In such cases the practitioner’s ef- forts to promote generalization revolves around ensuring that the target behavior contacts reinforcement in the gen- eralization setting. Five of the 13 tactics for promoting

generalized behavior change described in this chapter in- volve some form of arranging or contriving for the target behavior to be reinforced in the generalization setting.

Teach Behavior to Levels Required by Natural Contingencies

Baer (1999) suggested that a common mistake practi- tioners make when attempting to employ natural contin- gencies of reinforcement is failing to teach the behavior change well enough so that it contacts the contingency.

Sometimes behavior changes that seem to need general- ization may only need better teaching. Try making the students fluent, and see if they still need further support for generalization. Fluency may consist of any or all of the following: high rate of performance, high accuracy of performance, fast latency, given the opportunity to re- spond, and strong response. (p. 17)

A new behavior may occur in the generalization set- ting but fail to make contact with the naturally existing contingencies of reinforcement. Common variables that diminish contact with reinforcement in the generaliza- tion setting include the accuracy of the behavior, the di- mensional quality of the behavior (i.e., frequency, duration, latency, magnitude), and the form (topography) of the behavior. The practitioner may need to enhance the learner’s performance in one or more of these vari- ables to ensure that the new behavior will meet the nat- urally existing contingencies of reinforcement. For example, when given a worksheet to complete at his desk, a student’s behavior that is consistent with the following dimensions is unlikely to contact reinforcement for com- pleting the task, even if the student has the ability to com- plete each worksheet item accurately.

• Latency too long. A student who spends 5 minutes “daydreaming” before he begins reading the direc- tions may not finish in time to obtain reinforcement.

• Rate too low. A student who needs 5 minutes to read the directions for an independent seatwork as- signment that his peers read in less than 1 minute may not finish in time to obtain reinforcement.

• Duration too brief. A student who can work with- out direct supervision for only 5 minutes at a time will not be able to complete any task requiring more than 5 minutes of independent work.

The solution for this kind of generalization problem, if not always simple, is straightforward. The behavior change must be made more fluent: The learner must be taught to emit the target behavior at a rate commensurate with the naturally occurring contingency, with more

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accuracy, within a shorter latency, and/or at a greater mag- nitude. Generalization planning should include identifi- cation of the levels of performance necessary to access existing criteria for reinforcement.

Program Indiscriminable Contingencies

Applied behavior analysts purposely design and imple- ment interventions so the learner receives consistent and immediate consequences for emitting the target behav- ior. Although consistent and immediate consequences are often necessary to help the learner acquire new behav- ior, those very contingencies can impede generalization and maintenance. The clear, predictable, and immediate consequences that are typically part of systematic in- struction can actually work against generalized respond- ing. This is most likely to occur when a newly acquired skill has not yet contacted naturally existing contingen- cies of reinforcement, and the learner can discriminate when the instructional contingencies are absent in the generalization settings. If the presence or absence of the controlling contingencies in the generalization setting is obvious or predictable to the learner (“Hey, the game’s off. There’s no need to respond here/now”), the learner may stop responding in the generalization setting, and the behavior change the practitioner worked so hard to develop may cease to occur before it can contact the nat- urally existing contingency of reinforcement.

An indiscriminable contingency is one in which the learner cannot discriminate whether the next response will produce reinforcement. As a tactic for promoting generalization and maintenance, programming indis- criminable contingencies involves contriving a contin- gency in which (a) reinforcement is contingent on some, but not all, occurrences of the target behavior in the gen- eralization setting, and (b) the learner is unable to pre- dict which responses will produce reinforcement.

The basic rationale for programming indiscriminable contingencies is to keep the learner responding often enough and long enough in the generalization setting for the target behavior to make sufficient contact with the naturally existing contingencies of reinforcement. From that point on, the need to program contrived contingen- cies to promote generalization will be moot. Applied be- havior analysts use two related techniques to program indiscriminable contingencies: intermittent schedules of reinforcement and delayed rewards.

Intermittent Schedules of Reinforcement. A newly learned behavior often must occur repeatedly over a period of time in the generalization setting be- fore it contacts a naturally existing contingency of rein- forcement. During that time, an extinction condition

exists for responses emitted in the generalization set- ting. The current or most recent schedule of reinforce- ment for a behavior in the instructional setting plays a significant role in how many responses will be emitted in the generalization setting prior to reinforcement. Be- haviors that have been under continuous schedules of reinforcement (CRF) show very limited response main- tenance under extinction. When reinforcement is no longer available, responding is likely to decrease rapidly to prereinforcement levels. On the other hand, behaviors with a history of intermittent schedules of reinforcement often continue to be emitted for relatively long periods of time after reinforcement is no longer available (e.g., Dunlap & Johnson, 1985; Hoch, McComas, Thompson, & Paone, 2002).

An experiment by Koegel and Rincover (1977, Ex- periment II) showed the effects of intermittent schedules of reinforcement on response maintenance in a general- ization setting. The participants were six boys diagnosed with autism and severe to profound mental retardation, ages 7 to 12 years, who had participated in a previous study on generalization and had showed generalized re- sponding in the extra-therapy setting used in that exper- iment (Rincover & Koegel, 1975). As in Experiment I by Koegel and Rincover (1977) described earlier in this chapter, one-on-one training trials were conducted with each child and the trainer seated at a table in a small room, and generalization trials were conducted by an un- familiar adult standing outside on the lawn, surrounded by trees. Two types of imitative response class consisted of (a) nonverbal imitation (e.g., raising arm) in response to an imitative model and the verbal instruction, “Do this” and (b) touching a body part in response to verbal in- structions such as, “Touch your nose.” After acquiring an imitation response, each child was given additional trials on one of three randomly chosen schedules of reinforce- ment: CRF, FR 2, or FR 5. Only after these additional training trials were the children taken outside to assess re- sponse maintenance. Once outside, trials were conducted until the child’s correct responding had decreased to 0%, or was maintained at 80% correct or above for 100 con- secutive trials.

Behaviors that were most recently on a CRF sched- ule in the instructional setting underwent extinction quickly in the generalization setting (see Figure 28.7). Generalized responding occurred longer for the FR 2 trained behavior, and longer still for behavior that had been shifted to an FR 5 schedule in the instructional set- ting. The results showed clearly that the schedule of re- inforcement in the instructional setting had a predictable effect on responding in the absence of reinforcement in the generalization setting: The thinner the schedule in the instructional setting, the longer the response maintenance in the generalization setting.

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100

P er

ce n

t C

or re

ct R

es p

on d

in g

CRF Child 4

FR 2 Child 5

FR 2 Child 6

(Same Child)

FR 5FR 5

Child 6

Child 7

50 100 200 300 400 500 Trials

Figure 28.7 Percent of correct responses by three children in a generalization setting as a function of the schedule of reinforcement used during the final sessions in an instructional setting. From “Research on the Differences between Generalization and Maintenance in Extra-Therapy Responding” by R. L. Koegel and A. Rincover, 1977, Journal of Applied Behavior Analysis, 10, p. 8. Copyright 1977 by the Society for the Experimental Analysis of Behavior, Inc. Reprinted by permission.

The defining feature of all intermittent schedules of reinforcement is that only some responses are reinforced, which means, of course, that some responses go unrein- forced. Thus, one possible explanation for the mainte- nance of responding during periods of extinction for behaviors developed under intermittent schedules is the relative difficulty of discriminating that reinforcement is no longer available. Thus, the unpredictability of an in- termittent schedule may account for the maintenance of behavior after the schedule is terminated.

Practitioners should recognize that although all in- discriminable contingencies of reinforcement involve in- termittent schedules, not all schedules of intermittent reinforcement are indiscriminable. For example, although the FR 2 and FR 5 schedules of reinforcement used by Koegel and Rincover (1977) were intermittent, many learners would soon be able to discriminate whether re- inforcement would follow their next response. In con- trast, a student whose behavior is being supported by a VR 5 schedule of reinforcement cannot determine whether his next response will be reinforced.

Delayed Rewards. Stokes and Baer (1977) sug- gested that not being able to discriminate in what set- tings a behavior will be reinforced is similar to not being able to discriminate whether the next response will be reinforced. They cited an experiment by Schwarz and Hawkins (1970) in which each day after school a sixth- grade girl was shown videotapes of her behavior in that

day’s math class and received praise and token rein- forcement for improvements in her posture, reducing the number of times she touched her face, and speaking with sufficient volume to be heard by others. Reinforce- ment after school was contingent on behaviors emitted during math class only, but comparable improvements were noted in spelling class as well. The generalization data were taken from videotapes that were made of the girl’s behavior in spelling class but were never shown to her. Stokes and Baer hypothesized that because rein- forcement was delayed (the behaviors that produced praise and tokens were emitted during math class but were not rewarded until after school), it may have been difficult for the student to discriminate when improved performance was required for reinforcement. They sug- gested that the generalization across settings of the target behaviors may have been a result of the indiscriminable nature of the response-to-reinforcement delay.

Delayed rewards and intermittent schedules of rein- forcement are alike in two ways: (a) Reinforcement is not delivered each time the target behavior is emitted (only some responses are followed by reinforcement), and (b) there are no clear stimuli to signal the learner which current responses will produce reinforcement. A delayed reward contingency differs from intermittent re- inforcement in that instead of delivering the consequence immediately following an occurrence of the target be- havior, the reward is provided after a period of time has elapsed (i.e., a response-to-reward delay). Receiving the

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delayed reward is contingent on the learner having per- formed the target behavior in the generalization setting during an earlier time period. With an effective delayed reward contingency, the learner cannot discriminate when (or where, depending on the details of the contingency) the target behavior must be emitted in order to receive reinforcement. As a result, to have the best chance to re- ceive the reward later, the learner must “be good all day” (Fowler & Baer, 1981).

Two similar studies by Freeland and Noell (1999, 2002) investigated the effects of delayed rewards on the maintenance of students’ mathematics performance. Par- ticipants in the second study were two third-grade girls who had been referred by their teacher for help with mathematics. That target behavior for both students was writing answers to single-digit addition problems with sums to 18. The researchers used a multiple-treatment reversal design to compare the effects of five conditions on the number of correct digits written as answers to sin- gle-digit addition problems during daily 5-minute work periods (e.g., writing “11” as the answer to “5 + 6 = ?” counted as two digits correct).

• Baseline: Green worksheets; no programmed con- sequences; students were told they could attempt as many or as few problems as they wanted.

• Reinforcement: Blue worksheets with a goal num- ber at the top indicating the number of correct dig- its needed to choose a reward in the “goody box”; each student’s goal number was the median num- ber of correct digits on the last three worksheets; all worksheets were graded after each session.

• Delay 2: White worksheets with goal number; after every two sessions, one of the two worksheets completed by each student was randomly selected for grading; reinforcement was contingent on meeting highest median of three consecutive ses- sions up to that point in the study.

• Delay 4: White worksheets with goal number and same procedures as Delay 2 except that worksheets were not graded until four sessions had been completed, at which time one of each student’s previous four worksheets was randomly selected and graded.

• Maintenance: White worksheets with goal number as before; no worksheets were graded and no feed- back or rewards for performance were given.

The fact that different colored worksheets were used for each condition in this study made it easy for the stu- dents to predict the likelihood of reinforcement. A green worksheet meant no “goody bag”—and no feedback at all—no matter how many correct digits were written. However, meeting one’s performance criterion on a white

worksheet sometimes produced reinforcement. This study provides powerful evidence of the importance of having contingencies in the instructional setting “look like” the contingencies in effect in the generalization setting(s) in two ways: (a) Both students showed large decreases in performance when baseline conditions were reinstated, and immediate drops during a second return to baseline; and (b) the students continued completing math prob- lems at a high rate during the maintenance condition, even though no reinforcement was provided. (See Figure 28.8.)

When the delayed (indiscriminable) contingencies were implemented, all students demonstrated levels of correct responding at or above levels during the rein- forcement phase. When the students were exposed to maintenance conditions, Amy maintained high levels of responding for 18 sessions with variable performance over the final six sessions, and Kristen showed a gradu- ally increasing rate of performance over 24 sessions. The results demonstrated that behavior with an indiscrim- inable contingency can be maintained at the same rate as with a predictable schedule, and with greater resistance to extinction.

Delayed consequences have been used to promote the setting/situation generalization and response mainte- nance of a wide range of target behaviors, including aca- demic and vocational tasks by individuals with autism (Dunlap, Koegel, Johnson, & O’Neill, 1987), young chil- dren’s toy play, social initiations, and selection of healthy snacks (R. A. Baer, Blount, Dietrich, & Stokes, 1987; R. A. Baer, Williams, Osnes, & Stokes, 1984; Osnes, Guevremont, & Stokes, 1986), restaurant trainees’ re- sponding appropriately to coworkers’ initiations (Grossi et al., 1994); and performance on reading and writing tasks (Brame, 2001; Heward, Heron, Gardner, & Prayzer, 1991).

The effective use of delayed consequences can re- duce (or even eliminate in some instances) the learner’s ability to discriminate when a contingency is or is not in effect. As a result, the learner needs to “be good” (i.e., emit the target behavior) all the time. If an effective con- tingency can be made indiscriminable across settings and target behaviors, the learner will also have to “be good” everywhere, with all of his or her relevant skills.

Following are four examples of classroom applica- tions of indiscriminable contingencies involving delayed rewards. Each of these examples also features an inter- dependent group contingency (see Chapter 26) by mak- ing rewards for the whole class contingent on the performance of randomly selected students.

• Spinners and dice. A procedure such as the follow- ing can make academic seatwork periods more ef- fective. Every few minutes (e.g., on a VI 5-minute

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Figure 28.8 Number of correct digits per minute by two third-grade students while answering math problems during base- line (BL), reinforcement contingent on performance on one randomly selected worksheet after each session (RF), rein- forcement contingent on performance on one randomly selected worksheet after every two (D2) or four (D4) sessions, and maintenance conditions. From “Programming for Maintenance: An Investigation of Delayed Intermittent Reinforcement and Common Stimuli to Create Indiscriminable Contingencies” by J. T. Freeland and G. H. Noell, 2002, Journal of Behavioral Education, 11, p. 13. Copyright 2002 by Human Sciences Press. Reprinted by permission.

schedule), the teacher (a) randomly selects a stu- dent’s name, (b) walks to that student’s desk and has the student spin a spinner or roll a pair of dice, (c) counts backward from the worksheet problem or item the student is currently working on by the number shown on the spinner or dice, and (d) gives a token to the student if that problem or item is correct. Students who immediately begin to work on the assignment and work quickly but carefully throughout the seatwork period are most likely to obtain reinforcers under this indiscriminable contingency.

• Story fact recall game. Many teachers devote 20 to 30 minutes per day to sustained silent reading (SSR), a period when students can read silently from books of their choice. A story fact recall game can encourage students to read with a pur- pose during SSR. At the end of the SSR period the teacher asks several randomly selected students a question about the book they are reading. For ex-

ample, a student who is reading Chapter 3 of Eliza- beth Winthrop’s The Castle in the Attic might be asked, What did William give the Silver Knight to eat? (Answer: bacon and toast). A correct answer is praised by the teacher, applauded by the class, and earns a marble in a jar toward a reward for the whole class. Students do not know when they will be called on or what they might be asked (Brame, Bicard, Heward, & Greulich, 2007).

• Numbered heads together. Collaborative learning groups (small groups of students working together on a joint learning activity) can be effective, but teachers should use procedures that motivate all students to participate. A technique called num- bered heads together can ensure that all students actively participate (Maheady, Mallete, Harper, & Saca, 1991). Students are seated in heterogeneous groups of three or four, and each student is given the number 1, 2, 3, or 4. The teacher asks the class a question, and each group discusses the problem

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and comes up with an answer. Next, the teacher randomly selects a number from 1 to 4 and then calls on one or more students with that number to answer. It is important that every person in the group knows the answer to the question. This strat- egy promotes cooperation within the group rather than competition. Because all students must know the answer, group members help each other under- stand not only the answer, but also the how and why behind it. Finally, this strategy encourages in- dividual responsibility.

• Intermittent grading. Most students do not receive sufficient practice writing, and when students do write, the feedback they receive is often ineffective. One reason for this may be that providing detailed feedback on a daily composition by each student in the class requires more time and effort than even the most dedicated teacher can give. A procedure called intermittent grading offers one solution to this problem (Heward, Heron, Gardner, & Prayzer, 1991). Students write for 10 to 15 minutes each day, but instead of reading and evaluating every student’s paper, the teacher provides detailed feed- back on a randomly selected 20 to 25% of students’ daily compositions. The students whose papers were graded earn points based on individualized performance criteria, and bonus points are given to the class contingent on the quality of the selected and graded papers (e.g., if the authors of four of the five papers that were graded met their individ- ual criteria). Students’ papers that were graded can be used as a source of instructional examples for the next lesson.

The success of a delayed reward tactic in promoting generalization and maintenance rests on (a) the indis- criminability of the contingency (i.e., the learner cannot tell exactly when emitting the target behavior in the gen- eralization setting will produce a reward at a later time), and (b) the learner understanding the relation between his emitting the target behavior at an earlier time and re- ceiving a reward later. A delayed rewards intervention may not be effective with some learners with severe cog- nitive disabilities.

Guidelines for Programming Indiscriminable Contingencies. Practioners should consider these guidelines when implementing indiscriminable con- tingencies:

• Use continuous reinforcement during the initial stages of acquiring new behaviors or when strengthening little-used behaviors.

• Systematically thin the schedule of reinforcement based on the learner’s performance (see Chap-

ter 13). Remember that the thinner the schedule of reinforcement is, the more indiscriminable it is (e.g., an FR 5 schedule is more indiscriminable than an FR 2 schedule); and variable schedules of reinforcement (e.g., VR and VI schedules) are more indiscriminable than fixed schedules are (e.g., FR and FI schedules).

• When using delayed rewards, begin by delivering the reinforcer immediately following the target be- havior and gradually increase the response-to- reinforcement delay.

• Each time a delayed reward is delivered, explain to the learner that he is receiving the reward for spe- cific behaviors he performed earlier. This helps to build and strengthen the learner’s understanding of the rule describing the contingency.

When selecting reinforcers to use during instruction, practitioners should try to use or eventually shift to the same reinforcers that the learner will acquire in the gen- eralization environment. The reinforcer itself may serve as a discriminative stimulus for the target behavior (e.g., Koegel & Rincover, 1977)

Set Behavior Traps

Some contingencies of reinforcement are especially pow- erful, producing substantial and long-lasting behavior changes. Baer and Wolf (1970) called such contingen- cies behavior traps. Using a mouse trap as an analogy, they described how a householder has only to exert a rel- atively small amount of behavioral control over the mouse—getting the mouse to smell the cheese—to pro- duce a behavior change with considerable (in this case, complete) generalization and maintenance.

A householder without a trap can, of course, still kill a mouse. He can wait patiently outside the mouse’s hole, grab the mouse faster than the mouse can avoid him, and then apply various forms of force to the unfortunate animal to accomplish the behavioral change desired. But this performance requires a great deal of competence: vast patience, super-coordination, extreme manual dexterity, and a well-suppressed squeamishness. By contrast, a householder with a trap need very few ac- complishments: If he can merely apply the cheese and then leave the trap where the mouse is likely to smell that cheese, in effect he has guaranteed general(ized) change in the mouse’s future behavior.

The essence of a trap, in behavioral terms, is that only a relatively simple response is necessary to enter the trap, yet once entered, the trap cannot be resisted in creating general behavior change. For the mouse, the entry response is merely to smell the cheese. Everything

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proceeds from there almost automatically. (p. 321, em- phasis added)

Behavioral trapping is a fairly common phenomenon that everyone experiences from time to time. Behavior traps are particularly evident in the activities we “just can- not get (or do) enough of.” The most effective behavior traps share four essential features: (a) They are “baited” with virtually irresistible reinforcers that “lure” the student to the trap; (b) only a low-effort response already in the student’s repertoire is necessary to enter the trap; (c) in- terrelated contingencies of reinforcement inside the trap motivate the student to acquire, extend, and maintain tar- geted academic and/or social skills (Kohler & Green- wood, 1986); and (d) they can remain effective for a long time because students show few, if any, satiation effects.

Consider the case of the “reluctant bowler.” A young man is persuaded to fill in as a substitute for a friend’s bowling team. He has always regarded bowling as uncool. And bowling looks so easy on television that he does not see how it can be considered a real sport. Nevertheless, he agrees to go, just to help out this one time. During the evening he learns that bowling is not as easy as he had al- ways assumed (he has a history of reinforcement for ath- letic challenges) and that several people he would like to get to know are avid bowlers (i.e., it is a mixed doubles league). Within a week he has purchased a custom-fitted bowling ball, a bag, and shoes; practiced twice on his own; and signed up for the next league season.

The reluctant bowler example illustrates the funda- mental nature of behavior traps: easy to enter and diffi- cult to exit. Some naturally existing behavior traps can lead to maladaptive behaviors, such as alcoholism, drug addiction, and juvenile delinquency. The everyday term vicious circle refers to the natural contingencies of rein- forcement that operate in destructive behavior traps. Prac- titioners, however, can learn to create behavior traps that help students develop positive, constructive knowledge and skills. Alber and Heward (1996), who provided guidelines for creating successful traps, gave the follow- ing example of an elementary teacher creating a behav- ior trap that took advantage of a student’s penchant for playing with baseball cards.

Like many fifth graders struggling with reading and math, Carlos experiences school as tedious and unre- warding. With few friends of his own, Carlos finds that even recess offers little reprieve. But he does find solace in his baseball cards, often studying, sorting and playing with them in class. His teacher, Ms. Greene, long ago lost count of the number of times she had to stop an in- structional activity to separate Carlos and his beloved baseball cards. Then one day, when she approached Car- los’ desk to confiscate his cards in the middle of a lesson on alphabetization, Ms. Greene discovered that Carlos had already alphabetized all the left-handed pitchers in

the National League! Ms. Greene realized she’d found the secret to sparking Carlos’ academic development.

Carlos was both astonished and thrilled to learn that Ms. Greene not only let him keep his baseball cards at his desk, but also encouraged him to “play with them” during class. Before long, Ms. Greene had incorporated baseball cards into learning activities across the curricu- lum. In math, Carlos calculated batting averages; in geography, he located the hometown of every major leaguer born in his state; and in language arts, he wrote letters to his favorite players requesting an autographed photo. Carlos began to make significant gains academi- cally and an improvement in his attitude about school was also apparent.

But school became really fun for Carlos when some of his classmates began to take an interest in his knowl- edge of baseball cards and all the wonderful things you could do with them. Ms. Greene helped Carlos form a classroom Baseball Card Club, giving him and his new friends opportunities to develop and practice new social skills as they responded to their teacher’s challenge to think of new ways to integrate the cards into the curricu- lum. (p. 285)

Ask People in the Generalization Setting to Reinforce the Behavior

The problem may be simply that the natural community [of reinforcement] is asleep and needs to be awakened and turned on.

—Donald M. Baer (1999, p. 16).

Sometimes a potentially effective contingency of rein- forcement in the generalization setting is not operating in a form available to the learner no matter how often or well she performs the target behavior. The contingency is there, but dormant. One solution for this kind of prob- lem is to inform key people in the generalization setting of the value and importance of their attention to the learner’s efforts to acquire and use new skills and ask them to help.

For example, a special education teacher who has been helping a student learn how to participate in class discussions by providing repeated opportunities for prac- tice and feedback in the resource room could inform the teachers in the general education classes that the student attends about the behavior change program and ask them to look for and reinforce any reasonable effort by the stu- dent to participate in their classrooms. A small amount of contingent attention and praise from these teachers may be all that is needed to achieve the desired generalization of the new skill.

This simple but often effective technique for pro- moting generalized behavior was evident in the study by Stokes and colleagues (1974) in which staff members re- sponded to the children’s waving response by saying,

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“Hello, (name).” Approximately 20 such generalization probes were conducted each day with each child.

Williams, Donley, and Keller (2000) gave mothers of two preschool children with autism explicit instruc- tions on providing models, response prompts, and rein- forcement to their children who were learning to ask questions about hidden objects (e.g., “What’s that?”, “Can I see it?”).

Contingent praise and attention from significant oth- ers can add to the effectiveness of other strategies already in place in the generalization environment. Broden, Hall, and Mitts (1971) showed this in the self-monitoring study described in Chapter 27. After self-recording had im- proved eighth-grader Liza’s study behavior during his- tory class, the researchers asked Liza’s teacher to praise Liza for study behavior whenever he could during history class. Liza’s level of study behavior during this Self- Recording Plus Praise condition increased to a mean of 88% and was maintained at levels nearly as high during a subsequent Praise Only condition (see Figure 27.3).

Teach the Learner to Recruit Reinforcement

Another way to “wake up” a potentially powerful but dor- mant natural contingency of reinforcement is to teach the learner to recruit reinforcement from significant others. For example, Seymour and Stokes (1976) taught delin- quent girls to work more productively in the vocational training area of a residential institution. However, obser- vation showed that staff at the institution gave the girls no praise or positive interaction regardless of the quality of their work. The much-needed natural community of re- inforcement to ensure the generalization of the girls’ im- proved work behaviors was not functioning. To get around this difficulty, the experimenters trained the girls to use a simple response that called the attention of staff members to their work. With this strategy, staff praise for good work increased. Thus, teaching the girls an addi- tional response that could be used to recruit reinforce- ment enabled the target behavior to come into contact with natural reinforcers that would serve to extend and maintain the desired behavior change.

Students of various ages and abilities have learned to recruit teacher and peer attention for performing a wide range of tasks in classroom and community settings; preschoolers with developmental delays for completing pre-academic tasks and staying on task during transitions (Connell, Carta, & Baer, 1993; Stokes, Fowler, & Baer, 1978) as well as students with learning disabilities (Alber, Heward, & Hippler, 1999; Wolford, Alber, & Heward, 2001), students with behavioral disorders (Morgan, Young, & Goldstein, 1983), students with mental retar-

dation performing academic tasks in regular classrooms (Craft, Alber, & Heward, 1998), and secondary students with mental retardation for improved work performance in vocational training settings (Mank & Horner, 1987).

Craft and colleagues (1998) assessed the effects of recruitment training on academic assignments for which students recruited teacher attention. Four elementary stu- dents were trained by their special education teacher (the first author) when, how, and how often to recruit teacher attention in the general education classroom. Training consisted of modeling, role-playing, error correction, and praise in the special education classroom. The students were taught to show their work to the teacher or ask for help two to three times per work page, and to use appro- priate statements such as “How am I doing?” or “Does this look right?”

Data on the frequency of student recruiting and teacher praise statements were collected during a daily 20-minute homeroom period in a general education class- room. During this period, the general education students completed a variety of independent seatwork tasks (read- ing, language arts, math) assigned by the general educa- tion teacher, while the four special education students completed spelling worksheets assigned by the special education teacher, an arrangement that had been estab- lished prior to the experiment. If students needed help with their assignments during homeroom, they took their work to the teacher’s desk and asked for help.

The effects of recruitment training on the children’s frequency of recruiting and on the number of praise state- ments they received from the classroom teacher are shown in Figure 28.9. Recruiting across students in- creased from a mean rate of 0.01 to 0.8 recruiting re- sponses per 20-minute session during baseline to a mean rate of 1.8 to 2.7 after training. Teacher praise statements received by the students increased from a mean rate of 0.1 to 0.8 praise statements per session during baseline to a mean rate of 1.0 to 1.7 after training. The ultimate mean- ing and outcome of the intervention was in the increased amount and improved accuracy of all four of the students’ academic work (see Figure 6.9).

For a review of research on recruiting and sugges- tions for teaching children to recruit reinforcement from significant others, see Alber and Heward (2000). Box 28.2, “Look, Teacher, I’m All Finished!” provides sug- gestions for teaching students to recruit teacher attention.

Mediate Generalization

Another strategy for promoting generalized behavior change is to arrange for some thing or person to act as a medium that ensures the transfer of the target behavior from the instructional setting to the generalization setting.

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Classrooms are extremely busy places, and even the most conscientious teachers can easily overlook students’ im- portant academic and social behaviors. Research shows that teachers are more likely to pay attention to a dis- ruptive student than to one who is working quietly and productively (Walker, 1997). It is hard for teachers to be aware of students who need help, especially low- achieving students who are less likely to ask for help (Newman & Golding, 1990).

Although teachers in general education classrooms are expected to adapt instruction to serve students with disabilities, this is not always the case. Most secondary teachers interviewed by Schumm and colleagues (1995) believed that students with disabilities should take re- sponsibility for obtaining the help they need. Thus, know- ing how to politely recruit teacher attention and assistance can help students with disabilities function more inde- pendently and actively influence the quality of instruction they receive.

Who Should Be Taught to Recruit?

Although most students would probably benefit from learning to recruit teacher praise and feedback, here are some ideal candidates for recruitment training:

Withdrawn Willamena. Willamena seldom asks a teacher anything. Because she is so quiet and well behaved, her teachers sometimes forget she’s in the room.

In-a-Hurry Harry. Harry is usually half-done with a task before his teacher finishes explaining it. Racing through his work allows him to be the first to turn it in. But his work is often incomplete and filled with errors, so he doesn’t hear much praise from his teacher. Harry would benefit from re- cruitment training that includes self-checking and self-correction.

Shouting Shelly. Shelly has just finished her work, and she wants her teacher to look at it—right now! But Shelly doesn’t raise her hand. She gets her teacher’s attention—and disrupts most of her classmates—by shouting across the room. Shelly should be taught appropriate ways to solicit teacher attention.

Pestering Pete. Pete always raises his hand, waits quietly for his teacher to come to his desk, and then politely asks, “Have I done this right?” But he repeats this routine a dozen or more times in a

20-minute period, and his teachers find it annoy- ing. Positive teacher attention often turns into rep- rimands. Recruitment training for Pete will teach him to limit the number of times he cues his teach- ers for attention.

How to Get Started

1. Identify target behaviors. Students should recruit teacher attention for target behaviors that are val- ued and therefore likely to be reinforced, such as writing neatly and legibly, working accurately, completing assigned work, cleaning up at transi- tions, and making contributions when working in a cooperative group.

2. Teach self-assessment. Students should self-assess their work before recruiting teacher attention (e.g., Sue asks herself, “Is my work complete?”). After the student can reliably distinguish between com- plete and incomplete work samples, she can learn how to check the accuracy of her work with an- swer keys or checklists of the steps or components of the academic skill, or how to spot-check two or three items before asking the teacher to look at it.

3. Teach appropriate recruiting. Teach students when, how, and how often to recruit and how to respond to the teacher after receiving attention.

• When? Students should signal for teacher atten- tion after they have completed and self-checked a substantial part of their work. Students should also be taught when not to try to get their teacher’s attention (e.g., when the teacher is working with another student, talking to another adult, or taking the lunch count).

• How? The traditional hand raise should be part of every student’s recruiting repertoire. Other methods of gaining attention should be taught depending on teacher preferences and routines in the general education classroom (e.g., have students signal they need help by standing up a small flag on their desks; expect students to bring their work to the teacher’s desk for help and feedback).

• How often? While helping Withdrawn Willa- mena learn to seek teacher attention, don’t turn her into a Pestering Pete. How often a student should recruit varies across teachers and activi- ties (e.g., independent seatwork, cooperative

Box 28.2 “Look, Teacher, I’m All Finished!”

Teaching Students to Recruit Teacher Attention

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learning groups, whole-class instruction). Direct observation in the classroom is the best way to establish an optimal rate of recruiting; it is also a good idea to ask the regular classroom teacher when, how, and with what frequency she prefers students to ask for help.

• What to say? Students should be taught several statements that are likely to evoke positive feed- back from the teacher (e.g., “Please look at my work.” “Did I do a good job?” “How am I doing?”). Keep it simple, but teach the student to vary her verbal cues so she will not sound like a parrot.

• How to respond? Students should respond to their teacher’s feedback by establishing eye con- tact, smiling, and saying, “Thank you.” Polite appreciation is very reinforcing to teachers and will increase the likelihood of more positive at- tention the next time.

4. Model and role-play the complete sequence. Begin by providing students with a rationale for recruiting (e.g., the teacher will be happy you did a good job, you will get more work done, your grades might improve). Thinking aloud while modeling is a good way to show the recruiting se- quence. While performing each step, say, “Okay, I’ve finished my work. Now I’m going to check it. Did I put my name on my paper? Yes. Did I do all

the problems? Yes. Did I follow all the steps? Yes. Okay, my teacher doesn’t look busy right now. I’ll raise my hand and wait quietly until she comes to my desk.” Have another student pretend to be the regular classroom teacher and come over to you when you have your hand up. Say, “Mr. Patterson, please look at my work.” The helper says, “Oh, you did a very nice job.” Then smile and say, “Thank you, Mr. Patterson.” Role-play with praise and offer corrective feedback until the student correctly performs the entire sequence on several consecutive trials.

5. Prepare students for alternate responses. Of course, not every recruiting student attempt will result in teacher praise; some recruiting responses may even be followed by criticism (e.g., “This is all wrong. Pay better attention the next time.”). Use role-playing to prepare students for these pos- sibilities and have them practice polite responses (e.g., “Thank you for helping me with this”).

6. Promote generalization to the regular classroom. The success of any recruitment training effort de- pends on the student actually using his or her new skill in the regular classroom.

Adapted from “Recruit it or lose it! Training students to recruit con- tingent teacher attention” by S. R. Alber and W. L. Heward, 1997, Inter- vention in School and Clinic, 5, pp. 275–282. Used with permission.

Two tactics for implementing this strategy are contriving a mediating stimulus and teaching the learner to mediate her own generalization through self-management.

Contrive a Mediating Stimulus

One tactic for mediating generalization is to bring the target behavior under the control of a stimulus in the in- structional setting that will function in the generalization setting to reliably prompt or aid the learner’s performance of the target behavior. The stimulus selected for this im- portant role may exist already in the generalization envi- ronment, or it may be a new stimulus added to the instruction program that subsequently goes with the learner to the generalization setting. Whether it is a nat- urally existing component of the generalization setting or an added element to the instructional setting, to effec- tively mediate generalization, a contrived mediating stimulus must be (a) made functional for the target be- havior during instruction and (b) transported easily to the generalization setting (Baer, 1999). The mediating stim- ulus is functional for the learner if it reliably prompts or

aids the learner in performing the target behavior; the mediating stimulus is transportable if it easily goes with the learner to all important generalization settings.

Naturally existing features of generalization settings that are used as contrived mediating stimuli may be phys- ical objects or people. Van den Pol and colleagues (1981) used paper napkins, a common feature of any fast-food restaurant, as a contrived mediating stimulus. They taught students that a paper napkin was the only place to put food. In this way, the researchers eliminated the added challenge and difficulty of teaching the students to dis- criminate clean tables from dirty tables, to sit only at clean tables, or to wipe dirty tables and then program- ming the generalization and maintenance of those be- haviors. By contriving the special use of the napkin, only one response had to be trained, and napkins then served as mediating stimulus for that behavior.

When choosing a stimulus to be made common to both teaching and social generalization setting(s), prac- titioners should consider using people. In addition to being a requisite feature of social settings, people are transportable and important sources of reinforcement for

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Figure 28.9 Number of recruiting responses (data points) and teacher praise statement (bars) per 20-minute seatwork sessions. Target recruiting rate was two to three responses per session. Asterisks indicate when each student was trained in the resource room. From “Teaching Elementary Students with Developmental Disabilities to Recruit Teacher Attention in a General Education Classroom: Effects on Teacher Praise and Academic Productivity” by M. A. Craft, S. R. Alber, and W. L. Heward, 1998, Journal of Applied Behavior Analysis, 31, p. 407. Copyright 1998 by the Society for the Experimental Analysis of Behavior, Inc. Reprinted by permission.

many behaviors. A study by Stokes and Baer (1976) is a good example of the potential effects of the evocative ef- fects of the presence of a person in the generalization set- ting who had a functional role in the learner’s acquisition of the target behavior in the instructional setting. Two preschool children with learning disabilities learned word-recognition skills while working as reciprocal peer tutors for one another. However, neither child showed re- liable generalization of those new skills in nontraining settings until the peer with whom he had learned the skills was present in the generalization setting.

Some contrived mediating stimuli serve as much more than response prompts; they are prosthetic devices that assist the learner in performing the target behavior. Such devices can be especially useful in promoting gen-

eralization and maintenance of complex behaviors and extend response chains by simplifying a complex situa- tion. Three common forms are cue cards, photographic activity schedules, and self-operated prompting devices.

Sprague and Horner (1984) gave the students in their study cue cards to aid them in operating a vending ma- chine without another person’s assistance. The cue cards, which had food and drink logos on one side and pictures of quarters paired with prices on the other, not only were used during instruction and generalization probes, but also were kept by the students at the end of the program. A follow-up 18 months after the study was completed re- vealed that five of the six students still carried a cue card and were using vending machines independently.

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MacDuff, Krantz, and McClannahan (1993) taught four boys with autism ages 9 to 14 to use photographic activity schedules when performing domestic-living skills such as vacuuming and table setting and for leisure ac- tivities such as using manipulative toys. Prior to training with photographic activity schedules, the boys

were dependent on ongoing supervision and verbal prompts to complete self-help, housekeeping, and leisure activities. . . . In the absence of verbal prompts from supervising adults, it appeared that stimulus con- trol transferred to photographs and materials that were available in the group home. When the study ended, all 4 boys were able to display complex home-living and recreational repertoires for an hour, during which time they frequently changed tasks and moved to different areas of their group home without adults’ prompts. Pho- tographic activity schedules, . . . became functional dis- criminative stimuli that promoted sustained engagement after training ceased and fostered generalized respond- ing to new activity sequences and novel leisure materi- als. (pp. 90, 97)

Numerous studies have shown that learners across a wide range of age and cognitive levels, including stu- dents with severe intellectual disabilities, can learn to use personal audio playback devices to independently per- form a variety of academic, vocational, and domestic tasks (e.g., Briggs et al., 1990; Davis, Brady, Williams, & Burta, 1992; Grossi, 1998; Mechling & Gast, 1997; Post, Storey, & Karabin, 2002; Trask-Tyler, Grossi, & Heward, 1994). The popularity of personal music devices such as “Walkman-style” tape players and iPods enables a person to listen to a series of self-delivered response prompts in a private, normalized manner that does not impose on or bother others.

Teach Self-Management Skills

The most potentially effective approach to mediating generalized behavior changes rests with the one element that is always present in every instructional and gener- alization setting—the learner herself. Chapter 27 de- scribed a variety of self-management tactics that people can use to modify their own behavior. The logic of using self-management to mediate generalized behavior changes goes like this: If the learner can be taught a be- havior (not the original target behavior, but another be- havior—a controlling response from a self-management perspective) that serves to prompt or reinforce the target behavior in all the relevant settings, at all appropriate times, and in all of its relevant forms, then the general- ization of the target behavior is ensured. But as Baer and Fowler (1984) warned:

Giving a student self-control responses designed to mediate the generalization of some critical behavior

changes does not ensure that those mediating responses will indeed be used. They are, after all, just responses: they, too, need generalization and maintenance just as do the behavior changes that they are meant to general- ize and maintain. Setting up one behavior to mediate the generalization of another behavior may succeed— but it may also represent a problem in guaranteeing the generalization of two responses, where before we had only the problem of guaranteeing the generalization of one! (p. 149)

Train to Generalize If generalization is considered as a response itself, then a reinforcement contingency may be placed on it, the same as with any other operant.

—Stokes and Baer (1977, p. 362)

Training “to generalize” was one of the eight proactive strategies for programming generalized behavior change in Stokes and Baer’s (1977) conceptual scheme (which also included the nonstrategy, train and hope). The quotation marks around “to generalize” signified that the authors were hypothesizing about the possibilities of treating “to generalize” as an operant response and that they recog- nized “the preference of behaviorists to consider general- ization to be an outcome of behavioral change, rather than as a behavior itself” (p. 363). Since the publication of Stokes and Baer’s review, basic and applied research has demonstrated the pragmatic value of their hypothesis (e.g., Neuringer, 1993, 2004; Ross & Neuringer, 2002; Shahan & Chase, 2002). Two tactics that applied behavior analysts have used are reinforcing response variability and in- structing the learner to generalize.

Reinforce Response Variability

Response variability can help a person solve problems. A person who can improvise by emitting a variety of re- sponses is more likely to solve problems encountered when a standard response form fails to obtain reinforce- ment (e.g., Arnesen, 2000; Marckel, Neef, & Ferreri, 2006; Miller & Neuringer, 2000; Shahan & Chase, 2002). Response variability also may result in behavior that is valued because it is novel or creative (e.g., Goetz & Baer, 1973; Holman, Goetz, & Baer, 1977; Pryor, Haag, and O’Reilly, 1969). Response variability may expose a per- son to sources of reinforcement and contingencies not accessible by more constricted forms of responding. The additional learning that results from contacting those con- tingencies further expands the person’s repertoire.

One direct way to program desired response gener- alization is to reinforce response variability when it oc- curs. The contingency between response variability and reinforcement can be formalized with a lag reinforce-

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ment schedule (Lee, McComas, & Jawar, 2002). On a lag reinforcement schedule, reinforcement is contingent on a response being different in some defined way from the previous response (a Lag 1 schedule) or a specified number of previous responses (Lag 2 or more). Cam- milleri and Hanley (2005) used a lag reinforcement contingency to increase varied selections of classroom activities by two typically developing girls, who were se- lected to participate in the study because they spent the vast majority of their time engaged in activities not sys- tematically designed to result in a particular set of skills within the curriculum.

At the beginning of each 60-minute session, the chil- dren were told they could select any activity and could

switch activities at any time. A timer sounded every 5 minutes to prompt activity choices. During baseline there were no programmed consequences for selecting any of the activities. Intervention consisted of a lag rein- forcement schedule in which the first activity selection and each subsequent novel selection were followed by the teacher handing the student a green card that she could exchange later for 2 minutes of teacher attention (result- ing in a Lag 12 contingency, which was reset if all 12 ac- tivities were selected within a session).

Activity selections during baseline showed little vari- ability, with both girls showing strong preferences for stack- able blocks (Figure 28.10 shows results for one of the girls). When the lag contingency was introduced, both girls

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Figure 28.10 Number of novel activity selections (top panel), percentage of intervals of engagement in programmed (italicized) and nonprogrammed activities (middle panel; shaded cells indicate activities for which there was some engagement), and number of academic units completed (bottom panel). From “Use of a Lag Differential Reinforcement Contingency to Increase Varied Selections of Classroom Activities” by A. P. Cammilleri and G. P. Hanley, 2005, Journal of Applied Behavior Analysis, 38, p. 114. Copyright 2005 by the Society for the Experimental Analysis of Behavior, Inc. Reprinted by permission.

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immediately selected and engaged in more diverse activi- ties. The researchers noted that “an indirect but important outcome of this shift in time allocation was a marked in- crease in the number of academic units completed” (p. 115).

Instruct the Learner to Generalize

The simplest and least expensive of all tactics for pro- moting generalized behavior change is to “tell the subject about the possibility of generalization and then ask for it” (Stokes & Baer, 1977, p. 363). For example, Ninness and colleagues (1991) explicitly told three middle school stu- dents with emotional disturbance to use self-management procedures they had learned in the classroom to self-assess and self-record their behavior while walking from the lunchroom to the classroom. Hughes and colleagues (1995) used a similar procedure to promote generaliza- tion: “At the close of each training session, peer teachers reminded participants to self-instruct when they wished to talk to someone” (p. 207). Likewise, at the conclusion of each training session conducted in the special education classroom on how to recruit assistance from peers during cooperative learning groups, Wolford and colleagues (2001) prompted middle school students with learning disabilities to recruit peer assistance at least two times but not more than four times during each cooperative learn- ing group in the language arts classroom.

To the extent that generalizations occur and are them- selves generalized, a person might then become skilled at generalizing newly acquired skills, or in the words of Stokes and Baer (1977), become a “generalized generalizer.”

Modifying and Terminating Successful Interventions With most successful behavior change programs it is im- possible, impractical, or undesirable to continue the in- tervention indefinitely. Withdrawal of a successful intervention should be carried out in a systematic fashion, guided by the learner’s performance of the target behav- ior in the most important generalization settings. Gradu- ally shifting from the contrived conditions of the intervention to the typical, everyday environment will in- crease the likelihood that the learner will maintain the new behavior patterns. When deciding how soon and how swiftly to withdraw intervention components, practi- tioners should consider factors such as the complexity of the intervention, the ease or speed with which behavior changed, and the availability of naturally existing con- tingencies of reinforcement for the new behavior.

This shift from intervention conditions to the postin- tervention environment can be made by modifying one or

more of the following components, each representing one part of the three-term contingency:

• Antecedents, prompts, or cue-related stimuli

• Task requirements and criteria

• Consequences or reinforcement variables

Although the order in which intervention compo- nents are withdrawn may make little or no difference, in most programs it is probably best to make all task-related requirements as similar as possible to those of the post- intervention environment before withdrawing significant antecedent or consequence components of the interven- tion. In this way the learner will be emitting the target behavior at the same level of fluency that will be required after the complete intervention has been withdrawn.

A behavior change program carried out many years ago by a graduate student in one of our classes illus- trates how the components of a program can be gradu- ally and systematically withdrawn. An adult male with developmental disabilities took an inordinate amount of time to get dressed each morning (40 to 70 minutes during baseline), even though he possessed the skills needed to dress himself. Intervention began with a con- struction paper clock hung by his bed with the hands set to indicate the time by which he had to be fully dressed to receive reinforcement. Although the man could not tell time, he could discriminate whether the position of the hands on the real clock nearby matched those on his paper clock. Two task-related intervention elements were introduced to increase the likelihood of initial success. First, he was given fewer and easier clothes to put on each morning (e.g., no belt, slip-on loafers instead of shoes with laces). Second, based on his baseline performance, he was initially given 30 min- utes to dress himself, even though the objective of the program was for him to be completely dressed within 10 minutes. An edible reinforcer paired with verbal praise was used first on a continuous schedule of reinforce- ment. Figure 28.11 shows how each aspect of the inter- vention (antecedent, behavior, and consequence) was modified and eventually withdrawn completely, so that by the program’s end the man was dressing himself completely within 10 minutes without the aid of extra clocks or charts or contrived reinforcement other than a naturally existing schedule of intermittent praise from staff members.

Rusch and Kazdin (1981) described a method for systematically withdrawing intervention components while simultaneously assessing response maintenance that they called “partial-sequential withdrawal.” Martella, Leonard, Marchand-Martella, and Agran (1993) used a partial-sequential withdrawal of various components of a self-monitoring intervention they had implemented to

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Figure 28.11 An example of modifying and withdrawing components of an independent morning dressing program for an adult with developmental disabilities to facilitate mainte- nance and generalization.

help Brad, a 12-year-old student with mild mental retar- dation, reduce the number of negative statements (e.g., “I hate this @#!%ing calculator,” “Math is crappy”) he made during classroom activities. The self-management intervention consisted of Brad (a) self-recording nega- tive statements on a form during two class periods, (b) charting the number on a graph, (c) receiving his choice from a menu of “small” reinforcers (items costing 25 cents or less), and (d) when his self-recorded data were in agreement with the trainer and at or below a gradually reducing criterion level for four consecutive sessions, he was allowed to choose a “large” reinforcer (more than 25 cents). After the frequency of Brad’s negative state- ments was reduced, the researchers began a four-phase partial-sequential withdrawal of the intervention. In the first phase, charting and earning of “large” reinforcers were withdrawn; in the second phase, Brad was required to have zero negative statements in both periods to re- ceive a daily “small” reinforcer; in the third phase, Brad used the same self-monitoring form for both class peri- ods instead of one form for each period as before, and the small reinforcer was no longer provided; and in the fourth phase (the follow-up condition), all components of the intervention were withdrawn except for the self- monitoring form without a criterion number highlighted. Brad’s negative statements remained low throughout the gradual and partial withdrawal of the intervention (see Figure 28.12).

A word of caution is in order regarding the termi- nation of successful behavior change programs. Achiev- ing socially significant improvements in behavior is a defining purpose of applied behavior analysis. Addi- tionally, those improved behaviors should be maintained and should show generalization to other relevant settings and behaviors. In most instances, achieving optimal gen-

eralization of the behavior change will require most, if not all, of the intervention components to be withdrawn. However, practitioners, parents, and others responsible for helping children learn important behaviors are some- times more concerned with whether and with how a po- tentially effective intervention will eventually be withdrawn than they are with whether it will produce the needed behavior change. Considering how a pro- posed intervention will lend itself to eventual withdrawal or blending into the natural environment is important and consistent with everything recommended throughout this book. And clearly, when the choice is between two or more interventions of potentially equal effectiveness, the intervention that is most similar to the natural envi- ronment and the easiest to withdraw and terminate should be given first priority. However, an important be- havior change should not go unmade because complete withdrawal of the intervention required to achieve it may never be possible. Some level of intervention may al- ways be required to maintain certain behaviors, in which case attempts to continue the necessary programming must be made.

The Sprague and Horner (1984) study on teaching generalized vending machine use provides another ex- ample of this point. The six students with moderate to severe mental retardation who participated in the pro- gram were given cue cards to aid them in operating a vending machine without another person’s assistance. The cue cards, which had food and drink logos on one side and pictures of quarters paired with prices on the other, not only were used during instruction and gener- alization probes, but also were kept by the students at the end of the program. Five of the six students still carried a cue card and were using vending machines indepen- dently 18 months after the study had ended.

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Guiding Principles for Promoting Generalized Outcomes Regardless of the specific tactics selected and applied, we believe that practitioners’ efforts to promote general- ized behavior change will be enhanced by adherence to five guiding principles:

• Minimize the need for generalization as much as possible.

• Conduct generalization probes before, during, and after instruction.

• Involve significant others whenever possible.

• Promote generalization with the least intrusive, least costly tactics possible.

• Contrive intervention tactics as needed to achieve important generalized outcomes.

Minimize the Need for Generalization

Practitioners should reduce the need for generalization to untaught skills, settings, and situations as much as possi- ble. Doing so requires thoughtful, systematic assessment of what behavior changes are most important. Practition- ers should prioritize the knowledge and skills that will most often be required of the learner and the settings and

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situations in which the learner will most often benefit from using those skills. In addition to the environment(s) in which the learner is currently functioning, practition- ers should consider the environments in which the learner will function in the immediate future and later in life.

The most critical behavior changes should not be relegated to the not-for-certain technology of general- ization. The most important skill-setting-stimulus com- binations should always be taught directly and, when possible, taught first. For example, a training program to teach a young adult with disabilities to ride the pub- lic bus system should use the bus routes the person will take most often (e.g., to and from her home, school, job site, and community recreation center) as teaching ex- amples. Instead of providing direct instruction on routes to destinations the learner may visit only occasionally after training, use those routes as generalization probes. Achieving a high level of response maintenance on the trained routes will still be a challenge.

Probe for Generalization Before, During, and After Instruction

Generalization probes should be conducted prior to, dur- ing, and after instruction.

Probe Before Instruction

Generalization probes conducted before instruction be- gins may reveal that the learner already performs some or all of the needed behaviors in the generalization setting, thereby reducing the scope of the teaching task. It is a mistake to assume that because a learner does not per- form a particular behavior in the instructional setting, she does not or cannot do it in the generalization setting(s). Preinstruction generalization probe data are the only ob- jective basis for knowing that the learner’s performance of the target behavior after instruction is in fact a gener- alized outcome.

Probes before instruction enable observation of the contingencies that operate in the generalization setting. Knowledge of such information may contribute to a more effective treatment or instruction.

Probe During Instruction

Generalization probes during instruction reveal if and when generalization has occurred and if and when in- struction can be terminated or shifted in focus from ac- quisition to maintenance. For example, a teacher who finds that a student can solve untaught examples of a par- ticular type of algebraic equation after receiving instruc- tion on just a few examples and shifts instruction to the next type of equation in the curriculum, will cover more

of the algebra program effectively than will a teacher who continues to present additional examples.

Results of probes during instruction also show whether generalization is not occurring, thereby indicat- ing that a change in instructional strategies is needed. For example, when Sprague and Horner (1984) observed that one student’s poor performance on the first generalization probe was caused by a ritualistic pattern of inserting coins, they incorporated repeated practice on the coin in- sertion step during a training session and the student’s performance on the generalization task improved greatly.

Probing can often be made more efficient by con- triving opportunities for the learner to use her new knowl- edge or skill. For example, instead of waiting for (and perhaps missing) naturally occurring opportunities for the learner to use her new conversational skills in the gen- eralization environment, a practitioner could enlist the assistance of a “confederate” peer to approach the learner. Ninness, Fuerst, and Rutherford, (1991) used contrived generalization probes in which students were provoked or distracted to test the extent to which they used a set of self-management skills.

Contrived generalization probes can also be used as primary measures of acquisition and generalization. For example, Miltenberger and colleagues (2005) contrived opportunities to measure and teach children’s use of gun safety skills by placing a gun in home and school loca- tions where the children would find it. If a child did not execute the target safety skills when finding a gun (i.e., do not touch it, get away from the gun, and tell an adult), the trainer entered the room and conducted an in situ training session, asking the child what he should have done and rehearsing the entire sequence five times.

Probe After Instruction

Generalization probes after instruction has ended reveal the extent of response maintenance. The question of how long probes should be conducted after instruction has ended should be answered by factors such as the sever- ity of the target behavior, the importance of the behavior change to the person’s quality of life, the strength and consistency of the response maintenance obtained by probes to date, and so forth. The need for long-term as- sessment of maintenance is especially critical for severe behavior problems. In some cases, maintenance probes over many months and several years may be indicated (e.g., Derby et al., 1997; Foxx, Bittle, & Faw, 1989; Wag- man, Miltenberger, & Woods, 1995).

If conducting systematic generalization and mainte- nance probes seems too difficult or too contrived, the practitioner should consider the relative importance of the target behavior to the student or client. If a behavior is important to target for intervention then assessing the

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generalized outcomes of that intervention is worth what- ever effort that assessment requires.

Involve Significant Others All persons are potential teachers of all sorts of behavior changes. Just because you are designated as “teacher” or “behavior analyst” does not mean that you have an ex- clusive franchise on the ability to make deliberate be- havior changes. In fact, there is no possibility of such a franchise. Everyone in contact contributes to everyone else’s behavior, both to its changes and its maintenance.

— Donald M. Baer (1999, p. 12)

Teaching for generalized outcomes is a big job, and prac- titioners should try to get as much help as they can. People are almost always around where and when im- portant behaviors need to be prompted and reinforced; and when social behaviors are targeted, people are there by definition.

In virtually every behavior change program people other than the participant and the behavior analyst are in- volved, and their cooperation is crucial to the success of the program. Foxx (1996) stated that in programming success- ful behavior change interventions, “10% is knowing what to do; 90% is getting people to do it. . . . Many programs are unsuccessful because these percentages have been reversed” (p. 230). Although Foxx was referring to the challenge and importance of getting staff to implement programs with the consistency and fidelity needed for success, the same holds for the involvement of significant others.

Baer (1999) suggested identifying others who will or may be involved in a behavior change program as active sup- porters or tolerators. An active supporter is someone natu- rally present in the generalization setting who helps promote the generalization and maintenance of the target behavior by doing specific things. Active supporters help facilitate the desired generalized outcomes by arranging opportunities for the learner to use or practice the new skill, giving cues and response prompts for the behavior, and providing rein- forcement for performance of the target behavior.

Active supporters for a behavior change program to teach independent eating to a child with severe disabili- ties might include one or two key people in the school’s cafeteria, a volunteer or aide who works regularly with the child, the child’s parents, and an older sibling. These people are vital parts of the teaching team. If optimal generalization is to occur, the active supporters must see to it that the learner has many opportunities to use the new skill in the generalization environments they share, and the natural reinforcers they control in those environ- ments (e.g., praise, touch, smiles, companionship) must be used as consequences for the target behavior. It is not necessary, and probably not desirable, to limit an active

supporters list, to school staff, family members, and peers. They may not be regularly available in all the environ- ments and situations in which generalization is desired.

A tolerator is someone in the generalization setting who agrees not to behave in ways that would impede the generalization plan. Tolerators should be informed that the learner will be using a new skill in the generalization en- vironment and asked to be patient. In an independent eat- ing program a list of tolerators would likely include some members of the child’s family, school cafeteria staff, and peers. Beyond the home and school environments the be- havior analyst should consider the possible role of the gen- eral public with whom the child may find herself sharing a table or dining area in a public restaurant. The learner’s initial sloppiness and slowness (she may always be slop- pier and slower than most people) as she makes the first at- tempts beyond the familiar contingencies of home and school might result in various responses from strangers that could punish the new eating skills. Being stared at, laughed at, talked about, told to hurry up, or even offered assistance could reduce the possibility of generalization. Certainly, the behavior analyst can inform various school staff and family members of the ongoing eating program and request that they not interfere with the child’s attempts to eat independently. But the public at large is another issue. It is impossible to inform everyone of the program. However, by considering the types of intolerant behaviors that the learner may encounter in the generalization set- ting, the teaching program can be constructed to include practice under such intolerant conditions. Instructional tri- als might be contrived to reinforce the learner for ignoring rude remarks and continuing to eat independently.

Use the Least Intrusive, Least Costly Tactics Possible

Behavior analysts should use less intrusive and less costly tactics to promote generalization before using more in- trusive and costly tactics. As noted previously, simply reminding students to use their new skills in the general- ization setting is the easiest and least expensive of all methods that might promote generalization. Although practitioners should never assume that telling the learner to generalize will produce the desired outcomes, neither should they fail to include such a simple and cost-free tactic. Similarly, incorporating some of the most relevant features of the generalization setting into the instructional setting (i.e., programming common stimuli) often helps produce the needed generalization and is less costly than conducting instruction in the natural setting (e.g., Neef, Lensbower et al., 1990; van den Pol et al., 1981).

Not only is using less costly tactics good conserva- tion of the limited resources available for teaching, but

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also less intrusive interventions with fewer moving parts are easier to withdraw. Systematic generalization probes will determine whether generalization has occurred and whether more elaborate and intrusive intervention and supports are needed.

Contrive Intervention Tactics as Needed to Achieve Important Generalized Outcomes

A practitioner should not be so concerned about intru- siveness that she fails to implement a potentially effective intervention or procedures that will achieve important outcomes for the learner. Therefore, if necessary, prac- tioners should disregard the previous guideline and con- trive as many instruction and generalization tactics as necessary to enable the learner to generalize and maintain critical knowledge and skills.

Rather than lament the lack of generalization or blame the learner for his inability to show generalized behavior changes, the behavior analyst should work to arrange whatever socially valid contingencies may be needed to extend and maintain the target behavior.

Some Final Words of Wisdom from Don Baer

The most difficult and important challenge facing behav- ioral practitioners is helping learners achieve generalized change in socially significant behaviors. A behavior

change—no matter how important initially—is of little value to the learner if it does not last over time, is not emit- ted in appropriate settings and situations, or occurs in re- stricted form when varied topographies are desired.

Research during the past 30 years has developed and advanced the knowledge base of what Stokes and Baer (1977) described as an “implicit technology of general- ization” into an increasingly explicit and effective set of strategies and tactics for promoting generalized behav- ior change. Knowledge of these methods, combined with knowledge of the basic principles and behavior change tactics described throughout this book, provides behav- ior analysts with a powerful approach for helping peo- ple enjoy healthy, happy, and productive lives.

We end this chapter with a dually wise observation by Don Baer (1999), in which he pointed out a funda- mental truth about the relation between a person’s expe- rience (in this case, the nature of a lesson) and what is learned or not learned from that experience. Like Skin- ner before him, Baer wisely reminded us not to blame the learner for not behaving as we think he should.

Learning one aspect of anything never means that you know the rest of it. Doing something skillfully now never means that you will always do it well. Resisting one temptation consistently never means that you now have character, strength, and discipline. Thus, it is not the learner who is dull, learning disabled, or immature, because all learners are alike in this regard: no one learns a generalized lesson unless a generalized lesson is taught. (p. 1) [emphasis in original]

Summary Generalized Behavior Change: Definitions and Key Concepts

1. Generalized behavior change has taken place if trained be- havior occurs at other times or in other places without hav- ing to be retrained completely in those time or places, or if functionally related behaviors occur that were not taught directly.

2. Response maintenance refers to the extent to which a learner continues to perform a behavior after a portion or all of the intervention responsible for the behavior’s initial appearance in the learner’s repertoire has been terminated.

3. Setting/situation generalization refers to the extent to which a learner emits the target behavior in settings or situations that are different from the instructional setting.

4. The instructional setting is the environment where instruc- tion occurs and encompasses all aspects of the environment,

planned or unplanned, that may influence the learner’s ac- quisition and generalization of the target behavior.

5. A generalization setting is any place or stimulus situation that differs from the instructional setting in some mean- ingful way and in which performance of the target behav- ior is desired.

6. Response generalization refers to the extent to which a learner emits untrained responses that are functionally equivalent to the trained response.

7. Some interventions yield significant and widespread gen- eralized effects across time, settings, and other behaviors; others produce circumscribed changes in behavior with limited endurance and spread.

8. Undesirable setting/situation generalization takes two common forms: overgeneralization, in which the behavior has come under control of a stimulus class that is too broad, and faulty stimulus control, in which the behavior

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comes under the control of an irrelevant antecedent stimulus.

9. Undesired response generalization occurs when any of a learner’s untrained but functionally equivalent responses produce undesirable outcomes.

10. Other types of generalized outcomes (e.g., stimulus equiv- alence, contingency adduction, and generalization across subjects) do not fit easily into categories of response maintenance, setting/situation generalization, and re- sponse generalization.

11. The generalization map is a conceptual framework for combining and categorizing the various types of gener- alized behavior change (Drabman, Hammer, & Rosen- baum, 1979).

Planning for Generalized Behavior Change

12. The first step in promoting generalized behavior changes is to select target behaviors that will meet naturally exist- ing contingencies of reinforcement.

13. A naturally existing contingency is any contingency of re- inforcement (or punishment) that operates independent of the behavior analyst’s or practitioner’s efforts, including socially mediated contingencies contrived by other peo- ple and already in effect in the relevant setting.

14. A contrived contingency is any contingency of reinforce- ment (or punishment) designed and implemented by a be- havior analyst to achieve the acquisition, maintenance, and/or generalization of a targeted behavior change.

15. Planning for generalization includes identifying all the de- sired behavior changes and all the environments in which the learner should emit the target behavior(s) after direct training has ceased.

16. Benefits of developing the planning lists include a better understanding of the scope of the teaching task and an op- portunity to prioritize the most important behavior changes and settings for direct instruction.

Strategies and Tactics for Promoting Generalized Behavior Change

17. Researchers have developed and advanced what Stokes and Baer (1977) called an “implicit technology of gener- alization” into an increasingly explicit and effective set of methods for promoting generalized behavior change.

18. The strategy of teaching sufficient examples requires teaching a subset of all of the possible stimulus and re- sponse examples and then assessing the learner’s perfor- mance on untrained examples.

19. A generalization probe is any measurement of a learner’s performance of a target behavior in a setting and/or stimu- lus situation in which direct training has not been provided.

20. Teaching sufficient stimulus examples involves teaching the learner to respond correctly to more than one example

of an antecedent stimulus and probing for generalization to untaught stimulus examples.

21. As a general rule, the more examples the practitioner uses during instruction, the more likely the learner will be to re- spond correctly to untrained examples or situations.

22. Having the learner practice a variety of response topogra- phies helps ensure the acquisition of desired response forms and promotes response generalization. Often called multiple exemplar training, this tactic typically incorpo- rates numerous stimulus examples and response variations.

23. General case analysis is a systematic method for select- ing teaching examples that represent the full range of stim- ulus variations and response requirements in the generalization setting.

24. Negative, or “don’t do it,” teaching examples help learn- ers identify stimulus situations in which the target behav- ior should not be performed.

25. Minimum difference negative teaching examples, which share many characteristics with positive teaching exam- ples, help eliminate “generalization errors” due to over- generalization and faulty stimulus control.

26. The greater the similarity between the instructional set- ting and the generalization setting, the more likely the tar- get behavior will be emitted in the generalization setting.

27. Programming common stimuli means including in the in- structional setting stimulus features typically found in the generalization setting. Practitioners can identify possible stimuli to make common by direct observation in the gen- eralization setting and by asking people who are familiar with the generalization setting.

28. Teaching loosely—randomly varying noncritical aspects of the instructional setting within and across teaching ses- sions—(a) reduces the likelihood that a single or small group of noncritical stimuli will acquire exclusive con- trol over the target behavior and (b) makes it less likely that the learner’s performance will be impeded or “thrown off” by the presence of a “strange” stimulus in the gen- eralization setting.

29. A newly learned behavior may fail to contact an existing contingency of reinforcement because it has not been taught well enough. The solution for this kind of general- ization problem is to teach the learner to emit the target be- havior at the rate, accuracy, topography, latency, duration, and/or magnitude required by the naturally occurring con- tingencies of reinforcement.

30. The use of intermittent schedules of reinforcement and de- layed rewards can create indiscriminable contingencies, which promote generalized responding by making it dif- ficult for the learner to discriminate whether the next re- sponse will produce reinforcement.

31. Behavior traps are powerful contingencies of reinforce- ment with four defining features: (a) They are “baited”

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Chapter 28 Generalization and Maintenance of Behavior Change 655

with virtually irresistible reinforcers; (b) only a low-effort response already in the student’s repertoire is needed to enter the trap; (c) interrelated contingencies of reinforce- ment inside the trap motivate the student to acquire, ex- tend, and maintain targeted skills; and (d) they can remain effective for a long time.

32. One way to wake up an existing but inoperative contin- gency of reinforcement is to ask key people in the gener- alization setting to attend to and praise the learner’s performance of the target behavior.

33. Another tactic for waking up a natural contingency of re- inforcement is to teach the learner how to recruit rein- forcement in the generalization setting.

34. One tactic for mediating generalization is to bring the tar- get behavior under the control of a contrived stimulus in the instructional setting that will reliably prompt or aid the learner’s performance of the target behavior in the gen- eralization setting.

35. Teaching a learner self-management skills with which he can prompt and maintain targeted behavior changes in all relevant settings at all times is the most potentially effective approach to mediating generalized behavior changes.

36. The strategy of training to generalize is predicated on treat- ing “to generalize” as an operant response class that, like any other operant, is selected and maintained by contin- gencies of reinforcement.

37. One tactic for promoting response generalization is to re- inforce response variability. On a lag reinforcement sched- ule, reinforcement is contingent on a response being different in some defined way from the previous response (a Lag 1 schedule) or a specified number of previous re- sponses (Lag 2 or more).

38. The simplest and least expensive tactic for promoting gen- eralized behavior change is to tell the learner about the usefulness of generalization and then instruct him to do so.

Modifying and Terminating Successful Interventions

39. With most successful behavior change programs it is im- possible, impractical, or undesirable to continue the in- tervention indefinitely.

40. The shift from formal intervention procedures to a normal everyday environment can be accomplished by gradually withdrawing elements comprising the three components of the training program: (a) antecedents, prompts, or cue- related stimuli; (b) task modifications and criteria; and (c) consequences or reinforcement variables.

41. An important behavior change should not go unmade be- cause complete withdrawal of the intervention required to achieve it may never be possible. Some level of interven- tion may always be required to maintain certain behav- iors, in which case attempts must be made to continue necessary programming.

Guiding Principles for Promoting Generalized Outcomes

42. Efforts to promote generalized behavior change will be enhanced by adhering to five guiding principles:

• Minimize the need for generalization as much as possible.

• Conduct generalization probes before, during, and after instruction.

• Involve significant others whenever possible.

• Promote generalized behavior change with the least in- trusive, least costly tactics possible.

• Contrive intervention tactics as needed to achieve im- portant generalized outcomes.

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